CN115865992A - Wisdom water conservancy on-line monitoring system - Google Patents
Wisdom water conservancy on-line monitoring system Download PDFInfo
- Publication number
- CN115865992A CN115865992A CN202310188750.5A CN202310188750A CN115865992A CN 115865992 A CN115865992 A CN 115865992A CN 202310188750 A CN202310188750 A CN 202310188750A CN 115865992 A CN115865992 A CN 115865992A
- Authority
- CN
- China
- Prior art keywords
- data
- monitoring
- module
- value
- monitoring system
- 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.)
- Granted
Links
Images
Classifications
-
- 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
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention provides an intelligent water conservancy on-line monitoring system which comprises a data acquisition module, a network service module, a processing module, a scheduling module and an execution module, wherein the data acquisition module is used for acquiring data; the data acquisition module comprises a plurality of sensors configured on site and monitoring data provided by remote sensing image equipment; the network service module provides transmission support of high-speed network data for other modules of the monitoring system; monitoring multiple items of water conservancy data in a monitored water area by the data acquisition module in a first period T1 and a second period T2 when an alarm state occurs; meanwhile, historical monitoring data and current monitoring values are analyzed, and a prediction model is adopted for prediction so as to set a work task target and make scheduling arrangement.
Description
Technical Field
The invention relates to the technical field of water management and monitoring, in particular to an intelligent water conservancy on-line monitoring system.
Background
The water resource is a basic resource for city construction and development, and the shortage of water resources all over the world at present forces the management requirements of various countries on the water resource to be further improved, and the good city water management can effectively promote the city construction and development.
Referring to the related published technical solutions, the technical solution with publication number KR1020130113249A proposes a monitoring system that moves with water flow to various waters by using a floating monitoring device for flow measurement, and the system is simultaneously connected to the data networks of the national bureau of meteorology and water conservancy departments and shares and cooperatively analyzes the data; the technical scheme of the publication number WO2017109294A1 provides mineral monitoring based on an X-ray technology by monitoring at the bank or the bed of a river, and is used for analyzing the water quality state of the river and realizing multi-region collaborative analysis; the technical scheme of publication No. CN115112536A proposes a method for rapidly measuring suspended load sand content, which comprises establishing a corresponding relation between turbidity and sand content in a laboratory, and arranging a transparent tester on site in a water area to be tested to test actual water quality and water flow, thereby measuring the sand content in the water area.
The technical scheme refers to a system for acquiring and processing related data of a water conservancy project; however, there are few references to a wide range of measurements and monitoring systems that use data to perform relevant work task settings and scheduling.
The foregoing discussion of the background art is intended only to facilitate an understanding of the present invention. This discussion is not an acknowledgement or admission that any of the material referred to is part of the common general knowledge.
Disclosure of Invention
The invention aims to provide an intelligent water conservancy online monitoring system which comprises a data acquisition module, a network service module, a processing module, a scheduling module and an execution module; the data acquisition module comprises a plurality of sensors configured on site and monitoring data provided by remote sensing image equipment; the network service module provides transmission support of high-speed network data for other modules of the monitoring system; monitoring multiple items of water conservancy data in a monitored water area through the data acquisition module according to a first period T1; when early warning data appear, quickly positioning the appearing area of the early warning data, and improving the monitoring period to be T2; meanwhile, historical monitoring data and current monitoring values are analyzed, and a prediction model established by mechanical learning is adopted for prediction so as to set a work task target and make scheduling arrangement; and collecting execution feedback on the task execution and making further future predictions to evaluate the results of the task execution for a previous time; meanwhile, all data in the monitoring system are rapidly shared and displayed for relevant departments, so that efficient and consistent transmission of information is ensured.
The invention adopts the following technical scheme:
an intelligent water conservancy online monitoring system comprises a data acquisition module, a network service module, a processing module, a scheduling module and an execution module; among them, in the case of a high-frequency,
the data acquisition module comprises the field data acquisition module and is used for acquiring the monitoring data of the target project site by configuring a plurality of sensors on the target project monitoring site; the remote sensing data acquisition module is used for acquiring remote sensing monitoring data of a target water conservancy area by adopting a remote sensing monitoring technology; the data acquisition module transmits the acquired data to the network service module through network communication;
the network service module is based on the Internet or an intranet of a related water conservancy department, and provides network operation service by adopting an Internet of things gateway and a corresponding server;
the processing module is used for processing, exchanging, analyzing, sharing and storing the collected monitoring data and submitting a data analysis result to the scheduling module;
the scheduling module is used for formulating a work task according to the data analysis result and combining with the personnel scheduling system, and sending the work task to related personnel or departments so as to schedule and arrange the personnel;
the execution module is used for receiving execution feedback of relevant workers after the work tasks are executed, wherein the execution feedback comprises execution time and specific implementation conditions; the execution module sends the execution feedback to the processing module;
the processing module continuously updates the current monitoring value of the target project and combines the execution feedback to update the data analysis result; the processing module stores a prediction model; obtaining a first predicted value corresponding to a current time by inputting historical monitoring data into the prediction model; and obtaining a second predicted value corresponding to a future time by inputting the historical monitoring data and the current monitoring value into the prediction model; comparing the first predicted value with the current monitoring value and comparing the second predicted value with the future monitoring value, evaluating whether the state or progress of the target project is healthy or not, or further sending out early warning information; updating the work task and scheduling arrangement according to the difference value of the two numerical values;
preferably, the processing steps of the monitoring system include:
s100: receiving data, namely receiving monitoring data acquired by the data acquisition module in a first period T1;
s200: the processing module analyzes historical monitoring data and obtains a first predicted value, and the first predicted value is compared with a current monitoring value to obtain a first difference value; thereby formulating a first task objective and a first scheduling according to the first difference value;
s300: sending the first task objective and a first scheduling schedule to relevant personnel or departments for execution; and receiving corresponding execution feedback;
s400: analyzing by the processing module by combining the historical monitoring data, the current monitoring value and the execution feedback, and acquiring a second predicted value;
s500: comparing the second predicted value with a future monitoring value at a future designated moment to obtain a second difference value; evaluating an execution result of the first task object according to the second difference value;
preferably, the predictive model comprises: a supervised machine learning model, a multimodal gaussian process regression model, a non-linear regression model, a process driven model, a residual network based machine learning model, a weight based machine learning model, a generator countermeasure network based machine learning model, or any combination of two or more of the models;
preferably, step S200 includes:
inputting historical monitoring data and a current monitoring value into a preset fault node model to obtain a fault node matched with the fault node model; analyzing the geographical position corresponding to the fault node, and defining an early warning area in a specified range; the fault node model is set by a related technician according to the standard range of parameters corresponding to a plurality of ring node nodes in the design of the current water conservancy project;
preferably, the method further comprises the step S200 of:
adjusting the monitoring period of the early warning area to a second period T2, and
；
preferably, in step S300, the method includes storing information such as historical monitoring data, a current monitoring value, a first predicted value, a second predicted value, a fault node, a geographical location corresponding to the fault node, and an early warning area in a server in the network service module, and a client of a related person or unit obtains and displays one or more items of information from the server;
preferably, the workflow of the scheduling module and the execution module includes the following steps:
e100: the scheduling module sends a first task target and a first scheduling arrangement to a corresponding partition management unit;
e200: after receiving a corresponding regulation and control instruction, the partition management unit dispatches a worker to work on the early warning area based on the first task target according to the first scheduling arrangement;
e300: after the work is finished, the execution feedback is sent to the network service module, and the network service module records and stores information;
preferably, the network service module includes:
the Internet of things gateway is used for solving the data acquisition and processing problems of the sensing equipment in the data acquisition modules;
the server is used for processing data circulation and caching and comprises a database used for storing monitoring data;
through a system intranet of a water department, an internet of things gateway and a corresponding server, the processing module is matched to work together, so that the circulation and unified processing of collected data, analyzed data, managed data and display data in a network are improved;
in some embodiments, the field data acquisition module includes one or more of a flow rate sensor, a water level sensor, a water pressure sensor, and a water turbidity sensor; according to actual water conservancy project requirements, different sensors are selected to be configured on site, water affair data are collected periodically, and then the data are collected through an equipment console or a special control box (PLC);
preferably, the field data acquisition module comprises one or more of a flow rate sensor, a water level sensor, a water pressure sensor and a water quality turbidity sensor;
preferably, the remote sensing data acquisition module comprises one or more satellite images adopting resource three, high score one and high score two.
The beneficial effects obtained by the invention are as follows:
1. the monitoring system disclosed by the invention combines data acquired by field data acquisition and data acquired by remote sensing data to monitor a target water conservancy region in a multi-period and time-sequence manner, so that the monitoring frequency and the monitoring load are effectively balanced;
2. the monitoring system adopts a prediction model established by mechanical learning, uses historical monitoring data, current monitoring data and execution feedback of tasks as input, evaluates and predicts a monitored project so as to make a coping strategy as early as possible and schedule and execute the strategy;
3. the monitoring system is connected with an internal network of a water conservancy relevant department in a butt joint mode, and data transmission is carried out by combining a high-speed network, so that rapid circulation of collected data, analyzed data and processed data is realized; the method is different from the data circulation mode that the prior monitoring system can carry out next-stage processing only through a large amount of forwarding and detention, and improves the efficiency of the monitoring system;
4. the combined scheme of the software and the hardware of the monitoring system adopts a modularized design, so that the monitoring system can be conveniently replaced or upgraded in the future, and the use cost is effectively reduced.
Drawings
The invention will be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the embodiments. Like reference numerals designate corresponding parts throughout the different views.
FIG. 1 is a schematic view of a monitoring system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of data acquisition and processing steps of the monitoring system in an embodiment of the present invention;
FIG. 3 is a diagram illustrating the operation steps of the scheduling module and the execution module according to an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating a training process of the prediction model according to an embodiment of the present invention;
fig. 5 is a schematic diagram illustrating each of the prediction data in the embodiment of the present invention, in which fig. (a) is a diagram for performing calculation of the first prediction value a 'using the historical monitoring data, and fig. (B) is a diagram for performing calculation of the second prediction value B' using the historical monitoring data and the current monitoring data.
The reference numbers illustrate:
100-a data acquisition module; 110-a web services module; 120-a processing module; 130-a scheduling module; 140-execution module.
Detailed Description
In order to make the technical solution and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the embodiments thereof; it should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. Other systems, methods, and/or features of the present embodiments will become apparent to those skilled in the art upon review of the following detailed description. It is intended that all such additional systems, methods, features and advantages be included within this description, be within the scope of the invention, and be protected by the accompanying claims. Additional features of the disclosed embodiments are described in, and will be apparent from, the detailed description that follows.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it is to be understood that if there is an orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", etc. based on the orientation or positional relationship shown in the drawings, it is only for convenience of describing the present invention and simplifying the description, but not for indicating or implying that the referred device or assembly must have a specific orientation.
The first embodiment is as follows:
as shown in fig. 1, the monitoring system for intelligent water conservancy comprises a data acquisition module 100, a network service module 110, a processing module 120, a scheduling module 130 and an execution module 140; among them, in the case of a high-frequency,
the data acquisition modules 100 are denoted by 100a and 100b in the drawing, and the number of the data acquisition modules can be multiple; the data acquisition module comprises a field data acquisition module and is used for acquiring monitoring data of a target project field by configuring a plurality of sensors on the target project monitoring field; the system also comprises a remote sensing data acquisition module which acquires remote sensing monitoring data of a target water conservancy area by adopting a remote sensing monitoring technology; the data acquisition module transmits the acquired data to the network service module through network communication;
the network service module is based on the Internet or an intranet of a related water conservancy department, is carried out by adopting an Internet of things gateway and a corresponding server and is used for realizing the transmission of digital information and the protection of data;
the processing module is used for processing, exchanging, analyzing, sharing and storing the collected monitoring data and submitting a data analysis result to the scheduling module;
the scheduling module is used for formulating a work task according to the data analysis result and in combination with the personnel scheduling system, and sending the work task to related personnel or departments so as to schedule the personnel;
the execution module is used for receiving execution feedback of relevant workers after the work tasks are executed, wherein the execution feedback comprises execution time and specific implementation conditions; the execution module sends the execution feedback to the processing module;
the processing module continuously updates the current monitoring value of the target project and combines the execution feedback to update the data analysis result; the processing module stores a prediction model; obtaining a first predicted value corresponding to a current time by inputting historical monitoring data into the prediction model; and obtaining a second predicted value corresponding to a future time by inputting the historical monitoring data and the current monitoring value into the prediction model; comparing the first predicted value with the current monitoring value and comparing the second predicted value with the future monitoring value, evaluating whether the state or progress of the target project is healthy or not, or further sending out early warning information; updating the work task and scheduling arrangement according to the difference value of the two numerical values;
preferably, as shown in fig. 2, the processing steps of the monitoring system include:
s100: receiving data, namely receiving monitoring data acquired by the data acquisition module in a first period T1;
s200: the processing module analyzes historical monitoring data and obtains a first predicted value, and the first predicted value is compared with a current monitoring value to obtain a first difference value; thereby formulating a first task objective and a first scheduling according to the first difference value;
s300: sending the first task objective and a first scheduling schedule to relevant personnel or departments for execution; and receiving corresponding execution feedback;
s400: analyzing by the processing module by combining the historical monitoring data, the current monitoring value and the execution feedback, and acquiring a second predicted value;
s500: comparing the second predicted value with a future monitoring value at a future designated moment to obtain a second difference value; evaluating an execution result of the first task objective according to the second difference value;
preferably, the predictive model comprises: a supervised machine learning model, a multimodal gaussian process regression model, a non-linear regression model, a process driven model, a residual network based machine learning model, a weight based machine learning model, a generator countermeasure network based machine learning model, or any combination thereof;
preferably, step S200 includes:
inputting historical monitoring data and a current monitoring value into a preset fault node model to obtain a fault node matched with the fault node model; analyzing the geographical position corresponding to the fault node, and defining an early warning area in a specified range; the fault node model is set by a related technician according to the standard range of parameters corresponding to a plurality of ring node nodes in the design of the current water conservancy project;
preferably, the method further comprises the step S200 of:
adjusting the monitoring period of the early warning area to a second period T2, wherein T2 is less than T1;
preferably, in step S300, the method includes storing information such as historical monitoring data, a current monitoring value, a first predicted value, a second predicted value, a fault node, a geographical location corresponding to the fault node, and an early warning area in a server in the network service module, and a client of a related person or unit obtains and displays one or more items of information from the server;
preferably, as shown in fig. 3, the workflow of the scheduling module and the executing module includes the following steps:
e100: the scheduling module sends a first task target and a first scheduling arrangement to a corresponding partition management unit;
e200: after receiving a corresponding regulation and control instruction, the partition management unit dispatches a worker to work on the early warning area based on the first task target according to the first scheduling arrangement;
e300: after the work is finished, the execution feedback is sent to the network service module, and the network service module records and stores information;
preferably, the network service module includes:
the Internet of things gateway is used for solving the data acquisition and processing problems of the sensing equipment in the data acquisition modules;
the server is used for processing data circulation and caching and comprises a database used for storing monitoring data;
the system intranet of the water department, the gateway of the Internet of things and the corresponding server are matched with the processing module to work together, so that the circulation and the unified processing of the collected data, the analyzed data, the managed data and the display data in the network are improved;
in some embodiments, the field data acquisition module includes one or more of a flow rate sensor, a water level sensor, a water pressure sensor, and a water turbidity sensor; according to actual water conservancy project requirements, different sensors are selected to be configured on site, water affair data are collected periodically, and then the data are collected through an equipment console or a special control box (PLC);
furthermore, remote non-contact monitoring of large-scale water conservancy projects can be carried out by adopting a remote sensing technology; the remote sensing technology utilizes the characteristic of collecting the light spectrum information and uses different remote sensing wave bands formed by various wavelengths from ultraviolet, visible light, infrared, far infrared to radar and the like to reflect various types of light reflection information in the ground or space; after the data information of different wave bands is processed by a computer and information is extracted, a large amount of various professional information can be generated, such as large-scale understanding of conditions of water, vegetation, a water system, geology, disasters, a ground structure, water and soil loss, coastal erosion and the like, water conditions and flood monitoring in all days and the like; the common remote sensing satellite resources in China are analyzed by adopting one or more than one satellite images of a resource III, a resource HI and a resource HI;
by combining field data and remote sensing data, multi-dimensional monitoring can be carried out on the diuresis projects from macro to micro, and comprehensiveness and height informatization of data acquisition are realized;
in some embodiments, the web services module 110, the processing module 120, the scheduling module 130, and the execution module 140 may be based on one or more cloud computing servers and establish a cloud management platform; the cloud management platform integrates data acquisition, exchange, service, sharing and utilization, and comprises standards such as data acquisition and storage, exchange and service interfaces and components such as data service, a storage library, identity codes and the like, the cloud technology is applied to the remote server cluster through networked connection and control, and services of operation programs are processed on line, so that dynamic management of water conservancy projects can be realized, data, videos, position information and the like of ash returning target projects are acquired and arranged in real time, the data, videos, position information and the like are transmitted to the cloud platform in time and are analyzed, processed, stored, backed up, summarized and arranged in a corresponding module, a safety production management information control system can download and call data in a database at any time, the processes of production safety monitoring, water conservancy project information real-time query, data inter-visit, accident early warning, disaster rescue, accident investigation processing and the like are realized, technical promotion and improvement of water conservancy project management departments on the water conservancy project management level are promoted, and water environment is comprehensively and scientifically managed.
The second embodiment:
this embodiment should be understood to include at least all of the features of any of the embodiments described above and further refinements thereto:
in the past, technical personnel are required to manually, closely and continuously monitor the change of a plurality of monitoring data in the water conservancy project monitoring process, and related technical personnel need a large amount of professional knowledge, identify possible problems of the water conservancy project in time and determine appropriate countermeasures;
with the development of informatization technology, a big data algorithm is used to cooperate with the application of machine learning technology, and a prediction model can be established by adopting mechanical learning and is used for predicting future data aiming at historical data and current data;
one embodiment of establishing the predictive model based on machine learning is illustrated; as shown in fig. 4;
in step 410, the processing module receives a monitoring data set for training the prediction model, wherein the monitoring data set comprises a plurality of historical monitoring data of a plurality of monitoring items divided by time sequence; these data are historical data accumulated over time; the type of monitoring data may include climate data, such as temperature, wind speed, light; a plurality of index data about the water body can be further included, such as flow, velocity, direction, clarity, etc.; further, image data of the water body, such as reflected light illumination; further comprising remote sensing data;
in step 420, based on the received monitoring data set, a relevant technician specifies each monitoring index in the monitoring data set as a feature vector, and generates a set of feature values of each feature vector based on the monitoring data set;
in steps 430 and 440, in some embodiments, the method includes dividing the monitoring data set into a training set and a validation set; the training set is used for training the model, and the verification set is used for performing parameter adjustment and optimization on a plurality of weight values in the model;
further, the predictive model may include one or more statistical or machine learning models; in some embodiments, the predictive model comprises a supervised machine learning model, a multimodal gaussian process regression model, a non-linear regression model, a process driven model, a residual network based machine learning model, a weight based machine learning model, a generative confrontation machine learning model, or any combination thereof;
in some embodiments, the non-linear regression model comprises a high-dimensional non-linear parametric function; in view of multiple characteristic values of similar water conservancy projects at different implementation places, including the history of geographic positions, climatic conditions and the like, model parameters can be adjusted to best predict future characteristic values;
in some embodiments, the process-driven models include one or more water-body-action process-driven models of a given known law, which can model and predict the course of change of the water body given historical and future inputs; for example, in known watercourses, variations in flow velocity, flow rate, etc. in different segments; for different water conservancy projects, the model parameters can be adjusted according to actual projects;
in some embodiments, the weight-based machine learning model includes weights of a learning and prediction process driven model and a data driven model; to predict the time series of plant features, the machine learning model may use a set of deep neural networks whose structure may be an artificial Recurrent Neural Network (RNN) or a Long Short Term Memory (LSTM) architecture; these models may also include Convolutional Neural Networks (CNNs) to process various image-based data;
in some embodiments, the machine learning models based on generating the countermeasure network include two sets of machine learning models; a model using historical data of partial characteristics of the water conservancy project as input to generate prediction data; and another model for distinguishing between predicted data and observed data; the two models are iteratively trained, so that the predicted data of the model for predicting data is closer to the actually occurring data; both models can be trained using deep neural networks, non-linear parameterized functions, or related hydraulic computed functions whose weights can be iteratively adjusted.
Example three:
this embodiment should be understood to include at least all of the features of any of the embodiments described above and further refinements thereto:
the analysis of the monitored data and the prediction of future data based on the predictive model may be implemented by the following exemplary description; as shown in fig. 5;
for each target item, the monitoring system may obtain a plurality of data sets, including: monitoring a current monitoring value A obtained at the current time, a first predicted value A 'based on predicted data of the current time, a second predicted value B' of the predicted specified future time, and a predicted specified future time target C; each of these data sets will be described below; and these data sets are based at least in part on the predictive model;
the current monitoring value a is a numerical value of a certain characteristic obtained by monitoring the target item at the current time (for example, specific data of a certain time node, or an average value of the current day, the current week, the current month, and the like); the data set may be obtained by a data acquisition module as described herein;
the first predicted value A' is a numerical value of a certain characteristic predicted by the monitoring system at the current time based on historical data obtained by monitoring a target project in the past; and according to a preferred embodiment of the present invention, the first predicted value a' may be obtained using the prediction model; as shown in fig. 5 (a), the monitoring system may correspond to a plurality of past times x-3 ，T x-2 ，T x-1 ]As input features, inputting into the predictive model; the prediction model can predict and output at T x The value of the moment, namely the first predicted value A'; in some embodiments, the predicted first predicted value a' may be interpreted as "how the monitored data should now be";
the second predicted value B' is an output characteristic value of a certain characteristic in the future time predicted by the monitoring system based on historical data obtained by monitoring the target item in the past and data actually measured at present; and according to a preferred embodiment of the present invention, the second predictive value B' may be obtained using the predictive model; as also shown in fig. 5 (B), the system may correspond to a plurality of past times and a current time x-3 ，T x-2 ，T x-1 ，T x ]As input features, inputting into the predictive model; the predictive model may output a future time T x+1 Is predictedData, namely a second predicted value B'; in some embodiments, the second predictor, B', may be interpreted as "what the monitored data will look like at a future time";
projected future time target C refers to a numerical value of a process that monitors data changes between one or more future times in order to assess the progress of the change in achievement for the target task; for example, given a set of task goals (e.g., achieving specified metric data within three months), the monitoring system may determine what feature value the metric data should reach each month; in some embodiments, the projected future time target C may be interpreted as "how the process should be between the factory from present to future time";
further, the processing module may evaluate the result of the task objective from the above values, and may perform a process check to ensure that the task objective changes according to a predetermined trajectory in the process, so as to update the first task objective and the first scheduling arrangement to the second task objective and the second scheduling arrangement in time;
furthermore, the monitoring system displays a series of data by using images so as to achieve a more intuitive monitoring data display effect.
In the above embodiments, the description of each embodiment has its own emphasis, and reference may be made to the related description of other embodiments for parts that are not described or recited in any embodiment.
Although the invention has been described above with reference to various embodiments, it should be understood that many changes and modifications may be made without departing from the scope of the invention. That is, the methods, systems, and devices discussed above are examples. Various configurations may omit, substitute, or add various procedures or components as appropriate. For example, in alternative configurations, the methods may be performed in an order different than that described, and/or various components may be added, omitted, and/or combined. Moreover, features described with respect to certain configurations may be combined in various other configurations, as different aspects and elements of the configurations may be combined in a similar manner. Further, elements therein may be updated as technology evolves, i.e., many elements are examples and do not limit the scope of the disclosure or claims.
Specific details are given in the description to provide a thorough understanding of the exemplary configurations including implementations. However, configurations may be practiced without these specific details, for example, well-known circuits, processes, algorithms, structures, and techniques have been shown without unnecessary detail in order to avoid obscuring the configurations. This description provides example configurations only, and does not limit the scope, applicability, or configuration of the claims. Rather, the foregoing description of the configurations will provide those skilled in the art with an enabling description for implementing the described techniques. Various changes may be made in the function and arrangement of elements without departing from the spirit or scope of the disclosure.
In conclusion, it is intended that the foregoing detailed description be regarded as illustrative rather than limiting, and that it be understood that these examples are illustrative only and are not intended to limit the scope of the invention. After reading the description of the present invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.
Claims (10)
1. The utility model provides an wisdom water conservancy on-line monitoring system which characterized in that: the monitoring system comprises a data acquisition module, a network service module, a processing module, a scheduling module and an execution module; wherein the content of the first and second substances,
the data acquisition module comprises a field data acquisition module and is used for acquiring monitoring data of a target project field by configuring a plurality of sensors on the target project monitoring field; the system also comprises a remote sensing data acquisition module which acquires remote sensing monitoring data of a target water conservancy area by adopting a remote sensing monitoring technology; the data acquisition module transmits the acquired data to the network service module through network communication;
the network service module is based on the Internet or an intranet of a related water conservancy department, and provides network operation service by adopting an Internet of things gateway and a corresponding server, so that the transmission of digital information and the protection of data are realized;
the processing module is used for processing, exchanging, analyzing, sharing and storing the collected monitoring data and submitting a data analysis result to the scheduling module;
the scheduling module is used for formulating a work task according to the data analysis result and in combination with the personnel scheduling system, and sending the work task to related personnel or departments so as to schedule the personnel;
the execution module is used for receiving execution feedback of relevant workers after the work tasks are executed, wherein the execution feedback comprises execution time and specific implementation conditions; the execution module sends the execution feedback to the processing module;
the processing module continuously updates the current monitoring value of the target project and combines the execution feedback to update the data analysis result; the processing module stores a prediction model; obtaining a first predicted value corresponding to a current time by inputting historical monitoring data into the prediction model; and obtaining a second predicted value corresponding to a future time by inputting the historical monitoring data and the current monitoring value into the prediction model; comparing the first predicted value with the current monitoring value and comparing the second predicted value with the future monitoring value, evaluating whether the state or progress of the target project is healthy or not, or further sending out early warning information; and updating the work task and scheduling arrangement according to the difference value of the two values.
2. The monitoring system of claim 1, wherein the monitoring system performs steps comprising:
s100: receiving data, namely receiving monitoring data acquired by the data acquisition module in a first period T1;
s200: the processing module analyzes historical monitoring data and obtains a first predicted value, and the first predicted value is compared with a current monitoring value to obtain a first difference value; thereby formulating a first task objective and a first scheduling according to the first difference value;
s300: sending the first task objective and a first scheduling schedule to relevant personnel or departments for execution; and receiving corresponding execution feedback;
s400: analyzing by the processing module by combining the historical monitoring data, the current monitoring value and the execution feedback, and acquiring a second predicted value;
s500: at a future designated moment, comparing the second predicted value with a future monitoring value to obtain a second difference value; and evaluating the execution result of the first task target according to the second difference value.
3. The monitoring system of claim 2, wherein the predictive model comprises: a supervised machine learning model, a multi-modal gaussian process regression model, a non-linear regression model, a process driven model, a residual network based machine learning model, a weight based machine learning model, or a machine learning model based on generating a countermeasure network, or any combination of two or more of these models.
4. The monitoring system according to claim 3, wherein in step S200 comprises:
inputting historical monitoring data and a current monitoring value into a preset fault node model to obtain a fault node matched with the fault node model; analyzing the geographical position corresponding to the fault node, and defining an early warning area in a specified range; and the fault node model is set by related technicians according to the standard ranges of parameters corresponding to the plurality of ring node nodes in the design of the current water conservancy project.
5. The monitoring system of claim 4, further comprising in step S200:
adjusting the monitoring period of the early warning area to a second period T2, and enabling
。
6. The monitoring system according to claim 5, wherein in step S300, the monitoring system includes a server for storing information such as historical monitoring data, current monitoring value, first predicted value, second predicted value, fault node and geographical location and early warning area corresponding to the fault node in the network service module, and the client of the relevant person or unit obtains and displays one or more items of information from the server.
7. The monitoring system of claim 6, wherein the workflow of the scheduling module and the execution module comprises the steps of:
e100: the scheduling module sends a first task target and a first scheduling arrangement to a corresponding partition management unit;
e200: after receiving a corresponding regulation and control instruction, the partition management unit dispatches a worker to work on the early warning area based on the first task target according to the first scheduling arrangement;
e300: and after the work is finished, the execution feedback is sent to the network service module, and the network service module records and stores the information.
8. The monitoring system of claim 7, wherein the web services module comprises:
the Internet of things gateway is used for solving the data acquisition and processing problems of the sensing equipment in the data acquisition modules;
the server is used for processing data circulation and caching and comprises a database used for storing monitoring data;
through the system intranet of water affair department, thing networking gateway and corresponding server, the cooperation of processing module's common work improves the circulation and the unified treatment effeciency of data collection, analysis data, management data and show data in the network.
9. The monitoring system of claim 8, wherein the field data collection module includes one or more of a flow rate sensor, a water level sensor, a water pressure sensor, and a water turbidity sensor.
10. The monitoring system of claim 9, wherein the telemetry data acquisition module includes one or more satellite images using resource three, high-score one, high-score two.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310188750.5A CN115865992B (en) | 2023-03-02 | 2023-03-02 | Intelligent water conservancy online monitoring system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310188750.5A CN115865992B (en) | 2023-03-02 | 2023-03-02 | Intelligent water conservancy online monitoring system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115865992A true CN115865992A (en) | 2023-03-28 |
| CN115865992B CN115865992B (en) | 2023-08-04 |
Family
ID=85659637
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310188750.5A Active CN115865992B (en) | 2023-03-02 | 2023-03-02 | Intelligent water conservancy online monitoring system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115865992B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116320833A (en) * | 2023-05-24 | 2023-06-23 | 广州耐奇电气科技有限公司 | Heat supply pipe network monitoring method based on Internet of things technology |
| CN117196159A (en) * | 2023-11-07 | 2023-12-08 | 山东辰智电子科技有限公司 | Intelligent water service partition metering system based on Internet big data analysis |
| CN117234909A (en) * | 2023-09-08 | 2023-12-15 | 浪潮智慧科技有限公司 | Water conservancy application software test system based on computer system |
| CN117539187A (en) * | 2023-12-20 | 2024-02-09 | 湖南博禹水电科技开发有限公司 | Remote data monitoring system and method based on Internet of things technology |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR3046265A1 (en) * | 2015-12-29 | 2017-06-30 | Thales Sa | SYSTEM FOR MONITORING AN INDUSTRIAL INSTALLATION; ASSOCIATED CONFIGURATION AND MONITORING METHODS |
| CN109067879A (en) * | 2018-08-08 | 2018-12-21 | 四川理工学院 | A kind of Internet of Things multi-parameter water quality on-line monitoring system |
| CN109474069A (en) * | 2018-09-12 | 2019-03-15 | 国网浙江省电力有限公司嘉兴供电公司 | A kind of distributed power generation station state monitoring method |
| CN110377447A (en) * | 2019-07-17 | 2019-10-25 | 腾讯科技（深圳）有限公司 | A kind of abnormal deviation data examination method, device and server |
| CN110390429A (en) * | 2019-07-15 | 2019-10-29 | 重庆邮电大学 | A kind of quality of water environment forecasting system neural network based and method |
| CN215340800U (en) * | 2021-03-26 | 2021-12-28 | 中建智能技术有限公司 | Basin management system |
| CN114021822A (en) * | 2021-11-09 | 2022-02-08 | 莫毓昌 | Clean energy power generation power prediction method and system |
| WO2022048050A1 (en) * | 2020-09-06 | 2022-03-10 | 厦门理工学院 | Big data information collection system and usage method |
| WO2022048168A1 (en) * | 2020-09-03 | 2022-03-10 | 上海上讯信息技术股份有限公司 | Training method and device for failure prediction neural network model |
| CN114372777A (en) * | 2022-01-06 | 2022-04-19 | 中交上海航道局有限公司 | Wisdom water affairs thing networking on-line monitoring system |
| CN114726751A (en) * | 2022-04-07 | 2022-07-08 | 广州汇智通信技术有限公司 | Intelligent early warning method, system, equipment and storage medium for resource quality monitoring |
| CN115240394A (en) * | 2022-09-22 | 2022-10-25 | 国网湖北省电力有限公司经济技术研究院 | Method and system for monitoring and early warning water level of accident oil pool of transformer substation |
-
2023
- 2023-03-02 CN CN202310188750.5A patent/CN115865992B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR3046265A1 (en) * | 2015-12-29 | 2017-06-30 | Thales Sa | SYSTEM FOR MONITORING AN INDUSTRIAL INSTALLATION; ASSOCIATED CONFIGURATION AND MONITORING METHODS |
| CN109067879A (en) * | 2018-08-08 | 2018-12-21 | 四川理工学院 | A kind of Internet of Things multi-parameter water quality on-line monitoring system |
| CN109474069A (en) * | 2018-09-12 | 2019-03-15 | 国网浙江省电力有限公司嘉兴供电公司 | A kind of distributed power generation station state monitoring method |
| CN110390429A (en) * | 2019-07-15 | 2019-10-29 | 重庆邮电大学 | A kind of quality of water environment forecasting system neural network based and method |
| CN110377447A (en) * | 2019-07-17 | 2019-10-25 | 腾讯科技（深圳）有限公司 | A kind of abnormal deviation data examination method, device and server |
| WO2022048168A1 (en) * | 2020-09-03 | 2022-03-10 | 上海上讯信息技术股份有限公司 | Training method and device for failure prediction neural network model |
| WO2022048050A1 (en) * | 2020-09-06 | 2022-03-10 | 厦门理工学院 | Big data information collection system and usage method |
| CN215340800U (en) * | 2021-03-26 | 2021-12-28 | 中建智能技术有限公司 | Basin management system |
| CN114021822A (en) * | 2021-11-09 | 2022-02-08 | 莫毓昌 | Clean energy power generation power prediction method and system |
| CN114372777A (en) * | 2022-01-06 | 2022-04-19 | 中交上海航道局有限公司 | Wisdom water affairs thing networking on-line monitoring system |
| CN114726751A (en) * | 2022-04-07 | 2022-07-08 | 广州汇智通信技术有限公司 | Intelligent early warning method, system, equipment and storage medium for resource quality monitoring |
| CN115240394A (en) * | 2022-09-22 | 2022-10-25 | 国网湖北省电力有限公司经济技术研究院 | Method and system for monitoring and early warning water level of accident oil pool of transformer substation |
Non-Patent Citations (2)
| Title |
|---|
| 孙兆兵;王保良;冀海峰;黄志尧;李海青;: "基于概率组合的水质预测方法", no. 10 * |
| 贺骥;王海锋;郭利娜;康健;: "遥感技术在水利强监管领域的应用研究", 水利发展研究, no. 01, pages 205 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116320833A (en) * | 2023-05-24 | 2023-06-23 | 广州耐奇电气科技有限公司 | Heat supply pipe network monitoring method based on Internet of things technology |
| CN116320833B (en) * | 2023-05-24 | 2023-09-19 | 广州耐奇电气科技有限公司 | Heat supply pipe network monitoring method based on Internet of things technology |
| CN117234909A (en) * | 2023-09-08 | 2023-12-15 | 浪潮智慧科技有限公司 | Water conservancy application software test system based on computer system |
| CN117234909B (en) * | 2023-09-08 | 2024-06-11 | 浪潮智慧科技有限公司 | Water conservancy application software test system based on computer system |
| CN117196159A (en) * | 2023-11-07 | 2023-12-08 | 山东辰智电子科技有限公司 | Intelligent water service partition metering system based on Internet big data analysis |
| CN117539187A (en) * | 2023-12-20 | 2024-02-09 | 湖南博禹水电科技开发有限公司 | Remote data monitoring system and method based on Internet of things technology |
| CN117539187B (en) * | 2023-12-20 | 2024-04-12 | 湖南博禹水电科技开发有限公司 | Remote data monitoring system and method based on Internet of things technology |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115865992B (en) | 2023-08-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115865992B (en) | Intelligent water conservancy online monitoring system | |
| KR102159692B1 (en) | solar photovoltatic power generation forecasting apparatus and method based on big data analysis | |
| CN116129366B (en) | Digital twinning-based park monitoring method and related device | |
| Alaswad et al. | A review on condition-based maintenance optimization models for stochastically deteriorating system | |
| CN117495210A (en) | Highway concrete construction quality management system | |
| Jalalkamali | Using of hybrid fuzzy models to predict spatiotemporal groundwater quality parameters | |
| CN117787658B (en) | Water resource scheduling system based on irrigation district river flow abnormal condition | |
| CN117852896B (en) | Construction supervision risk control early warning system and method | |
| CN117371952B (en) | Multi-project collaborative management system | |
| CN113807951A (en) | Transaction data trend prediction method and system based on deep learning | |
| Plant et al. | Combining expert system and GIS technology to implement a state-transition model of oak woodlands | |
| Malykhina et al. | Digital twin technology as a basis of the industry in future | |
| Badrun et al. | The development of smart irrigation system with IoT, cloud, and Big Data | |
| CN117829382A (en) | Intelligent prediction method and system for highway construction progress | |
| CN117669895A (en) | Highway engineering environment influence evaluation system | |
| CN117370919A (en) | Remote monitoring system for sewage treatment equipment | |
| CN114169794B (en) | Method and device for evaluating pollution possibility, computer equipment and storage medium | |
| Wu et al. | Physics-informed ai surrogates for day-ahead wind power probabilistic forecasting with incomplete data for smart grid in smart cities | |
| Namrata et al. | Data-Driven Hyperparameter Optimized Extreme Gradient Boosting Machine Learning Model for Solar Radiation Forecasting | |
| JP7396133B2 (en) | Parameter adjustment device, inference device, parameter adjustment method, and parameter adjustment program | |
| Anwar et al. | Integrating Artificial Intelligence and Environmental Science for Sustainable Urban Planning | |
| Reddy | Reducing the risks in geotechnical engineering using artificial intelligence techniques | |
| Nair et al. | Bayesian Models for Weather Prediction: Using Remote Sensing Data to Improve Forecast Accuracy | |
| Kim et al. | A study on facility maintenance information composition and sophisticated technology utilization measures | |
| RU2680869C2 (en) | Method for automated data collection and preparation for monitoring and modelling complex technical system |
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 |