Freezing load identification system for hydraulic structure in cold region

Abstract

The invention discloses a cold region hydraulic structure freezing load identification system. The system comprises a sensor module, a data acquisition and transmission unit, a data processing center, a display and early warning module and a remote monitoring terminal. The sensor module comprises a temperature sensor, a strain sensor and a displacement sensor and can monitor the physical quantity of the hydraulic building in all directions; the data acquisition and transmission unit ensures efficient acquisition and stable transmission of data; the data processing center analyzes the data through preprocessing and a machine learning algorithm; the display and early warning module visually displays information and early warning; and the remote monitoring terminal supports remote monitoring. The system can accurately identify the freezing load of the hydraulic structure in the cold region, guarantees the safe and stable operation of the hydraulic structure in the cold region environment, overcomes the defects of a traditional monitoring means, and improves the safety management level of a water conservancy project.

Description

Technical Field

[0001] This invention relates to the field of ice load identification for hydraulic structures in cold regions, specifically to a system for identifying ice loads on hydraulic structures in cold regions. Background Technology

[0002] Hydraulic structures in cold regions are subject to freezing loads during winter operation, a long-standing problem in the field of hydraulic engineering. Due to the special climatic conditions of cold regions, low winter temperatures cause water bodies around hydraulic structures to freeze easily. The formation and development of ice layers exert various loads on the structures, such as static ice pressure and dynamic ice pressure. These loads cause deformation, strain, and displacement of the hydraulic structures, thus affecting their structural safety and stability. Traditional monitoring methods often rely on manual observation and simple instruments, resulting in inaccurate and untimely monitoring data, and an inability to comprehensively reflect the dynamic changes of freezing loads. For example, manual observation is difficult to continuously monitor hydraulic structures, easily missing key load change information; some simple instruments can only monitor single physical quantities and cannot comprehensively consider multiple parameters such as temperature, strain, and displacement, making it difficult to accurately assess the overall situation of freezing loads. At the same time, for some complex hydraulic structures, traditional monitoring methods are insufficient to meet the needs of refined management and real-time monitoring, leading to significant safety hazards in the maintenance and management of hydraulic structures in cold regions, posing a potential threat to the normal operation and service life of the projects. Summary of the Invention

[0003] The purpose of this invention is to provide a system for identifying freezing loads on hydraulic structures in cold regions, in order to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a system for identifying freezing loads on hydraulic structures in cold regions, comprising the following components: The sensor module consists of a temperature sensor array, a strain sensor array, and a displacement sensor. The temperature sensor array is evenly distributed at different depths of the hydraulic structure to monitor the temperature changes of the water surrounding the structure and the structure itself in real time, accurately recording temperature fluctuations over time to provide basic data for subsequent analysis of the freezing process. The strain sensor array is installed at key stress-bearing parts of the structure to accurately capture structural strain information caused by freezing loads. The displacement sensors are arranged on the surface of the structure and at the connection points with the foundation to measure the displacement changes of the structure under the influence of freezing loads, including horizontal and vertical displacements. These sensors collect physical parameters related to freezing loads in a comprehensive and multi-dimensional manner. Data acquisition and transmission unit: Connected to the aforementioned sensor modules, this unit is responsible for acquiring data generated by the sensors at high speed and with high precision, and transmitting this data quickly and stably to the data processing center via wired or wireless transmission methods, such as fiber optic communication or 5G communication. This unit has a data caching function, which can temporarily store data when the transmission network experiences a brief failure, and resend the data after the network is restored, ensuring the integrity and continuity of the data. Data Processing Center: After receiving data from the data acquisition and transmission unit, the center first preprocesses the data, including noise reduction, filtering, and normalization, to improve data quality. Then, it uses a pre-set ice load identification model based on machine learning algorithms, such as a deep neural network model or a support vector machine model, to analyze and calculate the processed data. Combined with the structural parameters and material properties of the building, it identifies the magnitude, direction, and distribution parameters of the ice load currently acting on the hydraulic structure. Display and Early Warning Module: Displays the freezing load information identified by the data processing center in an intuitive way, such as showing real-time load data curves and load distribution cloud maps on a large screen. At the same time, this module is set with an early warning threshold. When the identified freezing load exceeds the preset safety threshold, it immediately issues an audible and visual alarm signal to remind relevant personnel to take timely countermeasures to ensure the safe and stable operation of hydraulic structures in cold regions.

[0005] Furthermore, the temperature sensors in the temperature sensor array are high-precision thermistor sensors with a measurement accuracy of ±0.1℃ and a response time of less than 1 second. Each temperature sensor is waterproof and freeze-resistant, enabling it to operate stably for extended periods in harsh environments with low temperatures and humidity. The temperature sensor array is arranged vertically along the building, from 1 meter above the water surface to 5 meters underwater, with one sensor every 0.5 meters. Horizontally, it is evenly distributed in key areas susceptible to freezing, according to the building's structural characteristics, ensuring comprehensive coverage of areas where temperature changes may occur. This provides abundant and accurate data for the precise analysis of the freezing process.

[0006] Furthermore, the strain sensors in the strain sensor array are fiber Bragg grating strain sensors; these sensors have strong anti-electromagnetic interference capabilities and high sensitivity, enabling them to detect minute strain changes with an accuracy of up to 1 με. When installing strain sensors, for different types of hydraulic structures, such as gravity dams and arch dams, the key stress points determined by finite element analysis are arranged accordingly. For gravity dams, the sensors are placed at key points on the upstream and downstream faces of the dam body, the dam heel, and the dam toe; for arch dams, the sensors are densely arranged at key locations such as the arch crown beam and arch seat, thereby accurately capturing the strain information of the structure under freezing loads.

[0007] Furthermore, the displacement sensor is a laser displacement sensor with a measurement accuracy of ±0.01mm; a displacement sensor is installed at regular intervals along the dam crest and slope on the building surface to monitor horizontal displacement; displacement sensors are pre-embedded at the connection between the building and the foundation to measure vertical displacement; the displacement sensor and the data acquisition and transmission unit are wirelessly transmitted and equipped with an independent power module to ensure continuous and stable operation in harsh environments and to obtain real-time and accurate displacement change data of the building under the influence of freezing load.

[0008] Furthermore, the data acquisition frequency of the data acquisition and transmission unit can be adjusted according to actual needs. In the early stage of freezing and the rapid change stage, the acquisition frequency is set to above 10Hz to acquire data at a high frequency and accurately capture rapid changes in temperature, strain, and displacement. In the relatively stable stage of freezing, the acquisition frequency can be reduced to 1Hz-5Hz to reduce the amount of data transmitted and storage pressure while ensuring data integrity. The data cache capacity of this unit is not less than 1GB, which can meet the data caching requirements for at least 24 hours and ensure that data is not lost in the event of a transmission network failure.

[0009] Furthermore, in the preprocessing stage of the data processing center, wavelet transform denoising algorithm is used for denoising. Based on the noise characteristics of different sensor data, appropriate wavelet basis functions are selected to effectively remove high-frequency noise and low-frequency interference signals. Adaptive filtering algorithm is used for filtering. The filtering parameters are automatically adjusted according to the real-time changes of the data to retain effective signals while further improving the quality of the data. Normalization process maps the data collected by different types of sensors to the [0,1] interval to eliminate the difference in data dimensions and provide standardized data input for the subsequent ice load identification model.

[0010] Furthermore, the ice load identification model based on machine learning algorithms uses a large amount of historical measured data and numerical simulation data as training samples during the training process. The historical measured data covers relevant parameter data of hydraulic structures under different years, seasons, and climatic conditions. The numerical simulation data simulates response data under various ice load conditions by establishing accurate hydraulic structure models. The model is repeatedly trained and optimized using these data to adjust the model parameters, improve the model's generalization ability and recognition accuracy, and enable it to accurately identify ice load parameters under various complex conditions.

[0011] Furthermore, the large-screen display of the display and early warning module adopts a high-resolution LCD screen with a resolution of no less than 1920×1080, which can clearly display real-time load data curves and load distribution cloud map information. At the same time, the display interface classifies and displays different types of load data and sets different color labels, making it convenient for operators to intuitively and quickly obtain key information. The early warning threshold is determined by comprehensive analysis based on the design standards of hydraulic structures, structural strength, and historical freezing load data, ensuring that early warnings are issued in a timely manner when the freezing load reaches a level that may endanger the safety of the structure.

[0012] Furthermore, the system also includes a remote monitoring terminal connected to the data processing center via the Internet. The remote monitoring terminal can be a computer or a mobile smart device, allowing relevant management personnel to view real-time data, historical data, and early warning information of the freezing load of hydraulic structures anytime and anywhere. At the same time, personalized reminder functions can be set on the remote monitoring terminal, such as notifying management personnel in a timely manner via SMS or push notification when the freezing load approaches the warning threshold, thereby realizing remote real-time monitoring and management of hydraulic structures in cold regions.

[0013] Furthermore, the system employs standardized interface designs between its various modules; the interface between the sensor module and the data acquisition and transmission unit follows industry-standard data transmission protocols to ensure data transmission stability and compatibility; the interface between the data acquisition and transmission unit and the data processing center adopts high-speed data transmission interface standards to meet the needs of rapid transmission of large amounts of data; and the interface between the data processing center and the display and early warning modules, as well as the remote monitoring terminal, uses open network communication protocols to facilitate system expansion and upgrades and enable easy integration with other related systems.

[0014] Compared with the prior art, the beneficial effects of the present invention are: By employing a combination of temperature sensor arrays, strain sensor arrays, and displacement sensors within the sensor module, multi-dimensional monitoring of the physical quantities of hydraulic structures can be achieved. The rational arrangement of the temperature sensor array can accurately grasp the temperature changes of the water surrounding the structure and the structure itself, providing detailed data support for analyzing the freezing formation process and helping to predict the generation and development trend of freezing loads. The high-sensitivity arrangement of the strain sensor array can promptly detect structural strain caused by freezing loads, enabling maintenance personnel to detect potential safety hazards before significant structural deformation occurs. Displacement sensors can directly reflect the displacement changes of the structure under freezing loads, providing crucial evidence for assessing the overall structural stability.

[0015] The data acquisition and transmission unit's data caching function and efficient transmission method ensure data integrity and continuity, overcoming the problem of unstable transmission networks in harsh environments. Even in the event of network failure, data is guaranteed not to be lost and can be retransmitted later, providing complete data support for subsequent data analysis.

[0016] The data processing center uses advanced machine learning algorithms to analyze preprocessed data. Based on models trained from historical measured data and numerical simulation data, it can accurately identify the magnitude, direction, and distribution of freezing loads, providing reliable data for subsequent structural safety assessments and decisions, and improving the ability to analyze and judge complex freezing loads.

[0017] The display and early warning module presents the analysis results intuitively, displaying various data curves and cloud maps on a large screen for easy understanding by management personnel. When the load exceeds the preset safety threshold, timely audible and visual alarms alert relevant personnel to take emergency measures and prevent accidents. Simultaneously, the remote monitoring terminal allows management personnel to monitor hydraulic structures remotely in real time, regardless of location. This improves management flexibility and efficiency, reduces maintenance costs for hydraulic structures in cold regions, extends their service life, and ensures the safe and stable operation of water conservancy projects in cold areas. Attached Figure Description

[0018] Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1 This invention provides a technical solution: a freezing load identification system for hydraulic structures in cold regions. The system aims to accurately, comprehensively, and in real-time monitor and identify the freezing loads experienced by hydraulic structures during winter operation in cold regions, thereby ensuring the safe and stable operation of the structures. The system mainly includes a sensor module, a data acquisition and transmission unit, a data processing center, a display and early warning module, and a remote monitoring terminal. These components cooperate to jointly complete the tasks of monitoring, analyzing, and issuing early warnings for freezing loads.

[0021] Sensor module The temperature sensor array of this system uses high-precision thermistor sensors. These sensors have high measurement accuracy (up to ±0.1℃) and fast response characteristics with a response time of less than 1 second. They also have excellent waterproof and antifreeze properties, enabling them to work continuously and stably in harsh environments with low temperatures and humidity in cold regions.

[0022] Regarding the placement of sensors on hydraulic structures, considering the close relationship between the formation of freezing loads and the temperature of the water body and the structure, temperature sensors are arranged vertically from 1 meter above the water surface to 5 meters underwater. Specifically, a sensor is placed at a interval of 0.5 meters to capture temperature gradient changes at different depths in detail. Horizontally, based on the structural characteristics of the hydraulic structures and past experience data, temperature sensors are evenly distributed in key areas susceptible to freezing, such as the water-facing side of the structure, corners, and connections to the foundation. For different types of hydraulic structures, such as gravity dams, temperature sensors are also placed at the dam crest, upstream and downstream faces of the dam body, dam heel, and dam toe. For arch dams, in addition to the above conventional locations, temperature sensors are also placed at key locations such as the arch crown beam and arch abutment to ensure comprehensive coverage of the temperature field of the entire structure.

[0023] During installation, the temperature sensors are mounted using a specially designed fixing device. For underwater sensors, a waterproof sealed chamber is used for encapsulation, ensuring good contact between the sensor and water while preventing moisture intrusion that could affect its performance. For sensors above water, they are installed in a specialized protective enclosure to withstand harsh weather conditions such as wind and snow. The sensor's signal lines are connected to the data acquisition and transmission unit via waterproof sealed conduits, ensuring the stability and reliability of signal transmission.

[0024] The strain sensor array uses fiber Bragg grating strain sensors, which have the advantages of strong anti-electromagnetic interference capability, high sensitivity, and accuracy up to 1με. They are suitable for the complex electromagnetic environment of hydraulic structures in cold regions and for scenarios that require high-precision strain measurement.

[0025] When deploying strain sensors, the first step is to perform a stress analysis on the hydraulic structure using finite element analysis to identify key stress points. For gravity dams, the key stress points are typically located at the dam heel and toe, as these areas are prone to stress concentration under the combined effects of water pressure and freezing loads. Therefore, strain sensors are densely deployed at the dam heel and toe. Simultaneously, sensors are evenly distributed at certain intervals along the upstream and downstream faces of the dam, based on the structural stress characteristics and past experience. The spacing is determined according to the dam's dimensions and stress conditions, generally between 1 and 5 meters. For arch dams, the stress characteristic is the transfer of loads to the arch abutment. Therefore, strain sensors are strategically deployed at the arch abutment and crown beam, and their density is appropriately increased along the arch direction according to the stress distribution pattern to accurately capture the structure's strain information.

[0026] Strain sensors are installed using either surface bonding or embedded methods. For surface bonding, the bonding surface is first sanded, cleaned, and treated. Specialized adhesive is then used to firmly attach the sensor to the structural surface, ensuring good strain transfer between the sensor and the structure. For embedded installation, the sensor is accurately embedded in the predetermined position during concrete pouring, ensuring it can deform in tandem with the structure after the concrete hardens. Simultaneously, proper lead-out and protection of the signal cable are ensured to prevent damage during construction.

[0027] The displacement sensor uses a laser displacement sensor, which has a measurement accuracy of ±0.01mm and can accurately measure the displacement changes of buildings in the horizontal and vertical directions.

[0028] On the surface of the building, horizontal displacement sensors are arranged at certain intervals along the dam crest and slope, with the spacing depending on the length and importance of the building, generally ranging from 3 to 10 meters. For the connection between the building and the foundation, displacement sensors are pre-embedded at the connection point between the foundation and the bottom of the building to monitor vertical displacement, ensuring that they can accurately reflect the settlement or uplift of the building relative to the foundation.

[0029] The installation of displacement sensors requires precise adjustment of their measurement direction and reference to ensure accurate measurement results. After installation, they are calibrated using standard blocks with known displacement values ​​to establish an accurate correspondence between the output signal and the actual displacement. Simultaneously, the displacement sensor is equipped with an independent power module, typically a rechargeable lithium battery, providing a stable power supply and featuring a low-battery alarm function for timely replacement or recharging, ensuring continuous and stable operation in cold environments. Wireless transmission is used between the sensor and the data acquisition and transmission unit via low-power Bluetooth or Zigbee protocols. This method avoids complex wiring, adapts to the complex environments of cold regions, and reduces the risk of line damage due to environmental factors.

[0030] Data acquisition and transmission unit The data acquisition and transmission unit is responsible for collecting data from the sensor modules. It features multi-channel data acquisition capabilities, allowing simultaneous connection to multiple sensors and employing different acquisition methods based on the data type and volume of each sensor. For temperature sensors, a high-precision analog-to-digital converter (ADC) is used for data acquisition, ensuring the accuracy of the acquired temperature data, based on the sensor's measurement accuracy and frequency. For strain sensors, a fiber Bragg grating demodulator demodulates the signal from the fiber Bragg grating strain sensor, converting the optical signal into processable strain data. For displacement sensors, a dedicated signal receiving module receives the signal from the laser displacement sensor and converts it into a digital signal.

[0031] The data acquisition frequency of this unit can be flexibly adjusted according to actual needs. In the early stages of ice formation and during rapid changes, when temperature, strain, and displacement fluctuate dramatically, the acquisition frequency is set to above 10Hz to capture these rapidly changing parameters. For example, in the early stages of a sudden drop in winter temperature or the initial freezing of ice, a higher acquisition frequency can capture rapid temperature drops, sudden increases in strain, and minute changes in displacement. In the relatively stable stages of freezing, such as when the ice layer thickness reaches a certain level and becomes relatively stable, the acquisition frequency can be reduced to 1Hz-5Hz to reduce the amount of data and the burden on data processing, while also avoiding redundant data storage.

[0032] The data acquisition and transmission unit has a time synchronization function. Through the built-in high-precision clock chip, it assigns a precise timestamp to each acquired data point, ensuring the consistency of data from different sensors in time. This is crucial for subsequent data analysis and accurate identification of freezing loads, because the generation and development of freezing loads is a process closely related to time, and data at different time points have different physical meanings.

[0033] In terms of data transmission, this unit can employ various transmission methods. For short-distance transmission, wired transmission is preferred, such as transmitting data to the data processing center via shielded twisted-pair cables or fiber optic cables. This method offers high transmission speed and strong anti-interference capabilities, making it suitable for use inside or near buildings where there are good wiring conditions. When long-distance data transmission is required, wireless transmission methods such as 5G communication and satellite communication can be used. Taking 5G communication as an example, the high speed and low latency characteristics of the 5G network are utilized to send the collected data to the data processing center in a timely manner. During transmission, the data is encapsulated into specific data packets containing information such as sensor identification, collection time, and data values ​​to ensure data integrity and traceability.

[0034] This unit also features a data caching function with a capacity of at least 1GB. When network transmission fails, the collected data is stored in the cache. The cache uses a first-in, first-out (FIFO) storage method; when cache space is insufficient, the oldest data is overwritten first to ensure the most up-to-date data is stored. Once the network recovers, the cached data is resent to the data processing center in sequence according to the data's timestamp to guarantee data continuity and integrity. Simultaneously, to ensure reliable data transmission, a data verification and retransmission mechanism is employed. Verification information is attached when sending data. The receiver verifies the received data; if errors or loss are found, a retransmission is requested from the sender, thus ensuring high reliability of data transmission.

[0035] Data Processing Center After receiving data from the data acquisition and transmission unit, the data processing center first performs data preprocessing. For denoising, a wavelet transform denoising algorithm is used, selecting appropriate wavelet basis functions based on the noise characteristics of different sensor data. For example, for temperature sensor data, which is relatively less affected by environmental electromagnetic interference but may contain some random noise, the Daubechies wavelet basis function can be selected for denoising, effectively removing high-frequency noise. For strain and displacement sensor data, which may be affected by environmental vibrations during measurement, the Symlet wavelet basis function is used for denoising, better removing low-frequency and high-frequency noise. The data is decomposed into sub-bands of different frequencies using wavelet transform. Based on the frequency range of the noise, the coefficients of the noise sub-bands are set to zero or thresholded, and then the denoised data is obtained through inverse wavelet transform.

[0036] For filtering operations, an adaptive filtering algorithm is employed. This algorithm automatically adjusts the filtering parameters based on real-time changes in the data. For stable signals, a smaller filtering bandwidth is used to retain more effective information; for rapidly changing signals, such as temperature and strain data in the early stages of freezing, the filtering bandwidth is automatically increased to ensure that rapidly changing data is not over-smoothed. In the specific implementation, the signal's changing trend is estimated based on the data from the previous and current moments, and then parameters such as the filter's cutoff frequency are adjusted accordingly.

[0037] Data normalization is the process of mapping data collected by different types of sensors to the [0,1] interval. For temperature data, the collected temperature values ​​are linearly mapped to the [0,1] interval based on the known temperature measurement range (e.g., -30℃ to 20℃). For strain data, mapping is performed based on the measurement range of the strain sensor (e.g., -1000με to 1000με). For displacement data, corresponding normalization operations are performed based on the measurement range of the displacement sensor (e.g., -100mm to 100mm) to eliminate dimensional differences in the data and provide a unified standard input for subsequent analysis and processing.

[0038] This system employs a machine learning-based model for identifying freezing loads, such as a deep neural network (DNN) or a support vector machine (SVM). During the model training phase, a large amount of historical measured data and numerical simulation data are used as training samples. The historical measured data includes temperature, strain, and displacement data of hydraulic structures during different cold seasons over the past few years, as well as corresponding known freezing load data. This data comes from previous monitoring records and field experiments. The numerical simulation data is generated by establishing a high-precision finite element model of the hydraulic structure, considering various boundary conditions and different freezing load conditions, such as different ice thicknesses, ice temperatures, and ice pressure distributions, to simulate the corresponding temperature, strain, and displacement response data.

[0039] For deep neural network models, the network structure can be designed as a multi-layer structure, including an input layer, multiple hidden layers, and an output layer. The number of nodes in the input layer depends on the dimensionality of the preprocessed data, such as the amount of temperature, strain, and displacement data. The number of hidden layers and nodes can be adjusted according to the actual situation, generally using 3-5 hidden layers, with the number of nodes in each layer ranging from tens to hundreds. The optimal structure can be determined through multiple trials and optimizations. During training, the backpropagation algorithm is used to calculate the error of the network output based on the input of the training samples and the known freezing load output. The weights and biases of the network are adjusted using the gradient descent algorithm to continuously optimize the network performance. Simultaneously, to prevent overfitting, Dropout technology is used to randomly discard some neurons during training, improving the model's generalization ability.

[0040] For the Support Vector Machine (SVM) model, the preprocessed data is first mapped to a high-dimensional feature space, and the nonlinear problem is transformed into a linear problem using a kernel function. Different kernel functions, such as the Radial Basis Function (RBF) kernel function and the polynomial kernel function, can be selected depending on the application scenario and data characteristics. By solving the optimization problem, the optimal hyperplane is found to distinguish samples under different freezing load conditions. During training, cross-validation is used to divide the training samples into training and validation sets. The model parameters, such as the kernel function parameters and penalty factors, are adjusted based on the performance of the validation set to achieve the best classification or regression results.

[0041] The trained model, in practical applications, uses preprocessed data as input to calculate the magnitude, direction, and distribution of ice loads on current hydraulic structures. For example, for a large gravity dam, the model can predict the magnitude and distribution of static ice pressure on the dam body, as well as the magnitude and direction of dynamic ice pressure caused by ice movement, based on input dam body temperature, strain, and displacement data. Simultaneously, the model can also consider structural parameters of the structure, such as the dam's geometry, the material's elastic modulus, and Poisson's ratio, using these parameters as auxiliary inputs to improve the accuracy of load identification.

[0042] Display and Early Warning Module The large-screen display in the display and early warning module uses a high-resolution LCD screen with a resolution of no less than 1920×1080, capable of clearly displaying various information. On the display interface, real-time temperature data is shown as a time-temperature curve, with different colored curves representing temperature sensor data at different depths, allowing for intuitive observation of temperature change trends and temperature gradients at different depths. Strain data is displayed as a strain contour map on the structural outline of the hydraulic structure, with color intensity indicating strain magnitude, thus quickly locating areas of high strain. Displacement data is displayed as a displacement vector map, showing the magnitude and direction of displacement at the corresponding locations on the structure.

[0043] For displaying freezing loads, the identified freezing load magnitude, direction, and distribution are presented as a load distribution cloud map, using different colors and color gradients to represent the load magnitude and distribution. Operators can intuitively see the concentrated areas and weak points of the load. Simultaneously, historical data curves are displayed on the interface. By selecting different time ranges, load data for different time periods can be viewed for trend analysis and comparative analysis.

[0044] In addition, the display interface will show the system's operating status information, such as the working status of each sensor, data acquisition frequency, and data transmission status, which will facilitate the maintenance and management of the system by administrators.

[0045] This module sets early warning thresholds, which are determined through a comprehensive analysis of the design standards, structural strength, and historical ice load data of hydraulic structures. Different types of hydraulic structures, such as gravity dams and arch dams, have different ice load capacities, thus requiring the determination of corresponding early warning thresholds based on specific design parameters. For example, for gravity dams, early warning thresholds for static ice pressure and dynamic ice pressure are set based on their designed compressive strength, shear strength, and historical maximum allowable load; for arch dams, corresponding load early warning thresholds are set considering the stress characteristics and structural stability of the arch.

[0046] When the data processing center detects that the freezing load exceeds the preset warning threshold, the display and warning module will immediately issue an audible and visual alarm signal. The alarm uses a high-brightness red warning light and a high-decibel buzzer. The warning light is installed in a prominent position in the monitoring room, and the buzzer can be set with different frequencies and volumes to ensure it attracts the attention of relevant personnel even in noisy environments. Simultaneously, a prominent alarm message will pop up on the display interface, detailing the type and location of the load exceeding the threshold, reminding management personnel to take appropriate emergency measures, such as ice-breaking measures, adjusting the building's operational status, or arranging for professional personnel to conduct on-site inspections.

[0047] Remote monitoring terminal Remote monitoring terminals can be smart devices such as computers and mobile phones, connected to the data processing center via the internet. Dedicated monitoring software is installed on these terminals, featuring user login and access control functions. Different levels of administrators have different operating permissions, ensuring system security.

[0048] Managers can view real-time, historical, and early warning data on the frost load of hydraulic structures at any time via remote monitoring terminals. The software interface displays various information in intuitive charts and data lists, similar to the display and early warning modules, but with an optimized layout to suit different device screen sizes. For example, on mobile phones, a simple layout and swipe operation facilitate viewing different information; on computers, more detailed information and complex charts can be displayed.

[0049] For remote monitoring terminals, a data download function is also provided, allowing administrators to download data of interest to local devices for further analysis and archiving. Downloaded data can be in common formats such as CSV and Excel files, facilitating subsequent processing with other software.

[0050] Personalized alert functions can be set on the remote monitoring terminal. When the freezing load approaches the warning threshold, the system will promptly notify management personnel via SMS, push notifications, and other means. For example, when the load reaches 80% of the warning threshold, an SMS alert will be sent to management personnel; when it reaches 90%, an emergency push notification will be sent, and a pop-up notification will appear on the mobile phone, prompting management personnel to take immediate action. Furthermore, management personnel can set different alert thresholds and alert methods according to their needs to meet various management requirements.

[0051] The remote monitoring terminal can also receive system maintenance information sent by the data processing center, such as sensor fault information and data transmission fault information, which makes it convenient for managers to remotely arrange maintenance personnel to perform system maintenance and repair, reduce monitoring interruption time caused by system failures, and improve system reliability and availability.

[0052] Before system installation, a detailed survey of the hydraulic structure and site environment is conducted to determine the optimal installation locations for the sensor modules and the layout of the data acquisition and transmission units. Based on the survey results, a detailed installation plan is developed, including the number, type, and installation locations of the sensors, wiring paths, and the establishment of the data transmission network.

[0053] For the sensor module, install the temperature sensor, strain sensor, and displacement sensor sequentially according to the specific implementation method described above, ensuring secure installation and accurate positioning. During installation, protect and label the signal lines of each sensor to prevent signal interference and confusion. Simultaneously, perform preliminary testing on the sensors to check their normal operation, including verifying that the sensor output signals are within the normal range and checking for faults such as short circuits and open circuits.

[0054] The data acquisition and transmission unit should be installed in a suitable location, such as near the sensors or in an equipment room within a building, depending on the site conditions. During installation, ensure a stable power supply and reliable connection to the sensors. For wired transmission, complete the cabling and test the cables to ensure continuity and interference resistance. For wireless transmission, configure the wireless communication module and ensure normal communication with the sensors and data processing center.

[0055] The data processing center is typically installed in the monitoring room, where servers, storage devices, and other hardware are assembled and connected to ensure the normal operation of the hardware system. Large-screen displays for the display and early warning modules are installed in a prominent position in the monitoring room for easy observation. Display devices, audible and visual alarm devices, and other related equipment are connected to the data processing center to ensure proper display and alarm functions. For remote monitoring terminals, monitoring software is installed on the appropriate devices, and network configuration is completed to ensure a connection with the data processing center.

[0056] After system installation, system debugging is performed. First, hardware debugging is conducted, involving a comprehensive check of the hardware equipment of each module, including sensor signal output, data acquisition and transmission unit functions, and the hardware performance of the data processing center. Sensors are then calibrated and standardized using standard testing equipment to ensure measurement accuracy and precision. For example, a high-precision thermometer is used to calibrate the temperature sensor, a standard specimen with known strain is used to calibrate the strain sensor, and a standard device with known displacement is used to calibrate the displacement sensor.

[0057] Next, software debugging was performed. The software system of the data processing center was started, and the normality of data receiving, storage, processing, and analysis functions was checked. By simulating sensor input data, the data processing center's data processing and the calculation results of the freezing load identification model were checked to see if they met expectations. The software of the display and early warning module was tested to check whether the display interface could display various data and information normally. Different test data were set to check whether the early warning function could be triggered normally. For the software of the remote monitoring terminal, it was checked whether it could connect normally to the data processing center, receive and display data normally, and trigger the reminder function.

[0058] During the debugging process, repeated testing and adjustments were made to ensure the coordinated operation of all parts of the system. For example, when testing the data transmission function of the data acquisition and transmission unit, network failures were artificially created to check whether its data caching and retransmission functions were normal; when testing the processing function of the data processing center, different abnormal data were input to check its anomaly handling capability and data processing stability; when testing the display and early warning module, different load conditions were simulated to check the accuracy and timeliness of its display and early warning.

[0059] Applications in gravity dams For gravity dams in cold regions, this ice load identification system can play an important role in monitoring and protection. Gravity dams in cold regions face threats from static ice pressure and dynamic ice pressure. Static ice pressure may cause local damage to the upstream face of the dam, while dynamic ice pressure may cause vibration and displacement of the dam.

[0060] In practical applications, the temperature sensor array monitors the temperature of the water surrounding the dam and the dam itself in detail. For example, at the beginning of winter, as the air temperature drops, the water temperature in front of the dam also decreases. The temperature sensors can accurately capture changes in water temperature. When the water temperature approaches freezing point, it means that ice may be forming soon, providing early warning information for the subsequent freezing load. Strain sensors deployed on the upstream face of the dam closely monitor the strain on the dam surface caused by ice pressure. When static ice begins to exert pressure on the dam, the strain sensors can detect an increase in strain on the dam surface, especially at the dam heel and toe, where the strain change is more significant due to stress concentration.

[0061] Displacement sensors monitor the dam's displacement. As static ice pressure gradually increases, the dam may experience slight horizontal displacement, which the sensors record. Simultaneously, vertical displacement sensors at the connection point between the dam's base and foundation monitor settlement or uplift, preventing foundation instability due to freezing.

[0062] The data acquisition and transmission unit collects and transmits data from these sensors in a timely manner. The data processing center analyzes and processes this data, using an ice load identification model to accurately identify the magnitude and distribution of static ice pressure. For example, based on the temperature gradient and strain changes of the dam body, combined with parameters such as the elastic modulus of the structure, the distribution of static ice pressure is determined. When the ice layer is subjected to water flow or wind, generating dynamic ice pressure, the system monitors the vibration frequency and displacement changes of the dam body, and uses a model to calculate the magnitude and direction of the dynamic ice pressure.

[0063] The display and early warning module presents this information intuitively. Managers can view temperature distribution cloud maps, strain distribution cloud maps, displacement vector maps, and load distribution cloud maps on the upstream face of the dam via a large screen. When static ice pressure or dynamic ice pressure exceeds a preset safety threshold, the system will issue an alarm, reminding managers to take measures such as icebreaking and water level adjustment. Simultaneously, the remote monitoring terminal allows managers to monitor the dam's condition remotely at any time. For example, when managers receive an early warning message, even if they are not on-site, they can remotely direct on-site personnel to perform corresponding operations, such as starting icebreakers to break ice or opening floodgates to adjust the water level in front of the dam, preventing further increases in static ice pressure and ensuring the safe operation of the gravity dam.

[0064] Applications in arch dams In the application of arch dams, the arrangement of sensors and the focus of monitoring differ. The structural characteristics of arch dams determine that they mainly transfer loads to the arch abutments through the action of the arches, therefore the arch abutments are the key monitoring points.

[0065] Temperature sensors are strategically placed not only on the dam surface and in the water, but also near the arch abutments, as temperature changes at the abutments affect the dam's structural performance, especially during winter when frost heave can adversely impact the structure. Strain sensors are densely arranged at the arch crown beam and abutments to monitor the dam's stress state. When the dam is subjected to freezing loads, the arch crown beam deforms, and the strain sensors capture these strain changes. Analysis of this strain data allows for assessment of the dam's structural stress.

[0066] Displacement sensors on arch dams primarily monitor the displacement of the arch crown and the horizontal displacement of the arch abutments. The displacement of the arch crown may reflect the deformation of the entire arch dam, while the horizontal displacement of the arch abutments is crucial for determining the stability of the arch dam. During freezing, as ice forms and develops, the arch abutments may be subjected to lateral ice pressure, leading to horizontal displacement. Displacement sensors can accurately measure this displacement information.

[0067] The data acquisition and transmission unit transmits various monitoring data of the arch dam to the data processing center. The data processing center uses a trained ice load identification model, combined with the arch dam's structural parameters such as the arch's radius, thickness, and material properties, to analyze the ice loads acting on the arch dam. For example, based on the strain and displacement information of the arch abutments, the magnitude and direction of the ice pressure on the abutments are calculated; and based on the displacement and strain of the arch crown, the overall stability of the arch dam structure is assessed.

[0068] The display and early warning module presents the arch dam's monitoring information in an intuitive way, such as displaying the temperature, strain, and displacement information of the arch abutments on the display interface, and showing the load situation of the arch dam through a load distribution cloud map. When the load exceeds the threshold, an audible and visual alarm is issued to remind management personnel to take appropriate measures, such as taking insulation measures near the arch abutments to prevent frost heave damage. The remote monitoring terminal allows management personnel to remotely monitor the condition of the arch dam. In case of abnormalities, the remote monitoring terminal can be used to coordinate personnel to maintain the arch dam, such as adding heating devices at the arch abutments to mitigate the impact of freezing loads.

[0069] Application in sluice gates In cold regions, sluice gates and piers are subject to freezing loads during winter operation. The application of this system in sluice gates primarily focuses on monitoring the gates and piers.

[0070] Temperature sensors are deployed on both sides of the gate, at different heights of the gate piers, and in the water within the gate chamber to monitor temperature changes. When the water temperature in the gate chamber drops, the temperature sensors record the temperature data, providing a basis for predicting the freezing conditions of the gate and gate piers. Strain sensors are placed on the surface of the gate and at key stress points of the gate piers. Since the operation of the sluice gate may cause stress changes, and freezing loads also generate additional stress on the gate and gate piers, strain sensors can monitor these stress changes. For example, when the gate is closed, it is subjected to static ice pressure, causing strain on the gate surface; strain sensors can accurately detect the magnitude and distribution of these strains.

[0071] Displacement sensors monitor changes in the opening and closing positions of the gates, as well as the displacement of the gate piers. When the gates are unable to open or close properly due to ice pressure, the displacement sensors can detect abnormal displacement. Simultaneously, the gate piers may experience slight displacement under freezing loads, affecting the normal operation of the sluice gate; the displacement sensors record this displacement information.

[0072] The data acquisition and transmission unit sends the monitoring data of the sluice gate to the data processing center. The data processing center analyzes the data and, based on the structural characteristics and material parameters of the gate and piers, uses a freezing load identification model to determine the freezing load on the gate and piers. For example, based on the strain and displacement data of the gate, combined with temperature changes, it determines whether the gate will jam due to freezing, and based on the strain and displacement of the piers, it assesses the structural safety of the piers.

[0073] The display and early warning module shows various information about the sluice gate, including the gate's temperature-time curve, the strain distribution cloud map of the gate piers, and the gate's displacement vector map. It issues an early warning signal when the gate's operation is affected by freezing or when the structural safety of the gate piers is threatened. The remote monitoring terminal allows management personnel to operate the sluice gate remotely. For example, if gate jamming is detected, they can remotely control the heating device to heat the gate or remotely adjust the water level in the gate chamber to ensure the normal operation of the sluice gate.

Claims

1. A system for identifying freezing loads on hydraulic structures in cold regions, characterized in that, It includes the following parts: The sensor module consists of a temperature sensor array, a strain sensor array, and a displacement sensor. The temperature sensor array is evenly distributed at different depths of the hydraulic structure to monitor the temperature changes of the water surrounding the structure and the structure itself in real time, accurately recording temperature fluctuations over time to provide basic data for subsequent analysis of the freezing process. The strain sensor array is installed at key stress-bearing parts of the structure to accurately capture structural strain information caused by freezing loads. The displacement sensors are arranged on the surface of the structure and at the connection points with the foundation to measure the displacement changes of the structure under the influence of freezing loads, including horizontal and vertical displacements. These sensors collect physical parameters related to freezing loads in a comprehensive and multi-dimensional manner. Data acquisition and transmission unit: Connected to the aforementioned sensor modules, this unit is responsible for acquiring data generated by the sensors at high speed and with high precision, and transmitting this data quickly and stably to the data processing center via wired or wireless transmission methods, such as fiber optic communication or 5G communication. This unit has a data caching function, which can temporarily store data when the transmission network experiences a brief failure, and resend the data after the network is restored, ensuring the integrity and continuity of the data. Data Processing Center: After receiving data from the data acquisition and transmission unit, the center first preprocesses the data, including noise reduction, filtering, and normalization, to improve data quality. Then, it uses a pre-set ice load identification model based on machine learning algorithms, such as a deep neural network model or a support vector machine model, to analyze and calculate the processed data. Combined with the structural parameters and material properties of the building, it identifies the magnitude, direction, and distribution parameters of the ice load currently acting on the hydraulic structure. Display and Early Warning Module: Displays the freezing load information identified by the data processing center in an intuitive way, such as showing real-time load data curves and load distribution cloud maps on a large screen. At the same time, this module is set with an early warning threshold. When the identified freezing load exceeds the preset safety threshold, it immediately issues an audible and visual alarm signal to remind relevant personnel to take timely countermeasures to ensure the safe and stable operation of hydraulic structures in cold regions.

2. The ice load identification system for hydraulic structures in cold regions according to claim 1, characterized in that, The temperature sensor array employs high-precision thermistor sensors with a measurement accuracy of ±0.1℃ and a response time of less than 1 second. Each sensor is waterproof and freeze-resistant, enabling long-term stable operation in harsh environments with low temperatures and humidity. Vertically, the array is positioned along the building, from 1 meter above the water surface to 5 meters underwater, with sensors spaced 0.5 meters apart. Horizontally, the array is evenly distributed in key areas susceptible to freezing, according to the building's structural characteristics, ensuring comprehensive coverage of areas prone to temperature changes and providing abundant and accurate data for precise analysis of the freezing process.

3. The ice load identification system for hydraulic structures in cold regions according to claim 1, characterized in that, The strain sensors in the strain sensor array are fiber Bragg grating strain sensors. These sensors are characterized by strong anti-electromagnetic interference capability and high sensitivity, capable of detecting minute strain changes with an accuracy of up to 1 με. When installing strain sensors, they are arranged according to the key stress points determined by finite element analysis for different types of hydraulic structures, such as gravity dams and arch dams. For gravity dams, they are arranged at key points on the upstream and downstream faces of the dam body, the dam heel, and the dam toe. For arch dams, they are densely arranged at key locations such as the arch crown beam and arch seat, thereby accurately capturing the strain information of the structure under freezing load.

4. The ice load identification system for hydraulic structures in cold regions according to claim 1, characterized in that, The displacement sensor is a laser displacement sensor with a measurement accuracy of ±0.01mm. A displacement sensor is installed at regular intervals along the dam crest and slope on the building surface to monitor horizontal displacement. Displacement sensors are pre-embedded at the connection between the building and the foundation to measure vertical displacement. The displacement sensors and the data acquisition and transmission unit are wirelessly transmitted and equipped with an independent power module to ensure continuous and stable operation in harsh environments and to obtain real-time and accurate displacement change data of the building under the influence of freezing load.

5. A system for identifying freezing loads on hydraulic structures in cold regions according to claim 1, characterized in that, The data acquisition and transmission unit's data acquisition frequency can be adjusted according to actual needs. In the early stages of freezing and during rapid changes, the acquisition frequency is set to above 10Hz to acquire data at high frequencies and accurately capture rapid changes in temperature, strain, and displacement. In the relatively stable stage of freezing, the acquisition frequency can be reduced to 1Hz-5Hz to reduce data transmission volume and storage pressure while ensuring data integrity. The unit's data cache capacity is no less than 1GB, which can meet the data caching requirements for at least 24 hours, ensuring that data is not lost in the event of a transmission network failure.

6. The ice load identification system for hydraulic structures in cold regions according to claim 1, characterized in that, In the preprocessing stage of the data processing center, wavelet transform denoising algorithm is used for denoising. According to the noise characteristics of different sensor data, appropriate wavelet basis functions are selected to effectively remove high-frequency noise and low-frequency interference signals. Adaptive filtering algorithm is used for filtering. The filtering parameters are automatically adjusted according to the real-time changes of the data to retain effective signals and further improve the data quality. Normalization process maps the data collected by different types of sensors to the [0,1] interval to eliminate the difference in data dimensions and provide standardized data input for the subsequent ice load identification model.

7. A system for identifying freezing loads on hydraulic structures in cold regions according to claim 1, characterized in that, The ice load identification model based on machine learning algorithms uses a large amount of historical measured data and numerical simulation data as training samples during the training process. The historical measured data covers relevant parameter data of hydraulic structures under different years, seasons, and climatic conditions. The numerical simulation data simulates the response data under various ice load conditions by establishing accurate hydraulic structure models. The model is repeatedly trained and optimized using these data to adjust the model parameters, improve the model's generalization ability and recognition accuracy, and enable it to accurately identify ice load parameters under various complex conditions.

8. A system for identifying freezing loads on hydraulic structures in cold regions according to claim 1, characterized in that, The large-screen display of the display and early warning module uses a high-resolution LCD screen with a resolution of no less than 1920×1080, which can clearly display real-time load data curves and load distribution cloud map information. At the same time, the display interface classifies and displays different types of load data and sets different color labels, so that operators can intuitively and quickly obtain key information. The early warning threshold is determined by comprehensive analysis based on the design standards of hydraulic structures, structural strength, and historical freezing load data to ensure that early warnings are issued in a timely manner when the freezing load reaches a level that may endanger the safety of the structure.

9. A system for identifying freezing loads on hydraulic structures in cold regions according to claim 1, characterized in that, The system also includes a remote monitoring terminal connected to the data processing center via the Internet. The remote monitoring terminal can be a computer or a mobile smart device, allowing relevant management personnel to view real-time data, historical data, and early warning information of the freezing load of hydraulic structures anytime and anywhere. At the same time, personalized reminder functions can be set on the remote monitoring terminal, such as notifying management personnel in a timely manner via SMS or push message when the freezing load approaches the warning threshold, thereby realizing remote real-time monitoring and management of hydraulic structures in cold regions.

10. A system for identifying freezing loads on hydraulic structures in cold regions according to claim 1, characterized in that, The system adopts a standardized interface design between its various modules; the interface between the sensor module and the data acquisition and transmission unit follows the industrial common data transmission protocol to ensure the stability and compatibility of data transmission. The interface between the data acquisition and transmission unit and the data processing center adopts a high-speed data transmission interface standard to meet the needs of rapid transmission of large amounts of data; the interface between the data processing center and the display and early warning module and the remote monitoring terminal adopts an open network communication protocol, which facilitates system expansion and upgrades and can be easily integrated with other related systems.

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