CN115865992B - Intelligent water conservancy online monitoring system - Google Patents
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Abstract
The invention provides 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, wherein the data acquisition module is used for acquiring data of a water conservancy; 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 high-speed network data transmission support for other modules of the monitoring system; the data acquisition module is used for monitoring a plurality of items of water conservancy data in the monitored water area in a first period T1 and a second period T2 when an alarm state occurs; and meanwhile, analyzing the historical monitoring data and the current monitoring value, and predicting by adopting a prediction model to set a work task target and schedule.
Description
Technical Field
The invention relates to the technical field of water management monitoring, in particular to an intelligent water conservancy online monitoring system.
Background
The water resource is a basic resource for urban construction and development, and the shortage of the water resource worldwide at present forces the management requirement of each country on the water resource to be further improved, and the good urban water management can effectively promote the urban construction and development.
Consult the relevant technical scheme that has already been disclosed, the technical scheme of publication number KR1020130113249A proposes a and utilizes the floating monitoring device to move to each waters and carry on the monitoring system of the flow measurement along with the rivers, this system is connected to the data network of the meteorological office of the country, water conservancy office department at the same time and carries on sharing and collaborative analysis to the data; the technical proposal with the publication number of WO2017109294A1 proposes that mineral monitoring based on X-ray technology is carried out on the side of a river or a river bed by monitoring, so as to analyze the water quality state of the river and realize the collaborative analysis of multiple areas; the technical scheme with the publication number of CN115112536A provides a rapid measurement method for the sand content of suspended solids, wherein the corresponding relation between turbidity and sand content is established in a laboratory, and a transparent tester is arranged on site in a water area to be tested to test the actual water quality and water flow, so that the sand content in the water area is measured.
The technical proposal refers to a system for collecting and processing related data of water conservancy projects; however, there is no mention of a wide range of measurements, as well as monitoring systems that utilize data in making relevant work task settings and scheduling.
The foregoing discussion of the background art is intended to facilitate an understanding of the present invention only. This discussion is not an admission or admission that any of the material referred to was 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 high-speed network data transmission support for other modules of the monitoring system; monitoring a plurality of items of water conservancy data in a monitored water area by a first period T1 through the data acquisition module; when the early warning data appear, the appearance area of the early warning data are rapidly positioned, and the monitoring period is improved 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; and collecting execution feedback on task execution and making further future predictions to evaluate the results of task execution at a previous time; meanwhile, all data in the monitoring system are rapidly shared and displayed for related departments, so that efficient and consistent information transmission 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 these, the first and second,
the data acquisition module comprises the 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, wherein remote sensing monitoring data of the target water conservancy area are acquired by adopting a remote sensing monitoring technology; the data acquisition module transmits acquired data to the network service module through network communication;
the network service module provides network operation service by adopting an Internet of things gateway and a corresponding server based on the Internet or an internal network of a related water conservancy department;
the processing module is used for carrying out data processing, exchange, analysis, sharing and storage on the collected monitoring data and submitting a data analysis result to the scheduling module;
the scheduling module is used for formulating work tasks according to the data analysis result and combining with a personnel scheduling system, and sending the work tasks to related personnel or departments so as to schedule the personnel;
the execution module is used for receiving execution feedback of related staff after executing the work task, including 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 item and combines the execution feedback so as to update the data analysis result; the processing module stores a prediction model; obtaining a first predicted value corresponding to the 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; based on the comparison of the first predicted value and the current monitoring value and the comparison of the second predicted value and the future monitoring value, whether the state or progress of the target project is healthy or not is evaluated, or early warning information is further sent out; according to the difference value of the two values, updating the work task and the scheduling arrangement;
preferably, the processing steps of the monitoring system include:
s100: the data receiving is used for receiving the 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 schedule according to the first variance value;
s300: transmitting the first task goal and the first schedule to related personnel or departments for execution; and receives corresponding execution feedback;
s400: the processing module analyzes the historical monitoring data, the current monitoring value and the execution feedback, and obtains a second predicted value;
s500: comparing the second predicted value with a future monitoring value at a future appointed time to obtain a second difference value; evaluating the execution result of the first task target according to the second difference value;
preferably, the prediction model includes: a supervised machine learning model, a multimodal gaussian process regression model, a nonlinear regression model, a process driven model, a residual network-based machine learning model, a weight-based machine learning model, a generated countermeasure network-based machine learning model, or any combination of two or more models;
preferably, in step S200, it includes:
inputting the historical monitoring data and the current monitoring value into a preset fault node model to obtain a fault node matched with the fault node model; analyzing the geographic position corresponding to the fault node, and defining an early warning area of a designated range; the fault node model is set by related technicians according to standard ranges of parameters corresponding to a plurality of link nodes in the design of the current water conservancy project;
preferably, in step S200, further comprising:
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, an early warning area, and the like in a server in the network service module, and acquiring and displaying one or more items of information from the server by a client of a related person or unit;
preferably, the workflow of the scheduling module and the execution module comprises the following steps:
e100: the scheduling module sends the first task target and the first scheduling arrangement to a corresponding partition management unit;
e200: after receiving the 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 completed, the execution feedback is sent to the network service module, and the network service module records and stores the information;
preferably, the network service module includes:
the gateway of the Internet of things 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 flow and caching, and comprises a database for storing monitoring data;
through a system intranet of a water service department, an Internet of things gateway and a corresponding server, the cooperation of the processing modules improves the circulation and unified processing of collected data, analyzed data, management data and display data in a network;
in some embodiments, the on-site data acquisition module includes employing one or more of a flow rate sensor, a water level sensor, a water pressure sensor, and a water quality turbidity sensor; according to actual water conservancy project requirements, different sensors are selected to be configured on site, water service data are periodically collected, and then the data are collected through an equipment console or a special control box (PLC);
preferably, the on-site 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 of resource III, high score I and high score II.
The beneficial effects obtained by the invention are as follows:
1. the monitoring system combines the data acquired by field data and remote sensing data to monitor the target water conservancy area 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 the monitored item so as to make a coping strategy as early as possible and schedule the execution of the strategy;
3. the monitoring system of the invention realizes the rapid circulation of data acquisition, data analysis and data processing by interfacing with the internal network of the water conservancy related department and combining with the adoption of a high-speed network for data transmission; the monitoring system is different from the data flow mode that the previous monitoring system can carry out the next stage of processing only through a large amount of forwarding and detention, and the efficiency of the monitoring system is improved;
4. the combined scheme of the software and the hardware of the monitoring system adopts a modularized design, and can be replaced or lifted conveniently during replacement and upgrading in future, thereby effectively reducing the use cost.
Drawings
The invention will be further understood from the following description taken 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 diagram of a monitoring system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the steps of data acquisition and processing of the monitoring system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating working steps of the scheduling module and the executing module according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a training process of a prediction model according to an embodiment of the present invention;
fig. 5 is a schematic diagram illustrating each predicted data according to an embodiment of the present invention, where fig. (a) is a calculation of a first predicted value a 'using historical monitoring data, and fig. (B) is a calculation of a second predicted value B' using the historical monitoring data and current monitoring data.
Reference numerals illustrate:
100-a data acquisition module; 110-a network service module; 120-a processing module; 130-a scheduling module; 140-execution module.
Detailed Description
In order to make the technical scheme and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the following examples thereof; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. Other systems, methods, and/or features of the present embodiments will be or become apparent to one with skill in the art upon examination 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 following detailed description.
The same or similar reference numbers in the drawings of embodiments of the invention correspond to the same or similar components; in the description of the present invention, it should 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, this is for convenience of description and simplification of the description, rather than to indicate or imply that the apparatus or component referred to must have a specific orientation.
Embodiment one:
as shown in fig. 1, an intelligent water conservancy online monitoring system 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 these, the first and second,
the number of the data acquisition modules 100 is denoted by 100a and 100b in the figure, and the number of the data acquisition modules can be a plurality of data acquisition modules; 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 the target water conservancy area by adopting a remote sensing monitoring technology; the data acquisition module transmits acquired data to the network service module through network communication;
the network service module is based on the Internet or an internal network 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 carrying out data processing, exchange, analysis, sharing and storage on the collected monitoring data and submitting a data analysis result to the scheduling module;
the scheduling module is used for formulating work tasks according to the data analysis result and combining with a personnel scheduling system, and sending the work tasks to related personnel or departments so as to schedule the personnel;
the execution module is used for receiving execution feedback of related staff after executing the work task, including 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 item and combines the execution feedback so as to update the data analysis result; the processing module stores a prediction model; obtaining a first predicted value corresponding to the 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; based on the comparison of the first predicted value and the current monitoring value and the comparison of the second predicted value and the future monitoring value, whether the state or progress of the target project is healthy or not is evaluated, or early warning information is further sent out; according to the difference value of the two values, updating the work task and the scheduling arrangement;
preferably, as shown in fig. 2, the processing steps of the monitoring system include:
s100: the data receiving is used for receiving the 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 schedule according to the first variance value;
s300: transmitting the first task goal and the first schedule to related personnel or departments for execution; and receives corresponding execution feedback;
s400: the processing module analyzes the historical monitoring data, the current monitoring value and the execution feedback, and obtains a second predicted value;
s500: comparing the second predicted value with a future monitoring value at a future appointed time to obtain a second difference value; evaluating the execution result of the first task target according to the second difference value;
preferably, the prediction model includes: a supervised machine learning model, a multimodal gaussian process regression model, a nonlinear regression model, a process driven model, a residual network-based machine learning model, a weight-based machine learning model, a generated countermeasure network-based machine learning model, or any combination of the foregoing;
preferably, in step S200, it includes:
inputting the historical monitoring data and the current monitoring value into a preset fault node model to obtain a fault node matched with the fault node model; analyzing the geographic position corresponding to the fault node, and defining an early warning area of a designated range; the fault node model is set by related technicians according to standard ranges of parameters corresponding to a plurality of link nodes in the design of the current water conservancy project;
preferably, in step S200, further comprising:
adjusting the monitoring period of the early warning area to be a second period T2, wherein T2 is smaller 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, an early warning area, and the like in a server in the network service module, and acquiring and displaying one or more items of information from the server by a client of a related person or unit;
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 the first task target and the first scheduling arrangement to a corresponding partition management unit;
e200: after receiving the 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 completed, the execution feedback is sent to the network service module, and the network service module records and stores the information;
preferably, the network service module includes:
the gateway of the Internet of things 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 flow and caching, and comprises a database for storing monitoring data;
the system intranet of the water service department, the gateway of the Internet of things and the corresponding server are matched with the common work of the processing module to improve the circulation and unified processing of collected data, analyzed data, management data and display data in the network;
in some embodiments, the on-site data acquisition module includes employing one or more of a flow rate sensor, a water level sensor, a water pressure sensor, and a water quality turbidity sensor; according to actual water conservancy project requirements, different sensors are selected to be configured on site, water service data are periodically collected, and then the data are collected through an equipment console or a special control box (PLC);
furthermore, remote sensing technology is adopted to carry out remote non-contact monitoring of large-scale water conservancy projects; the remote sensing technology utilizes the characteristic of light spectrum information acquisition, and uses different remote sensing wave bands formed by various wavelengths of ultraviolet, visible light, infrared, far infrared, 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 and extracted by a computer, a large amount of various professional information is generated, such as the wide-range understanding of the conditions of water body, vegetation, water system, geology, disasters, ground structure, water and soil loss, coastal erosion and the like, the full-weather water condition, flood monitoring and the like; the common remote sensing satellite resources in China comprise one or more satellite images of resources III, high score I and high score II for analysis;
by combining field data and remote sensing data, the multi-dimensional monitoring of the diuresis project can be carried out from macroscopic level to microscopic level, so that the comprehensiveness and the high informatization of data acquisition are realized;
in some embodiments, the network service 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 build a cloud management platform; the cloud management platform integrates the data acquisition, exchange, service, sharing and utilization basis, comprises the data acquisition and storage, exchange and service interfaces and other standards, and components such as data service, a storage library and an identity code, the cloud technology is applied to the on-line processing of the service of an operation program through networking connection and control of a remote server cluster, the dynamic management of water conservancy projects can be realized, data, videos, position information and the like of the dust collection target projects are acquired and managed in real time, the data, videos, position information and the like are transmitted to corresponding modules in the cloud platform in time for analysis processing, storage backup and induction arrangement, the safety production management information control system can download and call the data in a database at any time, the production safety monitoring, the real-time inquiry of the water conservancy project information, the mutual visit of data, the early warning, the disaster emergency rescue, the accident investigation and the like are realized, the technical improvement and the improvement of the water conservancy project management level by a water conservancy management department are promoted, and the comprehensive and scientific management of the water environment is realized.
Embodiment two:
this embodiment should be understood to include at least all of the features of any one of the foregoing embodiments, and further improvements thereto:
monitoring of water conservancy projects has previously required technicians to perform manual close and continuous monitoring of changes in a plurality of monitored data, and the related technicians need a great deal of expertise and timely identify possible problems of the water conservancy projects and determine appropriate countermeasures;
with the development of informatization technology, a large data algorithm is used in combination with the application of a machine learning technology, and a prediction model can be established by adopting machine learning and used for predicting future data according to historical data and current data;
an embodiment of establishing the predictive model based on a machine learning manner is exemplarily described; as shown in fig. 4;
in step 410, the processing module receives a monitoring dataset for training the predictive model, including a plurality of historical monitoring data for a plurality of monitoring items divided in time sequence; these data are historical data accumulated over time; the type of monitoring data may include climate data such as temperature, wind speed, illumination; a plurality of index data about the body of water, such as flow, velocity, direction, sharpness, etc., may also be included; further, image data of the body of water, such as reflected illuminance, may be included; further comprising remote sensing data;
in step 420, based on the received monitoring dataset, each monitoring index in the monitoring dataset is explicitly used as a feature vector by a relevant technician, and a set of feature values of each feature vector is generated based on the monitoring dataset;
in steps 430 and 440, in some embodiments, including dividing the monitoring dataset into a training set and a validation set; the training set is used for training the model, and the verification set is used for parameter adjustment optimization of 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 includes a supervised machine learning model, a multimodal gaussian process regression model, a nonlinear regression model, a process driven model, a residual network-based machine learning model, a weight-based machine learning model, a generative machine learning model, or any combination thereof;
in some embodiments, the nonlinear regression model includes a high-dimensional nonlinear parameter function; in view of the plurality of characteristic values of similar water conservancy projects at different implementation sites, including the history of geographic locations, climatic conditions, etc., model parameters can be adjusted to best predict future characteristic values;
in some embodiments, the process driven models include one or more water body effect process driven models of a given known law that can simulate and predict the course of a water body change given historical and future inputs; for example, in known river courses, variations in flow rates, etc. in different river segments; for different water conservancy projects, the model parameters of the water conservancy projects can be adjusted according to actual projects;
in some embodiments, the weight-based machine learning model includes weights for learning and predicting process driven models and data driven models; to predict the time series of plant features, machine learning models may use a set of deep neural networks, the structure of which may be an artificial Recurrent Neural Network (RNN) or long-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 model based on generating the countermeasure network includes two sets of machine learning models; a model takes historical data of part of characteristics of the water conservancy project as input and is used for generating prediction data; while another model is used to distinguish between predicted and observed data; the two models are subjected to iterative training, so that the predicted data of the model for predicting the data are closer to the actually occurring data; both models may be trained using deep neural networks, nonlinear parameterized functions, or functions of related water conservancy calculations where weights may be iteratively adjusted.
Embodiment III:
this embodiment should be understood to include at least all of the features of any one of the foregoing embodiments, and further improvements thereto:
among them, analysis of the monitored data and prediction of future data based on the prediction model can 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: the method comprises the steps of monitoring a obtained current monitoring value A at a current time, a first predicted value A 'based on predicted current time data, a second predicted value B' of predicted specified future time and a predicted specified future time target C; each of these data sets will be described below; and these datasets are based at least in part on the predictive model;
the current monitoring value a is a value of a certain feature obtained by monitoring a target item at a current time (for example, specific data of a certain time node, or an average value of the current day, the current week and the current month, etc.); the data set may be obtained by a data acquisition module as described herein;
the first predicted value A' refers to the value of a certain feature predicted by the monitoring system at the current time based on historical data obtained by past monitoring of a target item; 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 figure 5 (a), the monitoring system may correspond to a plurality of time instants [.. x-3 ，T x-2 ，T x-1 ]As input features, into the predictive model; the prediction model can predict and output the prediction model at T x The value of the moment, i.e. the first predicted value a'; in some embodiments, the predicted first predicted value a' may be interpreted as "what the monitored data should be at present";
the second predicted value B' is an output characteristic value of a certain characteristic at a future time under the condition that the monitoring system monitors the obtained historical data and the current measured data in the past on the basis of the target item; and according to a preferred embodiment of the present invention, the second predicted value B' may be obtained using the prediction model; as also shown in fig. 5 (B), the system may correspond to the past times and the present times [.. x-3 ，T x-2 ，T x-1 ，T x ]As input features, into the predictive model; the predictive model may output a signal at a future time T x+1 I.e. the second predicted value B'; in some embodiments, the second predictive value B' may be interpreted as "what the monitored data would be at a future time";
the predicted future time target C refers to a value of a process that monitors data changes between one or more future times in order to evaluate the progress of the implementation change to the target task; for example, given a set of task goals (e.g., implementing specified metric data in three months), the monitoring system may determine what the metric data should reach at each month; in some embodiments, the predicted future time target C may be interpreted as "what the process should be from the present to the future time of the plant";
further, the processing module may evaluate the results of the task targets from the above values, and may perform a process check to ensure that the task targets in the process are changed according to a predetermined trajectory, and update the first task target and the first schedule to the second task target and the second schedule in time;
furthermore, the monitoring system presents a series of data using images so as to achieve a more visual monitoring data display effect.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
While the invention has been described above with reference to various embodiments, it should be understood that many changes and modifications can 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, replace, or add various procedures or components as appropriate. For example, in alternative configurations, the methods may be performed in a different order than 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, such as different aspects and elements of the configurations may be combined in a similar manner. Furthermore, as the technology evolves, elements therein may be updated, i.e., many of the elements are examples, and do not limit the scope of the disclosure or the claims.
Specific details are given in the description to provide a thorough understanding of exemplary configurations involving implementations. However, configurations may be practiced without these specific details, e.g., well-known circuits, processes, algorithms, structures, and techniques have been shown without unnecessary detail in order to avoid obscuring configurations. This description provides only an example configuration and does not limit the scope, applicability, or configuration of the claims. Rather, the foregoing description of the configuration 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.
It is intended that the foregoing detailed description be regarded as illustrative rather than limiting, and that it be understood that it is intended that it be regarded as illustrative rather than limiting. Various changes and modifications to the present invention may be made by one skilled in the art after reading the teachings herein, and such equivalent changes and modifications are intended to fall within the scope of the invention as defined in the appended claims.
Claims (4)
1. An intelligent water conservancy on-line monitoring system which is 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 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 the target water conservancy area by adopting a remote sensing monitoring technology; the data acquisition module transmits acquired data to the network service module through network communication;
the network service module is based on the Internet or an internal network of a related water conservancy department, adopts an Internet of things gateway and a corresponding server to provide network operation service and is used for realizing the transmission of digital information and the protection of data;
the processing module is used for carrying out data processing, exchange, analysis, sharing and storage on the collected monitoring data and submitting a data analysis result to the scheduling module;
the scheduling module is used for formulating work tasks according to the data analysis result and combining with a personnel scheduling system, and sending the work tasks to related personnel or departments so as to schedule the personnel;
the execution module is used for receiving execution feedback of related staff after executing the work task, including execution time and specific implementation conditions; the execution module sends the execution feedback to the processing module;
the monitoring system comprises the following execution steps:
s100: the data receiving is used for receiving the 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 schedule according to the first variance value;
s300: transmitting the first task goal and the first schedule to related personnel or departments for execution; and receives corresponding execution feedback;
s400: the processing module analyzes the historical monitoring data, the current monitoring value and the execution feedback, and obtains a second predicted value;
s500: comparing the second predicted value with a future monitoring value at a future appointed time to obtain a second difference value; evaluating the execution result of the first task target according to the second difference value;
the processing module continuously updates the current monitoring value of the target item and combines the execution feedback so as to update the data analysis result; the processing module stores a prediction model; obtaining a first predicted value corresponding to the 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; based on the comparison of the first predicted value and the current monitoring value and the comparison of the second predicted value and the future monitoring value, whether the state or progress of the target project is healthy or not is evaluated, or early warning information is further sent out; according to the difference value of the two values, updating the work task and the scheduling arrangement;
the processing module receives a monitoring dataset for training the predictive model, including a plurality of historical monitoring data for a plurality of monitoring items divided by time sequence; based on the received monitoring data set, determining each monitoring index in the monitoring data set as a characteristic vector by a related technician, and generating a set of characteristic values of each characteristic vector based on the monitoring data set; the processing module divides the monitoring data set into a training set and a verification set; the training set is used for training the model, and the verification set is used for parameter adjustment optimization of a plurality of weight values in the model;
wherein, in step S200, it includes:
inputting the historical monitoring data and the current monitoring value into a preset fault node model to obtain a fault node matched with the fault node model; analyzing the geographic position corresponding to the fault node, and defining an early warning area of a designated range; the fault node model is set by related technicians according to standard ranges of parameters corresponding to a plurality of link nodes in the design of the current water conservancy project; the step S200 further includes: adjusting the monitoring period of the early warning area to be a second period T2, and enabling T2 to be less than T1;
in step S300, the method comprises the steps of pre-determining historical monitoring data, current monitoring value, first predicted value and second predicted value
The measured value, the fault node, the geographic position corresponding to the fault node and the early warning area information are stored in a server in the network service module, and a client of a relevant person or unit acquires and displays one or more items of information from the server;
the workflow of the scheduling module and the execution module comprises the following steps:
e100: the scheduling module sends the first task target and the first scheduling arrangement to a corresponding partition management unit;
e200: after receiving the 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 completed, the execution feedback is sent to the network service module, and the network service module records and stores the information;
the network service module includes:
the gateway of the Internet of things 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 flow and caching, and comprises a database for storing monitoring data;
through a system intranet of a water service department, an Internet of things gateway and a corresponding server, the system intranet, the Internet of things gateway and the corresponding server cooperate with the common work of the processing modules, the circulation and unified processing efficiency of collected data, analyzed data, management data and display data in a network are improved;
for each target item, the monitoring system may obtain a plurality of data sets, including: the method comprises the steps of monitoring a obtained current monitoring value A at a current time, a first predicted value A 'based on predicted current time data, a second predicted value B' of predicted specified future time and a predicted specified future time target C; each of these data sets will be described below; and these datasets are based at least in part on the predictive model;
the current monitoring value A is a value of a certain feature obtained by monitoring the target item at the current time; the first predicted value A' refers to the value of a certain feature predicted by the monitoring system at the current time based on historical data obtained by past monitoring of a target item; the second predicted value B' is an output characteristic value of a certain characteristic at a future time under the condition that the monitoring system monitors the obtained historical data and the current measured data in the past on the basis of the target item; the predicted future time target C refers to a value of a process that monitors data changes between one or more future times in order to evaluate the progress of the implementation change to the target task;
the processing module evaluates the results of the task targets from the above values and performs a process check to ensure that the in-process task targets are changed according to the established trajectory, and timely updates the first task target and the first schedule to the second task target and the second schedule.
2. The monitoring system of claim 1, wherein the predictive model comprises: supervision machine
A 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 generated countermeasure network-based machine learning model, or any combination of two or more of these models.
3. The monitoring system of claim 2, wherein the field data acquisition module comprises a mining module
One or more of a flow rate sensor, a water level sensor, a water pressure sensor and a water quality turbidity sensor are used.
4. The monitoring system of claim 3, wherein the remote sensing data acquisition module comprises
One or more satellite images of the third resource, the first high score and the second high score are adopted.
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| 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 |
| 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 |
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