CN116611786B - Sewage treatment strategy adjusting system based on artificial intelligence - Google Patents
Sewage treatment strategy adjusting system based on artificial intelligence Download PDFInfo
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- CN116611786B CN116611786B CN202310571664.2A CN202310571664A CN116611786B CN 116611786 B CN116611786 B CN 116611786B CN 202310571664 A CN202310571664 A CN 202310571664A CN 116611786 B CN116611786 B CN 116611786B
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- 239000010865 sewage Substances 0.000 title claims abstract description 108
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 34
- 238000011269 treatment regimen Methods 0.000 title abstract description 15
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 196
- 239000000463 material Substances 0.000 claims abstract description 133
- 239000000835 fiber Substances 0.000 claims abstract description 114
- 238000011282 treatment Methods 0.000 claims abstract description 64
- 238000000746 purification Methods 0.000 claims abstract description 37
- 230000007246 mechanism Effects 0.000 claims abstract description 34
- 239000002245 particle Substances 0.000 claims abstract description 15
- 238000013527 convolutional neural network Methods 0.000 claims description 47
- 238000011049 filling Methods 0.000 claims description 32
- 238000003860 storage Methods 0.000 claims description 21
- 230000009471 action Effects 0.000 claims description 16
- 238000013507 mapping Methods 0.000 claims description 14
- 238000001914 filtration Methods 0.000 claims description 10
- 238000004065 wastewater treatment Methods 0.000 claims description 10
- 238000000034 method Methods 0.000 claims description 5
- 230000001360 synchronised effect Effects 0.000 claims description 5
- 238000004891 communication Methods 0.000 claims description 4
- 238000012360 testing method Methods 0.000 claims description 4
- 238000004088 simulation Methods 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 2
- 238000012545 processing Methods 0.000 claims description 2
- 230000001105 regulatory effect Effects 0.000 abstract 1
- 239000002351 wastewater Substances 0.000 description 10
- 238000005189 flocculation Methods 0.000 description 8
- 230000016615 flocculation Effects 0.000 description 8
- 230000000875 corresponding effect Effects 0.000 description 5
- NWZSZGALRFJKBT-KNIFDHDWSA-N (2s)-2,6-diaminohexanoic acid;(2s)-2-hydroxybutanedioic acid Chemical compound OC(=O)[C@@H](O)CC(O)=O.NCCCC[C@H](N)C(O)=O NWZSZGALRFJKBT-KNIFDHDWSA-N 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- IKDUDTNKRLTJSI-UHFFFAOYSA-N hydrazine monohydrate Substances O.NN IKDUDTNKRLTJSI-UHFFFAOYSA-N 0.000 description 4
- 238000012432 intermediate storage Methods 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 2
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 2
- FAPWRFPIFSIZLT-UHFFFAOYSA-M Sodium chloride Chemical compound [Na+].[Cl-] FAPWRFPIFSIZLT-UHFFFAOYSA-M 0.000 description 2
- 229920004933 Terylene® Polymers 0.000 description 2
- 238000005273 aeration Methods 0.000 description 2
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- 238000002156 mixing Methods 0.000 description 2
- 239000005020 polyethylene terephthalate Substances 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 229920002972 Acrylic fiber Polymers 0.000 description 1
- VEXZGXHMUGYJMC-UHFFFAOYSA-M Chloride anion Chemical compound [Cl-] VEXZGXHMUGYJMC-UHFFFAOYSA-M 0.000 description 1
- 239000004743 Polypropylene Substances 0.000 description 1
- 239000006004 Quartz sand Substances 0.000 description 1
- 229920002978 Vinylon Polymers 0.000 description 1
- XKMRRTOUMJRJIA-UHFFFAOYSA-N ammonia nh3 Chemical compound N.N XKMRRTOUMJRJIA-UHFFFAOYSA-N 0.000 description 1
- RHZUVFJBSILHOK-UHFFFAOYSA-N anthracen-1-ylmethanolate Chemical compound C1=CC=C2C=C3C(C[O-])=CC=CC3=CC2=C1 RHZUVFJBSILHOK-UHFFFAOYSA-N 0.000 description 1
- 239000003830 anthracite Substances 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000003197 catalytic effect Effects 0.000 description 1
- 239000003245 coal Substances 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 238000006477 desulfuration reaction Methods 0.000 description 1
- 230000023556 desulfurization Effects 0.000 description 1
- 239000000945 filler Substances 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 239000002223 garnet Substances 0.000 description 1
- 238000007037 hydroformylation reaction Methods 0.000 description 1
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- 239000011780 sodium chloride Substances 0.000 description 1
- 238000001179 sorption measurement Methods 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01D—SEPARATION
- B01D24/00—Filters comprising loose filtering material, i.e. filtering material without any binder between the individual particles or fibres thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0464—Convolutional networks [CNN, ConvNet]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
- Y02W10/00—Technologies for wastewater treatment
- Y02W10/10—Biological treatment of water, waste water, or sewage
Abstract
The invention relates to a sewage treatment strategy adjustment system based on artificial intelligence, comprising: the quantitative learning device is used for acquiring an artificial intelligent model for performing intelligent prediction on the number of the fiber ball filter materials; the particle prediction mechanism is used for inputting various water surface reference information of a target water surface to be subjected to sewage treatment and various configuration data of a single fiber ball filter material into the artificial intelligent model so as to analyze the number of the fiber ball filter materials required for completely purifying the water body where the target water surface is located. The sewage treatment strategy regulating system based on artificial intelligence is simple and convenient to operate and wide in application. The number of the fiber ball filter materials required to be used for completing the complete purification of the water body of the target water surface can be intelligently predicted based on various water surface reference information of the target water surface of the sewage treatment to be executed and various configuration data of the single fiber ball filter material, so that a great deal of labor cost and time cost are avoided, and the dilemma of insufficient purification or excessive filter materials is solved.
Description
Technical Field
The invention relates to the field of sewage treatment, in particular to a sewage treatment strategy adjusting system based on artificial intelligence.
Background
The common products for sewage treatment are: quartz sand filter material, anthracite filter material, polyaluminium chloride, activated carbon, honeycomb inclined tube filler, fiber ball filter material, garnet sand and the like.
The fiber ball filter material is a spherical filter material formed by bundling terylene, polypropylene or acrylic fibers, and has the characteristics of good elastic effect, high-hydroformylation vinylon, sizing terylene as a raw material, no floating surface, large gap, long working period, small head loss and the like. The fiber ball filter material is treated by repeatedly contacting a great amount of biological groups attached to the surface of the fiber ball filter material with oxygenated sewage to degrade suspended matters and organic matters in the sewage, and is widely used in various water treatment industries.
For example, the invention of application publication number CN113998820a discloses a water treatment integrated machine device for coal mine, comprising a precipitation tank, a fiber ball filter, a cartridge filter and a dosing filter box; the water inlet at the lower part of the settling tanks is connected with a water pump of a mine sump or a water drainage gate through a pipeline and takes water from the water pump, and each settling tank comprises a plurality of layers of filter screens which are vertically overlapped; the water inlet at the upper part of the fiber ball filters is communicated with the water outlet at the upper part of the sedimentation tank, and each fiber ball filter comprises a plurality of layers of fiber ball filter plates which are vertically overlapped; the cartridge filter is connected with a water outlet at the lower part of the fiber ball filter, and the cartridge filter comprises a filter core; the water inlet of the dosing filter box is communicated with the water outlet of the cartridge filter, each dosing filter box comprises a plurality of filter layers which are arranged in parallel left and right, and the filter function of each filter layer is different; the water treatment integrated machine equipment can comprehensively treat underground wastewater conveyed by a water pump of a mine sump or a drainage gate. The invention with the application publication number of CN113562913A discloses a treatment method of hydrazine hydrate production wastewater, which comprises the steps of introducing the hydrazine hydrate production wastewater into a fiber ball for filtering and impurity removing, then carrying out resin adsorption on the wastewater to remove most of COD, and carrying out three-phase catalytic oxidation on the adsorbed wastewater to reduce the COD, ammonia nitrogen and the like in the wastewater to be below the emission standard. The method solves the problem that the wastewater is difficult to discharge up to standard in the hydrazine hydrate production enterprises, greatly reduces the treatment cost of the wastewater, realizes high recycling of sodium chloride in the wastewater produced by the hydrazine hydrate, saves resources, does not produce secondary pollution in the whole process, and has high sewage treatment efficiency and low treatment cost. The invention with the application publication number of CN112979018A provides a synchronous treatment device for ship domestic sewage and desulfurization wastewater, which comprises a mixing tank, a flocculation pump, flocculation air floatation equipment, a compressed air inlet, an intermediate storage tank, a filtering pump, filtering ultraviolet integrated equipment and a collecting storage tank. The flocculation air floatation device comprises a flocculation tank and an air floatation tank, wherein a dosing pump is arranged at the inlet of the top of the flocculation tank, an aeration device is arranged at the bottom of the air floatation tank, and a compressed air inlet is connected with the aeration device. The filtering ultraviolet integrated equipment comprises a filter layer and an ultraviolet layer which are sequentially arranged from top to bottom, a plurality of fiber balls are arranged in the filter layer, and a plurality of ultraviolet lamp equipment are arranged on the side wall of the ultraviolet layer. The outlet of the mixing tank is communicated with the inlet of the flocculation air floatation device through the flocculation pump, the outlet of the flocculation air floatation device is communicated with the inlet of the intermediate storage tank, the outlet of the intermediate storage tank is communicated with the inlet of the filter pump, the outlet of the filter pump is communicated with the top inlet of the filter ultraviolet integrated device, and the bottom outlet of the filter ultraviolet integrated device is communicated with the inlet of the collection storage tank.
The technical solutions already disclosed are not listed here.
However, when the fiber ball filter material is used to perform the input of the fiber ball filter material to each water body to be purified, due to the complexity of the internal environment of the water body, for example, different factors such as different water depths and water surface areas, the quantity of the fiber ball filter materials actually required by each water body to be purified is difficult to predict in advance, the fiber ball filter material needs to be continuously input to test the purifying effect, and whether the water body is completely purified is judged, obviously, the continuously input purifying effect test mode is very complicated, wastes a great amount of labor cost and time cost, and also easily causes the difficulty of insufficient purifying or excessive filtering materials.
Disclosure of Invention
Therefore, the invention provides a sewage treatment strategy adjusting system based on artificial intelligence, which can predict the number of fiber ball filter materials required to be used for completing the complete purification of the water body of the target water surface based on various water surface reference information of the target water surface to be treated by sewage and various configuration data of single fiber ball filter materials by adopting an artificial intelligence model, and is particularly critical that the artificial intelligence model is a convolution neural network subjected to multiple learning for a set number of times, and the value of the set number of times is positively associated with the filling volume of the single fiber ball filter materials, so that the purification effect of each water body and the cost saving of the fiber ball filter materials are considered.
According to an aspect of the invention, the system comprises:
the water surface capturing device is used for acquiring various water surface reference information of the target water surface for sewage treatment to be executed, wherein the various water surface reference information of the target water surface for sewage treatment to be executed comprises the water surface area, the average water depth, the maximum water depth and the water temperature of the target water surface for sewage treatment to be executed;
the filter material analysis device is used for acquiring the filling height, the water inlet height, the filling volume, the water inlet volume and the water filtering speed of the single fiber ball filter material for performing sewage treatment and outputting various configuration data of the single fiber ball filter material;
the quantitative learning device is used for acquiring an artificial intelligent model for performing intelligent prediction on the number of required fiber ball filter materials, wherein the artificial intelligent model is a convolutional neural network subjected to repeated learning for a set number of times, and the value of the set number of times is positively associated with the filling volume of a single fiber ball filter material;
the content storage mechanism is connected with the quantitative learning device and used for storing the artificial intelligent model;
the particle prediction mechanism is respectively connected with the water surface capturing device, the filter material analysis device and the content storage mechanism and is used for inputting various water surface reference information of a target water surface to be subjected to sewage treatment and various configuration data of a single fiber ball filter material into the artificial intelligent model to execute the artificial intelligent model, obtaining the number of the fiber ball filter materials required by the sewage purification of the water body of the target water surface where the sewage treatment to be performed is output by the artificial intelligent model and outputting the number of the fiber ball filter materials as a predicted filter material number;
the artificial intelligent model is a convolutional neural network subjected to repeated learning for a set number of times, and the forward correlation between the value of the set number of times and the filling volume of the single fiber ball filter material comprises the following steps: in each learning of the convolutional neural network, the number of fiber ball filter materials used by the whole water body with the sewage purification is taken as the output of the convolutional neural network, and each item of configuration data of a single fiber ball filter material and each item of water surface reference information corresponding to the whole water body with the sewage purification are taken as the input of the convolutional neural network so as to complete a single learning action of the convolutional neural network.
The sewage treatment strategy adjusting system based on artificial intelligence is simple and convenient to operate and wide in application, and the number of fiber ball filter materials required for complete purification of the water body of the target water surface can be intelligently predicted based on various water surface reference information of the target water surface to be subjected to sewage treatment and various configuration data of single fiber ball filter materials, so that a great deal of labor cost and time cost are avoided, and the dilemma of insufficient purification or excessive filter materials is solved.
Drawings
Embodiments of the present invention will be described below with reference to the accompanying drawings.
Fig. 1 is a schematic view showing an internal structure of an artificial intelligence based sewage treatment strategy adjustment system according to a first embodiment of the present invention.
Fig. 2 is a schematic diagram showing an internal structure of an artificial intelligence based sewage treatment strategy adjustment system according to a second embodiment of the present invention.
Fig. 3 is a schematic view showing an internal structure of an artificial intelligence based sewage treatment strategy adjustment system according to a third embodiment of the present invention.
Detailed Description
Embodiments of the artificial intelligence based wastewater treatment strategy adjustment system of the present invention will be described in detail with reference to the accompanying drawings.
First embodiment
Fig. 1 is a schematic view showing an internal structure of an artificial intelligence based sewage treatment strategy adjustment system according to a first embodiment of the present invention, the system comprising:
the water surface capturing device is used for acquiring various water surface reference information of the target water surface for sewage treatment to be executed, wherein the various water surface reference information of the target water surface for sewage treatment to be executed comprises the water surface area, the average water depth, the maximum water depth and the water temperature of the target water surface for sewage treatment to be executed;
illustratively, the water surface capturing device comprises a plurality of collecting components for respectively collecting the water surface area, the average depth of the water body, the maximum depth value and the water body temperature of the target water surface on which the sewage treatment is to be performed;
for example, the plurality of collecting components are a first collecting component, a second collecting component, a third collecting component and a fourth collecting component, respectively, and are used for collecting the water surface area, the average depth of the water body, the maximum value of the depth and the water body temperature of the target water surface for sewage treatment to be executed respectively;
the filter material analysis device is used for acquiring the filling height, the water inlet height, the filling volume, the water inlet volume and the water filtering speed of the single fiber ball filter material for performing sewage treatment and outputting various configuration data of the single fiber ball filter material;
the quantitative learning device is used for acquiring an artificial intelligent model for performing intelligent prediction on the number of required fiber ball filter materials, wherein the artificial intelligent model is a convolutional neural network subjected to repeated learning for a set number of times, and the value of the set number of times is positively associated with the filling volume of a single fiber ball filter material;
the content storage mechanism is connected with the quantitative learning device and used for storing the artificial intelligent model;
the particle prediction mechanism is respectively connected with the water surface capturing device, the filter material analysis device and the content storage mechanism and is used for inputting various water surface reference information of a target water surface to be subjected to sewage treatment and various configuration data of a single fiber ball filter material into the artificial intelligent model to execute the artificial intelligent model, obtaining the number of the fiber ball filter materials required by the sewage purification of the water body of the target water surface where the sewage treatment to be performed is output by the artificial intelligent model and outputting the number of the fiber ball filter materials as a predicted filter material number;
the artificial intelligent model is a convolutional neural network subjected to repeated learning for a set number of times, and the forward correlation between the value of the set number of times and the filling volume of the single fiber ball filter material comprises the following steps: in each learning of the convolutional neural network, the number of fiber ball filter materials used by the whole water body with the sewage purification is taken as the output of the convolutional neural network, and each item of configuration data of a single fiber ball filter material and each item of water surface reference information corresponding to the whole water body with the sewage purification are taken as the input of the convolutional neural network so as to complete a single learning action of the convolutional neural network.
Second embodiment
Fig. 2 is a schematic diagram showing an internal structure of an artificial intelligence based sewage treatment strategy adjustment system according to a second embodiment of the present invention, including the following components:
the water surface capturing device is used for acquiring various water surface reference information of the target water surface for sewage treatment to be executed, wherein the various water surface reference information of the target water surface for sewage treatment to be executed comprises the water surface area, the average water depth, the maximum water depth and the water temperature of the target water surface for sewage treatment to be executed;
the filter material analysis device is used for acquiring the filling height, the water inlet height, the filling volume, the water inlet volume and the water filtering speed of the single fiber ball filter material for performing sewage treatment and outputting various configuration data of the single fiber ball filter material;
the quantitative learning device is used for acquiring an artificial intelligent model for performing intelligent prediction on the number of required fiber ball filter materials, wherein the artificial intelligent model is a convolutional neural network subjected to repeated learning for a set number of times, and the value of the set number of times is positively associated with the filling volume of a single fiber ball filter material;
the content storage mechanism is connected with the quantitative learning device and used for storing the artificial intelligent model;
the particle prediction mechanism is respectively connected with the water surface capturing device, the filter material analysis device and the content storage mechanism and is used for inputting various water surface reference information of a target water surface to be subjected to sewage treatment and various configuration data of a single fiber ball filter material into the artificial intelligent model to execute the artificial intelligent model, obtaining the number of the fiber ball filter materials required by the sewage purification of the water body of the target water surface where the sewage treatment to be performed is output by the artificial intelligent model and outputting the number of the fiber ball filter materials as a predicted filter material number;
the synchronous control mechanism is respectively connected with the quantitative learning device, the content storage mechanism and the particle prediction mechanism and is used for realizing the synchronous control of the actions of the quantitative learning device, the content storage mechanism and the particle prediction mechanism;
for example, the synchronization control means may employ a falling edge of a rectangular wave to realize synchronization control of the actions of the quantitative learning device, the content storage means, and the particle predicting means.
Third embodiment
Fig. 3 is a schematic view showing an internal structure of an artificial intelligence based sewage treatment strategy adjustment system according to a third embodiment of the present invention, including the following components:
the water surface capturing device is used for acquiring various water surface reference information of the target water surface for sewage treatment to be executed, wherein the various water surface reference information of the target water surface for sewage treatment to be executed comprises the water surface area, the average water depth, the maximum water depth and the water temperature of the target water surface for sewage treatment to be executed;
the filter material analysis device is used for acquiring the filling height, the water inlet height, the filling volume, the water inlet volume and the water filtering speed of the single fiber ball filter material for performing sewage treatment and outputting various configuration data of the single fiber ball filter material;
the quantitative learning device is used for acquiring an artificial intelligent model for performing intelligent prediction on the number of required fiber ball filter materials, wherein the artificial intelligent model is a convolutional neural network subjected to repeated learning for a set number of times, and the value of the set number of times is positively associated with the filling volume of a single fiber ball filter material;
the content storage mechanism is connected with the quantitative learning device and used for storing the artificial intelligent model;
the particle prediction mechanism is respectively connected with the water surface capturing device, the filter material analysis device and the content storage mechanism and is used for inputting various water surface reference information of a target water surface to be subjected to sewage treatment and various configuration data of a single fiber ball filter material into the artificial intelligent model to execute the artificial intelligent model, obtaining the number of the fiber ball filter materials required by the sewage purification of the water body of the target water surface where the sewage treatment to be performed is output by the artificial intelligent model and outputting the number of the fiber ball filter materials as a predicted filter material number;
and the data communication interface is respectively connected with the quantitative learning device, the content storage mechanism and the particle prediction mechanism and is used for establishing parallel communication links between the quantitative learning device, the content storage mechanism and the particle prediction mechanism.
Next, a further explanation of the specific structure of the artificial intelligence-based sewage treatment strategy adjustment system of the present invention will be continued.
In an artificial intelligence based wastewater treatment strategy adjustment system according to various embodiments of the invention:
executing an artificial intelligent model for intelligently predicting the number of fiber ball filter materials, wherein the artificial intelligent model is a convolutional neural network subjected to repeated learning for a set number of times, and the forward correlation between the value of the set number of times and the filling volume of a single fiber ball filter material comprises the following steps: expressing the forward association relation between the value of the set times and the filling volume of the single fiber ball filter material by adopting an information mapping formula;
the forward association relation between the value of the set times and the filling volume of the single fiber ball filter material is expressed by adopting an information mapping formula, and the forward association relation comprises the following steps: using a numerical simulation mode to simulate and test the information mapping formula;
the forward association relation between the value of the set times and the filling volume of the single fiber ball filter material is expressed by adopting an information mapping formula, and the forward association relation comprises the following steps: in the information mapping formula, the value of the set times is used as the output content of the information mapping formula, and the filling volume of the single fiber ball filter material is used as the input content of the information mapping formula.
In an artificial intelligence based wastewater treatment strategy adjustment system according to various embodiments of the invention:
in each learning of the convolutional neural network, taking the number of fiber ball filter materials used by the whole water body with the sewage purification as the output of the convolutional neural network, taking each item of configuration data of a single fiber ball filter material and each item of water surface reference information corresponding to the whole water body with the sewage purification as the input of the convolutional neural network, so as to complete a single learning action of the convolutional neural network, wherein the single learning action comprises the following steps: and simulating the quantity of fiber ball filter materials used by the whole water body with the sewage purification by adopting a MATLAB tool box to serve as the output of the convolutional neural network, and taking each item of configuration data of a single fiber ball filter material and each item of water surface reference information corresponding to the whole water body with the sewage purification as the input of the convolutional neural network so as to complete the single learning action of the convolutional neural network.
In an artificial intelligence based wastewater treatment strategy adjustment system according to various embodiments of the invention:
inputting various water surface reference information of a target water surface to be subjected to sewage treatment and various configuration data of a single fiber ball filter material into the artificial intelligent model to execute the artificial intelligent model, obtaining the number of the fiber ball filter materials required for finishing sewage purification of the water body where the target water surface to be subjected to sewage treatment is positioned and output by the artificial intelligent model as a predicted filter material number, wherein the method comprises the following steps: before various water surface reference information of the target water surface to be subjected to sewage treatment and various configuration data of the single fiber ball filter material are input into the artificial intelligent model, binary value conversion processing is respectively carried out on the various water surface reference information of the target water surface to be subjected to sewage treatment and the various configuration data of the single fiber ball filter material.
And in an artificial intelligence based wastewater treatment strategy adjustment system according to various embodiments of the invention:
the positive correlation of the value of the set times and the filling volume of the single fiber ball filter material comprises the following steps: in each learning of the convolutional neural network, taking the number of fiber ball filter materials used by the whole water body with the sewage purification as the output of the convolutional neural network, taking each item of configuration data of a single fiber ball filter material and each item of water surface reference information corresponding to the whole water body with the sewage purification as the input of the convolutional neural network, so as to complete a single learning action of the convolutional neural network, wherein the single learning action comprises the following steps: the multiple learning of the convolutional neural network corresponds to multiple different whole water bodies which are positioned at different positions and finish sewage purification respectively.
In addition, in the sewage treatment strategy adjustment system based on artificial intelligence, inputting various water surface reference information of a target water surface of sewage treatment to be performed and various configuration data of a single fiber ball filter material into the artificial intelligence model to perform the artificial intelligence model, obtaining the number of fiber ball filter materials required for purifying sewage of a water body where the target water surface of sewage treatment to be performed is output by the artificial intelligence model and outputting the number as a predicted filter material number comprises: the number of the fiber ball filter materials required for purifying the sewage of the water body where the target water surface for completing the sewage treatment is located and output by the artificial intelligent model is in a binary number representation form.
The invention has the technical advantages that:
(1) Acquiring various water surface reference information of a target water surface on which sewage treatment is to be performed and various configuration data of a single fiber ball filter material, so as to provide intelligent prediction basic data for the number of fiber ball filter materials required for performing sewage purification on a water body on which the target water surface on which sewage treatment is to be performed is to use a plurality of fiber ball filter materials;
(2) Acquiring an artificial intelligent model for performing intelligent prediction of the number of fiber ball filter materials, wherein the artificial intelligent model is a convolutional neural network subjected to repeated learning for a set number of times, and the value of the set number of times is positively correlated with the filling volume of a single fiber ball filter material, so that the effectiveness of an intelligent prediction result is ensured;
(3) Inputting various water surface reference information of a target water surface to be subjected to sewage treatment and various configuration data of a single fiber ball filter material into an artificial intelligent model to execute the artificial intelligent model, obtaining the number of the fiber ball filter materials required for finishing sewage purification of the water body where the target water surface to be subjected to sewage treatment is positioned and outputting the number as a predicted filter material number, thereby providing a plurality of predictions of the matched number for the whole water body to be subjected to sewage purification and avoiding insufficient purification or filter material waste.
While embodiments have been described with reference to several exemplary embodiments, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this invention. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the disclosure, the drawings and the appended claims. In addition to various variations and modifications in the component parts and/or arrangements, other alternative uses will be apparent to those skilled in the art.
Claims (9)
1. An artificial intelligence based wastewater treatment strategy adjustment system, the system comprising:
the water surface capturing device is used for acquiring various water surface reference information of the target water surface for sewage treatment to be executed, wherein the various water surface reference information of the target water surface for sewage treatment to be executed comprises the water surface area, the average water depth, the maximum water depth and the water temperature of the target water surface for sewage treatment to be executed;
the filter material analysis device is used for acquiring the filling height, the water inlet height, the filling volume, the water inlet volume and the water filtering speed of the single fiber ball filter material for performing sewage treatment and outputting various configuration data of the single fiber ball filter material;
the quantitative learning device is used for acquiring an artificial intelligent model for performing intelligent prediction on the number of required fiber ball filter materials, wherein the artificial intelligent model is a convolutional neural network subjected to repeated learning for a set number of times, and the value of the set number of times is positively associated with the filling volume of a single fiber ball filter material;
the content storage mechanism is connected with the quantitative learning device and used for storing the artificial intelligent model;
the particle prediction mechanism is respectively connected with the water surface capturing device, the filter material analysis device and the content storage mechanism and is used for inputting various water surface reference information of a target water surface to be subjected to sewage treatment and various configuration data of a single fiber ball filter material into the artificial intelligent model to execute the artificial intelligent model, obtaining the number of the fiber ball filter materials required by the sewage purification of the water body of the target water surface where the sewage treatment to be performed is output by the artificial intelligent model and outputting the number of the fiber ball filter materials as a predicted filter material number;
the artificial intelligent model is a convolutional neural network subjected to repeated learning for a set number of times, and the forward correlation between the value of the set number of times and the filling volume of the single fiber ball filter material comprises the following steps: in each learning of the convolutional neural network, the number of fiber ball filter materials used by the whole water body with the sewage purification is taken as the output of the convolutional neural network, and each item of configuration data of a single fiber ball filter material and each item of water surface reference information corresponding to the whole water body with the sewage purification are taken as the input of the convolutional neural network so as to complete a single learning action of the convolutional neural network.
2. The artificial intelligence based wastewater treatment policy adjustment system according to claim 1, wherein the system further comprises:
and the synchronous control mechanism is respectively connected with the quantitative learning device, the content storage mechanism and the particle prediction mechanism and is used for realizing the synchronous control of the actions of the quantitative learning device, the content storage mechanism and the particle prediction mechanism.
3. The artificial intelligence based wastewater treatment policy adjustment system according to claim 1, wherein the system further comprises:
and the data communication interface is respectively connected with the quantitative learning device, the content storage mechanism and the particle prediction mechanism and is used for establishing parallel communication links between the quantitative learning device, the content storage mechanism and the particle prediction mechanism.
4. An artificial intelligence based sewage treatment policy adjustment system according to any of claims 1-3, wherein:
executing an artificial intelligent model for intelligently predicting the number of fiber ball filter materials, wherein the artificial intelligent model is a convolutional neural network subjected to repeated learning for a set number of times, and the forward correlation between the value of the set number of times and the filling volume of a single fiber ball filter material comprises the following steps: and expressing the forward association relation between the value of the set times and the filling volume of the single fiber ball filter material by adopting an information mapping formula.
5. The artificial intelligence based wastewater treatment policy adjustment system according to claim 4, wherein:
the forward association relation between the value of the set times and the filling volume of the single fiber ball filter material is expressed by adopting an information mapping formula, and the forward association relation comprises the following steps: and carrying out simulation and test of the information mapping formula by using a numerical simulation mode.
6. The artificial intelligence based wastewater treatment policy adjustment system according to claim 5, wherein:
the forward association relation between the value of the set times and the filling volume of the single fiber ball filter material is expressed by adopting an information mapping formula, and the forward association relation comprises the following steps: in the information mapping formula, the value of the set times is used as the output content of the information mapping formula, and the filling volume of the single fiber ball filter material is used as the input content of the information mapping formula.
7. An artificial intelligence based sewage treatment policy adjustment system according to any of claims 1-3, wherein:
in each learning of the convolutional neural network, taking the number of fiber ball filter materials used by the whole water body with the sewage purification as the output of the convolutional neural network, taking each item of configuration data of a single fiber ball filter material and each item of water surface reference information corresponding to the whole water body with the sewage purification as the input of the convolutional neural network, so as to complete a single learning action of the convolutional neural network, wherein the single learning action comprises the following steps: and simulating the quantity of fiber ball filter materials used by the whole water body with the sewage purification by adopting a MATLAB tool box to serve as the output of the convolutional neural network, and taking each item of configuration data of a single fiber ball filter material and each item of water surface reference information corresponding to the whole water body with the sewage purification as the input of the convolutional neural network so as to complete the single learning action of the convolutional neural network.
8. An artificial intelligence based sewage treatment policy adjustment system according to any of claims 1-3, wherein:
inputting various water surface reference information of a target water surface to be subjected to sewage treatment and various configuration data of a single fiber ball filter material into the artificial intelligent model to execute the artificial intelligent model, obtaining the number of the fiber ball filter materials required for finishing sewage purification of the water body where the target water surface to be subjected to sewage treatment is positioned and output by the artificial intelligent model as a predicted filter material number, wherein the method comprises the following steps: before various water surface reference information of the target water surface to be subjected to sewage treatment and various configuration data of the single fiber ball filter material are input into the artificial intelligent model, binary value conversion processing is respectively carried out on the various water surface reference information of the target water surface to be subjected to sewage treatment and the various configuration data of the single fiber ball filter material.
9. An artificial intelligence based sewage treatment policy adjustment system according to any of claims 1-3, wherein:
the positive correlation of the value of the set times and the filling volume of the single fiber ball filter material comprises the following steps: in each learning of the convolutional neural network, taking the number of fiber ball filter materials used by the whole water body with the sewage purification as the output of the convolutional neural network, taking each item of configuration data of a single fiber ball filter material and each item of water surface reference information corresponding to the whole water body with the sewage purification as the input of the convolutional neural network, so as to complete a single learning action of the convolutional neural network, wherein the single learning action comprises the following steps: the multiple learning of the convolutional neural network corresponds to multiple different whole water bodies which are positioned at different positions and finish sewage purification respectively.
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