CN117077870B - Water resource digital management method based on artificial intelligence - Google Patents

Water resource digital management method based on artificial intelligence Download PDF

Info

Publication number
CN117077870B
CN117077870B CN202311316438.6A CN202311316438A CN117077870B CN 117077870 B CN117077870 B CN 117077870B CN 202311316438 A CN202311316438 A CN 202311316438A CN 117077870 B CN117077870 B CN 117077870B
Authority
CN
China
Prior art keywords
water resource
model
data
water
digital management
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202311316438.6A
Other languages
Chinese (zh)
Other versions
CN117077870A (en
Inventor
赵亮
赵茹
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Derun Yuda Technology Co ltd
Original Assignee
Beijing Derun Yuda Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Derun Yuda Technology Co ltd filed Critical Beijing Derun Yuda Technology Co ltd
Priority to CN202311316438.6A priority Critical patent/CN117077870B/en
Publication of CN117077870A publication Critical patent/CN117077870A/en
Application granted granted Critical
Publication of CN117077870B publication Critical patent/CN117077870B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • 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
    • 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
    • G06N3/042—Knowledge-based neural networks; Logical representations of neural networks
    • 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
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
    • 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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • 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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/067—Enterprise or organisation modelling
    • 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—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • General Physics & Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Health & Medical Sciences (AREA)
  • Marketing (AREA)
  • General Health & Medical Sciences (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Software Systems (AREA)
  • Operations Research (AREA)
  • Mathematical Physics (AREA)
  • General Engineering & Computer Science (AREA)
  • Game Theory and Decision Science (AREA)
  • Computing Systems (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Development Economics (AREA)
  • Quality & Reliability (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Molecular Biology (AREA)
  • Educational Administration (AREA)
  • Primary Health Care (AREA)
  • Water Supply & Treatment (AREA)
  • Public Health (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a water resource digital management method based on artificial intelligence, which relates to the technical field of water resource digital management and comprises the steps of collecting water resource data and preprocessing; based on the preprocessed data, establishing a water resource digital management model; monitoring the water resource based on the water resource digital management model; the monitoring data is analyzed. The method can more accurately predict the future trend of the water resource by using the deep learning technology, realize the real-time monitoring and early warning of the water resource, and improve the efficiency and effect of water resource management.

Description

Water resource digital management method based on artificial intelligence
Technical Field
The invention relates to the technical field of water resource digital management, in particular to a water resource digital management method based on artificial intelligence.
Background
Water resource management is a technique for ensuring normal water use by residents, and normally judges whether the water resource can be normally used in a subsequent period of time according to factors affecting the normal water use.
In the prior art, when water resource management is performed, current water resource data is generally directly used for judgment, for example: the water consumption of several days in the future is judged according to the current water consumption, and the water consumption of several days in the future is judged according to the current water consumption of weather, so that the water resource management mode is rough and can not accurately judge the water resource use condition of a period in the future, and the artificial intelligence technology is more and more widely applied in the current society, so that the application of the artificial intelligence technology in the water resource digital management direction is a great difficulty.
Disclosure of Invention
The present invention has been made in view of the above-mentioned problems occurring in the existing artificial intelligence-based water resource digital management method.
Therefore, the invention aims to provide a water resource digital management method based on artificial intelligence.
In order to solve the technical problems, the invention provides the following technical scheme: a water resource digital management method based on artificial intelligence comprises the steps of collecting water resource data and preprocessing; based on the preprocessed data, establishing a water resource digital management model; monitoring the water resource based on the water resource digital management model; analyzing the monitoring data; the digital management model of the water resource comprises a water resource monitoring model, a deep neural network model is established, the formula is as follows,
in which W is 1 、W 2 、W 3 Is a weight matrix, b 1 、b 2 、b 3 Is a bias term, σ is a ReLU activation function, defined as σ (z) =max (0, z), x is input data, z is the net input of the neuron;
establishing a graph neural network model, training the graph neural network model by taking the output of the deep neural network model as the input of the graph neural network model to form a water resource monitoring model, wherein the formula is as follows,
in the formula, h r (l+1) Is the hidden state of the node r at the layer 1+1, N (r) is the neighbor of the node r, U is the weight matrix, b 4 Is an offset which is set to a value,is an activation function, h (x) l Is the output of the first layer in the deep neural network model;
using the DMA output, a weight value is generated for each node, as follows,
wherein q is r Refers to the weight value of the node r, h r Refers to an output value d generated by a node r in a water resource monitoring model r Is the output value that the DMA generates for node r,is a weight coefficient between 0 and 1;
establishing a loss function model, and training the water resource monitoring model based on an Adam optimizer;
in each training wheel, training data are input into the model in batches, loss is calculated, and then model parameters are updated by using an optimizer;
after each training round is completed, the performance of the model is evaluated using a validation set to prevent overfitting.
As a preferable scheme of the water resource digital management method based on artificial intelligence, the invention comprises the following steps: when the water resource monitoring model finds that the water resource data exceeds a preset threshold value, the system automatically sends real-time notification and early warning to related staff.
As a preferable scheme of the water resource digital management method based on artificial intelligence, the invention comprises the following steps: the loss function model is that,
wherein y is i Is the true value of the i-th sample,is the predicted value of the i-th sample, and n is the number of samples.
As a preferable scheme of the water resource digital management method based on artificial intelligence, the invention comprises the following steps: the water resource digital management model also comprises a water resource prediction model, the establishment of the water resource prediction model comprises the following steps,
establishing a water resource prediction framework based on the LSTM cyclic neural network;
using the mean square error as a loss function of the model, and using the mean absolute error to evaluate the predictive performance of the model;
model optimization was performed using Adam optimizer.
As a preferable scheme of the water resource digital management method based on artificial intelligence, the invention comprises the following steps: the water resource data comprises water consumption, water quality, water storage and weather information, and the pretreatment comprises data cleaning, data standardization and data coding.
As a preferable scheme of the water resource digital management method based on artificial intelligence, the invention comprises the following steps: the establishment of the water resource digital management model further comprises the following steps:
determining a data collection point to be monitored, wherein the data collection point comprises a water consumption metering device, water quality monitoring equipment, a water level meter and a meteorological sensor;
sensors and instruments are installed at each data collection point and used for monitoring water consumption, water quality, water storage and weather data in real time;
a central database is established for storing the data monitored in real time, and the data acquired by the sensor is transmitted to the central database through a network and is continuously updated;
based on the water resource data, establishing a water resource management model by using an artificial intelligence technology;
setting thresholds of water consumption, water quality, water storage and weather according to local water resource management policies and actual conditions;
the system monitors data in real time, inputs a water resource digital management model, and compares the result with a set threshold value.
As a preferable scheme of the water resource digital management method based on artificial intelligence, the invention comprises the following steps: the analysis of the monitoring data comprises the steps of,
acquiring real-time water resource data including water consumption, water quality, water storage and weather data from a central database, and preprocessing the data including standardization and coding;
training an isolated forest model by using the preprocessed data, wherein in the training process, a feature is randomly selected by the model, then a dividing point of the feature is randomly selected, the data is divided into two parts, and the process is repeatedly performed until all the data are isolated;
setting the number of trees and the maximum sample number to control the complexity and the running time of the model;
after training, detecting whether the output of the water resource digital management model is abnormal or not in real time by using the isolated forest model, inputting the output of the water resource digital management model into the isolated forest model, wherein the isolated forest model gives an abnormal score of each data point, the score is based on the isolated path length of the data point, and judging that the data is abnormal when the path length is smaller than a set length threshold value;
when abnormal data is detected, the system can immediately send real-time notification and early warning to related staff.
A computer device comprising a memory and a processor, said memory storing a computer program, characterized in that said processor, when executing said computer program, implements the steps of a method according to the artificial intelligence based water resource digitization management method.
A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor implements the steps of a method of digitally managing water resources based on artificial intelligence.
The invention has the beneficial effects that: by utilizing the deep learning technology, the future trend of the water resource can be predicted more accurately, the real-time monitoring and early warning of the water resource are realized, and the efficiency and the effect of the water resource management are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a flow chart of a method for digitally managing water resources based on artificial intelligence in example 1.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Example 1
Referring to fig. 1, a first embodiment of the present invention provides an artificial intelligence-based water resource digital management method, which includes the steps of:
s1, collecting water resource data and preprocessing;
s2, establishing a water resource digital management model based on the preprocessed data;
s3, monitoring the water resource based on the water resource digital management model;
s4, analyzing the monitoring data.
Specifically, the water resource data comprise water consumption, water quality, water storage and weather information, and the preprocessing comprises data cleaning, data standardization and data coding.
Further, in step S2, the water resource digital management model comprises a water resource monitoring model, the establishment of the water resource monitoring model comprises the following steps,
s21, establishing a deep neural network model, wherein the formula is as follows,
in which W is 1 、W 2 、W 3 Is a weight matrix, b 1 、b 2 、b 3 Is a bias term, σ is a ReLU activation function, defined as σ (z) =max (0, z), x is input data, z is the net input of the neuron;
s22, establishing a graph neural network model, training the graph neural network model by taking the output of the deep neural network model as the input of the graph neural network model to form a water resource monitoring model, wherein the formula is as follows,
in the formula, h r (l+1) Is the hidden state of the node r at the layer 1+1, N (r) is the neighbor of the node r, U is the weight matrix, b 4 Is an offset which is set to a value,is an activation function, h (x) l Is the output of the first layer in the deep neural network model;
s23, generating a weight value for each node by using the output of the DMA, wherein the formula is as follows,
wherein q is r Refers to the weight value of the node r, h r Refers to an output value d generated by a node r in a water resource monitoring model r Is the output value that the DMA generates for node r,is a weight coefficient between 0 and 1;
s24, establishing a loss function model, training the water resource monitoring model based on an Adam optimizer, wherein the loss function model is that,
wherein y is i Is the true value of the i-th sample,is the predicted value of the ith sample, n is the number of samples;
the Adam optimizer model is that,
wherein m is t And v t Is an estimate of the first and second moments of the gradient, beta 1 And beta 2 Is super parameter beta 1 =0.9，β 2 =0.999, a is the learning rate,is a constant, θ is a model parameter, g t For the gradient at time step t +.>And->Is the corrected estimated value of the first moment and the second moment;
s25, inputting training data into a model in batches in each training wheel, calculating loss, and updating model parameters by using an optimizer;
s26, after each training wheel is finished, evaluating the performance of the model by using a verification set so as to prevent over fitting.
It should be noted that the graph neural network model (GNN) can directly process the data of the graph structure, which enables it to capture complex topological relationships in the water resource network. Conventional neural networks often require the transformation of graph structure data into fixed-size vectors, which can lose some important structural information. While GNNs can operate directly on the graph to better retain this information, GNNs can generate a rich feature representation for each node through multi-layer graph convolution. This means that each node's information is not only based on its own properties, but also includes information of its neighbors. The aggregation of the neighbor information can help capture more distant dependency relationships, so that a more comprehensive network view is provided, the state of the water resource network is monitored in real time, prediction is performed, and the method is important for timely finding and processing potential water resource problems. Furthermore, the GNN can better generalize to unseen graph structure data by operating on nodes and edges on the graph, i.e. the GNN can monitor it even if certain specific water resource network configurations are not collected during training.
The deep neural network can extract advanced features from the original data, and the graph neural network can capture the relation between the features.
Preferably, when monitoring water resources, the system utilizes an artificial intelligent algorithm to perform resource allocation optimization, introduces an intelligent management system, performs information transmission and management through an electronic information processing device, automatically transmits real-time notification and early warning to related staff when the water resource monitoring model finds that water resource data exceeds a preset threshold, dynamically allocates water resources by adopting an enhanced learning algorithm or a genetic algorithm, and automatically adjusts a water resource allocation strategy according to the current water resource demand and supply condition so as to achieve optimal resource utilization efficiency.
Preferably, in step S2, the water resource digital management model further includes a water resource prediction model, and the building of the water resource prediction model includes the steps of,
a water resource prediction framework is established based on the LSTM cyclic neural network, the formula is as follows,
wherein f t ，i t ，o t The activation value of the forgetting gate, the activation value of the input gate and the activation value of the output gate are respectively C t Is the state of the cell at the current time step, C t-1 In the cell state of the last time step, h t Is the hidden state value of the current time step, h t-1 Is C t-1 For the cell state value of the last time step, W and b are weights and biases, x t For the input of the current time step,candidate values for new cell states;
using the mean square error as a loss function of the model, and using the mean absolute error to evaluate the predictive performance of the model;
model optimization was performed using Adam optimizer.
And in order to facilitate the detection of anomalies, an isolated forest model is built, comprising the steps of,
determining a data collection point to be monitored, wherein the data collection point comprises a water consumption metering device, water quality monitoring equipment, a water level meter and a meteorological sensor;
sensors and instruments are installed at each data collection point and used for monitoring water consumption, water quality, water storage and weather data in real time;
a central database is established for storing the data monitored in real time, and the data acquired by the sensor is transmitted to the central database through a network and is continuously updated;
based on the water resource data, establishing a water resource management model by using an artificial intelligence technology;
setting thresholds of water consumption, water quality, water storage and weather according to local water resource management policies and actual conditions;
the system monitors data in real time, inputs a water resource digital management model, and compares the result with a set threshold value.
The analysis of the monitoring data comprises the steps of,
acquiring real-time water resource data including water consumption, water quality, water storage and weather data from a central database, and preprocessing the data including standardization and coding;
training an isolated forest model by using the preprocessed data, wherein in the training process, a feature is randomly selected by the model, then a dividing point of the feature is randomly selected, the data is divided into two parts, and the process is repeatedly performed until all the data are isolated;
setting the number of trees and the maximum sample number to control the complexity and the running time of the model;
after training, detecting whether the output of the water resource digital management model is abnormal or not in real time by using the isolated forest model, inputting the output of the water resource digital management model into the isolated forest model, wherein the isolated forest model gives an abnormal score of each data point, the score is based on the isolated path length of the data point, and judging that the data is abnormal when the path length is smaller than a set length threshold value;
when abnormal data is detected, the system can immediately send real-time notification and early warning to related staff.
The method provided by the invention has the following beneficial effects: future trends in water resources can be predicted more accurately using deep learning techniques. Through the real-time monitoring and early warning system, abnormal conditions can be found and processed in time, so that possible loss is avoided. The water resource can be managed more efficiently by optimizing the resource allocation by the artificial intelligence algorithm. Providing real-time and accurate information for decision makers and helping them to make better decisions.
Example 2
A second embodiment of the invention, which is different from the previous embodiment, is:
the functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform 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, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Example 3
For the third embodiment of the present invention, in order to verify the advantageous effects of the present invention, scientific demonstration was performed through experiments, and experimental data are shown in table 1.
Table 1 table of experimental data
As can be seen from table 1, the my technical solution provides more efficient, accurate and safe water resource management than the prior art solutions, thereby exhibiting its advantages.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.

Claims (7)

1. A water resource digital management method based on artificial intelligence is characterized in that: comprising the steps of (a) a step of,
collecting water resource data and preprocessing;
based on the preprocessed data, establishing a water resource digital management model;
monitoring the water resource based on the water resource digital management model;
analyzing the monitoring data;
the water resource digital management model comprises a water resource monitoring model, the establishment of the water resource monitoring model comprises the following steps,
a deep neural network model is built, the formula is as follows,
，
in which W is 1 、W 2 、W 3 Is a weight matrix, b 1 、b 2 、b 3 Is a bias term, σ is a ReLU activation function, defined as σ (z) =max (0, z), x is input data, z is the net input of the neuron;
establishing a graph neural network model, training the graph neural network model by taking the output of the deep neural network model as the input of the graph neural network model to form a water resource monitoring model, wherein the formula is as follows,
，
in the formula, h r (l+1) Is the hidden state of node r at layer l+1, N (r) is the nodeNeighbor of point r, U is weight matrix, b 4 Is an offset which is set to a value,is an activation function, h (x) l Is the output of the first layer in the deep neural network model;
using the DMA output, a weight value is generated for each node, as follows,
，
wherein q is r Refers to the weight value of the node r, h r Refers to an output value d generated by a node r in a water resource monitoring model r Is the output value that the DMA generates for node r,is a weight coefficient between 0 and 1;
establishing a loss function model, and training the water resource monitoring model based on an Adam optimizer;
in each training wheel, training data are input into the model in batches, loss is calculated, and then model parameters are updated by using an optimizer;
after each training round is completed, the performance of the model is evaluated using a validation set to prevent overfitting.
2. The artificial intelligence based water resource digital management method as claimed in claim 1, wherein: when the water resource monitoring model finds that the water resource data exceeds a preset threshold value, the system automatically sends real-time notification and early warning to related staff.
3. The artificial intelligence based water resource digital management method as claimed in claim 2, wherein: the loss function model is that,
，
wherein y is i Is the true value of the i-th sample,is the predicted value of the i-th sample, and n is the number of samples.
4. The artificial intelligence based water resource digital management method as claimed in claim 3, wherein: the water resource digital management model also comprises a water resource prediction model, the establishment of the water resource prediction model comprises the following steps,
establishing a water resource prediction framework based on the LSTM cyclic neural network;
using the mean square error as a loss function of the model, and using the mean absolute error to evaluate the predictive performance of the model;
model optimization was performed using Adam optimizer.
5. The artificial intelligence based water resource digital management method as claimed in claim 4, wherein: the water resource data comprises water consumption, water quality, water storage and weather information, and the pretreatment comprises data cleaning, data standardization and data coding.
6. The artificial intelligence based water resource digital management method as claimed in claim 5, wherein: the establishment of the water resource digital management model further comprises the following steps:
determining a data collection point to be monitored, wherein the data collection point comprises a water consumption metering device, water quality monitoring equipment, a water level meter and a meteorological sensor;
sensors and instruments are installed at each data collection point and used for monitoring water consumption, water quality, water storage and weather data in real time;
a central database is established for storing the data monitored in real time, and the data acquired by the sensor is transmitted to the central database through a network and is continuously updated;
based on the water resource data, establishing a water resource management model by using an artificial intelligence technology;
setting thresholds of water consumption, water quality, water storage and weather according to local water resource management policies and actual conditions;
the system monitors data in real time, inputs a water resource digital management model, and compares the result with a set threshold value.
7. The artificial intelligence based water resource digital management method as claimed in claim 6, wherein: the analysis of the monitoring data comprises the steps of,
acquiring real-time water resource data including water consumption, water quality, water storage and weather data from a central database, and preprocessing the data including standardization and coding;
training an isolated forest model by using the preprocessed data, wherein in the training process, a feature is randomly selected by the model, then a dividing point of the feature is randomly selected, the data is divided into two parts, and the process is repeatedly performed until all the data are isolated;
setting the number of trees and the maximum sample number to control the complexity and the running time of the model;
after training, detecting whether the output of the water resource digital management model is abnormal or not in real time by using the isolated forest model, inputting the output of the water resource digital management model into the isolated forest model, wherein the isolated forest model gives an abnormal score of each data point, the score is based on the isolated path length of the data point, and judging that the data is abnormal when the path length is smaller than a set length threshold value;
when abnormal data is detected, the system can immediately send real-time notification and early warning to related staff.
CN202311316438.6A 2023-10-12 2023-10-12 Water resource digital management method based on artificial intelligence Active CN117077870B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202311316438.6A CN117077870B (en) 2023-10-12 2023-10-12 Water resource digital management method based on artificial intelligence

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202311316438.6A CN117077870B (en) 2023-10-12 2023-10-12 Water resource digital management method based on artificial intelligence

Publications (2)

Publication Number Publication Date
CN117077870A CN117077870A (en) 2023-11-17
CN117077870B true CN117077870B (en) 2023-12-22

Family

ID=88702724

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202311316438.6A Active CN117077870B (en) 2023-10-12 2023-10-12 Water resource digital management method based on artificial intelligence

Country Status (1)

Country Link
CN (1) CN117077870B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117309067B (en) * 2023-11-30 2024-02-09 长春职业技术学院 Water resource real-time monitoring method, system and electronic equipment

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111160430A (en) * 2019-12-19 2020-05-15 广东工业大学 Water resource optimization configuration method based on artificial intelligence algorithm
CN111553468A (en) * 2020-05-15 2020-08-18 南京大学 Method for accurately predicting effluent quality of sewage treatment plant
CN112801751A (en) * 2021-02-04 2021-05-14 重庆邮电大学 Personalized scenic spot recommendation method of multitask graph neural network
KR20220039595A (en) * 2020-09-22 2022-03-29 김남재 Multi point block remote monitoring system for watching unauthorized discharge of toxicity waste water based on internet of things and artifical intelligence
CN115827697A (en) * 2022-12-13 2023-03-21 中节能绿建环保科技有限公司 Intelligent community water resource management system based on big data and management method thereof
CN115906954A (en) * 2022-12-09 2023-04-04 北京数洋智慧科技有限公司 Multivariate time sequence prediction method and device based on graph neural network

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111160430A (en) * 2019-12-19 2020-05-15 广东工业大学 Water resource optimization configuration method based on artificial intelligence algorithm
CN111553468A (en) * 2020-05-15 2020-08-18 南京大学 Method for accurately predicting effluent quality of sewage treatment plant
KR20220039595A (en) * 2020-09-22 2022-03-29 김남재 Multi point block remote monitoring system for watching unauthorized discharge of toxicity waste water based on internet of things and artifical intelligence
CN112801751A (en) * 2021-02-04 2021-05-14 重庆邮电大学 Personalized scenic spot recommendation method of multitask graph neural network
CN115906954A (en) * 2022-12-09 2023-04-04 北京数洋智慧科技有限公司 Multivariate time sequence prediction method and device based on graph neural network
CN115827697A (en) * 2022-12-13 2023-03-21 中节能绿建环保科技有限公司 Intelligent community water resource management system based on big data and management method thereof

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
水资源***优化模型的确定;贾纯刚;野大君;;硅谷(第15期);215+222 *

Also Published As

Publication number Publication date
CN117077870A (en) 2023-11-17

Similar Documents

Publication Publication Date Title
CN111813084B (en) Mechanical equipment fault diagnosis method based on deep learning
CN112990556A (en) User power consumption prediction method based on Prophet-LSTM model
CN117077870B (en) Water resource digital management method based on artificial intelligence
CN111858231A (en) Single index abnormality detection method based on operation and maintenance monitoring
CN111582542B (en) Power load prediction method and system based on anomaly repair
CN111210128B (en) Wetland early warning method based on artificial intelligence and random self-adaptive threshold
CN110012009B (en) Internet of things intrusion detection method based on combination of decision tree and self-similarity model
CN110736968A (en) Radar abnormal state diagnosis method based on deep learning
CN112836720B (en) Building operation and maintenance equipment abnormality diagnosis method, system and computer readable storage medium
CN115760484A (en) Method, device and system for improving potential danger identification capability of power distribution station area and storage medium
CN117421684B (en) Abnormal data monitoring and analyzing method based on data mining and neural network
CN117113729B (en) Digital twinning-based power equipment online state monitoring system
CN112363896A (en) Log anomaly detection system
CN108961460B (en) Fault prediction method and device based on sparse ESGP (Enterprise service gateway) and multi-objective optimization
CN112100024B (en) Resource load abnormity detection method, device and equipment
CN114023399A (en) Air particulate matter analysis early warning method and device based on artificial intelligence
CN116383773A (en) Data center energy efficiency anomaly detection method, system and medium based on self-adaptive prediction interval
CN117494292A (en) Engineering progress management method and system based on BIM and AI large model
CN115964503B (en) Safety risk prediction method and system based on community equipment facilities
Dragomir et al. Adaptive Neuro-Fuzzy inference system for mid term prognostic error stabilization.
CN113268929B (en) Short-term load interval prediction method and device
CN117408394B (en) Carbon emission factor prediction method and device for electric power system and electronic equipment
Sheng et al. Network traffic anomaly detection method based on chaotic neural network
CN112215421A (en) Deep learning water quality index prediction method based on generation countermeasure network
CN116910559A (en) Index anomaly detection method for intelligent operation and maintenance application of power grid supercomputer center

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant