CN107040595B - Sewage treatment management method and system - Google Patents

Sewage treatment management method and system Download PDF

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Publication number
CN107040595B
CN107040595B CN201710242065.0A CN201710242065A CN107040595B CN 107040595 B CN107040595 B CN 107040595B CN 201710242065 A CN201710242065 A CN 201710242065A CN 107040595 B CN107040595 B CN 107040595B
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water quality
quality data
base station
data
layer
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CN107040595A (en
Inventor
张成彬
郑志根
蒋赛花
皋军
邵星
徐燕萍
王志宏
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Jiangsu Langte Environmental Protection Engineering Co ltd
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Jiangsu Three Environmental Protection Polytron Technologies Inc
Yancheng Institute of Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F9/00—Multistage treatment of water, waste water or sewage
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • 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
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • 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
    • G06N3/088—Non-supervised learning, e.g. competitive learning
    • 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
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
    • H04L67/025—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP] for remote control or remote monitoring of applications
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F1/00—Treatment of water, waste water, or sewage
    • C02F1/001—Processes for the treatment of water whereby the filtration technique is of importance
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F1/00—Treatment of water, waste water, or sewage
    • C02F1/30—Treatment of water, waste water, or sewage by irradiation
    • C02F1/32—Treatment of water, waste water, or sewage by irradiation with ultraviolet light
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F1/00—Treatment of water, waste water, or sewage
    • C02F1/52—Treatment of water, waste water, or sewage by flocculation or precipitation of suspended impurities
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F1/00—Treatment of water, waste water, or sewage
    • C02F2001/007—Processes including a sedimentation step
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F3/00—Biological treatment of water, waste water, or sewage
    • C02F3/30—Aerobic and anaerobic processes
    • 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
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A20/00—Water conservation; Efficient water supply; Efficient water use
    • Y02A20/152—Water filtration

Abstract

The invention provides a sewage treatment management method and a sewage treatment management system. The sewage treatment management method is applied to a sewage treatment management system. The sewage treatment management system comprises a sewage treatment control subsystem, a discharge device and a plurality of treatment devices. The sewage treatment control subsystem comprises a base station gateway, a server, a Web display device and a plurality of acquisition sensors. The plurality of processing devices are connected with the discharging device after being connected in sequence. The plurality of collecting sensors are respectively arranged in the discharging device and the plurality of processing devices. According to the sewage treatment management method and system, the signal intensity fingerprint electronic map and the sampling position electronic map are established, so that data acquisition can be realized, the position of the acquisition sensor for acquiring data can be positioned and displayed on the Web display device, the method and system are more intuitive, workers can monitor the whole process of sewage treatment in a monitoring center, abnormal problems can be found quickly, and coping treatment can be performed.

Description

Sewage treatment management method and system
Technical Field
The invention relates to the technical field of sewage treatment, in particular to a sewage treatment management method and system.
Background
Sewage treatment is a process of purifying sewage to meet the water quality requirement of discharging the sewage into a certain water body or reusing the sewage. Sewage treatment is widely applied to various fields such as buildings, agriculture, traffic, energy, petrifaction, environmental protection, urban landscape, medical treatment, catering and the like, and is increasingly used in daily life of common people. The existing sewage treatment plant occupies a large area, the water quality data and the environmental data of the sewage treatment are generally detected and recorded manually on the spot, and the monitoring is very inconvenient.
Disclosure of Invention
In view of the above, the present invention provides a method and a system for sewage treatment management to solve the above problems.
In order to achieve the purpose, the invention provides the following technical scheme:
a sewage treatment management method is applied to a sewage treatment management system, the sewage treatment management system comprises a sewage treatment control subsystem, a discharge device and a plurality of treatment devices, the sewage treatment control subsystem comprises a base station gateway, a server, a Web display device and a plurality of acquisition sensors, the plurality of treatment devices are connected with the discharge device after being sequentially connected, the plurality of acquisition sensors are respectively arranged in the discharge device and the plurality of treatment devices, and the method comprises the following steps:
the plurality of collecting sensors collect the water quality data discharged from the discharging device and the water quality data and environmental data processed by the plurality of processing devices;
the base station gateway sends a broadcast signal, wherein the broadcast signal comprises a base station node number of the base station gateway sending the broadcast signal;
the plurality of acquisition sensors receive the broadcast signals sent by the base station gateway, record the signal intensity of the received broadcast signals and the base station node number of the base station gateway sending the broadcast signals, and send the acquired processed water quality data, environmental data, discharged water quality data, the signal intensity of the received broadcast signals and the base station node number of the base station gateway sending the broadcast signals to the base station gateway;
the base station gateway receives and forwards the processed water quality data, the environment data, the discharged water quality data and the received signal strength of the broadcast signals sent by the plurality of acquisition sensors and the base station node number of the base station gateway sending the broadcast signals to the server;
the server prestores a plurality of sampling positions, the signal intensity of the broadcast signals sent by the base station gateway and the base station node number of the base station gateway sending the broadcast signals received by the plurality of acquisition sensors at each sampling position so as to establish a signal intensity fingerprint electronic map;
the Web display device prestores a sampling position electronic map, wherein the sampling position electronic map comprises the discharge device, a plurality of processing devices and the positions of the acquisition sensors in the discharge device and the processing devices;
the server receives the signal intensity of the broadcast signals received by the plurality of acquisition sensors and the base station node number of the base station gateway sending the broadcast signals, the signal intensity of the broadcast signals sent by the base station gateway and the base station node number of the base station gateway sending the broadcast signals are received by the plurality of pre-stored acquisition sensors at each sampling position, the sampling positions of the plurality of acquisition sensors are judged, and the processed water quality data, the environment data, the discharged water quality data and the sampling positions of the plurality of acquisition sensors are sent to the Web display device;
the Web display device receives the processed water quality data, the environment data, the discharged water quality data and the sampling positions of the plurality of acquisition sensors, and displays the sampling positions of the acquisition sensors, the acquired processed water quality data, the acquired environment data and the discharged water quality data at the corresponding positions of a prestored sampling position electronic map;
wherein, the water quality data processing comprises: first chemical oxygen demand, first total nitrogen volume, first total phosphorus volume, first ammonia nitrogen volume and first turbidity, environmental data includes temperature, dissolved oxygen concentration, pH value and mixed liquid sludge concentration, it includes to discharge water quality data: a second chemical oxygen demand, a second total nitrogen amount, a second total phosphorus amount, a second ammonia nitrogen amount, and a second turbidity.
Optionally, the server has pre-stored emission standards and a plurality of historical treated water quality data, environmental data and emission water quality data, the method further comprising the steps performed by the server of:
constructing a deep reliability network model according to historical treated water quality data, environmental data and discharged water quality data;
inputting the received processed water quality data and the received environmental data into the deep confidence network model for prediction to obtain predicted discharged water quality data;
and when the predicted discharge water quality data does not meet the discharge standard, controlling the corresponding treatment device to adjust treatment parameters so as to change the environmental data and the treatment water quality data of the corresponding treatment device.
Optionally, the method further comprises:
the server sends the predicted discharged water quality data to the Web display device;
the Web display device is also used for displaying the predicted discharged water quality data in the vicinity of the processed water quality data and the environmental data corresponding to the predicted discharged water quality data in an electronic map of sampling positions.
Optionally, the step of constructing a deep confidence network model from historical treated water quality data, environmental data and discharge water quality data comprises the steps performed by the server of:
normalizing the historical treated water quality data, the environmental data and the discharged water quality data;
taking the normalized historical processed water quality data and environmental data as input data, solving network parameters by using a contrast divergence algorithm, training three layers of RBMs layer by adopting an unsupervised greedy training method layer by layer, and constructing an initial deep belief network model;
and according to the normalized historical discharged water quality data, fine adjustment is carried out on the initial deep confidence network model by adopting a BP algorithm, the network parameters of the initial deep confidence network model are optimized, and the deep confidence network model is constructed.
Optionally, the step of inputting the received processed water quality data and environmental data into the deep confidence network model for prediction, and obtaining the predicted discharge water quality data includes the following steps performed by the server:
normalizing the received processed water quality data and the received environment data to obtain normalized processed water quality data and normalized environment data;
inputting the normalized processed water quality data and the normalized environment data into the deep confidence network model for prediction to obtain normalized predicted discharged water quality data;
and normalizing the normalized predicted discharged water quality data to obtain predicted discharged water quality data.
Optionally, the step of using the normalized historical processed water quality data and environmental data as input data, solving network parameters by using a contrastive divergence algorithm, training three layers of RBMs layer by using an unsupervised greedy training method layer by layer, and constructing an initial deep belief network model includes the following steps executed by the server:
initializing network parameters;
inputting normalized historical processed water quality data and environmental data serving as input data into a visual layer of a first layer RBM, and training the first layer RBM through a contrast divergence algorithm until an energy function is converged;
fixing network parameters of a first layer of RBM, taking an implicit layer of the first layer of RBM as a visual layer of a second layer of RBM, and training the second layer of RBM through a contrast divergence algorithm until an energy function is converged;
and fixing the network parameters of the second layer RBM, taking the hidden layer of the second layer RBM as the visual layer of the third layer RBM, and training the third layer RBM through a contrast divergence algorithm until the energy function is converged.
Optionally, the step of initializing the network parameter includes:
setting the number of RBM layers to be 3, and setting the number of RBM nodes in each layer;
the learning rate is 0.01, and the iteration cycle is 200;
will be offset by an amount aiAnd offset bjInitialization is 0;
interlayer connection weight wijSet to follow a normal distribution with a mean of 0 and a standard deviation of 1.
Optionally, the number of RBM layers is set to 3, and the step of setting the number of RBM nodes in each layer includes the following steps executed by the server:
the number of nodes of the visible layer of the first layer of RBM is equal to the number of input normalized historical processed water quality data and environment data;
the node number of the visible layer of the second layer of RBM and the node number of the visible layer of the third layer of RBM are equal to and more than or equal to the node number of the visible layer of the first layer of RBM;
the number of nodes of the hidden layer of the third layer RBM is 5.
A sewage treatment management system comprises a sewage treatment control subsystem, a discharge device and a plurality of treatment devices, wherein the sewage treatment control subsystem comprises a base station gateway, a server, a Web display device and a plurality of acquisition sensors;
the plurality of treatment devices are connected with the discharge device after being sequentially connected;
the plurality of acquisition sensors are respectively arranged in the discharging device and the plurality of processing devices and are used for acquiring the discharged water quality data in the discharging device and the processed water quality data and the environmental data in the plurality of processing devices;
the base station gateway is used for sending a broadcast signal, and the broadcast signal comprises a base station node number of the base station gateway sending the broadcast signal;
the plurality of acquisition sensors are also used for receiving the broadcast signals sent by the base station gateway, recording the signal intensity of the received broadcast signals and the base station node number of the base station gateway sending the broadcast signals, and sending the acquired processed water quality data, environmental data, discharged water quality data, the signal intensity of the received broadcast signals and the base station node number of the base station gateway sending the broadcast signals to the base station gateway;
the base station gateway is also used for receiving and forwarding the processed water quality data, the environment data, the discharged water quality data and the received signal strength of the broadcast signals sent by the plurality of acquisition sensors and sending the base station node number of the base station gateway of the broadcast signals to the server;
the server is used for prestoring a plurality of sampling positions, receiving the signal intensity of the broadcast signal sent by the base station gateway and the base station node number of the base station gateway sending the broadcast signal at each sampling position by the plurality of acquisition sensors so as to establish a signal intensity fingerprint electronic map;
the Web display device is used for prestoring a sampling position electronic map, and the sampling position electronic map comprises the discharge device, a plurality of processing devices and the positions of the acquisition sensors in the discharge device and the processing devices;
the server is further configured to receive the signal strength of the broadcast signal received by the plurality of acquisition sensors and the base station node number of the base station gateway sending the broadcast signal, which are sent by the base station gateway, compare the signal strength of the broadcast signal sent by the base station gateway and the base station node number of the base station gateway sending the broadcast signal, which are received by the plurality of acquisition sensors at each sampling position in advance, judge the sampling positions of the plurality of acquisition sensors, and send the processed water quality data, the environmental data, the discharged water quality data and the sampling positions of the plurality of acquisition sensors to the Web display device;
the Web display device is also used for receiving the processed water quality data, the environment data, the discharged water quality data and the sampling positions of the plurality of acquisition sensors, and displaying the sampling positions of the acquisition sensors, the acquired processed water quality data, the acquired environment data and the discharged water quality data at the corresponding positions of a prestored sampling position electronic map;
the server is also used for prestoring emission standards and a plurality of historical treated water quality data, environmental data and discharged water quality data, constructing a deep confidence network model according to the historical treated water quality data, the environmental data and the discharged water quality data, inputting the received treated water quality data and the environmental data into the deep confidence network model for prediction to obtain predicted discharged water quality data, and controlling corresponding treatment devices to adjust treatment parameters to change the environmental data and the treated water quality data of the corresponding treatment devices when the predicted discharged water quality data does not meet the emission standards;
wherein the plurality of acquisition sensors comprises: a plurality of chemical oxygen demand acquisition sensors, total nitrogen acquisition sensors, total phosphorus acquisition sensors, ammonia nitrogen acquisition sensors, turbidity acquisition sensors, temperature acquisition sensors, dissolved oxygen concentration acquisition sensors, PH acquisition sensors and mixed liquor sludge concentration acquisition sensors.
Optionally, the plurality of collecting sensors are integrated with a ZigBee module, the base station gateway is integrated with a ZigBee module and a WiFi module, the server is integrated with a WiFi module, the plurality of collecting sensors communicate with the base station gateway through a ZigBee technology, and the base station gateway communicates with the server through a WiFi technology.
According to the sewage treatment management method and system provided by the invention, the signal intensity fingerprint electronic map and the sampling position electronic map are established, so that not only can data acquisition be realized, but also the position of the acquisition sensor for acquiring data can be positioned and displayed on the Web display device, and the method and system are more intuitive, are beneficial to monitoring the whole process of sewage treatment by workers in a monitoring center, and are capable of rapidly finding abnormal problems and dealing with the abnormal problems.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments will be briefly described below. It is appreciated that the following drawings depict only some embodiments of the invention and are therefore not to be considered limiting of its scope, for those skilled in the art will be able to derive additional related drawings therefrom without the benefit of the inventive faculty.
Fig. 1 is a block diagram of a sewage treatment management system according to a preferred embodiment of the present invention.
Fig. 2 is a block diagram of a server according to a preferred embodiment of the present invention.
Fig. 3 is a block diagram of a Web display apparatus according to a preferred embodiment of the present invention.
FIG. 4 is a flow chart of a sewage treatment management method according to a preferred embodiment of the present invention.
FIG. 5 is a flow chart of another sewage treatment management method according to the preferred embodiment of the present invention.
Fig. 6 is a flowchart of the substeps of step S190 in fig. 5.
Fig. 7 is a flowchart of the substeps of substep S193 in fig. 6.
Fig. 8 is a flowchart of sub-steps of substep S1931 in fig. 7.
Fig. 9 is a flowchart of sub-steps of step S200 in fig. 5.
Icon: 1-a sewage treatment management system; 30-a discharge device; 50-a processing device; 11-a base station gateway; 13-a server; 15-a Web presentation means; 17-an acquisition sensor; 131-a memory; 133-a processor; 135-a network module; 151-login module; 153-an acquisition module; 155-inspection module.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely a few embodiments of the invention, and not all embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. In the description of the present invention, the terms "first," "second," "third," "fourth," and the like are used merely to distinguish one description from another, and are not to be construed as merely or implying relative importance.
Referring to fig. 1, an embodiment of the present invention provides a sewage treatment management system 1. The sewage treatment management system 1 includes a sewage treatment control subsystem, a discharge device 30, and a plurality of treatment devices 50. The sewage treatment control subsystem comprises a base station gateway 11, a server 13, a Web display device 15 and a plurality of acquisition sensors 17.
The plurality of processing devices 50 are connected to the discharging device 30 after being connected in sequence. The plurality of treatment devices 50 may include a coarse grid and sewage lift pump room, a fine grid, an aeration grit chamber, an AA0 reaction tank, a secondary sedimentation tank, a blower room, a second distribution well, a mixing tank, a mesh flocculation tank, an inclined plate sedimentation tank, a fiber rotary disc filter tank, a dosing room and an ultraviolet disinfection room. Wherein, the AAO reaction tank comprises an anaerobic tank, an anoxic tank and an aerobic tank. The plurality of processing devices 50 are connected to the discharging device 30 after being connected in sequence.
The plurality of collecting sensors 17 are respectively disposed in the discharging device 30 and the plurality of processing devices 50, and are configured to collect the discharged water quality data in the discharging device 30 and the processed water quality data and the environmental data in the plurality of processing devices 50. Wherein the plurality of acquisition sensors 17 comprises: a plurality of chemical oxygen demand acquisition sensors, total nitrogen acquisition sensors, total phosphorus acquisition sensors, ammonia nitrogen acquisition sensors, turbidity acquisition sensors, temperature acquisition sensors, dissolved oxygen concentration acquisition sensors, PH acquisition sensors and mixed liquor sludge concentration acquisition sensors. Will according to the actual demand a plurality of collection sensor 17 set up respectively in discharging equipment 30 and a plurality of processing apparatus 50 are interior, for example can set up a plurality of chemical oxygen demand collection sensor, total nitrogen volume collection sensor, total phosphorus volume collection sensor, ammonia nitrogen volume collection sensor, turbidity collection sensor in thick grid and sewage elevator pump room for obtain into water quality of water data. A plurality of chemical oxygen demand acquisition sensors, total nitrogen acquisition sensors, total phosphorus acquisition sensors, ammonia nitrogen acquisition sensors, turbidity acquisition sensors, temperature acquisition sensors, dissolved oxygen concentration acquisition sensors, PH acquisition sensors and mixed liquid sludge concentration acquisition sensors are flexibly arranged in the fine grating, the aeration grit chamber, the AA0 reaction tank, the secondary sedimentation tank, the blower room, the second distribution well, the mixing tank, the grid flocculation tank, the inclined plate sedimentation tank, the fiber rotary disc filter tank, the dosing room and the ultraviolet disinfection room, and are used for acquiring the data of the treated water quality. A plurality of chemical oxygen demand collection sensors, total nitrogen collection sensors, total phosphorus collection sensors, ammonia nitrogen collection sensors, and turbidity collection sensors may be provided in the discharge device 30 for obtaining the discharge water quality data.
Wherein, the water quality data processing comprises: a first chemical oxygen demand, a first total nitrogen amount, a first total phosphorus amount, a first ammonia nitrogen amount, and a first turbidity. The environmental data includes temperature, dissolved oxygen concentration, PH, and mixed liquor sludge concentration. The discharge water quality data includes: a second chemical oxygen demand, a second total nitrogen amount, a second total phosphorus amount, a second ammonia nitrogen amount, and a second turbidity.
In the above description, the terms "first" and "second" are used only for distinguishing the description, and respectively indicate parameters within the treatment device 50 and the discharge device 30. For example, the first chemical oxygen demand represents the chemical oxygen demand within the treatment device 50, including historical chemical oxygen demand and actual chemical oxygen demand within the treatment device 50. The second chemical oxygen demand represents the chemical oxygen demand within discharge device 30, including historical chemical oxygen demand, predicted and actual chemical oxygen demand within discharge device 30.
The base station gateway 11 is configured to send a broadcast signal, where the broadcast signal includes a base station node number of the base station gateway 11 that sends the broadcast signal. Optionally, the base station gateway 11 is integrated with a ZigBee module, and the plurality of acquisition sensors 17 are integrated with a ZigBee module. The plurality of acquisition sensors 17 and the base station gateway 11 communicate by the ZigBee technology.
The plurality of collecting sensors 17 are further configured to receive the broadcast signal sent by the base station gateway 11, record the signal intensity of the received broadcast signal and the base station node number of the base station gateway 11 that sends the broadcast signal, and send the collected processed water quality data, environmental data, discharged water quality data, the signal intensity of the received broadcast signal and the base station node number of the base station gateway 11 that sends the broadcast signal to the base station gateway 11.
The base station gateway 11 is further configured to receive and forward the processed water quality data, the environmental data, the discharged water quality data, the signal strength of the received broadcast signal, and the base station node number of the base station gateway 11 that transmits the broadcast signal, which are sent by the plurality of collecting sensors 17, to the server 13. Optionally, the base station gateway 11 is further integrated with a WiFi module, and the server 13 is integrated with a WiFi module. The base station gateway 11 and the server 13 communicate via WiFi technology.
The server 13 is configured to prestore a plurality of sampling locations, where the plurality of acquisition sensors 17 receive, at each of the sampling locations, the signal strength of the broadcast signal sent by the base station gateway 11 and the base station node number of the base station gateway 11 that sends the broadcast signal, so as to establish a signal strength fingerprint electronic map.
The server 13 may be, but is not limited to, a web server, a database server, an ftp (file transfer protocol) server, and the like. Referring to fig. 2, the server 13 may include a memory 131, a processor 133 and a network module 135.
The memory 131, the processor 133 and the network module 135 are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 131 includes at least one software functional module which can be stored in the memory 131 in the form of software or firmware (firmware), and the processor 133 executes various functional applications and data processing by running the software programs and modules stored in the memory 131, that is, implements the sewage treatment management method in the embodiment of the present invention.
The Memory 131 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 131 is used for storing a program, and the processor 133 executes the program after receiving an execution instruction. The memory 131 includes a database in which a plurality of historical treated water quality data, environmental data, and discharged water quality data are prestored.
The processor 133 may be an integrated circuit chip having signal processing capabilities. The Processor 133 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like. But may also be a Digital Signal Processor (DSP)), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The various methods, steps and logic blocks disclosed in embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor 133 may be any conventional processor or the like.
The network module 135 is used for establishing a communication connection between the server 13 and an external communication terminal through a network, and implementing the transceiving operation of network signals and data. The network module 135 is integrated with a WiFi module, and the base station gateway 11 and the server 13 communicate by WiFi technology.
And the Web display device 15 is used for prestoring an electronic map of the sampling position. The electronic map of sampled locations includes the locations of the discharge devices 30, the plurality of processing devices 50, and the location of each of the plurality of acquisition sensors 17 within the discharge devices 30 and the plurality of processing devices 50. I.e. the electronic map of sampled positions comprises the positions of the respective acquisition sensors 17, processing means 50 and discharge means 30.
The server 13 is further configured to receive the signal strength of the broadcast signal received by the plurality of collection sensors 17 sent by the base station gateway 11 and the base station node number of the base station gateway 11 sending the broadcast signal, compare the signal strength of the broadcast signal sent by the base station gateway 11 with the base station node number of the base station gateway 11 sending the broadcast signal, which is pre-stored in each sampling position of the plurality of collection sensors 17, determine the sampling positions of the plurality of collection sensors 17, and send the processed water quality data, the environmental data, the discharged water quality data and the sampling positions of the plurality of collection sensors 17 to the Web display device 15.
The Web display device 15 is further configured to receive the processed water quality data, the environmental data, the discharged water quality data and the sampling positions of the plurality of collecting sensors 17, and display the sampling positions of the collecting sensors 17, the collected processed water quality data, the collected environmental data and the collected discharged water quality data at corresponding positions of a pre-stored electronic map of the sampling positions.
The Web presentation device 15 is mainly responsible for data presentation, accessing the server 13, and obtaining stored data from the server 13. Optionally, referring to fig. 3, the Web presentation apparatus 15 includes a login module 151, a collection module 153, and a verification module 155. The Web presentation means 15 may be provided at a monitoring center of a sewage treatment plant. The login module 151 is used for user login. Entering a different username and password at the login module 151 may enter the acquisition module 153 or the verification module 155. The acquisition module 153 is configured to pre-store a sampling location electronic map, send an acquisition instruction to the server 13, and control the server 13 to establish a signal strength fingerprint electronic map. After base station gateway 11 is deployed in advance, the acquisition time is set, and server 13 is controlled to acquire the signal intensity of the broadcast signal sent by base station gateway 11 and the base station node number of base station gateway 11 sending the broadcast signal, which are received by each acquisition sensor 17 at different sampling positions, so as to establish a signal intensity fingerprint electronic map. The sampling position refers to a position within the discharge device 30 or the processing device 50 of the acquisition sensor 17. The inspection module 155 is configured to send an inspection instruction to the server 13, receive the processed water quality data, the environmental data, the discharged water quality data and the sampling positions of the plurality of acquisition sensors 17 sent by the server 13, and display the sampling positions of the acquisition sensors 17 and the acquired processed water quality data, the environmental data and the discharged water quality data at corresponding positions of a pre-stored electronic map of the sampling positions.
With the above arrangement, the processed water quality data, the environmental data, or the discharged water quality data collected by each of the collection sensors 17 can be displayed beside each of the collection sensors 17 on the electronic map of the sampling position displayed by the Web display device 15. Or the collected processed water quality data, the collected environmental data or the collected discharged water quality data can be displayed by clicking the icon of the collecting sensor 17 on the electronic map of the sampling position. By adopting the design, the system is more intuitive, and is beneficial to monitoring the whole process of sewage treatment in a monitoring center by workers, finding abnormal problems quickly and dealing with the problems.
The server 13 is further configured to pre-store a discharge standard and a plurality of historical treated water quality data, environmental data, and discharged water quality data, construct a confidence network model according to the historical treated water quality data, the environmental data, and the discharged water quality data, input the received treated water quality data and the received environmental data into the confidence network model for prediction, obtain predicted discharged water quality data, and control the corresponding processing device 50 to adjust processing parameters to change the environmental data and the treated water quality data of the corresponding processing device 50 when the predicted discharged water quality data does not meet the discharge standard, thereby improving the processing efficiency of the processing device 50 and the quality of discharged water.
Please refer to fig. 4, which is a flowchart illustrating a sewage treatment management method according to a preferred embodiment of the present invention. The method steps defined by the process related to the method can be implemented by the sewage treatment management system 1. The specific flow shown in fig. 4 will be described in detail below.
In step S110, the plurality of collection sensors 17 collect the discharge water quality data in the discharge device 30 and the treated water quality data and the environmental data in the plurality of treatment devices 50.
Wherein, the water quality data processing comprises: first chemical oxygen demand, first total nitrogen volume, first total phosphorus volume, first ammonia nitrogen volume and first turbidity, environmental data includes temperature, dissolved oxygen concentration, pH value and mixed liquid sludge concentration, it includes to discharge water quality data: a second chemical oxygen demand, a second total nitrogen amount, a second total phosphorus amount, a second ammonia nitrogen amount, and a second turbidity.
Step S120, the base station gateway 11 transmits a broadcast signal, where the broadcast signal includes a base station node number of the base station gateway 11 that transmits the broadcast signal.
Step S130, the plurality of collection sensors 17 receive the broadcast signal sent by the base station gateway 11, record the signal intensity of the received broadcast signal and the base station node number of the base station gateway 11 that sends the broadcast signal, and send the collected processed water quality data, environmental data, discharged water quality data, the signal intensity of the received broadcast signal and the base station node number of the base station gateway 11 that sends the broadcast signal to the base station gateway 11.
Step S140, the base station gateway 11 receives and forwards the processed water quality data, the environmental data, the discharged water quality data, the signal strength of the received broadcast signal and the base station node number of the base station gateway 11 that transmits the broadcast signal, which are sent by the plurality of collecting sensors 17, to the server 13.
Step S150, the server 13 prestores a plurality of sampling positions, the signal strength of the broadcast signal sent by the base station gateway 11 received at each of the sampling positions by the plurality of collecting sensors 17, and the base station node number of the base station gateway 11 sending the broadcast signal, so as to establish a signal strength fingerprint electronic map.
In step S160, the Web display device 15 prestores a sampling location electronic map, where the sampling location electronic map includes the discharge device 30, the plurality of processing devices 50, and the locations of the respective acquisition sensors 17 in the discharge device 30 and the plurality of processing devices 50.
Step S170, the server 13 receives the signal strength of the broadcast signal received by the plurality of collection sensors 17 sent by the base station gateway 11 and the base station node number of the base station gateway 11 sending the broadcast signal, compares the signal strength of the broadcast signal sent by the base station gateway 11 with the pre-stored signal strength of the broadcast signal received by the plurality of collection sensors 17 at each sampling position and the base station node number of the base station gateway 11 sending the broadcast signal, determines the sampling positions of the plurality of collection sensors 17, and sends the processed water quality data, the environmental data, the discharged water quality data and the sampling positions of the plurality of collection sensors 17 to the Web display device 15.
Step S180, the Web display device 15 receives the processed water quality data, the environmental data, the discharged water quality data and the sampling positions of the plurality of collecting sensors 17, and displays the sampling positions of the collecting sensors 17, the collected processed water quality data, the collected environmental data and the collected discharged water quality data at the corresponding positions of the pre-stored electronic map of the sampling positions.
The server 13 prestores the discharge standard and a plurality of historical processed water quality data, environmental data and discharged water quality data, referring to fig. 5, the method further includes step S190, step S200, step S210, step S220 and step S230.
In step S190, the server 13 constructs a confidence network model according to the historical processed water quality data, the environmental data, and the discharged water quality data.
Referring to fig. 6, step S190 includes sub-step S191, sub-step S193, and sub-step S195.
And a substep S191 of normalizing the historical treated water quality data, environmental data and discharged water quality data.
Because the historical treated water quality data, the environmental data and the discharged water quality data have different factors such as unit and value, before constructing the deep confidence network model, the historical treated water quality data, the environmental data and the discharged water quality data need to be normalized, and the normalization formula can be as follows:
Figure BDA0001267266230000181
in the formula, xiOne of treated water quality data, environmental data and discharge water quality data representing history; x is the number ofminDenotes xiThe minimum value of the corresponding type of data; x is the number ofmaxDenotes xiThe maximum value of the corresponding type of data; x is the number ofi' denotes normalized xi. For example, xiRepresenting a first chemical oxygen demand; x is the number ofminRepresents the minimum value of all the first chemical oxygen demand; x is the number ofmaxRepresents the maximum value of all the first chemical oxygen demand; x is the number ofi' denotes normalized xi。
And a substep S193, taking the normalized historical processed water quality data and the normalized environmental data as input data, solving network parameters by using a contrast divergence algorithm, adopting an unsupervised layer-by-layer greedy training method, training three layers of RBMs layer by layer, and constructing an initial deep belief network model.
Referring to fig. 7, the sub-step S193 includes a sub-step S1931, a sub-step S1933, a sub-step S1935, and a sub-step S1937.
S1931, initializing network parameters.
Referring to fig. 8, the substep S1931 includes a substep S19311, a substep S19313, a substep S19315, and a substep S19317.
In the substep S19311, the number of RBM layers is set to 3, and the number of RBM nodes in each layer is set.
The node number of the visible layer of the first layer of RBM is equal to the number of the inputted normalized historical processed water quality data and environment data, the node number of the visible layer of the second layer of RBM is equal to the node number of the visible layer of the third layer of RBM and is more than or equal to the node number of the visible layer of the first layer of RBM, and the node number of the hidden layer of the third layer of RBM is 5. For example, if the number of the inputted normalized historical processed water quality data and environmental data is 100, the number of nodes in the visible layer of the first-layer RBM is 100. The number of nodes of the visible layer of the second layer RBM and the visible layer of the third layer RBM is more than or equal to 100, for example, 100 and 200. The number of nodes of the hidden layer of the third layer RBM is 5.
In substep S19313, the learning rate is 0.01 and the iteration cycle 200 is repeated.
Substep S19315, biasing by an amount aiAnd offset bjThe initialization is 0.
Substep S19317, interlayer connection weight wijSet to follow a normal distribution with a mean of 0 and a standard deviation of 1.
And a substep S1933 of inputting the normalized historical processed water quality data and the environmental data serving as input data into a visual layer of the first layer RBM, and training the first layer RBM through a contrast divergence algorithm until an energy function converges.
And a substep S1935 of fixing the network parameters of the first layer of RBMs, taking the hidden layer of the first layer of RBMs as the visual layer of the second layer of RBMs, and training the second layer of RBMs through a contrast divergence algorithm until the energy function converges.
And a substep S1937 of fixing the network parameters of the second layer RBM, taking the hidden layer of the second layer RBM as the visual layer of the third layer RBM, and training the third layer RBM through a contrast divergence algorithm until the energy function converges.
And a substep S195, fine-tuning the initial deep confidence network model by adopting a BP algorithm according to the normalized historical discharged water quality data, optimizing the network parameters of the initial deep confidence network model, and constructing the deep confidence network model.
And constructing a loss function according to the normalized historical discharged water quality data and the predicted discharged water quality data output by the initial deep credibility network model. Optionally, the loss function is a cross-entropy loss function. When the activation function is Sigmoid, compared with the mean square error loss function, the cross entropy loss function is adopted, so that the problem of low convergence speed of the BP algorithm is solved. And carrying out multiple times of fine adjustment on the whole network parameter by adopting a BP algorithm until the loss function value is smaller than a threshold value, and taking the fine-adjusted network parameter as the network parameter of the deep-credibility network model.
Step S200, the server 13 inputs the received processed water quality data and the received environment data into the deep confidence network model for prediction, and predicted discharged water quality data is obtained.
Referring to fig. 9, step S200 includes sub-step S201, sub-step S203, and sub-step S205.
And a substep S201 of normalizing the received processed water quality data and the received environment data to obtain normalized processed water quality data and normalized environment data.
Wherein, when normalizing the received processed water quality data and the environmental data in the substep S201, the normalization formula is the same as that in the substep S191.
And a substep S203, inputting the normalized processed water quality data and the normalized environment data into the deep confidence network model for prediction to obtain normalized predicted discharged water quality data.
And a substep S205 of normalizing the normalized predicted discharged water quality data to obtain predicted discharged water quality data.
Wherein, when the normalization prediction discharge water quality data is denormalized, the adopted denormalization formula is as follows:
x″′i＝x″i×(xmax-xmin)+xmin
in the formula, x ″)iRepresenting the normalized predicted effluent quality data; x ″)iDenotes denormalization x ″iAnd obtaining the data of the predicted discharge water quality.
Step S210, when the predicted discharge water quality data does not meet the discharge standard, the server 13 controls the corresponding processing device 50 to adjust the processing parameters, so as to change the environmental data and the processed water quality data of the corresponding processing device 50.
In step S220, the server 13 sends the predicted discharged water quality data to the Web presentation device 15.
In step S230, the Web showing device 15 is further configured to display the predicted discharged water quality data in the vicinity of the processed water quality data and the environmental data corresponding to the predicted discharged water quality data in the electronic map of the sampling location.
The predicted discharged water quality data is displayed in the vicinity of the treated water quality data and the environmental data corresponding to the predicted discharged water quality data, that is, in the vicinity of the collecting sensor 17 in the corresponding treatment apparatus 50 in the electronic map of the sampling position. With this design, the processing effect of the corresponding processing device 50 is displayed more intuitively. And when the predicted discharge water quality data does not meet the discharge standard, controlling the corresponding treatment device 50 to adjust treatment parameters so as to change the environmental data and the treatment water quality of the corresponding treatment device 50. Optionally, the Web presentation means 15 may also display the predicted discharge water quality data in the vicinity of the corresponding collection sensor 17 within the discharge device 30 in an electronic map of the sampling location.
According to the sewage treatment management method and system provided by the invention, the signal intensity fingerprint electronic map and the sampling position electronic map are established, so that not only can data acquisition be realized, but also the position of the acquisition sensor 17 for acquiring data can be positioned and displayed on the Web display device 15, and the method and system are more intuitive, are beneficial to monitoring the whole process of sewage treatment in a monitoring center by workers, and are capable of rapidly finding abnormal problems and dealing with the abnormal problems. In addition, the sewage treatment management method and the sewage treatment management system provided by the invention are also based on the deep confidence network model, and the association of the discharged water quality data, the treated water quality data and the environmental data is established according to the historical treated water quality data, the environmental data and the essential characteristics of the discharged water quality data, so that the accuracy of the discharge water quality prediction is improved, and the operation is simple and easy to realize. Meanwhile, the server 13 controls the corresponding processing device 50 to adjust the processing parameters when the predicted discharge water quality data does not meet the discharge standard, so as to change the environmental data and the processed water quality data of the corresponding processing device 50, thereby improving the processing efficiency of the processing device 50 and the quality of the discharge water.
In the embodiments provided in the present invention, it should be understood that the disclosed server and method can be implemented in other ways. The server and method embodiments described above are merely illustrative, for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of servers, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, various electronic devices, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element. The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. The utility model provides a sewage treatment management method, its characterized in that is applied to sewage treatment management system, sewage treatment management system includes sewage treatment control subsystem, discharging equipment and a plurality of processing apparatus, sewage treatment control subsystem includes basic station gateway, server, Web display device and a plurality of acquisition sensor, a plurality of processing apparatus connect gradually the back with discharging equipment connects, a plurality of acquisition sensor set up respectively in discharging equipment and a plurality of processing apparatus, the method includes:
the plurality of collecting sensors collect the water quality data discharged from the discharging device and the water quality data and environmental data processed by the plurality of processing devices;
the base station gateway sends a broadcast signal, wherein the broadcast signal comprises a base station node number of the base station gateway sending the broadcast signal;
the plurality of acquisition sensors receive the broadcast signals sent by the base station gateway, record the signal intensity of the received broadcast signals and the base station node number of the base station gateway sending the broadcast signals, and send the acquired processed water quality data, environmental data, discharged water quality data, the signal intensity of the received broadcast signals and the base station node number of the base station gateway sending the broadcast signals to the base station gateway;
the base station gateway receives and forwards the processed water quality data, the environment data, the discharged water quality data and the received signal strength of the broadcast signals sent by the plurality of acquisition sensors and the base station node number of the base station gateway sending the broadcast signals to the server;
the server prestores a plurality of sampling positions, the signal intensity of the broadcast signals sent by the base station gateway and the base station node number of the base station gateway sending the broadcast signals received by the plurality of acquisition sensors at each sampling position so as to establish a signal intensity fingerprint electronic map;
the Web display device prestores a sampling position electronic map, wherein the sampling position electronic map comprises the discharge device, a plurality of processing devices and the positions of the acquisition sensors in the discharge device and the processing devices;
the server receives the signal intensity of the broadcast signals received by the plurality of acquisition sensors and the base station node number of the base station gateway sending the broadcast signals, the signal intensity of the broadcast signals sent by the base station gateway and the base station node number of the base station gateway sending the broadcast signals are received by the plurality of pre-stored acquisition sensors at each sampling position, the sampling positions of the plurality of acquisition sensors are judged, and the processed water quality data, the environment data, the discharged water quality data and the sampling positions of the plurality of acquisition sensors are sent to the Web display device;
the Web display device receives the processed water quality data, the environment data, the discharged water quality data and the sampling positions of the plurality of acquisition sensors, and displays the sampling positions of the acquisition sensors, the acquired processed water quality data, the acquired environment data and the discharged water quality data at the corresponding positions of a prestored sampling position electronic map;
wherein, the water quality data processing comprises: first chemical oxygen demand, first total nitrogen volume, first total phosphorus volume, first ammonia nitrogen volume and first turbidity, environmental data includes temperature, dissolved oxygen concentration, pH value and mixed liquid sludge concentration, it includes to discharge water quality data: a second chemical oxygen demand, a second total nitrogen amount, a second total phosphorus amount, a second ammonia nitrogen amount, and a second turbidity.
2. The wastewater treatment management method according to claim 1, wherein the server is pre-stored with emission standards and a plurality of historical treatment water quality data, environmental data, and emission water quality data, the method further comprising the steps performed by the server of:
constructing a deep reliability network model according to historical treated water quality data, environmental data and discharged water quality data;
inputting the received processed water quality data and the received environmental data into the deep confidence network model for prediction to obtain predicted discharged water quality data;
and when the predicted discharge water quality data does not meet the discharge standard, controlling the corresponding treatment device to adjust treatment parameters so as to change the environmental data and the treatment water quality data of the corresponding treatment device.
3. The wastewater treatment management method according to claim 2, further comprising:
the server sends the predicted discharged water quality data to the Web display device;
the Web display device is also used for displaying the predicted discharged water quality data in the vicinity of the processed water quality data and the environmental data corresponding to the predicted discharged water quality data in an electronic map of sampling positions.
4. The wastewater treatment management method according to claim 2, wherein the step of constructing a deep belief network model from the historical treatment water quality data, environmental data, and discharge water quality data comprises the steps performed by the server of:
normalizing the historical treated water quality data, the environmental data and the discharged water quality data;
taking the normalized historical processed water quality data and environmental data as input data, solving network parameters by using a contrast divergence algorithm, training three layers of RBMs layer by adopting an unsupervised greedy training method layer by layer, and constructing an initial deep belief network model;
and according to the normalized historical discharged water quality data, fine adjustment is carried out on the initial deep confidence network model by adopting a BP algorithm, the network parameters of the initial deep confidence network model are optimized, and the deep confidence network model is constructed.
5. The wastewater treatment management method according to claim 4, wherein the step of inputting the received treatment water quality data and environmental data into the depth network model for prediction and obtaining the predicted discharge water quality data comprises the steps of, executed by the server:
normalizing the received processed water quality data and the received environment data to obtain normalized processed water quality data and normalized environment data;
inputting the normalized processed water quality data and the normalized environment data into the deep confidence network model for prediction to obtain normalized predicted discharged water quality data;
and normalizing the normalized predicted discharged water quality data to obtain predicted discharged water quality data.
6. The wastewater treatment management method of claim 4, wherein the step of using the normalized historical treatment water quality data and environmental data as input data, solving network parameters using a contrastive divergence algorithm, employing an unsupervised greedy-by-layer training method to train three-layer RBMs layer by layer, and constructing an initial deep belief network model comprises the steps performed by the server of:
initializing network parameters;
inputting normalized historical processed water quality data and environmental data serving as input data into a visual layer of a first layer RBM, and training the first layer RBM through a contrast divergence algorithm until an energy function is converged;
fixing network parameters of a first layer of RBM, taking an implicit layer of the first layer of RBM as a visual layer of a second layer of RBM, and training the second layer of RBM through a contrast divergence algorithm until an energy function is converged;
and fixing the network parameters of the second layer RBM, taking the hidden layer of the second layer RBM as the visual layer of the third layer RBM, and training the third layer RBM through a contrast divergence algorithm until the energy function is converged.
7. The wastewater treatment management method according to claim 6, wherein the step of initializing network parameters comprises:
setting the number of RBM layers to be 3, and setting the number of RBM nodes in each layer;
the learning rate is 0.01, and the iteration cycle is 200;
will be offset by an amount aiAnd offset bjInitialization is 0;
interlayer connection weight wijSet to follow a normal distribution with a mean of 0 and a standard deviation of 1.
8. The wastewater treatment management method according to claim 7, wherein the number of RBM layers is set to 3, and the step of setting the number of RBM nodes in each layer comprises the following steps performed by the server:
the number of nodes of the visible layer of the first layer of RBM is equal to the number of input normalized historical processed water quality data and environment data;
the node number of the visible layer of the second layer of RBM and the node number of the visible layer of the third layer of RBM are equal to and more than or equal to the node number of the visible layer of the first layer of RBM;
the number of nodes of the hidden layer of the third layer RBM is 5.
9. A sewage treatment management system is characterized by comprising a sewage treatment control subsystem, a discharge device and a plurality of treatment devices, wherein the sewage treatment control subsystem comprises a base station gateway, a server, a Web display device and a plurality of acquisition sensors;
the plurality of treatment devices are connected with the discharge device after being sequentially connected;
the plurality of acquisition sensors are respectively arranged in the discharging device and the plurality of processing devices and are used for acquiring the discharged water quality data in the discharging device and the processed water quality data and the environmental data in the plurality of processing devices;
the base station gateway is used for sending a broadcast signal, and the broadcast signal comprises a base station node number of the base station gateway sending the broadcast signal;
the plurality of acquisition sensors are also used for receiving the broadcast signals sent by the base station gateway, recording the signal intensity of the received broadcast signals and the base station node number of the base station gateway sending the broadcast signals, and sending the acquired processed water quality data, environmental data, discharged water quality data, the signal intensity of the received broadcast signals and the base station node number of the base station gateway sending the broadcast signals to the base station gateway;
the base station gateway is also used for receiving and forwarding the processed water quality data, the environment data, the discharged water quality data and the received signal strength of the broadcast signals sent by the plurality of acquisition sensors and sending the base station node number of the base station gateway of the broadcast signals to the server;
the server is used for prestoring a plurality of sampling positions, receiving the signal intensity of the broadcast signal sent by the base station gateway and the base station node number of the base station gateway sending the broadcast signal at each sampling position by the plurality of acquisition sensors so as to establish a signal intensity fingerprint electronic map;
the Web display device is used for prestoring a sampling position electronic map, and the sampling position electronic map comprises the discharge device, a plurality of processing devices and the positions of the acquisition sensors in the discharge device and the processing devices;
the server is further configured to receive the signal strength of the broadcast signal received by the plurality of acquisition sensors and the base station node number of the base station gateway sending the broadcast signal, which are sent by the base station gateway, compare the signal strength of the broadcast signal sent by the base station gateway and the base station node number of the base station gateway sending the broadcast signal, which are received by the plurality of acquisition sensors at each sampling position in advance, judge the sampling positions of the plurality of acquisition sensors, and send the processed water quality data, the environmental data, the discharged water quality data and the sampling positions of the plurality of acquisition sensors to the Web display device;
the Web display device is also used for receiving the processed water quality data, the environment data, the discharged water quality data and the sampling positions of the plurality of acquisition sensors, and displaying the sampling positions of the acquisition sensors, the acquired processed water quality data, the acquired environment data and the discharged water quality data at the corresponding positions of a prestored sampling position electronic map;
the server is also used for prestoring emission standards and a plurality of historical treated water quality data, environmental data and discharged water quality data, constructing a deep confidence network model according to the historical treated water quality data, the environmental data and the discharged water quality data, inputting the received treated water quality data and the environmental data into the deep confidence network model for prediction to obtain predicted discharged water quality data, and controlling corresponding treatment devices to adjust treatment parameters to change the environmental data and the treated water quality data of the corresponding treatment devices when the predicted discharged water quality data does not meet the emission standards;
wherein the plurality of acquisition sensors comprises: a plurality of chemical oxygen demand acquisition sensors, total nitrogen acquisition sensors, total phosphorus acquisition sensors, ammonia nitrogen acquisition sensors, turbidity acquisition sensors, temperature acquisition sensors, dissolved oxygen concentration acquisition sensors, PH acquisition sensors and mixed liquor sludge concentration acquisition sensors.
10. The sewage treatment management system of claim 9, wherein the plurality of collection sensors are integrated with a ZigBee module, the base station gateway is integrated with a ZigBee module and a WiFi module, the server is integrated with a WiFi module, the plurality of collection sensors and the base station gateway communicate via ZigBee technology, and the base station gateway and the server communicate via WiFi technology.
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