Hydrological monitoring method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of computers, and discloses a hydrological monitoring method and device, computer equipment and a storage medium, and the method comprises the steps: receiving hydrological observation data, reported by a sensor cluster, of a hydrological station at each sampling moment, and receiving video stream information, reported by a video monitoring system, of the hydrological station, processing the hydrological observation data to obtain observation data of each sensor and measured water level data of a water gauge in the hydrological station; analyzing the video stream information, determining a corresponding relation between a photo set in the video stream information and the observation data and the water level data, and calculating water level information of the water gauge; and inputting the water level data and the water level information of the water gauge into the trained neural network model, outputting a target water level, and monitoring the water level condition of the hydrometric station according to the target water level. According to the method, intelligent management is carried out by adopting an integrated mode of combining a sensor and video monitoring, so that the problems that hydrometric station information technology equipment is not strong and the perception is insufficient are effectively solved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a hydrological monitoring method, device, computer equipment and storage medium. Background Art

[0002] Water resources are the foundation for the development and survival of human resources and equipment, and the management and utilization of water resources are very important. As the main carrier and basic unit of hydrological monitoring, hydrological stations play the role of collecting, analyzing and transmitting hydrological data, providing an important basis for water resources management. The hydrological elements observed by hydrological stations include water level, flow velocity, flow direction, waves, sand content, water temperature, ice conditions, groundwater, water quality, etc., and meteorological elements include precipitation, evaporation, temperature, humidity, air pressure and wind, etc.

[0003] At present, hydrological stations mainly collect hydrological data through rain gauges, radar water level gauges, float water level gauges, radar wave flow meters, Doppler current profilers (Acoustic Doppler Current Profiler, ADCP), etc., which have problems such as aging equipment, single monitoring method, inconsistent data collection protocols, inaccurate data collection, and high risk of data leakage.

[0004] In addition, managers check hydrological information through on-site inspections and video surveillance, which is highly dependent on manpower, high cost, and unable to identify changes in hydrological data in real time, quickly, and accurately. Summary of the invention

[0005] In view of this, the present invention provides a hydrological monitoring method, device, computer equipment and storage medium to solve the above-mentioned series of problems in the current hydrological station's collection of hydrological data, as well as the high degree of dependence on manpower and high cost when viewing hydrological information, and the inability to identify changes in hydrological data in real time, quickly and accurately.

[0006] In a first aspect, the present invention provides a hydrological monitoring method, the method comprising:

[0007] Receiving hydrological observation data of the hydrological station at each sampling moment reported by the sensor cluster, and receiving video stream information of the hydrological station reported by the video monitoring system, the video stream information including a continuous set of photos taken of the hydrological station environment;

[0008] Processing the hydrological observation data to obtain observation data of each sensor and water level data of a water gauge in a measured hydrological station;

[0009] Parsing the video stream information, determining the correspondence between the photo set in the video stream information and the observation data and the water level data, and calculating the water level information of the water gauge according to the correspondence, the observation data and the water level data;

[0010] The water level data and the water level information of the water gauge are input into the trained neural network model, the target water level is output, and the water level condition of the hydrological station is monitored according to the target water level.

[0011] The hydrological monitoring method provided by the present invention has the following advantages:

[0012] By integrating sensor data and video stream information, the system can provide multi-dimensional hydrological data analysis, thereby providing a more comprehensive understanding of hydrological phenomena. Combining the photo collection of video surveillance with sensor data can more accurately determine water level changes and reduce the errors that may be caused by a single data source.

[0013] In addition, data collection and monitoring methods can avoid problems such as equipment aging, single monitoring methods, inconsistent data collection protocols, and inaccurate data collection. The system can calculate water level information in real time and predict the target water level through a neural network model, which helps to detect abnormal conditions in a timely manner and issue early warnings.

[0014] Automated data processing and water level prediction functions can reduce manual intervention and improve decision-making efficiency. The fusion of multi-source data improves the robustness of the system, and the system can still maintain normal operation even when some sensors fail. Through intuitive video streaming information and accurate water level data, users can understand the hydrological conditions more intuitively and improve user experience.

[0015] The automated monitoring and early warning system can reduce the frequency of manual inspections, thereby reducing operating costs. At the same time, through intelligent identification of video surveillance, the safety management capabilities around the hydrological station can be improved, and the intelligentization of the hydrological station can be achieved.

[0016] In an optional implementation, the receiving of hydrological observation data of the hydrological station at each sampling time reported by the sensor cluster includes:

[0017] Receiving the hydrological observation data reported by the sensor cluster in a wired manner, or,

[0018] Receiving the hydrological observation data reported by the sensor cluster via wireless LoRa, or,

[0019] The hydrological observation data is received through Beidou communication access. Specifically, in the above method of the present application, by supporting a variety of wired or wireless communication methods, the deployment of hydrological stations in various environments can be met. In this way, the problems of weak information technology equipment, insufficient perception, insufficient data security protection capabilities and insufficient intelligence levels of hydrological stations in related technologies can be solved. For example, in an environment with a wired network, a wired network can be used to collect sensor data to improve data security and data transmission stability. In an environment without a wired network, a wireless LoRa method can be used to collect sensor data, reduce the risk of data exposure to the public network, improve data security, and save costs.

[0020] In addition, the high-precision positioning capability of Beidou communication access can enhance the reliability of the system, ensure the accuracy and real-time nature of data collection, and may also reduce dependence on traditional communication networks and reduce communication costs. The use of wireless LoRa technology can reduce wiring costs. Wireless LoRa and Beidou communication access methods are not restricted by terrain and are suitable for complex and changeable natural environments, which increases the scope of application of the system. The maintenance cost of the wired method is relatively low, and troubleshooting is more direct. In the event that the wired connection may be interrupted, the wireless connection can be used as a backup to ensure the continuity and integrity of data transmission. Moreover, in an emergency, wireless communication can quickly restore data transmission and enhance the emergency response capability of the system. In summary, the above multi-mode data reception strategy significantly improves the performance, adaptability and practicality of the hydrological monitoring system.

[0021] In an optional implementation manner, the receiving of video stream information from a hydrological station reported by a video monitoring system includes:

[0022] In the case of wired communication, receiving the video stream information reported by the video surveillance system through a wired network;

[0023] Alternatively, in the case of wireless communication, the video stream information reported by the video surveillance system is received through a wireless bridge;

[0024] Alternatively, in the case where wireless communication is available but wireless bridge communication is not available, the video stream information is received via WLAN.

[0025] Specifically, similar to the above, in the above method of the present application, by supporting a variety of wired or wireless communication methods, the deployment of hydrological stations in various environments can be met. This can solve the problems of weak information technology equipment, insufficient perception, insufficient data security protection capabilities and insufficient intelligence levels in the related technologies of hydrological stations. For example, in an environment with a wired network, the video stream information reported by the video surveillance system can be received by means of a wired network to improve data security and the stability of data transmission. In an environment without a wired network, the video stream information reported by the video surveillance system is received through a wireless bridge to reduce the risk of data exposure to the public network, improve data security and save costs.

[0026] In the case of wireless communication but no wireless bridge communication, the video stream information is received via WLAN. In the case of poor or unavailable wired communication conditions, wireless bridges and WLAN provide alternatives, allowing the video surveillance system to be deployed in a wider range of environments.

[0027] Wireless communication methods, especially WLAN, are usually simpler to deploy than wired networks and are less expensive, making them particularly suitable for areas with complex terrain or difficult wiring. Wireless transmission usually has a faster response speed, allowing for quick reception of video stream information in emergency situations, facilitating quick response and decision-making. The choice of multiple communication methods provides redundancy for the system, and when one communication method fails, other methods can ensure the continuity of data transmission. Wireless communication reduces the need for maintenance of physical connections, reducing the maintenance cost and complexity of the system. Wired communication usually provides a more stable and higher data transmission rate, helping to ensure the quality and integrity of the video stream. In the case of unstable network conditions, wireless methods such as WLAN can provide a more reliable connection to avoid data loss caused by network fluctuations. Wireless communication makes remote monitoring more convenient, and users can access the video monitoring system anywhere through the network. The system can select the appropriate communication method based on the specific conditions of the site, whether it is wired or wireless, to ensure the continuity of video monitoring. Moreover, the support of multiple communication methods facilitates the future expansion of the system, making it easy to add new monitoring points or upgrade the system. The flexible video stream information reception method improves the overall performance of the hydrological station monitoring system and enhances the system's applicability, reliability and efficiency.

[0028] In an optional implementation manner, before processing the hydrological observation data, the method further includes:

[0029] Aligning the hydrological observation data with the plurality of photos in the photo set according to each sampling moment and the time point of the continuous photo set taken;

[0030] The processing of the hydrological observation data to obtain the observation data of each sensor and the water level data of the water gauge in the measured hydrological station includes:

[0031] The time-aligned hydrological observation data are processed to obtain the observation data of each sensor and the water level data of the measured water gauge, wherein the observation data includes the sampling data measured by each sensor and the sensor ID.

[0032] Specifically, through the above method, the precise correspondence between hydrological observation data and photo time points can be ensured, thereby improving the consistency and accuracy of the data. Through time alignment, researchers and engineers can save data synchronization and matching time and analyze data more efficiently. Moreover, the enhanced correlation between observation data and photos can also help to more accurately interpret and verify observation data, especially when analyzing water level changes and environmental conditions. In the event of water level anomalies, real-time aligned data can also help to respond quickly and take appropriate measures.

[0033] In addition, by processing the observation data and water gauge water level data, noise and inaccurate data can be removed and data quality can be improved. Combining photos and hydrological data can provide more intuitive data visualization, which helps to understand and interpret trends and patterns in the data. The combination of observation data and photos allows analysis to be conducted from multiple angles, such as water level changes, water flow speed, water quality conditions, etc. More accurate water level data can help optimize water resource management decisions, including irrigation, drainage and flood prevention. Furthermore, by processing the data consistently, the reliability of the entire monitoring system can be improved and errors and false alarms can be reduced.

[0034] In an optional implementation manner, after parsing the video stream information, the method further includes:

[0035] Parsing the video stream information to obtain a plurality of photos taken continuously in the photo collection;

[0036] Inputting the plurality of photos into a self-trained neural network model to obtain training results;

[0037] Determining whether the plurality of photos contain a water gauge target according to the training result;

[0038] If included, get the frame height of the water gauge target and the water gauge area image;

[0039] Detecting whether the water ruler area image contains the target character E through the self-trained neural network model;

[0040] If the target character E is included, then the average height EH of the target character E is calculated;

[0041] The step of calculating the water level information of the water gauge according to the corresponding relationship, the observation data and the water level data comprises:

[0042] Obtain the actual length eH of the target character E;

[0043] According to the corresponding relationship, the average height of the target character E and the actual length eH, the actual length of the water gauge is calculated, and the water level information including the actual length of the water gauge is generated.

[0044] Specifically, the automatic detection of water level information in water gauge photos reduces the burden of manual monitoring and improves the efficiency and accuracy of water level monitoring. The real-time analysis capability of the neural network model enables water level information to be updated in real time, which is crucial for hydrological events that require rapid response. Through automated target detection and water level calculation, data deviations caused by human errors are reduced. The use of neural network models can more accurately identify water gauge targets and characters, thereby improving the accuracy of water level information. Automated processing reduces the need for human resources and reduces long-term operating costs.

[0045] This method can be easily extended to multiple monitoring points and is suitable for large hydrological monitoring networks. By combining water level information with observation data and water level data, the availability and comprehensiveness of the data are improved. Accurate water level information provides strong support for decision-making such as water resource management, flood prevention and response. The neural network model can adapt to different lighting conditions and water gauge changes, enhancing the robustness of the system. The application of this method promotes the application and development of computer vision and machine learning technology in the field of hydrological monitoring.

[0046] In an optional implementation, the method further includes: acquiring the trained neural network model;

[0047] The step of obtaining a trained neural network model comprises:

[0048] Obtain the initial neural network model, as well as the prediction value and auxiliary value of the previous moment;

[0049] Obtain the current water level value t from the water level detection system or video monitoring system;

[0050] Merge the current moment input value and the previous moment auxiliary value to obtain the current moment merge output gate;

[0051] The current moment merge output gate is processed as follows:

[0052] Passing the current moment merge output gate through the first sigmoid neural network to obtain the current moment forget gate;

[0053] Pass the current moment merged output gate through the second sigmoid neural network to obtain the current moment input gate;

[0054] Passing the current moment merge output gate through the first tanh neural network to obtain the current moment state update value;

[0055] Perform a dot multiplication of the forget gate at the current moment and the predicted value at the previous moment to obtain;

[0056] Perform a dot multiplication of the current state update value and the current input gate to obtain;

[0057] Adding the above and the above point by point to obtain the current moment prediction value;

[0058] The current moment prediction value is calculated by a second tanh neural network, and is dot-multiplied with the current moment merge output gate to obtain the current moment auxiliary value;

[0059] The current moment prediction value and the current moment auxiliary value are used as input loops for training, and are detected by the cross entropy loss function until the difference between the measured true value and the predicted value reaches a preset value, then the loop is exited and the neural network model training is completed.

[0060] In an optional implementation, monitoring the water level status of the hydrological station according to the target water level includes:

[0061] Determining whether the target water level is within a safety line;

[0062] If yes, it is determined that the water level condition at the hydrological station is normal;

[0063] If not, it is determined that the water level condition is abnormal, an alarm message is generated, and the alarm message is sent to the alarm system.

[0064] Specifically, the system can monitor the water level of the hydrological station in real time to ensure timely detection of potential water level anomalies, thereby protecting people's lives and property. By timely determining whether the water level exceeds the safety line, early warning can be given to reduce the risk of disasters such as floods and waterlogging. When the water level is abnormal, the system can quickly generate an alarm message and send it to the alarm system, so that relevant departments can respond quickly and take necessary preventive measures. Through timely monitoring and alarming, property losses and casualties caused by excessively high or low water levels can be reduced.

[0065] In a second aspect, the present invention provides a hydrological monitoring device, the device comprising:

[0066] A receiving module, used to receive hydrological observation data of the hydrological station at each sampling moment reported by the sensor cluster, and receive video stream information of the hydrological station reported by the video monitoring system, wherein the video stream information includes a continuous set of photos taken of the hydrological station environment;

[0067] A processing module is used to process the hydrological observation data to obtain the observation data of each sensor and the water level data of the water gauge in the measured hydrological station;

[0068] A determination module, used for parsing the video stream information, determining the correspondence between the photo set in the video stream information and the observation data and the water level data, and calculating the water level information of the water gauge according to the correspondence, the observation data and the water level data;

[0069] The processing module is also used to input the water level data and the water level information of the water gauge into the trained neural network model, output the target water level, and monitor the water level status of the hydrological station according to the target water level.

[0070] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the hydrological monitoring method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0071] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the hydrological monitoring method of the first aspect or any corresponding embodiment thereof.

[0072] In addition, the present invention provides a computer program product, including computer instructions, which are used to enable a computer to execute the hydrological monitoring method of the above-mentioned first aspect or any corresponding embodiment thereof.

[0073] In a fifth aspect, the present invention provides a hydrological monitoring system, comprising at least one edge device, a sensor cluster, a video monitoring system and a server, wherein the sensor cluster, the video monitoring system and the edge device may be connected by wire or wirelessly. Each edge device is used to execute the hydrological monitoring method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0075] Figure 1 is a structural schematic diagram of a hydrological monitoring system according to an embodiment of the present invention;

[0076] Figure 2 is a flow chart of a hydrological monitoring method according to an embodiment of the present invention;

[0077] Figure 3 is a schematic diagram of input device information according to an embodiment of the present invention;

[0078] Figure 4 is a flow chart of another hydrological monitoring method according to an embodiment of the present invention;

[0079] Figure 5 is a flow chart of a method for calculating the actual length of a water gauge according to an embodiment of the present invention;

[0080] Figure 6 is a schematic diagram of a neural network model according to an embodiment of the present invention;

[0081] Figure 7 is a flow chart of an alarm method according to an embodiment of the present invention;

[0082] Figure 8 is a structural block diagram of a hydrological monitoring device according to an embodiment of the present invention;

[0083] Fig. 9 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0084] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0085] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0086] Furthermore, the terms “first” and “second” are used for descriptive purposes only and should not be understood as indicating or implying relative importance.

[0087] The technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0088] The technical solution of the present invention is applied to the detection environment of a hydrological station or a hydrological system, see Figure 1 As shown, it is a structural schematic diagram of a hydrological monitoring system according to an embodiment of the present invention. The system includes: a sensor set, a video monitoring system, an edge computing device, a server or a data center.

[0089] The edge computing device includes a data management system, which has functions such as data collection, data storage, and data compilation. Optionally, the data management system can be a functional module or functional software.

[0090] In addition, the edge computing device may be an edge device or an edge node, including but not limited to a client, a PC (personal computer) or a terminal device, etc. Optionally, the terminal device may also be a smart phone, a tablet computer, a foldable terminal, a wearable device with wireless communication function, a user device or a user equipment (UE), etc. This embodiment does not limit the specific device form of the terminal device.

[0091] In addition, the server can also be other network devices, such as a server cluster, a data center, a service node, etc.

[0092] Optionally, the above system may also include an alarm device or an alarm system, such as a speaker, a horn, etc., which is connected to the edge computing device and is used to issue an alarm when the water level at the hydrological station exceeds the safety line.

[0093] The server or data center includes a hydrological management platform, which can be a software platform or a hardware and software platform for analyzing and coordinating data reported by edge computing devices.

[0094] At present, the way sensor clusters report sampled observation data to edge computing devices is through remote terminal units (RTUs), which transmit the corresponding sensor data directly to the hydrological management platform via 4G / 5G. In this solution, each sensor needs to be equipped with a corresponding RTU device, which makes it impossible to manage it uniformly and the operating conditions cannot be monitored in real time. At the same time, the risk of data leakage is relatively high when data is transmitted via 4G / 5G.

[0095] In addition, the data of the water level gauge in the plan needs to be read and recorded by staff on-site or through video surveillance. When the corresponding data is entered into the provincial management platform, data entry errors are prone to occur.

[0096] In order to solve the above technical problems, an embodiment of the present invention provides an embodiment of a hydrological monitoring method. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0097] In this embodiment, a hydrological monitoring method is provided, which can be used in the above-mentioned edge computing device. Figure 2 is a flow chart of a hydrological monitoring method according to an embodiment of the present invention, the process comprising:

[0098] Step S101: receiving hydrological observation data of a hydrological station at each sampling time reported by a sensor cluster, and receiving video stream information of the hydrological station reported by a video monitoring system.

[0099] The video stream information includes a continuous set of photos taken of the hydrological station environment.

[0100] The sensor cluster includes: rainfall sensor or rain gauge, water level sensor, radar wave flow measurement, Doppler current profiler (ADCP) flow measurement and other sensors, such as marker float water level gauge, etc. Hydrological observation data is a general term for observation data collected by all sensors, which can be sampled data collected at different times, such as water level information.

[0101] The video monitoring system may include at least one camera or camera module for collecting the surrounding environment information of the hydrological station and taking a group of continuous photos or pictures to form a photo collection.

[0102] See also Figure 3As shown, in this embodiment of the hydrological supervision method based on the integration of sensors and video monitoring, the input device information includes not only the information of at least one sensor device, such as the device ID, parameters and data information corresponding to each sensor, etc. Similarly, the video monitoring device also includes the corresponding IP address and parameter information, etc.

[0103] In this step, the edge device can receive the hydrological observation data reported by the sensor cluster, or the video stream information of the hydrological station reported by the video monitoring system, through wired or wireless means.

[0104] Step S102: Process the hydrological observation data to obtain the observation data of each sensor and the water level data of the water gauge in the measured hydrological station.

[0105] The water level meter, flow meter, rain gauge and other sensors in the above sensor cluster are used to monitor the water level, flow, rainfall, etc. in real time. These sensors are generally arranged at key locations of reservoirs or hydrological stations to ensure the comprehensiveness and accuracy of the data.

[0106] Each sensor collects the observation data of the water gauge and sends it to the edge device. The edge device uses the water level meter to observe the changes in the water level line and obtain the water level data. For example, the observed data can be stored in the edge device through digital water gauge storage, water gauge digital coding storage, etc. Alternatively, the automatic recording and archiving function of the hydrological software can be used to organize all the data in a file and save it.

[0107] Data preprocessing: During the observation data transmission process, it may be necessary to perform preliminary processing on the transmitted data, such as data compression, format conversion, deduplication or interference removal, etc., to improve the transmission efficiency and compatibility of the observation data.

[0108] In addition, the water level data of the water gauge in the measured hydrological station, including information such as the water gauge height or water gauge length, can be obtained through a neural network model or a preset algorithm.

[0109] Step S103: parsing the video stream information, determining the correspondence between the photo set in the video stream information and the observation data and water level data, and calculating the water level information of the water gauge according to the correspondence, the observation data and the water level data.

[0110] The water level information of the water gauge includes water gauge height information, which is real water level data obtained after correcting the water gauge height measured by at least one sensor in the sensor set.

[0111] In this step, each photo in the video stream information is associated with the observation data and water level data collected by the sensor at the same time, and the associated observation data and water level data are used to calculate the water level information of the current water gauge under the data collected by the two systems, such as the water gauge length.

[0112] Step S104: input the water level data and the water level information of the water gauge into the trained neural network model, output the target water level, and monitor the water level status of the hydrological station according to the target water level.

[0113] The trained neural network model can be a trained LSTM model. The LSTM (Long Short-Term Memory) model is a special recurrent neural network (RNN) architecture that aims to solve the gradient vanishing and gradient exploding problems of traditional RNN when processing long sequence data. It captures the long-term dependency of information by introducing a memory unit (Cell State) and three key gating mechanisms (input gate, forget gate, output gate).

[0114] Monitor the water level status of the hydrological station according to the target water level, specifically including judging whether the target water level exceeds the safety warning line according to the target water level. If it exceeds the safety warning line, make corresponding alarm processing.

[0115] The hydrological monitoring method provided by the present invention has the following advantages:

[0116] By integrating sensor data and video stream information, the system can provide multi-dimensional hydrological data analysis, thereby providing a more comprehensive understanding of hydrological phenomena. Combining the photo collection of video surveillance with sensor data can more accurately determine water level changes and reduce the errors that may be caused by a single data source.

[0117] In addition, data collection and monitoring methods can avoid problems such as equipment aging, single monitoring methods, inconsistent data collection protocols, and inaccurate data collection. The system can calculate water level information in real time and predict the target water level through a neural network model, which helps to detect abnormal conditions in a timely manner and issue early warnings.

[0118] Automated data processing and water level prediction functions can reduce manual intervention and improve decision-making efficiency.

[0119] The fusion of multi-source data improves the robustness of the system, and the system can still maintain normal operation even when some sensors fail. Through intuitive video stream information and accurate water level data, users can understand the hydrological conditions more intuitively, improving user experience.

[0120] The automated monitoring and early warning system can reduce the frequency of manual inspections, thereby reducing operating costs. At the same time, through intelligent identification of video surveillance, the safety management capabilities around the hydrological station can be improved, and the intelligentization of the hydrological station can be achieved.

[0121] In this embodiment, in the above step S101: during the data collection process, all hydrological data of the hydrological station can be collected simultaneously through wired, wireless bridges, 4G / 5G, LoRa, Beidou and other communication methods, and the data of the hydrological station can be analyzed and processed, and simultaneously uploaded to the provincial management platform through the hydrological dedicated line, so as to reduce the risk of data exposure and improve the security of hydrological data.

[0122] Data security is mainly improved in two aspects. First, the local area network of the hydrological station is built on site through wired or wireless LoRa and wireless bridge to collect and aggregate data, prevent data from being transmitted through the public network, and reduce the risk of leakage; second, after the data is collected and aggregated to the edge computing device, the asymmetric RSA encryption algorithm is used to encrypt the data and decrypt it on the hydrological platform of the server. This embodiment does not limit the specific encryption process and decryption process.

[0123] Intelligent equipment supervision can simultaneously monitor the operating status of sensors and video surveillance equipment in real time, effectively identify the operating status of equipment, and intelligently manage the management area of ​​the hydrological station. If there are abnormalities such as strangers breaking in, floating objects in the river, etc., real-time alarms can be issued to improve the safety management level of the hydrological station, realize intelligent supervision of the hydrological station, and ultimately achieve "one station, one measurement, one station, one system".

[0124] In a possible implementation of this embodiment, the above-mentioned step S101: receiving the hydrological observation data of the hydrological station at each sampling time reported by the sensor cluster, includes: receiving the hydrological observation data reported by the sensor cluster through a wired method, or, receiving the hydrological observation data reported by the sensor cluster through a wireless LoRa method, or, receiving the hydrological observation data through a Beidou communication access method.

[0125] Similarly, the above step S101, receiving the video stream information of the hydrological station reported by the video monitoring system, includes: receiving the video stream information reported by the video monitoring system through a wired network in the case of wired communication.

[0126] Alternatively, when wireless communication is available, the video stream information reported by the video surveillance system is received through a wireless bridge; or, when wireless communication is available but wireless bridge communication is not available, the video stream information is received through a WLAN (Wireless Local Area Network).

[0127] Further, see Figure 3 and Figure 4 , the method comprising:

[0128] Obtain device information and classify the devices into sensor devices and video surveillance devices.

[0129] For video surveillance equipment, determine whether a wired network environment is available. If so, directly access the system through the wired network.

[0130] If there is no wired network environment, determine whether there are conditions for wireless bridge access;

[0131] If the conditions for wireless bridge transmission are met, the system is accessed through the wireless bridge, and video stream information is sent to the edge computing device through the wireless bridge.

[0132] If there is no wireless bridge environment, determine whether there is WLAN network transmission, such as 4G or 5G environment. If there is WLAN transmission, access the system through 4 / 5G to send video stream information to the edge computing device.

[0133] Optionally, in this embodiment, the video monitoring system supports collecting video streams in two ways: standard video protocol and RTSP (Real Time Streaming Protocol).

[0134] In addition, the video stream information is divided into continuous picture sets according to time periods; optionally, in one example, the audio segment uses a time period of 10 seconds, and the video is divided into picture sets of continuous pictures corresponding to 10 seconds.

[0135] For sensor devices, determine whether each sensor has wired communication conditions;

[0136] If wired communication conditions are available, access the system via a wired method, such as connecting to a USB interface, and transmit the hydrological observation data via the USB cable of the USB interface.

[0137] If the wired communication conditions are not met, it is determined whether the wireless LoRa conditions are met. If so, the edge computing device is connected via wireless LoRa.

[0138] Among them, wireless LoRa (Long Range Radio) is a low-power, long-distance wireless transmission technology, also known as low-power wide area network (LPWAN) wireless communication technology. LoRa technology uses spread spectrum modulation technology, especially Chirp Spread Spectrum (CSS) modulation technology. This technology improves the signal's anti-interference ability and transmission distance by spreading the data signal to a wider spectrum. LoRa uses unlicensed frequency bands, which enables it to communicate without applying for spectrum resources.

[0139] If the wireless LoRa conditions cannot be met, determine whether WLAN network transmission is available, that is, 4G / 5G environment. If so, access the system through 4G / 5G.

[0140] In addition, if none of the above transmission conditions are met, but the system is supported to access through Beidou communication. Optionally, in this embodiment, the system supports access through wired network, wireless bridge, WLAN network, such as 4G / 5G, Beidou, etc., and has good compatibility.

[0141] It should be noted that, in special circumstances, the use of Beidou communication transmission supports the setting of Beidou communication's unidirectional and bidirectional modes, collection cycle and collection frequency to save equipment resources and Beidou communication costs.

[0142] In a possible implementation of this embodiment, before processing the hydrological observation data, the above step S102 further includes: aligning the hydrological observation data with multiple photos in the photo set according to each sampling moment and the time point of the continuous photo set taken.

[0143] According to the time information, the hydrological observation data sampled by the sensor and at least one photo in the photo set are aligned in time to obtain the aligned hydrological observation data and photo set. For example, the sensor has N sampling moments, and the binding relationship between the hydrological observation data at each sampling moment and the corresponding photo taken at that moment is established, thereby establishing a binding between a group of photo sets and a group of hydrological observation data at N moments.

[0144] Step S103: Process the hydrological observation data to obtain the observation data of each sensor and the water level data of the water gauge in the measured hydrological station, including: processing the time-aligned hydrological observation data to obtain the observation data of each sensor and the water level data of the water gauge, wherein the observation data includes the sampling data measured by each sensor and the sensor ID.

[0145] Specifically, the edge node inputs the aligned hydrological observation data, such as hydrological observation data collected by at least one sensor at N moments, into the data management system to obtain the data corresponding to each sensor device ID, that is, the water level data observed by the sensor.

[0146] All files corresponding to the water gauge (such as upstream and downstream water gauge) in the video image set are separated according to the sensor ID data.

[0147] For example, as shown in the table below, it is the sampling data of water gauge 1 to water gauge 3 observed by sensors 1 to 3, that is, the hydrological observation data.

[0148] Sensor 1 Sensor 2 Sensor 3 Water gauge 1 A1 B1 C1 Water gauge 2 A2 B2 C2 Water gauge 3 A3 B3 C3

[0149] Among them, A1 represents the observation data of sensor 1 on water gauge 1 of the hydrological station, A2 represents the observation data of sensor 1 on water gauge 2 of the hydrological station, and A3 represents the observation data of sensor 1 on water gauge 3 of the hydrological station. Similarly, B1 represents the observation data of sensor 2 on water gauge 1 of the hydrological station, B2 represents the observation data of sensor 2 on water gauge 2 of the hydrological station, and B3 represents the observation data of sensor 2 on water gauge 3 of the hydrological station.

[0150] In this step, the observation data of different water gauges collected by different sensors 1 to 3 can be separated through the data management system, and the sampled observation data of each water gauge 1 to 3 in the image set can be separated, laying the foundation for the subsequent processing of the neural network model.

[0151] In addition, a specific implementation of the above step S103 includes: using a video intelligent recognition system, combining the observation data separated in the above table (such as A1-A3, B1-B3, C1-C3) with Figure 5 The method flow shown is used to obtain the corresponding water gauge water level information.

[0152] Specifically, see Figure 5 As shown, specifically including:

[0153] Step S201: parsing the video stream information to obtain a plurality of photos taken continuously in a photo collection.

[0154] Step S202: input multiple photos into the self-trained neural network model to obtain training results.

[0155] Specifically, the water gauge target is detected by autonomously training the yolov5 model.

[0156] Step S203: determining whether the plurality of photos contain a water gauge target according to the training result.

[0157] Step S204: If included, obtain the frame height WH of the water gauge target and the water gauge area image.

[0158] If not, return to step S201.

[0159] Step S205: Detect the target character E in the water gauge area image through the self-trained neural network model.

[0160] The autonomously trained neural network model may be a yolov5 neural network model, and the yolov5 neural network model is used to detect the character E in the water gauge area image.

[0161] Step S206: Determine whether the target character E exists.

[0162] Step S207: If the target character E is included, the average height EH of the target character E is calculated.

[0163] The above step S103: calculating the water level information of the water gauge according to the corresponding relationship, the observation data and the water level data, specifically includes:

[0164] Step S208: Obtain the actual length eH of the target character E.

[0165] Step S209: Calculate the actual length of the water gauge according to the corresponding relationship, the average height of the target character E and the actual length eH, and generate water level information including the actual length of the water gauge.

[0166] The calculation formula is: wH=eH*WH / EH, where wH is the actual length of the water gauge.

[0167] The method provided in this embodiment realizes real-time detection of the target character E in the water gauge area image through the self-trained neural network model, and calculates the corresponding actual length of the water gauge according to the detected target character E. The above process can be measured and calculated in real time by the edge computing node.

[0168] In addition, this method also automatically detects water level information in water gauge photos, reducing the burden of manual monitoring and improving the efficiency and accuracy of water level monitoring. The real-time analysis capability of the neural network model enables water level information to be updated in real time, which is crucial for hydrological events that require rapid response. Data deviations caused by human errors are reduced through automated target detection and water level calculation. The use of neural network models can more accurately identify water gauge targets and characters, thereby improving the accuracy of water level information. Automated processing reduces the need for human resources and reduces long-term operating costs.

[0169] This method improves the availability and comprehensiveness of data by combining water level information with observation data and water level data. Accurate water level information provides strong support for decision-making such as water resource management, flood prevention and response. The neural network model can adapt to different lighting conditions and water gauge changes, enhancing the robustness of the system. The application of this method promotes the application and development of computer vision and machine learning technology in the field of hydrological monitoring.

[0170] Furthermore, before the above step S104, it also includes obtaining a trained neural network model. Specifically, while retaining the original monitoring data collected by the hydrological station in full, the Kalman filter is used to consider the uncertainty factors of various errors, and the water level measured data and the video water gauge AI real-time recognition data are combined. The multi-sensor data fusion method based on the DS (Dempster-Shafer) evidence theory is used to eliminate the disturbance of uncertainty factors and the interference of some abnormal data through evidence grouping and grouped evidence weighted correction evidence synthesis, and the data fusion is performed after the interference is eliminated to achieve the optimal perception of the station status.

[0171] like Figure 6 Specifically, a specific implementation method of obtaining a trained neural network model includes:

[0172] First, the parameters of relevant hydrological stations are configured, and the parameter configuration includes: [water level tx: water level t, flow tx: flow t, flow velocity tx: flow velocity t, rainfall t+1-x: rainfall t+1, evaporation t+1-x: evaporation t+1][water level t+1, flow t+1, flow velocity t+1]; and [rainfall tx: rainfall t, evaporation tx: evaporation t, temperature tx: temperature t, wind speed tx: wind speed t][rainfall t+1, evaporation t+1].

[0173] In this embodiment, for the input parameters [rainfall t+1, evaporation t+1] or [water level t+1, flow t+1, flow velocity t+1], the following water level value prediction results are obtained.

[0174] The initial neural network model can be an LSTM model, and the process of training the LSTM model is as follows:

[0175] Step 1: Get the initial neural network model and the prediction value C at the previous moment t-1 and the auxiliary value h at the previous moment t-1 ;

[0176] Step 2: Obtain the current water level value t from the water level detection system or video monitoring system;

[0177] Step 3: Input the current time value x t and the auxiliary value h at the previous moment t-1 Merge and get the current merge output gate o t ;

[0178] Step 4: Merge the output gate o at the current moment t Perform the following processing:

[0179] Step 4-1: Merge the current moment into the output gate o tThrough the first sigmoid neural network (for example, including FC + activation function sigmoid), the current forget gate f is obtained t ; The current moment forget gate f t Used to control or abandon certain characteristics;

[0180] Step 4-2: Merge the current moment into the output gate o t Through the second sigmoid neural network (for example, including FC + activation function sigmoid), the current input gate i is obtained t ;

[0181] Step 4-3: Merge the current moment into the output gate o t Through the first tanh neural network (for example, including FC + activation function tanh1), the current state update value is obtained

[0182] Step 5: Forget the current moment t and the predicted value C at the previous moment t-1 Perform point multiplication to get f t ×C t-1 ;

[0183] Step 6: Update the current state value and the current input gate i t Perform the dot multiplication and we get

[0184] Step 7: Place the f t ×C t-1 and Add point by point to get the current prediction value C t ;

[0185] Step 8: Set the current prediction value C t Through the second tanh neural network operation (for example, including FC + activation function tanh2), and merged with the current moment output gate o t Perform dot multiplication to get the current auxiliary value h t ;

[0186] Among them, C is the predicted value and h is the auxiliary value.

[0187] The current predicted value C t and the current auxiliary value h t It is trained as an input loop and detected by the cross entropy loss function until the difference between the measured true value and the predicted value reaches a preset value. For example, when the preset value is 0.01, the loop is exited, the training of the neural network model is completed, and the corresponding water level value is output.

[0188] The expression of the above cross entropy loss function is:

[0189]

[0190] Among them, H(p, q) represents the cross entropy loss function, p(x i ) represents the true value of the measurement, q(x i ) represents the predicted value, corresponding to the above C t , n represents the total amount of data, i is a constant, and its value range is 1 to n. t It is C t Auxiliary value of , which is not calculated in the loss function.

[0191] In this embodiment, the trained deep neural network LSTM is used to reduce the data volume of one output Gate, and is directly multiplied by tanh to obtain the water level value. The analysis of a small amount of data or short-period data is realized at the hydrological station, and the water level data is obtained. Compared with the original hydrological data, the accuracy is improved by about 5cm, and the overall deviation is controlled within the range of 5cm, providing more complete data support for the management of hydrological resources and disaster prevention and mitigation.

[0192] The data analysis at the pilot hydrological station is as follows Figure 6 ,Through the trained LSTM, compared with the original detection results, the error range has been significantly reduced by reducing the model parameters (such as reducing the sigma operation), and the pressure on the use of computing resources has also been reduced.

[0193] See also Figure 7 As shown, the method provided in this embodiment also includes: performing real-time analysis and identification of the acquired video data through the video information intelligent identification system to determine the alarm information of abnormal behavior on site. Then, the identified alarm information is broadcasted on site through a loudspeaker to remind the on-site staff, and is sent to the supervisor and the superior hydrological supervision platform at the same time.

[0194] The above alarm information includes but is not limited to: abnormal situations such as intrusion, falls, fireworks, floating objects, etc. of the hydrological station. At the same time, the intruder is warned through the loudspeaker in the first time and pushed to the management personnel in real time.

[0195] The above step S104 monitors the water level condition of the hydrological station according to the target water level, including: determining whether the target water level is within the safety line; if so, determining that the water level condition of the hydrological station is normal; if not, determining that the water level condition is abnormal, generating an alarm message, and sending the alarm message to the alarm system, such as broadcasting through a loudspeaker, or reporting the abnormal water level status information to the hydrological management platform of the server.

[0196] The embodiment of the present invention also provides another alarm method, which includes: obtaining at least one picture from a video and preprocessing the picture; calling a deep learning algorithm through a target detection model to determine whether it is greater than a set threshold, if not, returning to the first step, otherwise, executing the next step. Output corresponding alarm information according to the threshold of different models, call corresponding voice alarm information according to different alarm information types, and push it to peripheral devices, such as speakers.

[0197] The method provided by the present invention realizes rapid deployment of applications. By deploying a hydrological data management platform and corresponding algorithm models through edge computing devices, functions such as full-factor hydrological perception access, data monitoring, data compilation, equipment condition management, and equipment operation and maintenance can be realized. The on-duty personnel of the hydrological station can view and display the display on site through a large screen, PC, etc., realizing "one station, one measurement, one station, one system".

[0198] In addition, this method also supports a variety of wired and wireless communication methods, which can meet the deployment of hydrological stations in various environments, and solve the problems of weak information technology equipment, insufficient perception, insufficient data security protection capabilities and insufficient intelligence levels in existing hydrological stations. At the same time, through intelligent identification of video surveillance, it can improve the security management capabilities around the hydrological stations and realize the intelligent improvement of the hydrological stations.

[0199] Specifically, in an environment with a wired network, you can use wired network and wireless LoRa to collect video surveillance data and sensor data respectively, improving data security and the stability of data transmission. In an environment without a wired network, you can use wireless bridges and wireless LoRa to collect video surveillance data and sensor data respectively, reducing the risk of data exposure to the public network, improving data security, and saving costs. In an environment without a wired network, you can use 4G / 5G and wireless LoRa to collect video surveillance data and sensor data respectively, improving data security and the stability of data parameters. In an environment without any network, you can use Beidou communication to collect hydrological station sensor data in real time and improve the stability of data transmission.

[0200] The method provided in this embodiment also supports the setting of the frequency of device data collection, and can be customized according to specific regulatory requirements, thereby reducing the use of unnecessary resources and improving overall utilization efficiency.

[0201] It supports normal communication in disaster environments, automatically identifies and determines the communication environment, automatically switches to Beidou communication, collects hydrological data in real time, and provides data support for handling harsh environments.

[0202] In addition, the use of three systems for independent data collection and analysis can also link information to achieve intelligent management of the hydrological station and improve the management efficiency of the hydrological station.

[0203] This embodiment provides a hydrological monitoring device, such as Figure 8 As shown, the device includes: a receiving module 810, a processing module 820 and a determining module 830. In addition, the device may also include other more or fewer modules, such as a sending module 840, which is not limited in this embodiment.

[0204] The receiving module 810 is used to receive the hydrological observation data of the hydrological station at each sampling time reported by the sensor cluster, and receive the video stream information of the hydrological station reported by the video monitoring system, wherein the video stream information includes a continuous set of photos taken of the hydrological station environment;

[0205] The processing module 820 is used to process the hydrological observation data to obtain the observation data of each sensor and the water level data of the water gauge in the measured hydrological station;

[0206] A determination module 830 is used to parse the video stream information, determine the correspondence between the photo set in the video stream information and the observation data and the water level data, and calculate the water level information of the water gauge according to the correspondence, the observation data and the water level data;

[0207] The processing module 820 is also used to input the water level data and the water level information of the water gauge into the trained neural network model, output the target water level, and monitor the water level status of the hydrological station according to the target water level.

[0208] In some optional implementations, the receiving module 810 is specifically configured to receive the hydrological observation data reported by the sensor cluster via a wired manner, or,

[0209] Receiving the hydrological observation data reported by the sensor cluster via wireless LoRa, or,

[0210] The hydrological observation data is received via Beidou communication access.

[0211] In some optional implementations, the receiving module 810 is specifically configured to receive the video stream information reported by the video surveillance system through a wired network when wired communication is available;

[0212] Alternatively, in the case of wireless communication, the video stream information reported by the video surveillance system is received through a wireless bridge;

[0213] Alternatively, in the case where wireless communication is available but wireless bridge communication is not available, the video stream information is received via WLAN.

[0214] In some optional embodiments, the processing module 820 is also used to align the hydrological observation data with the multiple photos in the photo set according to each sampling moment and the time point of the continuous photo set taken before processing the hydrological observation data; process the time-aligned hydrological observation data to obtain the observation data of each sensor and the water level data of the measured water gauge, wherein the observation data includes the sampling data measured by each sensor and the sensor ID.

[0215] In some optional implementations, the processing module 820 is further configured to parse the video stream information to obtain a plurality of photos taken continuously in the photo set; input the plurality of photos into the autonomously trained neural network model to obtain a training result;

[0216] Determine whether the multiple photos contain a water ruler target according to the training results; if so, obtain the frame height of the water ruler target and the water ruler area image; detect whether the water ruler area image contains a target character E through the self-trained neural network model; if the target character E is contained, calculate the average height of the target character E.

[0217] The determination module 830 is also used to obtain the actual length of the target character E; calculate the actual length of the water gauge according to the corresponding relationship, the average height and the actual length of the target character E, and generate the water level information including the actual length of the water gauge.

[0218] In some optional implementations, the processing module 820 is further used to obtain the trained neural network model;

[0219] Processing module 820 is specifically used to obtain the initial neural network model and obtain the prediction value C at the previous moment t-1 and the auxiliary value h at the previous moment t-1 ;

[0220] Obtain the current water level value t from the water level detection system or video monitoring system;

[0221] Input the current time value x t and the auxiliary value h at the previous moment t-1 Merge and get the current merge output gate o t ;

[0222] Merge the output gate o at the current moment t Perform the following processing:

[0223] Merge the current moment into the output gate o t Through the first sigmoid neural network, we get the current forget gate f t ;

[0224] Merge the current moment into the output gate o t Through the second sigmoid neural network, we get the current input gate i t ;

[0225] Merge the current moment into the output gate o t Through the first tanh neural network, the current state update value is obtained

[0226] The current moment forget gate f t and the predicted value C at the previous moment t-1 Perform point multiplication to get f t ×C t-1 ;

[0227] Update the current state value and the current input gate i t Perform the dot multiplication and we get

[0228] The f t ×C t-1 and stated Add point by point to get the current prediction value C t ;

[0229] The current moment prediction value C t Through the second tanh neural network operation, and merged with the current moment output gate o t Perform dot multiplication to get the current auxiliary value h t ;

[0230] The current moment prediction value C t and the current auxiliary value h t The training is performed as an input loop and detected by the cross entropy loss function until the difference between the measured true value and the predicted value reaches a preset value. Then, the loop is exited and the training of the neural network model is completed to obtain a trained neural network module, such as a trained LSTM model.

[0231] In some optional embodiments, the processing module 820 is specifically used to determine whether the target water level is within the safety line; if so, it is determined that the water level condition of the hydrological station is normal; if not, it is determined that the water level condition is abnormal, an alarm message is generated, and the alarm message is sent to the alarm system via the sending module 840.

[0232] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0233] The hydrological monitoring device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0234] The embodiment of the present invention provides a hydrological monitoring device, which can provide multi-dimensional hydrological data analysis by integrating sensor data and video stream information, so as to more comprehensively understand hydrological phenomena. Combining the photo collection of video monitoring with sensor data can more accurately determine water level changes and reduce the errors that may be caused by a single data source.

[0235] In addition, data collection and monitoring methods can avoid problems such as equipment aging, single monitoring methods, inconsistent data collection protocols, and inaccurate data collection. The system can calculate water level information in real time and predict the target water level through a neural network model, which helps to detect abnormal conditions in time and issue early warnings. Automated data processing and water level prediction functions can reduce manual intervention and improve decision-making efficiency. The fusion of multi-source data improves the robustness of the system, and the system can maintain normal operation even when some sensors fail. Through intuitive video streaming information and accurate water level data, users can understand the hydrological conditions more intuitively and improve user experience.

[0236] The automated monitoring and early warning system can reduce the frequency of manual inspections, thereby reducing operating costs. At the same time, through intelligent identification of video surveillance, the safety management capabilities around the hydrological station can be improved, and the intelligentization of the hydrological station can be achieved.

[0237] The embodiment of the present invention also provides a hydrological monitoring system, the structure of which is as described above. Figure 1 As shown, the system includes at least one edge device, a sensor cluster, a video surveillance system and a server, wherein the sensor cluster, the video surveillance system and the edge device can be connected by wire or wirelessly. Each edge device is used to perform the aforementioned Figures 2 to 5 The hydrological monitoring method flow is shown.

[0238] The specific implementation process refers to the description of the above embodiment and will not be repeated here.

[0239] The embodiment of the present invention also provides a computer device having the above Figure 8 The hydrological monitoring device shown.

[0240] See also Fig. 9, is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses for communication, and can be installed on a common motherboard or in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface).

[0241] In some optional embodiments, if desired, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig. 9 A processor 10 is taken as an example.

[0242] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0243] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the hydrological monitoring method shown in the above embodiment.

[0244] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0245] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0246] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Fig. 9 The example of connecting through bus is taken in the following.

[0247] The input device 30 can receive input digital or character information, and generate signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0248] In addition, the computer device also includes a communication interface, which is used for the computer device to communicate with other devices or communication networks, such as a sensor cluster or a video monitoring system, or can also communicate with a server through the communication interface.

[0249] Optionally, the above-mentioned computer device may be an edge device or an edge computing node, or an edge computing device.

[0250] It should be understood that the structure of the above-mentioned server or data center can be different from Fig. 9 The computer devices shown have the same structure, and this embodiment does not limit this.

[0251] An embodiment of the present invention also provides a computer-readable storage medium, and the above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented by downloading through a network and originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware.

[0252] The storage medium may be a disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid state drive, etc.; further, the storage medium may also include a combination of the above-mentioned types of memories. It is understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by the computer, processor or hardware, the hydrological monitoring method shown in the above embodiment is implemented.

[0253] Embodiments of the present application may also provide a computer program product, including computer program instructions, which, when executed by a processor, cause the processor to perform the steps in the above method. Wherein, the computer program product may be written in any combination of one or more programming languages ​​to perform program codes for performing the operations of the disclosed embodiments, wherein the programming languages ​​include object-oriented programming languages, such as Java, C++, etc., and also include conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on a user computing device, partially on a user device, as an independent software package, partially on a user computing device, partially on a remote computing device, or entirely on a remote computing device or server.

[0254] The above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, rather than to limit them. Although the embodiments of the present invention are described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A hydrological monitoring method, characterized in that: Applied to an edge device, the method comprises: Receiving hydrological observation data of the hydrological station at each sampling moment reported by the sensor cluster, and receiving video stream information of the hydrological station reported by the video monitoring system, wherein the video stream information includes a continuous set of photos taken of the hydrological station environment; Processing the hydrological observation data to obtain observation data of each sensor and water level data of a water gauge in a measured hydrological station; Parsing the video stream information, determining the correspondence between the photo set in the video stream information and the observation data and the water level data, and calculating the water level information of the water gauge according to the correspondence, the observation data and the water level data; The water level data and the water level information of the water gauge are input into the trained neural network model, the target water level is output, and the water level condition of the hydrological station is monitored according to the target water level.

2. The method according to claim 1, characterized in that The receiving of hydrological observation data of the hydrological station at each sampling time reported by the sensor cluster includes: Receiving the hydrological observation data reported by the sensor cluster in a wired manner, or, Receiving the hydrological observation data reported by the sensor cluster via wireless LoRa, or, The hydrological observation data is received via Beidou communication access.

3. The method according to claim 1, characterized in that: The receiving of video stream information from the hydrological station reported by the video monitoring system includes: In the case of wired communication, receiving the video stream information reported by the video surveillance system through a wired network; Alternatively, in the case of wireless communication, the video stream information reported by the video surveillance system is received through a wireless bridge; Alternatively, in the case where wireless communication is available but wireless bridge communication is not available, the video stream information is received via WLAN.

4. The method according to claim 1, characterized in that: Before the hydrological observation data is processed, the method further includes: Aligning the hydrological observation data with the plurality of photos in the photo set according to each sampling moment and the time point of the continuous photo set taken; The processing of the hydrological observation data to obtain the observation data of each sensor and the water level data of the water gauge in the measured hydrological station includes: The time-aligned hydrological observation data are processed to obtain the observation data of each sensor and the water level data of the measured water gauge, wherein the observation data includes the sampling data measured by each sensor and the sensor ID.

5. The method according to any one of claims 1 to 4, characterized in that: After parsing the video stream information, the method includes: Parsing the video stream information to obtain a plurality of photos taken continuously in the photo collection; Inputting the plurality of photos into a self-trained neural network model to obtain training results; Determining whether the plurality of photos contain a water gauge target according to the training result; If included, get the frame height of the water gauge target and the water gauge area image; Detecting whether the water ruler area image contains the target character E through the self-trained neural network model; If the target character E is included, then the average height of the target character E is calculated; The step of calculating the water level information of the water gauge according to the corresponding relationship, the observation data and the water level data comprises: Obtaining the actual length of the target character E; According to the corresponding relationship, the average height of the target character E and the actual length, the actual length of the water gauge is calculated, and the water level information including the actual length of the water gauge is generated.

6. The method according to any one of claims 1 to 4, characterized in that: The method further comprises: obtaining the trained neural network model; The step of obtaining a trained neural network model comprises: Get the initial neural network model and the predicted value C of the previous moment t-1 and the auxiliary value h at the previous moment t-1 ; Obtain the current water level value t from the water level detection system or video monitoring system; Input the current time value x t and the auxiliary value h at the previous moment t-1 Merge and get the current merge output gate o t ; Merge the output gate o at the current moment t Perform the following processing: Merge the current moment into the output gate o t Through the first sigmoid neural network, we get the current forget gate f t ; Merge the current moment into the output gate o t Through the second sigmoid neural network, we get the current input gate i t ; Merge the current moment into the output gate o t Through the first tanh neural network, the current state update value is obtained The current moment forget gate f t and the predicted value C at the previous moment t-1 Perform point multiplication to get f t ×C t-1 ; Update the current state value and the current input gate i t Perform the dot multiplication and we get The f t ×C t-1 and stated Add point by point to get the current prediction value C t ; The current moment prediction value C t Through the second tanh neural network operation, and merged with the current moment output gate o t Perform dot multiplication to get the current auxiliary value h t ; The current moment prediction value C t and the current auxiliary value h t The training is performed as an input loop and detected by the cross entropy loss function until the difference between the measured true value and the predicted value reaches a preset value. Then the loop is exited and the neural network model training is completed.

7. The method according to claim 1, characterized in that The step of monitoring the water level status of the hydrological station according to the target water level includes: Determining whether the target water level is within a safety line; If yes, it is determined that the water level condition at the hydrological station is normal; If not, it is determined that the water level condition is abnormal, an alarm message is generated, and the alarm message is sent to the alarm system.

8. A hydrological monitoring device, characterized in that: The device comprises: A receiving module, used to receive hydrological observation data of the hydrological station at each sampling moment reported by the sensor cluster, and receive video stream information of the hydrological station reported by the video monitoring system, wherein the video stream information includes a continuous set of photos taken of the hydrological station environment; A processing module is used to process the hydrological observation data to obtain the observation data of each sensor and the water level data of the water gauge in the measured hydrological station; A determination module, used for parsing the video stream information, determining the correspondence between the photo set in the video stream information and the observation data and the water level data, and calculating the water level information of the water gauge according to the correspondence, the observation data and the water level data; The processing module is also used to input the water level data and the water level information of the water gauge into the trained neural network model, output the target water level, and monitor the water level status of the hydrological station according to the target water level.

9. A computer device, characterized in that: comprising a memory and a processor, wherein the memory and the processor are connected; The memory stores computer instructions, and the processor executes the hydrological monitoring method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the hydrological monitoring method according to any one of claims 1 to 7.

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