H-ADCP-based rapid measurement method and system for hydrological flow velocity of river channel

Abstract

The invention relates to the technical field of hydrological measurement, in particular to an H-ADCP (H-Acoustic Doppler Current Profiler)-based rapid measurement method and system for the hydrological velocity of a river channel. The method comprises the following steps: acquiring structural data of a hydrological monitoring instrument and structural data of a solar panel; performing solar energy installation position analysis according to the solar panel structure data so as to obtain solar panel installation position data; extracting structural features of the instrument container according to the structural data of the hydrological monitoring instrument so as to obtain structural data of the instrument container; optimizing a battery layout module according to the structure of the instrument box so as to obtain data of the battery layout module; performing heat dissipation design on the instrument box structure according to the data of the battery layout module so as to obtain heat dissipation data of the instrument box structure; hydrological monitoring instrument energy supply stability analysis is carried out according to the solar panel installation position data and the instrument box structure heat dissipation data, and therefore hydrological monitoring instrument energy supply stability data are obtained. The invention realizes a river hydrological flow velocity measurement task based on a hydrological measurement technology.

Description

Technical Field

[0001] The invention relates to the technical field of hydrological measurement, and in particular to a method and system for rapid measurement of river hydrological flow velocity based on H-ADCP. Background Art

[0002] In traditional river monitoring systems, the measurement of water flow velocity mostly relies on current meters, floating object tracking methods, and static hydrological instruments. It has many disadvantages such as low measurement accuracy, long time consumption, complex operation, and limited coverage. Especially under complex river conditions, it is difficult to achieve comprehensive, fast, and accurate water flow velocity measurement. H-ADCP equipment usually relies on solar cells and batteries to provide energy support for long-term operation, but solar energy is affected by weather, geographical location, and seasonal changes, resulting in fluctuations in energy supply. The installation of H-ADCP equipment has high requirements for location, angle, and surrounding environment. Due to factors such as the irregular shape of the riverbed, changes in water depth, and water scouring, the installation and fixation of the equipment on the riverbed requires precise measurement and debugging difficulties. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a method and system for rapid measurement of river hydrological flow velocity based on H-ADCP to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a method for rapid measurement of river hydrological flow velocity based on H-ADCP includes the following steps:

[0005] Step S1: Acquire hydrological monitoring instrument structure data and solar panel structure data; perform solar installation position analysis based on the solar panel structure data to obtain solar panel installation position data;

[0006] Step S2: extracting the structural features of the instrument box according to the structural data of the hydrological monitoring instrument, thereby obtaining the structural data of the instrument box; optimizing the battery layout module according to the structure of the instrument box, thereby obtaining the battery layout module data; performing heat dissipation design on the instrument box structure according to the battery layout module data, thereby obtaining the heat dissipation data of the instrument box structure;

[0007] Step S3: analyzing the energy supply stability of the hydrological monitoring instrument according to the solar panel installation position data and the heat dissipation data of the instrument box structure, thereby obtaining the energy supply stability data of the hydrological monitoring instrument;

[0008] Step S4: acquiring monitoring data of the hydrological monitoring instrument; performing river hydrological flow rate monitoring continuity analysis on the monitoring data of the hydrological monitoring instrument according to the energy supply stability data of the hydrological monitoring instrument, thereby obtaining river hydrological flow rate monitoring continuity data;

[0009] Step S5: Perform velocity measurement error analysis based on the river hydrological velocity monitoring continuity data to obtain velocity measurement error data; perform river hydrological velocity correction based on the velocity measurement error data to obtain river hydrological velocity correction data, and upload it to the hydrological engineering management platform to execute the river hydrological velocity measurement task.

[0010] The present invention can ensure the optimal installation position of the solar panel by acquiring the structural data of the instrument, especially the structural characteristics and installation position analysis of the solar panel, minimize the light blocking and optimize the light time, which improves the power generation efficiency of the solar panel, thereby providing a stable energy supply for the H-ADCP and ensuring the long-term stable operation of the equipment. By optimizing the design of the internal space of the instrument box, especially the reasonable planning of the battery layout module, the internal space is more efficiently utilized. Through a reasonable battery layout, the risk of equipment overheating can be reduced, the heat dissipation effect can be optimized, thereby extending the life of the equipment and reducing the failure rate. Combining the solar panel installation position data with the instrument box heat dissipation structure, the stability of the instrument energy supply can be accurately analyzed, which is crucial for the long-term operation of the H-ADCP, because the stability of the energy supply directly affects the measurement accuracy and data continuity of the equipment, ensuring that the equipment can continuously collect hydrological data under various environmental conditions. By analyzing the stability data of the energy supply, it is possible to evaluate whether the monitoring data of the H-ADCP has continuity, ensuring that the monitoring of the river hydrological flow rate will not be interrupted due to energy shortage or equipment failure. This analysis result provides a basis for subsequent flow rate error correction, making the reliability of flow rate measurement higher. Error analysis based on monitoring data and correction of flow rate data can effectively improve measurement accuracy and correct measurement errors caused by energy supply fluctuations, environmental changes, etc. The corrected data is uploaded to the hydrological management platform to ensure the reliability and traceability of the final data, so that hydrological managers can quickly and accurately understand the hydrological conditions of the river.

[0011] Optionally, step S1 specifically includes:

[0012] Step S11: Acquire hydrological monitoring instrument structure data and solar panel structure data;

[0013] Step S12: analyzing the solar panel installation angle according to the solar panel structure data, thereby obtaining the solar panel installation angle data;

[0014] Step S13: performing solar panel installation light blocking analysis according to the solar panel installation angle data, thereby obtaining solar panel installation light blocking data;

[0015] Step S14: integrating the solar panel installation positions according to the solar panel installation angle data and the solar panel installation light blocking data, thereby obtaining the solar panel installation position data.

[0016] The present invention can fully understand the internal configuration and functional requirements by acquiring the structural data of the hydrological monitoring instrument and the structural characteristics of the solar panel. By analyzing the structural data, it is helpful to optimize the design of the solar panel to ensure that it can effectively receive sunlight and maximize the power generation efficiency. Through such structural optimization, a solid foundation can be laid for the subsequent energy supply to ensure the long-term stable operation of the H-ADCP equipment. The installation angle of the solar panel directly affects its light receiving efficiency. By analyzing the optimal installation angle, it can be ensured that the solar panel can receive sunlight to the greatest extent in different seasons and time periods. This analysis not only optimizes the energy acquisition efficiency, but also reduces the risk of equipment operation interruption caused by insufficient light, thereby enhancing the overall stability of the monitoring system. Performing light blocking analysis can identify obstacles (such as trees, buildings, etc.) that affect the light reception of the solar panel. By evaluating the data of light blocking, the installation position and angle of the solar panel can be further optimized to ensure that the equipment obtains sufficient sunlight in actual operation. This pre-analysis can reduce the problem of insufficient light in later actual operation, thereby improving the operation efficiency of the entire system. Combining the installation angle data of the solar panel with the light blocking data for location integration can ensure the selection of the best installation location. This integration process helps reduce the risk of insufficient light and improves the energy supply stability of the equipment. In addition, through reasonable installation location planning, future maintenance difficulties and costs can be reduced, improving the sustainability of the system.

[0017] Optionally, step S12 is specifically:

[0018] Step S121: extracting solar panel support structure features according to the solar panel structure data, thereby obtaining solar panel support structure data;

[0019] Step S122: Acquire river geographical latitude data and solar altitude angle data;

[0020] Step S123: performing seasonal solar orbit simulation according to the river geographical latitude data and the solar altitude angle data, thereby obtaining seasonal solar orbit data;

[0021] Step S124: adjusting the tilt angle of the solar panel support structure data according to the node solar orbit data, thereby obtaining the tilt angle data of the solar panel support;

[0022] Step S125: performing statistics of the light intensity at the tilt angle of the solar panel support according to the tilt angle data of the solar panel support, thereby obtaining the light intensity data at the tilt angle of the solar panel support;

[0023] Step S126: setting the solar panel installation angle according to the solar panel support tilt angle light intensity data, thereby obtaining the solar panel installation angle data.

[0024] The present invention extracts characteristic information of the bracket by analyzing the structural data of the solar panel, ensures that the design of the bracket can support the solar panel and adjust the angle, and this process helps to identify the strength, durability and stability requirements of the bracket, thereby optimizing the bracket design and improving the safety and stability of the overall system. Collecting the geographical latitude and solar altitude angle data of the river can provide basic data for subsequent solar orbit simulation, which enables subsequent analysis to be based on accurate geographical information to ensure that the solar panel can collect energy in the best position. Based on the simulation of seasonal solar orbits based on geographical latitude and solar altitude angle, it is possible to analyze the trajectory of the sun in different seasons and time periods. This analysis provides a scientific basis for adjusting the tilt angle of the bracket, ensuring that the solar panel can receive sunlight to the greatest extent and improve the power generation efficiency. By adjusting the tilt angle of the bracket through seasonal solar orbit data, the contact angle between the solar panel and the sunlight can be optimized. This adjustment can significantly improve the light receiving efficiency of the solar panel and ensure that the solar energy collection capacity reaches the best state in different seasons. Statistical analysis of light intensity based on the adjusted bracket tilt angle can evaluate the actual amount of light received by the solar panels at different angles. This data provides empirical evidence for the subsequent setting of the installation angle of the solar panels, which helps to ensure that the system always maintains efficient energy output during operation. The installation angle of the solar panel is set according to the light intensity data of the bracket tilt angle to ensure that the solar panel can achieve the best light reception under various environmental conditions. This step not only improves the energy efficiency of the solar panel, but also reduces the energy loss caused by improper angles, providing guarantee for the long-term stable operation of the hydrological monitoring system.

[0025] Optionally, step S13 is specifically:

[0026] Step S131: extracting solar panel installation azimuth angle features according to the solar panel installation angle data, thereby obtaining solar panel installation azimuth angle data;

[0027] Step S132: acquiring remote sensing data of the river monitoring area, and extracting bridge location features based on the remote sensing data of the river monitoring area, thereby obtaining bridge location data;

[0028] Step S133: performing light shielding path analysis on the solar panel installation azimuth data according to the bridge position data and the seasonal solar orbit data, thereby obtaining light shielding path data;

[0029] Step S134: Calculating the solar panel area according to the solar panel structure data, thereby obtaining solar panel area data;

[0030] Step S135: Calculate the solar panel light blocking ratio for the solar panel area data according to the light blocking path data, so as to obtain the solar panel installation light blocking data.

[0031] The present invention extracts the azimuth characteristics of solar panels by analyzing the installation angle data of solar panels. This process ensures that the installation of solar panels can make the most of sunlight resources, provides accurate basic data for subsequent illumination analysis, and helps to optimize the actual operation effect of solar panels. By acquiring remote sensing data, it is possible to fully understand the environmental characteristics and changes in the river monitoring area. The extraction of bridge position characteristics helps to evaluate the light blocking situation. This information lays a solid foundation for subsequent illumination analysis and ensures the scientificity and rationality of the installation orientation. By using bridge position data and seasonal solar orbit data to perform illumination blocking path analysis, it is possible to accurately identify the influence of bridges on solar panels in different time periods. This analysis can help optimize the installation position and orientation of solar panels, reduce light loss, and improve energy collection efficiency. By performing area calculation on the solar panel structure data, the solar panel area data obtained provides the necessary quantitative basis for the analysis of the light blocking ratio. This data helps to calculate the subsequent steps and ensures the accuracy and reliability of the light blocking analysis. Combining the light blocking path data and the solar panel area data to calculate the light blocking ratio can quantitatively analyze the light loss suffered by the solar panels in actual operation. This result provides a strong basis for adjusting the installation position or angle, which helps to improve the overall light energy conversion efficiency of the system.

[0032] Optionally, step S2 specifically includes:

[0033] Step S21: extracting the structural features of the instrument box according to the structural data of the hydrological monitoring instrument, thereby obtaining the structural data of the instrument box;

[0034] Step S22: constructing a three-dimensional spatial structure model of the instrument box according to the instrument box structure data, thereby obtaining a three-dimensional spatial structure model of the instrument box;

[0035] Step S23: marking the positions of the instrument components according to the three-dimensional spatial structure model of the instrument box, thereby obtaining the position data of the instrument components;

[0036] Step S24: simulating the component layout of the three-dimensional spatial structure model of the instrument box according to the instrument component position data, thereby obtaining the instrument component layout data;

[0037] Step S25: performing battery layout interval evaluation according to the instrument component layout data, thereby obtaining battery layout interval evaluation data;

[0038] Step S26: performing modular design according to the battery layout interval evaluation data, thereby obtaining battery layout module data;

[0039] Step S27: Perform heat dissipation analysis on the instrument box structure according to the battery layout module data, thereby obtaining heat dissipation data of the instrument box structure.

[0040] The present invention can provide accurate basic information for the design of the instrument box by extracting the structural data of the hydrological monitoring instrument, which ensures that the subsequent model construction and component layout can be based on real and accurate structural data, and improves the feasibility and rationality of the design. The construction of the three-dimensional spatial structure model provides an intuitive visualization platform to help designers better understand the spatial configuration of the instrument box. Through the three-dimensional model, more effective spatial analysis and layout optimization can be carried out, laying the foundation for subsequent component identification and layout simulation. Position identification of instrument components can clearly define the specific positions of each component in the instrument box, help avoid interference between components, ensure that each part can work effectively together in actual use, and improve the reliability and efficiency of the system. By simulating the component layout of the three-dimensional spatial structure model of the instrument box, the feasibility of different layout schemes can be intuitively evaluated, which can help optimize the relative positions of each component, ensure convenience during maintenance and operation, and reduce operational complexity. The evaluation of the battery layout interval helps to ensure that there is enough space between battery modules to avoid overheating and mutual interference, provides data support for subsequent modular design, ensures the safety and stability of the battery, and improves the work efficiency of the overall system. By implementing a modular design of the battery, the battery can be independently disassembled and replaced, which is convenient for later maintenance and troubleshooting. Modular design can also reduce the downtime of the overall equipment, improve the availability and operating efficiency of the equipment, and thus optimize the continuity and reliability of hydrological monitoring. Heat dissipation analysis of the instrument box structure can identify heat accumulation problems and ensure temperature control of the equipment during operation. Effective heat dissipation design helps to extend the service life of the equipment, prevent performance degradation or failure due to overheating, and improve the long-term stability of the hydrological monitoring system.

[0041] Optionally, step S27 is specifically:

[0042] Step S271: performing battery heating simulation according to the battery layout module data, thereby obtaining battery module heating data;

[0043] Step S272: drawing a heat distribution map of the battery module according to the heat generation data of the battery module, thereby obtaining a heat distribution map of the battery module;

[0044] Step S273: designing the air circulation path of the instrument box for the three-dimensional spatial structure model of the instrument box according to the heat distribution diagram of the battery module, thereby obtaining the air circulation path data of the instrument box;

[0045] Step S274: designing the heat sink of the instrument box for the three-dimensional spatial structure model of the instrument box according to the heat distribution diagram of the battery module, thereby obtaining the heat sink data of the instrument box;

[0046] Step S275: Perform heat dissipation integration of the instrument box structure according to the instrument box air circulation path data and the instrument box heat sink data, thereby obtaining the instrument box structure heat dissipation data.

[0047] The present invention can predict the heat generated by the battery under different working conditions by simulating the heat generation of the battery layout module. This data provides a scientific basis for the subsequent thermal management design, ensuring that potential problems caused by heat generation can be effectively dealt with and improving the safety and stability of the equipment. The drawing of the heat distribution map can intuitively display the temperature change of the battery module during operation. By analyzing the heat distribution, the heat concentration area can be quickly identified, and a targeted optimization solution can be provided for the heat dissipation design, thereby preventing failures caused by heat accumulation. The air flow path design based on the heat distribution map of the battery module can ensure the effective circulation of air inside the instrument box, help to take away excess heat, improve the overall heat dissipation efficiency, provide a guarantee for the continuous and stable operation of the equipment, and avoid performance degradation caused by excessive temperature. The design of the heat sink is a key step in enhancing the heat dissipation capacity. By combining the heat distribution map data, a heat sink that better meets actual needs can be designed to further improve the efficiency of heat dissipation, which not only helps to reduce the temperature of the equipment, but also prolongs the service life of the battery and other components, and ensures the stability of the equipment during long-term operation. According to the integration of the designed air flow path and the heat sink data, a systematic heat dissipation solution can be formed, which ensures the effective coordination of air flow and heat dissipation, and greatly improves the overall heat dissipation performance of the instrument box. By optimizing the heat dissipation design, the working reliability of the hydrological monitoring system in high temperature environments can be significantly improved, supporting its continuous monitoring tasks under harsh conditions.

[0048] Optionally, step S4 specifically includes:

[0049] Step S41: Acquire monitoring data of hydrological monitoring instruments;

[0050] Step S42: performing energy fluctuation statistics according to the energy supply stability data of the hydrological monitoring instrument, thereby obtaining energy supply fluctuation data of the hydrological monitoring instrument;

[0051] Step S43: performing a hydrological monitoring instrument equipment load analysis on the monitoring data of the hydrological monitoring instrument according to the energy supply fluctuation data of the hydrological monitoring instrument, thereby obtaining the hydrological monitoring instrument equipment load data;

[0052] Step S44: performing flow rate monitoring anomaly analysis on the monitoring data of the hydrological monitoring instrument according to the energy supply fluctuation data of the hydrological monitoring instrument, thereby obtaining flow rate monitoring anomaly data;

[0053] Step S45: Perform river hydrological flow rate monitoring continuity evaluation on the monitoring data of the hydrological monitoring instrument according to the load data of the hydrological monitoring instrument equipment and the flow rate monitoring abnormality data, so as to obtain river hydrological flow rate monitoring continuity data.

[0054] The present invention obtains monitoring data as the basis of the entire analysis process. By collecting the monitoring data of the hydrological monitoring instrument in real time, real field data can be provided for subsequent analysis, which is crucial to ensure the accuracy and reliability of the data. By performing fluctuation statistics on the energy supply stability data of the hydrological monitoring instrument, the fluctuation trend and pattern of the energy supply can be identified, and potential energy supply problems can be revealed, providing support for further fault warning and maintenance strategies, thereby improving the reliability of the monitoring equipment. Equipment load analysis based on energy supply fluctuation data can help evaluate the energy consumption and performance of the monitoring instrument under different conditions. By analyzing the equipment load data, the use strategy of the equipment can be optimized to ensure stable operation under various working conditions, reduce the failure rate, and improve work efficiency. Abnormal analysis of flow rate monitoring data can timely identify abnormal monitoring results and help detect whether the equipment has faults or whether there is interference during data collection. This analysis provides data support for maintaining and adjusting equipment, thereby improving the accuracy and reliability of flow rate monitoring. By comprehensively evaluating equipment load data and flow velocity monitoring anomaly data, the continuity of river hydrological flow velocity monitoring can be effectively judged, providing a basis for subsequent monitoring strategy adjustments and equipment maintenance, ensuring that water flow velocity monitoring can always remain efficient and accurate in dynamic and complex environments.

[0055] Optionally, step S43 is specifically:

[0056] Step S431: performing fluctuation amplitude statistics according to the energy supply fluctuation data of the hydrological monitoring instrument, thereby obtaining high-amplitude energy supply fluctuation data;

[0057] Step S432: performing fluctuation time frequency statistics according to the energy supply fluctuation data of the hydrological monitoring instrument, thereby obtaining high-frequency energy supply fluctuation time data;

[0058] Step S433: performing energy supply instability intersection operation according to the high-amplitude energy supply fluctuation data and the high-frequency energy supply fluctuation time data, thereby obtaining energy supply instability data;

[0059] Step S434: extracting sensor power consumption characteristics according to the monitoring data of the hydrological monitoring instrument, thereby obtaining sensor power consumption data;

[0060] Step S435: performing sensor overload operation analysis according to the sensor power consumption data and energy supply instability data, thereby obtaining sensor overload operation data;

[0061] Step S436: performing overload operation time statistics according to the sensor overload operation data, thereby obtaining the sensor overload operation time data;

[0062] Step S437: Perform a load analysis of the hydrological monitoring instrument sensor module on the monitoring data of the hydrological monitoring instrument according to the sensor overload operation time data, thereby obtaining the hydrological monitoring instrument equipment load data.

[0063] The present invention can identify high-amplitude fluctuations by performing amplitude statistics on energy supply fluctuation data. These data are helpful to timely discover potential energy supply problems, such as energy interruptions caused by extreme weather or other factors, and provide a basis for formulating early warning mechanisms and adjusting energy supply strategies. By statistically analyzing the time frequency of fluctuations, the time law of energy fluctuations can be analyzed, which helps to understand the instability of energy supply and provide data support for adjusting the working mode of monitoring instruments, thereby optimizing energy utilization efficiency. By performing intersection operations on high-amplitude and high-frequency energy supply fluctuation data, the instability of energy supply can be comprehensively evaluated, which helps to identify under which conditions energy supply is more likely to have problems, thereby strengthening monitoring and response measures for specific situations. Extracting sensor power consumption characteristics can help identify the energy consumption of each sensor under different working conditions, which is crucial for evaluating the adaptability of energy supply and sensor performance, and is helpful for energy optimization design and management. Combining sensor power consumption data with energy supply instability data for overload operation analysis can help identify the performance of sensors under high load conditions, and can detect equipment overload risks early, thereby avoiding equipment failure and data loss, and improving the stability of the monitoring system. Statistics on sensor overload operation time can help evaluate the actual operating status of the equipment. By understanding the duration of overload operation, maintenance personnel can better arrange maintenance work to ensure that the equipment is always in the best working condition and avoid frequent failures and maintenance interference. Load analysis of monitoring data based on sensor overload operation data can provide a scientific basis for the energy management of monitoring equipment, evaluate the load of equipment under different working conditions, formulate more reasonable energy supply and management plans, and improve the effectiveness and accuracy of hydrological monitoring.

[0064] Optionally, step S44 is specifically:

[0065] Step S441: performing statistics of energy supply fluctuation time periods according to the energy supply fluctuation data of the hydrological monitoring instrument, thereby obtaining data of high fluctuation time periods of energy supply of the hydrological monitoring instrument;

[0066] Step S442: extracting flow velocity monitoring features according to the monitoring data of the hydrological monitoring instrument, thereby obtaining flow velocity monitoring data;

[0067] Step S443: The hydrological monitoring instrument energy supply high fluctuation time period data is used to synchronize the flow velocity monitoring data with the flow velocity fluctuation monitoring time, thereby obtaining the flow velocity fluctuation monitoring time data;

[0068] Step S444: extracting flow velocity fluctuation features according to the flow velocity fluctuation monitoring time data, thereby obtaining flow velocity fluctuation data;

[0069] Step S445: performing flow rate monitoring loss identification according to the flow rate fluctuation data, thereby obtaining flow rate monitoring loss data;

[0070] Step S446: Calculate the abnormal flow rate monitoring ratio of the monitoring data of the hydrological monitoring instrument according to the flow rate monitoring loss data, so as to obtain abnormal flow rate monitoring data.

[0071] The present invention can identify the specific time period with large energy supply fluctuation during the operation of the equipment by statistically analyzing the energy supply fluctuation time period, which will help the operation and maintenance personnel to timely understand the energy supply problems that affect the monitoring accuracy, so as to take corresponding measures, such as adjusting the equipment operation mode or replenishing energy. Feature extraction of flow rate monitoring data is helpful to identify the changing trend and law of flow rate, help optimize the monitoring strategy, improve the reliability of data, and better understand the dynamic changes of the river. Time synchronization of the energy supply high fluctuation time period with the flow rate monitoring data can accurately associate the relationship between energy fluctuation and flow rate change, help to find the influence of energy fluctuation on the flow rate monitoring results, and ensure the accuracy and continuity of the data. Through the flow rate fluctuation feature extraction, the fluctuation of flow rate in different time periods can be understood. This information can not only help evaluate the quality of monitoring data, but also provide a basis for further analysis and decision-making, especially under complex conditions such as high water level or rapids. Monitoring loss identification through flow rate fluctuation data can timely find the missing of flow rate data. This ability is crucial to ensure data integrity, and helps operation and maintenance personnel to deal with data missing problems in a timely manner and prevent wrong decisions caused by data loss. Calculating the abnormal proportion based on the flow rate monitoring loss data helps to quantify the abnormal situations in flow rate monitoring and can help identify the vulnerability of the system under specific circumstances, thus providing a basis for improving the monitoring system and enhancing its reliability and stability.

[0072] Optionally, this specification also provides a river hydrological flow velocity rapid measurement method and system based on H-ADCP, which is used to perform the river hydrological flow velocity rapid measurement method based on H-ADCP as described above, and the river hydrological flow velocity rapid measurement system includes:

[0073] Solar installation analysis module: used to obtain the structural data of hydrological monitoring instruments and solar panel structure data; perform solar installation location analysis based on the solar panel structure data to obtain solar panel installation location data;

[0074] Instrument box heat dissipation analysis module: used to extract the structural features of the instrument box according to the structural data of the hydrological monitoring instrument, so as to obtain the structural data of the instrument box; optimize the battery layout module according to the structure of the instrument box, so as to obtain the battery layout module data; perform heat dissipation design on the instrument box structure according to the battery layout module data, so as to obtain the heat dissipation data of the instrument box structure;

[0075] Hydrological monitoring instrument energy supply analysis module: used to analyze the energy supply stability of the hydrological monitoring instrument based on the solar panel installation position data and the instrument box structure heat dissipation data, so as to obtain the energy supply stability data of the hydrological monitoring instrument;

[0076] River hydrological flow rate monitoring module: used to obtain monitoring data of hydrological monitoring instruments; conduct river hydrological flow rate monitoring continuity analysis on the monitoring data of hydrological monitoring instruments according to the energy supply stability data of hydrological monitoring instruments, so as to obtain river hydrological flow rate monitoring continuity data;

[0077] River hydrological flow velocity correction module: used to perform flow velocity measurement error analysis based on the river hydrological flow velocity monitoring continuity data, so as to obtain flow velocity measurement error data; perform river hydrological flow velocity correction based on the flow velocity measurement error data, so as to obtain river hydrological flow velocity correction data, and upload it to the hydrological engineering management platform to perform river hydrological flow velocity measurement tasks.

[0078] The H-ADCP-based rapid river hydrological flow velocity measurement method and system of the present invention can implement any one of the H-ADCP-based rapid river hydrological flow velocity measurement methods of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the H-ADCP-based rapid river hydrological flow velocity measurement method, and the internal modules of the system cooperate with each other to complete the task of rapid river hydrological flow velocity measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0080] Figure 1 The figure is a schematic flow chart of the steps of the method for rapid measurement of river hydrological flow velocity based on H-ADCP of the present invention;

[0081] Figure 2 Detailed step flow diagram of step S2 in the present invention;

[0082] Figure 3Detailed step flow diagram of step S23 in the present invention;

[0083] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0084] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0085] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0086] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0087] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for rapid measurement of river hydrological flow velocity based on H-ADCP, the method comprising the following steps:

[0088] Step S1: Acquire hydrological monitoring instrument structure data and solar panel structure data; perform solar installation position analysis based on the solar panel structure data to obtain solar panel installation position data;

[0089] In this embodiment, when obtaining the structural data of the hydrological monitoring instrument, three-dimensional modeling technology is used to generate high-precision three-dimensional point cloud data. The point cloud data is processed by professional software, and the relevant design drawings are loaded using CAD software to extract specific structural parameters (such as the height, width and thickness of the support frame). These structural parameters will be used to calculate the installation space of the solar panel and the position of the fixed bracket to ensure that the solar panel can be stably installed. The acquisition of the solar panel structure data is clear by consulting the product manuals and technical specifications of the relevant manufacturers to clarify the size, weight and installation requirements of the solar panel. In combination with these parameters, the installation height of the solar panel is first determined according to the height of the support frame to avoid the solar panel from being affected by ground obstacles. When analyzing the installation position, the characteristics of the installation environment must be fully considered. The environmental data acquisition equipment is used to obtain information such as the sunshine duration, sunshine angle and height of surrounding obstacles on the site. Based on the tilt angle and direction of the solar panel, calculations are performed in the solar radiation model to analyze the amount of solar energy received at different locations. Specifically, the sunshine duration and sunshine angle determine the optimal tilt angle and orientation of the solar panel, while the height of the obstacle is used to determine the shielding area that the solar panel needs to avoid. After inputting these parameters, the solar radiation model calculates the daily and monthly solar radiation intensity. Based on the calculation results and the actual limitations of the installation space, the optimization algorithm evaluates the advantages and disadvantages of each alternative installation location and finally determines an installation location that can fully utilize the space and maximize the amount of sunlight received.

[0090] Step S2: extracting the structural features of the instrument box according to the structural data of the hydrological monitoring instrument, thereby obtaining the structural data of the instrument box; optimizing the battery layout module according to the structure of the instrument box, thereby obtaining the battery layout module data; performing heat dissipation design on the instrument box structure according to the battery layout module data, thereby obtaining the heat dissipation data of the instrument box structure;

[0091] In this embodiment, the acquired hydrological monitoring instrument structure data will be imported into the finite element analysis software to extract the structural features of the instrument box. First, specific boundary conditions need to be set in the analysis, such as the fixing method and stress condition of the instrument box, as well as loading conditions (such as pressure and temperature applied by the external environment), which will be used to simulate the mechanical properties of the instrument box under actual working conditions. Through the application of these parameters, the analysis results will reveal the stress distribution and deformation of the material under different working conditions, thereby obtaining the structural data of the instrument box. Next, the battery layout module is optimized using an optimization algorithm (such as a genetic algorithm or a particle swarm optimization algorithm). The algorithm first arranges the battery reasonably based on the size and shape of the internal space of the instrument box, and parameters such as the size, shape and layout of the battery are used to calculate the optimal space utilization. In addition, the heat dissipation requirements will be comprehensively considered, and the heat dissipation effects under different layouts will be analyzed to ensure that there will be no overheating problems during work. The optimized battery layout module data will be iterated through the algorithm to obtain the optimal solution to ensure that the layout meets the space and heat dissipation requirements. The optimized layout is simulated using thermal analysis software, and parameters such as the power consumption and ambient temperature of the battery during work are set to calculate the temperature distribution of the battery and the surrounding area. Based on these temperature distribution data, the corresponding heat dissipation scheme is designed (such as setting up heat sinks or optimizing ventilation design), and the structural heat dissipation data of the instrument box is obtained accordingly to ensure that the instrument box can maintain normal temperature in different working environments.

[0092] Step S3: analyzing the energy supply stability of the hydrological monitoring instrument according to the solar panel installation position data and the heat dissipation data of the instrument box structure, thereby obtaining the energy supply stability data of the hydrological monitoring instrument;

[0093] In this embodiment, the obtained solar panel installation location data and the structural heat dissipation data of the instrument box will be combined and applied to analyze the stability of the energy supply of the hydrological monitoring instrument by establishing a mathematical model (such as a linear regression model). Specifically, the installation location data of the solar panel is used to calculate the light intensity, which is directly related to the power generation capacity of the solar panel. Changes in light intensity will affect the daily and monthly energy output of the solar panel, so it is necessary to use the solar radiation model to estimate the power generation under different weather conditions. Temperature, as another key parameter, affects the performance of electronic equipment and batteries. Excessive temperature may lead to increased energy consumption, while the heat dissipation capacity determines whether the instrument box can maintain normal operation under high temperature conditions. Therefore, in the model, temperature data will be combined with heat dissipation capacity to evaluate the actual energy demand of the equipment. Wind speed is an important environmental parameter that affects the heat dissipation efficiency of the instrument box. Higher wind speed can enhance the natural heat dissipation effect and reduce the temperature of the instrument box, otherwise more energy may be required to maintain the normal operation of the equipment. Historical meteorological data will be used to analyze the heat dissipation effect under different wind speed conditions, and combined with light intensity to calculate energy supply fluctuations. Through these parameters, the model can dynamically analyze the energy supply stability under different environmental conditions and set a fluctuation threshold (such as ±10%) to determine whether the power supply system is stable enough. Ultimately, the output energy supply stability data will provide a basis for system optimization and subsequent decision-making, ensuring that the instrument can still operate normally in an environment with large energy fluctuations.

[0094] Step S4: acquiring monitoring data of the hydrological monitoring instrument; performing river hydrological flow rate monitoring continuity analysis on the monitoring data of the hydrological monitoring instrument according to the energy supply stability data of the hydrological monitoring instrument, thereby obtaining river hydrological flow rate monitoring continuity data;

[0095] In this embodiment, after obtaining the monitoring data of the hydrological monitoring instrument, the data acquisition system will be used to record parameters such as flow velocity, flow rate and water level. Specifically, the flow velocity parameter is used to evaluate the hydrodynamic characteristics of the river, and the flow rate reflects the amount of water passing through the river in a unit time. The water level parameter can help determine the trend of water flow changes and the relationship with the flow velocity and flow rate. The time series data of these parameters will be processed by the time series analysis method. The flow velocity and flow rate data can be used to evaluate the working state and change trend of the monitoring system, and at the same time, the energy supply stability data is combined to judge the working continuity of the system under different energy supply conditions. Unstable energy supply may cause interruption or abnormality of the monitoring data, so when analyzing, these data will be used to detect the interruption point of monitoring continuity. In the actual analysis process, a time window (for example, 30 minutes) is set to perform segmented analysis on the flow velocity and flow rate data. This method ensures the integrity and consistency of the monitoring data within this time period. If missing values ​​or abnormal values ​​are found in the monitoring data, these data are supplemented and corrected using interpolation methods (such as linear interpolation or spline interpolation). In this way, the continuity data of river hydrological flow rate monitoring is finally obtained.

[0096] Step S5: Perform velocity measurement error analysis based on the river hydrological velocity monitoring continuity data to obtain velocity measurement error data; perform river hydrological velocity correction based on the velocity measurement error data to obtain river hydrological velocity correction data, and upload it to the hydrological engineering management platform to execute the river hydrological velocity measurement task.

[0097] In this embodiment, velocity measurement error analysis is carried out based on the continuity data of river hydrological velocity monitoring. Statistical analysis methods are used to calculate the mean, standard deviation and deviation of the monitoring data to identify the source of error in velocity measurement. An error threshold (for example, ±5%) is set, and the measured values ​​that exceed this range are marked. Then, based on the measurement error data, a correction algorithm (such as the least squares method) is applied to correct the velocity data to improve the measurement accuracy. The corrected data will be sorted and uploaded to the hydrological engineering management platform to perform subsequent river hydrological velocity measurement tasks. This process ensures the quality of the monitoring data and provides a reliable information basis for water resources management.

[0098] Optionally, step S1 specifically includes:

[0099] Step S11: Acquire hydrological monitoring instrument structure data and solar panel structure data;

[0100] In this embodiment, when obtaining the structural data of the hydrological monitoring instrument, three-dimensional modeling technology is used for processing. The hydrological monitoring instrument is scanned by laser scanning, and the generated three-dimensional point cloud data will be used to extract the size, shape, material and relative position of each component of the hydrological instrument. After loading the design drawings using CAD software, the structural parameters such as the height, width and thickness of the support frame can be specifically extracted. These structural data will play a key role in subsequent design and analysis. For example, the size and shape data of the instrument will help evaluate the space requirements and the stability of the support frame during installation. The parameters of the material can be used for mechanical analysis to simulate the stress and deformation of the instrument under various environmental conditions. For the structural data of the solar panel, the size, material, tilt angle and fixing method of the solar panel are recorded by consulting the manufacturer's product manual and technical specifications. These parameters can be used to evaluate whether the light receiving efficiency, mechanical stability and installation angle of the solar panel meet the on-site environmental conditions, thereby ensuring that the solar energy system provides a continuous and stable energy supply for the monitoring instrument. The integration and use of these data can not only optimize the installation design of the instrument, but also provide a solid foundation for subsequent analysis such as heat dissipation and energy supply.

[0101] Step S12: analyzing the solar panel installation angle according to the solar panel structure data, thereby obtaining the solar panel installation angle data;

[0102] In this embodiment, the installation angle of the solar panel is analyzed using a geometric analysis tool based on the solar panel structure data. The solar altitude angle and azimuth angle in the analysis area are set to 0° to 90° and 0° to 360°, respectively, to obtain various angles related to the lighting conditions. Using three-dimensional model reconstruction technology, the angle of the solar panel is combined with the historical lighting data to calculate the optimal received light intensity of the solar panel at different angles. In this process, the light intensity data at each installation angle is recorded, and the optimal installation angle data is obtained by comparing the light reception effects at different angles to ensure that the solar panel can obtain maximum light throughout the year.

[0103] Step S13: performing solar panel installation light blocking analysis according to the solar panel installation angle data, thereby obtaining solar panel installation light blocking data;

[0104] In this embodiment, light blocking analysis is performed based on the solar panel installation angle data. A digital elevation model (DEM) is established, which is built based on terrain data to accurately reflect the impact of terrain on light. The light source parameters in the model are set, including the altitude and azimuth of the sun, and a light propagation model (such as a radiation transfer model) is applied to simulate the lighting conditions at different installation angles. During the analysis process, the light blocking caused by surrounding buildings, trees or other obstacles is measured, and a threshold value (such as light intensity below 100W / m 2The data will help evaluate the impact of the installation angle on the amount of sunlight received.

[0105] Step S14: integrating the solar panel installation positions according to the solar panel installation angle data and the solar panel installation light blocking data, thereby obtaining the solar panel installation position data.

[0106] In this embodiment, the solar panel installation angle data and light blocking data are combined to perform an integrated analysis of the solar panel installation location. The two sets of data are imported into the geographic information system (GIS), and the spatial analysis tool is used to perform overlapping analysis to identify the best installation location under different environmental conditions. Comprehensive evaluation indicators are set, including light intensity, light blocking area, environmental adaptability, etc., and each potential location is scored using a weighted scoring model. The location with the highest comprehensive score is selected as the final installation location of the solar panel, and the corresponding installation location data is output to ensure efficient operation and stable power supply of the solar panel.

[0107] Optionally, step S12 is specifically:

[0108] Step S121: extracting solar panel support structure features according to the solar panel structure data, thereby obtaining solar panel support structure data;

[0109] In this embodiment, based on the obtained solar panel structure data, the finite element analysis method is used to extract the structural characteristics of the solar bracket. The geometric model of the bracket is established through three-dimensional modeling software, and the material, size, load-bearing capacity and other parameters of the bracket are accurately defined. For example: determine the material properties used in the bracket (such as steel, aluminum alloy, etc.) and input them into the finite element analysis software. The elastic modulus, yield strength and other properties of the material will be used to calculate the stress and deformation of the bracket under different external forces. Based on these data, it is possible to evaluate whether the structural strength of the bracket meets the design requirements and ensure the stability and safety of the bracket under various climatic conditions (such as wind loads). Using the mechanical properties of the material, the deformation and stress distribution of the bracket under different loads are analyzed to ensure that its design meets safety standards. The characteristic parameters of the bracket, such as height, width, support angle, etc., are output in the form of a data table to form complete solar panel bracket structure data.

[0110] Step S122: Acquire river geographical latitude data and solar altitude angle data;

[0111] In this embodiment, the geographical latitude data of the river is obtained by using a global positioning system (GPS) device, and the solar altitude angle data is obtained in combination with the data of the local meteorological monitoring station. The GPS device is placed near the river to record its longitude and latitude in real time. The data collection frequency is set to once per minute to ensure the acquisition of high-precision location data. At the same time, the solar altitude angle data is provided by the automatic weather station of the meteorological monitoring station. The weather station needs to be set in a place with a wide field of view to avoid being blocked by trees or buildings. Finally, the collected latitude data and solar altitude angle data are summarized to form a database, which provides a basis for subsequent seasonal solar orbit simulation.

[0112] Step S123: performing seasonal solar orbit simulation according to the river geographical latitude data and the solar altitude angle data, thereby obtaining seasonal solar orbit data;

[0113] In this embodiment, seasonal solar orbit simulation is performed using illumination simulation software based on the acquired river geographic latitude data and solar altitude angle data. The input latitude data and historical illumination records are used as parameters, the simulation time range is set to one year, and the altitude angle and azimuth angle of the sun in different time periods are calculated on a daily basis. The solar orbit algorithm is used to generate a solar trajectory diagram at each moment of each season, and the output contains the results of the altitude and azimuth data of the solar trajectory on different dates. These data are used to determine the optimal bracket installation angle to ensure that the solar panels can receive the maximum amount of light within a year.

[0114] Step S124: adjusting the tilt angle of the solar panel support structure data according to the node solar orbit data, thereby obtaining the tilt angle data of the solar panel support;

[0115] In this embodiment, the tilt angle of the solar panel bracket is adjusted according to the seasonal solar orbit data. An ideal tilt angle range (for example, 15° to 45°) is defined and compared with the solar altitude angle in the seasonal solar orbit data. An optimization algorithm, such as a genetic algorithm or a particle swarm optimization algorithm, is used to calculate the optimal bracket tilt angle in a specific season. This process ensures that the bracket receives the maximum amount of sunlight in different seasons through a mathematical model. The adjusted bracket tilt angle data will be recorded and used for subsequent light intensity statistics.

[0116] Step S125: performing statistics of the light intensity at the tilt angle of the solar panel support according to the tilt angle data of the solar panel support, thereby obtaining the light intensity data at the tilt angle of the solar panel support;

[0117] In this embodiment, according to the bracket tilt angle data, a light intensity measuring instrument (such as a photometer) is used to perform light intensity statistics. At the actual installation site, brackets with different tilt angles are set, and a photometer is used to measure the light intensity at each angle one by one. Each angle is measured at least three times to obtain the average value, and finally the light intensity data at different bracket tilt angles is formed. The recorded light intensity data will be used to evaluate the light reception effect of different bracket designs to ensure efficient energy conversion of solar panels.

[0118] Step S126: setting the solar panel installation angle according to the solar panel support tilt angle light intensity data, thereby obtaining the solar panel installation angle data.

[0119] In this embodiment, the installation angle of the solar panel is set according to the light intensity data of the solar panel bracket tilt angle. According to the light intensity statistics, the light receiving efficiency corresponding to each tilt angle is calculated, and a threshold is set (for example, the light intensity must be greater than 200W / m 2 ) to select the appropriate tilt angle. Select the tilt angle with the highest light intensity as the installation angle of the solar panel, and record this data in the installation plan to ensure that natural light can be fully utilized during the actual installation process to improve energy collection efficiency.

[0120] Optionally, step S13 is specifically:

[0121] Step S131: extracting solar panel installation azimuth angle features according to the solar panel installation angle data, thereby obtaining solar panel installation azimuth angle data;

[0122] In this embodiment, the azimuth angle features of the solar panel installation are extracted through three-dimensional modeling software based on the obtained solar panel installation angle data. The installation reference plane is defined and set to a horizontal plane. The solar panel model is imported into the software, the model is rotated according to the installation angle data, and the azimuth angle relative to the reference plane is calculated. The azimuth angle range is usually 0° to 360°, where 0° represents the north direction. The coordinate conversion formula in computer graphics is used to convert the position and posture of the solar panel in three-dimensional space, thereby obtaining the installation azimuth angle data of the solar panel.

[0123] Step S132: acquiring remote sensing data of the river monitoring area, and extracting bridge location features based on the remote sensing data of the river monitoring area, thereby obtaining bridge location data;

[0124] In this embodiment, remote sensing data of the river monitoring area is obtained, usually using high-resolution satellite images or drone aerial images as data sources. These remote sensing data need to be analyzed by image processing software. Import the remote sensing image, and use image recognition algorithms (such as edge detection and morphological processing) to identify the bridge structure in the image. In this process, the threshold of image processing is set to ensure that the bridge and the background can be accurately separated. By extracting the coordinate information of the bridge and recording its position (including longitude and latitude), the bridge position data is obtained. The bridge position data will be used as the basic parameter for subsequent illumination occlusion path analysis.

[0125] Step S133: performing light shielding path analysis on the solar panel installation azimuth data according to the bridge position data and the seasonal solar orbit data, thereby obtaining light shielding path data;

[0126] In this embodiment, the light blocking path analysis is performed based on the bridge position data and seasonal solar orbit data. The geographical coordinates of the bridge are combined with the solar altitude angle and azimuth data to draw the sun's light path in different time periods. Using the Geographic Information System (GIS) tool, the bridge position data and solar orbit data are input, and the bridge's light blocking is displayed through three-dimensional visualization. In this process, the range of variation of the solar altitude angle and azimuth angle is set to accurately analyze the time period and duration of the blocking. Finally, the light blocking path data is generated, and the blocking period and the corresponding sun angle are recorded to provide a basis for the subsequent calculation of the light blocking ratio.

[0127] Step S134: Calculating the solar panel area according to the solar panel structure data, thereby obtaining solar panel area data;

[0128] In this embodiment, the solar panel area is calculated based on the structural data of the solar panel. The length and width of the solar panel are confirmed and the accuracy of the data is ensured. Assuming that the standard size of the solar panel is 1.65 meters × 1 meter, the calculation formula for the panel area is: area = length × width. In this step, ensure that the actual size of the panel is verified to avoid inaccurate area calculation due to size error. The calculated solar panel area data will be recorded for subsequent calculation of the light blocking ratio.

[0129] Step S135: Calculate the solar panel light blocking ratio for the solar panel area data according to the light blocking path data, so as to obtain the solar panel installation light blocking data.

[0130] In this embodiment, the light blocking ratio of the solar panel is calculated based on the light blocking path data and the solar panel area data. The degree of influence of each blocking period in the light blocking path data on the solar panel is analyzed. The blocked area is determined by calculating the effective area of ​​the solar panel irradiated by sunlight during a specific time period. The ratio calculation formula is used: light blocking ratio = blocked area / panel area × 100%. In this process, ensure that the measurement of the blocked area is monitored in real time using an accurate light intensity sensor, and the light blocking data finally generated will provide a practical basis for the optimized installation of solar panels.

[0131] Optionally, step S2 specifically includes:

[0132] Step S21: extracting the structural features of the instrument box according to the structural data of the hydrological monitoring instrument, thereby obtaining the structural data of the instrument box;

[0133] In this embodiment, the structural features of the instrument box are extracted based on the acquired hydrological monitoring instrument structure data. This process first involves a detailed analysis of the material, size and design shape of the instrument box. Use computer-aided design (CAD) software to import the two-dimensional drawings of the instrument box and perform three-dimensional modeling. During the modeling process, specific geometric parameters such as length, width, height, wall thickness, etc. need to be set to ensure the accuracy of the model. According to the actual structure, the corresponding physical property data, including material type, density and strength, etc., are extracted. The final generated instrument box structure data will serve as the basis for subsequent modeling and layout.

[0134] Step S22: constructing a three-dimensional spatial structure model of the instrument box according to the instrument box structure data, thereby obtaining a three-dimensional spatial structure model of the instrument box;

[0135] In this embodiment, the three-dimensional spatial structure model of the instrument box is constructed using CAD software or three-dimensional modeling software (such as SolidWorks or AutoCAD) using the instrument box structure data. According to the extracted dimension data, the outer contour of the instrument box is drawn in the software, and the thickness of the inner wall and the outer wall is set according to the design requirements. Ensure that every detail of the model is consistent with the structural data, including openings, connecting parts, etc. After the construction is completed, the rationality of the structure is verified by calculating the parameters such as the volume and surface area of ​​the model to ensure the feasibility and functionality of the model.

[0136] Step S23: marking the positions of the instrument components according to the three-dimensional spatial structure model of the instrument box, thereby obtaining the position data of the instrument components;

[0137] In this embodiment, the instrument component position is identified based on the constructed three-dimensional spatial structure model of the instrument box. This involves accurately calibrating the specific position of each internal component (such as sensors, controllers, batteries, etc.). In the three-dimensional modeling software, the installation position of each component is determined based on the shape and function of the component to ensure that it can be reasonably laid out in space without interfering with each other. After the component position is identified, the coordinate position and corresponding installation angle of each component are recorded, and these data will be used for subsequent component layout simulation.

[0138] Step S24: simulating the component layout of the three-dimensional spatial structure model of the instrument box according to the instrument component position data, thereby obtaining the instrument component layout data;

[0139] In this embodiment, based on the instrument component position data, the component layout simulation is performed on the three-dimensional spatial structure model of the instrument box. In CAD or 3D modeling software, each component is placed in the previously marked position by dragging and positioning tools. Use interference detection tools to ensure that there is no physical interference and spatial conflict between components. In the process of simulating component layout, aspects such as cable routing, cooling system and service access need to be considered to ensure the rationality and practicality of the layout. Finally, the completed component layout data is recorded for subsequent battery layout evaluation.

[0140] Step S25: performing battery layout interval evaluation according to the instrument component layout data, thereby obtaining battery layout interval evaluation data;

[0141] In this embodiment, the battery layout spacing assessment is performed based on the instrument component layout data. The size and volume information of the battery is determined, and then the space required for the battery and the safe spacing of adjacent components are identified in the three-dimensional model. According to the thermal management requirements and electrical connection requirements, the minimum spacing between batteries (usually in centimeters) is set, and it is ensured that the heat dissipation performance of the overall instrument box is not affected. After the assessment is completed, the battery layout spacing assessment data is recorded to provide a basis for modular design.

[0142] Step S26: performing modular design according to the battery layout interval evaluation data, thereby obtaining battery layout module data;

[0143] In this embodiment, the modular design of the battery is performed based on the battery layout interval evaluation data. This process uses CAD software to optimize the battery layout and design the battery as a detachable module for easy replacement and maintenance. The standardized size of the module is set to ensure that the modular design can meet safety and stability requirements while maintaining performance. In addition, during the design process, the cooling airflow and electrical connections between the batteries are considered to ensure that the module can dissipate heat efficiently. Finally, the battery layout module data is formed as the basis for subsequent heat dissipation analysis.

[0144] Step S27: Perform heat dissipation analysis on the instrument box structure according to the battery layout module data, thereby obtaining heat dissipation data of the instrument box structure.

[0145] In this embodiment, the heat dissipation analysis of the instrument box structure is performed based on the battery layout module data. Thermal analysis software (such as ANSYS or CFD tools) is used to simulate the heat distribution inside the instrument box. In this process, the power consumption data and heat dissipation parameters of each component need to be input to establish a thermal model. By calculating the heat flow, temperature distribution and heat dissipation efficiency, the heat dissipation capacity of the instrument box under normal operating conditions is evaluated. Ensure that the heat dissipation design can effectively reduce the operating temperature of the components to extend the service life of the equipment and ensure stable operation of the system. Finally, the heat dissipation analysis results are recorded to provide a basis for optimized design.

[0146] Optionally, step S27 is specifically:

[0147] Step S271: performing battery heating simulation according to the battery layout module data, thereby obtaining battery module heating data;

[0148] In this embodiment, according to the battery layout module data, a battery heating simulation is performed to obtain the battery module heating data. In this step, the operating conditions of the battery, including input current, load power, and working time, must be determined first, and modeling is performed using thermal simulation software (such as ANSYS or COMSOL Multiphysics). The thermal characteristic parameters of the battery, including specific heat capacity, thermal conductivity, and ambient temperature, are input into the software. After the thermal model is constructed, the power dissipation parameters under the operating state are set. By performing thermal analysis on the battery under specific load conditions and simulating the heating process, the battery module heating data is finally obtained. The specific values ​​include the temperature change rate and the heat transfer rate.

[0149] Step S272: drawing a heat distribution map of the battery module according to the heat generation data of the battery module, thereby obtaining a heat distribution map of the battery module;

[0150] In this embodiment, the heat distribution map of the battery module is drawn according to the heat generation data of the battery module. The heat distribution map is generated by thermal analysis software. By inputting the heat generation data of the battery under different working conditions, the software will automatically calculate and visualize the temperature field distribution. The heat distribution map needs to identify the temperatures of different areas, including the battery surface temperature, heat concentration area and heat dissipation area. Set the drawing standard, such as using color gradient to represent the temperature range, so that the high temperature area and the low temperature area are clear at a glance. The final generated heat distribution map will provide basic data for the subsequent air circulation path design and heat sink design.

[0151] Step S273: designing the air circulation path of the instrument box for the three-dimensional spatial structure model of the instrument box according to the heat distribution diagram of the battery module, thereby obtaining the air circulation path data of the instrument box;

[0152] In this embodiment, the air circulation path of the instrument box is designed for the three-dimensional spatial structure model of the instrument box according to the heat distribution diagram of the battery module. In this process, the heating position and heat distribution of the battery are first determined, and then the effective air circulation channel is identified in the three-dimensional model. The computational fluid dynamics (CFD) tool is used to simulate the air flow, and parameters such as air flow rate, temperature and density are input to calculate the efficiency and ventilation effect of the circulation path. Among them, different air flow rate values ​​are set to simulate the air flow under various ventilation conditions. By inputting the flow rate parameters, the CFD tool can calculate the flow pattern of air in the instrument box and evaluate how the air moves between different channels to determine the effectiveness of these channels. By adjusting the flow rate, observe its effect on the heat removal effect to determine the optimal flow rate setting. The heating position of the battery module and the heat generated by it are converted into corresponding temperature distribution and input into the CFD model. This parameter is used to simulate the temperature changes in different areas to help analyze which areas may form high-temperature retention points. By monitoring the temperature changes, it can be evaluated whether the design effectively removes the heat from the high-temperature area. The density of the air changes with temperature changes, so the density of the air at different temperatures needs to be input during the simulation. The CFD tool automatically calculates the density of the air based on the input temperature and flow rate, and uses this to predict the buoyancy and flow characteristics of the air. Through the density parameter, the CFD model can more accurately simulate the process of hot air rising and cold air sinking, thereby optimizing the air flow path design. The design should ensure that the air flow can take away the heat from the high temperature area while preventing heat from being trapped in the box. Finally, record the air flow path data to ensure the rationality and operability of the design.

[0153] Step S274: designing the heat sink of the instrument box for the three-dimensional spatial structure model of the instrument box according to the heat distribution diagram of the battery module, thereby obtaining the heat sink data of the instrument box;

[0154] In this embodiment, the instrument box heat sink is designed for the three-dimensional spatial structure model of the instrument box according to the heat distribution diagram of the battery module. The material and shape of the heat sink are selected based on the heating characteristics of the battery. The material should have good thermal conductivity, such as aluminum or copper. The thickness, number of fins and arrangement of the heat sink need to be considered in the design to ensure that it can maximize contact with the air and improve the heat transfer efficiency. The heat dissipation performance is simulated by thermal analysis software to evaluate the impact of different design parameters on the heat dissipation effect. The heat sink data finally generated includes the heat dissipation area, volume and expected thermal conductivity, providing a basis for subsequent heat dissipation integration.

[0155] Step S275: Perform heat dissipation integration of the instrument box structure according to the instrument box air circulation path data and the instrument box heat sink data, thereby obtaining the instrument box structure heat dissipation data.

[0156] In this embodiment, the heat dissipation integration of the instrument box structure is performed based on the air circulation path data and the heat sink data. The design of the air circulation path and the heat sink is integrated into the overall structure of the instrument box to ensure that the air flow can pass through the heat sink and effectively take away the heat. Use 3D modeling software, combine the air flow simulation results and the heat sink design, and optimize the overall layout of the heat dissipation system. Evaluate the thermal management performance of the integrated design under actual working conditions to ensure that the system can operate stably within the predetermined operating temperature range. Finally, record the comprehensive heat dissipation data of the instrument box structure to provide a comprehensive design basis for actual production and application.

[0157] Optionally, step S4 specifically includes:

[0158] Step S41: Acquire monitoring data of hydrological monitoring instruments;

[0159] In this embodiment, the step of obtaining monitoring data of hydrological monitoring instruments involves data collection and recording. Configure hydrological monitoring instruments, including flow meters and other related sensors, to ensure that all equipment operates normally and is connected to the data recording system. Monitor data is collected regularly through the data acquisition system, and the monitoring content includes water flow velocity, flow, temperature, turbidity, etc. The time interval for data collection needs to be set to once per minute to ensure the timeliness and accuracy of the data. During the data recording process, the equipment status needs to be monitored in real time to ensure that the equipment can still accurately obtain data when environmental factors (such as water level changes, weather effects, etc.) change. Finally, a monitoring data file is generated to record all parameters and their corresponding timestamps, providing a basis for subsequent analysis.

[0160] Step S42: performing energy fluctuation statistics according to the energy supply stability data of the hydrological monitoring instrument, thereby obtaining energy supply fluctuation data of the hydrological monitoring instrument;

[0161] In this embodiment, energy fluctuation statistics are performed based on the energy supply stability data of the hydrological monitoring instrument to obtain the energy supply fluctuation data of the hydrological monitoring instrument. Real-time energy supply data is obtained from the energy management system, and the data includes battery voltage, current, power output, etc. Statistical analysis tools are used to analyze the historical data of battery energy supply, calculate the mean and standard deviation of energy fluctuations, and use them to set thresholds, for example, setting the standard deviation of energy fluctuations to 0.5V. When the actual fluctuation exceeds the threshold, it is marked as an abnormal fluctuation. By drawing the energy fluctuation curve, the trend and cycle of energy fluctuations are clarified, providing data support for subsequent load analysis.

[0162] Step S43: performing a hydrological monitoring instrument equipment load analysis on the monitoring data of the hydrological monitoring instrument according to the energy supply fluctuation data of the hydrological monitoring instrument, thereby obtaining the hydrological monitoring instrument equipment load data;

[0163] In this embodiment, based on the energy supply fluctuation data of the hydrological monitoring instrument, the load analysis of the hydrological monitoring instrument equipment is performed to obtain the equipment load data. It is necessary to identify the energy consumption data of the equipment under normal working conditions, and record the energy consumption of the equipment under different working modes (such as high-frequency monitoring and low-frequency monitoring). Then, combined with the energy fluctuation data, the load index of the equipment is calculated, including instantaneous load and average load. Use load analysis software (such as MATLAB or Excel) to model the data, calculate the load by multiplying power by time, and classify and analyze the load data to identify high load and low load states. Finally, generate a load analysis report to record the trend of load changes and its impact on equipment performance.

[0164] Step S44: performing flow rate monitoring anomaly analysis on the monitoring data of the hydrological monitoring instrument according to the energy supply fluctuation data of the hydrological monitoring instrument, thereby obtaining flow rate monitoring anomaly data;

[0165] In this embodiment, based on the energy supply fluctuation data of the hydrological monitoring instrument, the monitoring data of the hydrological monitoring instrument is analyzed for flow velocity monitoring anomalies to obtain flow velocity monitoring anomaly data. The flow velocity data is cross-checked with the energy fluctuation data using a monitoring data analysis tool. Define the normal range of flow velocity, for example, set the upper and lower limits of the flow velocity to 0.1m / s and 2.5m / s. During the comparison process, when the flow velocity data exceeds this range, it is marked as an abnormal flow velocity. At the same time, analyze the correlation between the energy supply fluctuation and the flow velocity data to identify whether there is a situation where energy fluctuations cause abnormal flow velocity. By generating an abnormality report, the occurrence time, duration and cause of the abnormal flow velocity are recorded to ensure the integrity and reliability of the data.

[0166] Step S45: Perform river hydrological flow rate monitoring continuity evaluation on the monitoring data of the hydrological monitoring instrument according to the load data of the hydrological monitoring instrument equipment and the flow rate monitoring abnormality data, so as to obtain river hydrological flow rate monitoring continuity data.

[0167] In this embodiment, the continuity of river hydrological flow rate monitoring is evaluated on the monitoring data of the hydrological monitoring instrument according to the load data of the hydrological monitoring instrument equipment and the abnormal flow rate monitoring data, so as to obtain the continuity data of river hydrological flow rate monitoring. The standard of continuity evaluation needs to be defined, for example, it is set that the proportion of missing flow rate data shall not exceed 5% during the monitoring period. By sorting the data during the monitoring period, the time period of missing data is identified, and its proportion of the total monitoring time is calculated. On this basis, combined with the equipment load situation, the stability of the equipment during the monitoring period is evaluated, the relationship between the load and the flow rate data is analyzed, and the reliability and validity of the monitoring data are determined. Finally, a continuity evaluation report is generated, which records in detail the integrity of the monitoring data and the recommended improvement measures, providing a reference for equipment maintenance and management.

[0168] Optionally, step S43 is specifically:

[0169] Step S431: performing fluctuation amplitude statistics according to the energy supply fluctuation data of the hydrological monitoring instrument, thereby obtaining high-amplitude energy supply fluctuation data;

[0170] In this embodiment, according to the energy supply fluctuation data of the hydrological monitoring instrument, the fluctuation amplitude statistics are performed to obtain high-amplitude energy supply fluctuation data. The energy supply fluctuation data set is extracted from the energy monitoring system, and the data should include the voltage, current and power values ​​at each time point. By setting a threshold, such as setting the fluctuation amplitude to exceed ±0.5V or ±10% power change, the situation is analyzed. In the data processing stage, a statistical analysis tool (such as Python's Pandas library) is used to calculate the fluctuation amplitude between each time point, screen out the time period when the fluctuation amplitude exceeds the set threshold, and record these data. Finally, a data list of high-amplitude energy supply fluctuations is formed to provide a basis for subsequent energy stability analysis.

[0171] Step S432: performing fluctuation time frequency statistics according to the energy supply fluctuation data of the hydrological monitoring instrument, thereby obtaining high-frequency energy supply fluctuation time data;

[0172] In this embodiment, according to the energy supply fluctuation data of the hydrological monitoring instrument, the fluctuation time frequency statistics are performed to obtain the high-frequency energy supply fluctuation time data. It is necessary to obtain the time series data of the energy supply fluctuation and set the frequency threshold, such as the case where the number of fluctuations occurs more than 5 times per minute. By counting the data, the time period that meets the frequency condition is counted. Use a time series analysis tool (such as MATLAB or R language) to process the time data, count the high-frequency fluctuation events within the set time range (such as one day or one week), and record the timestamps of these events. Finally, a data list of high-frequency energy supply fluctuation time is generated to provide a basis for energy supply instability analysis.

[0173] Step S433: performing energy supply instability intersection operation according to the high-amplitude energy supply fluctuation data and the high-frequency energy supply fluctuation time data, thereby obtaining energy supply instability data;

[0174] In this embodiment, an energy supply instability intersection operation is performed based on the high-amplitude energy supply fluctuation data and the high-frequency energy supply fluctuation time data to obtain energy supply instability data. Using the set operation method, events that simultaneously meet the high-amplitude fluctuation and high-frequency fluctuation in the same time period are identified. Using data analysis software (such as Excel or SQL) to perform intersection operations, extract time periods that exist in both data sets, and record the fluctuation characteristics of these time periods. Finally, a list of energy supply instability data is generated for subsequent analysis.

[0175] Step S434: extracting sensor power consumption characteristics according to the monitoring data of the hydrological monitoring instrument, thereby obtaining sensor power consumption data;

[0176] In this embodiment, based on the monitoring data of the hydrological monitoring instrument, the sensor power consumption characteristics are extracted to obtain the sensor power consumption data. The real-time power consumption data of the sensor is extracted from the monitoring system, including the current and voltage values ​​of each sensor. The power consumption of each sensor at different time points is calculated by the power formula (power = current × voltage). Combined with the monitoring data, the power consumption characteristics of each sensor in different operating modes are analyzed, the changing trend of power consumption and the maximum power consumption value are recorded, and a power consumption characteristic data table is generated. These data provide a basis for subsequent sensor overload operation analysis.

[0177] Step S435: performing sensor overload operation analysis according to the sensor power consumption data and energy supply instability data, thereby obtaining sensor overload operation data;

[0178] In this embodiment, the sensor overload operation analysis is performed based on the sensor power consumption data and the energy supply instability data to obtain the sensor overload operation data. The power consumption threshold for overload operation is set, for example, the power consumption is set to exceed 20% of the rated value as an overload sign. The power consumption data is combined with the energy instability data, and the sensor events in which the power consumption exceeds the threshold during the period of unstable energy supply are identified through logical operations. Use data analysis tools to classify and count these events, and record the time and duration of the overload event. Finally, a data report on the sensor overload operation is formed to facilitate subsequent equipment maintenance and management.

[0179] Step S436: performing overload operation time statistics according to the sensor overload operation data, thereby obtaining the sensor overload operation time data;

[0180] In this embodiment, the overload operation time statistics are performed based on the sensor overload operation data to obtain the sensor overload operation time data. The timestamp of the overload occurrence is extracted from the overload event list. These timestamps are sorted, the duration of each overload event is calculated, and the total overload duration in a specific time period is counted. Statistical software (such as SPSS or Python) is used to summarize the data, and by setting a time window (such as day, week or month), the sum of the overload operation time in each window is calculated, and a time statistics report is generated. This report provides important data for the subsequent operation stability evaluation of the sensor.

[0181] Step S437: Perform a load analysis of the hydrological monitoring instrument sensor module on the monitoring data of the hydrological monitoring instrument according to the sensor overload operation time data, thereby obtaining the hydrological monitoring instrument equipment load data.

[0182] In this embodiment, based on the sensor overload operation time data, the load analysis of the sensor module of the hydrological monitoring instrument is performed on the monitoring data of the hydrological monitoring instrument to obtain the load data of the hydrological monitoring instrument equipment. The sensor power consumption data and overload time data are required to calculate the load indicators of each sensor, such as real-time load and cumulative load. By setting the load calculation formula (load = power consumption / working time), the load performance of the sensor under different working conditions is analyzed. The data analysis tool is used to generate a trend chart of the equipment load, which visually displays the load changes of the equipment under different conditions, and finally forms an equipment load analysis report to guide subsequent equipment maintenance and optimization plans.

[0183] Optionally, step S44 is specifically:

[0184] Step S441: performing statistics of energy supply fluctuation time periods according to the energy supply fluctuation data of the hydrological monitoring instrument, thereby obtaining data of high fluctuation time periods of energy supply of the hydrological monitoring instrument;

[0185] In this embodiment, statistics of energy supply fluctuation time periods are performed based on the energy supply fluctuation data of the hydrological monitoring instrument to obtain data of high fluctuation time periods of the energy supply of the hydrological monitoring instrument. The energy supply fluctuation data is extracted from the monitoring system to ensure that the data contains timestamps, power values ​​and their fluctuation amplitudes. A fluctuation amplitude threshold is set, for example, exceeding ±15% is considered high fluctuation. Using data processing tools (such as Python's NumPy library), the statistical data of the fluctuation amplitude within a set time window (such as every hour or every minute) is calculated to filter out high fluctuation time periods. Finally, a time series data set containing high fluctuation time periods is generated to provide a basis for subsequent analysis of flow velocity monitoring.

[0186] Step S442: extracting flow velocity monitoring features according to the monitoring data of the hydrological monitoring instrument, thereby obtaining flow velocity monitoring data;

[0187] In this embodiment, based on the monitoring data of the hydrological monitoring instrument, flow rate monitoring feature extraction is performed to obtain flow rate monitoring data. Flow rate related data is extracted from the monitoring system to ensure that the data contains flow rate values, timestamps and related environmental parameters (such as water level, temperature). The flow rate data is preprocessed to remove abnormal values, and statistical methods (such as Z-score or IQR method) are used to identify and remove flow rate data that exceeds the normal range. Then, the mean, standard deviation, maximum and minimum values ​​of the flow rate are calculated, and a feature extraction report is generated to record the flow rate change trend and key feature data for subsequent flow rate fluctuation monitoring and abnormal identification.

[0188] Step S443: The hydrological monitoring instrument energy supply high fluctuation time period data is used to synchronize the flow velocity monitoring data with the flow velocity fluctuation monitoring time, thereby obtaining the flow velocity fluctuation monitoring time data;

[0189] In this embodiment, based on the data of the high fluctuation time period of energy supply of the hydrological monitoring instrument, the flow velocity monitoring data is synchronized with the flow velocity fluctuation monitoring time to obtain the flow velocity fluctuation monitoring time data. The high fluctuation time period is matched with the flow velocity monitoring data. Ensure that the time format of the two data sets is consistent, and use the timestamp alignment method to synchronize the flow velocity data with the energy fluctuation time period. By setting a time window (such as ±1 minute), confirm whether the flow velocity data within this time period exists, record the flow velocity data that matches the high fluctuation time period, and generate a time synchronization result set. Ultimately, the data provides an important time basis for flow velocity fluctuation feature extraction and monitoring loss identification.

[0190] Step S444: Extract flow velocity fluctuation features based on flow velocity fluctuation monitoring time data to obtain flow velocity fluctuation data; In this embodiment, extract flow velocity fluctuation features based on flow velocity fluctuation monitoring time data to obtain flow velocity fluctuation data. Extract relevant fluctuation features from the synchronous flow velocity data. Calculate the amplitude of flow velocity change in each time period, and use standard deviation, root mean square (RMS) and other methods to quantitatively describe the intensity and frequency of flow velocity fluctuations. Further analyze the fluctuation trend of flow velocity and identify any significant change patterns. Use chart tools (such as Matplotlib or Excel) to visualize fluctuation features, which is convenient for understanding the law of flow velocity changes and forming a detailed flow velocity fluctuation feature report to support subsequent monitoring loss identification and abnormality analysis.

[0191] Step S445: performing flow rate monitoring loss identification according to the flow rate fluctuation data, thereby obtaining flow rate monitoring loss data;

[0192] In this embodiment, flow rate monitoring loss identification is performed based on the flow rate fluctuation data to obtain flow rate monitoring loss data. Set the standard for flow rate monitoring loss, for example, there is no data record for the flow rate within a specified time period or the recorded value is lower than the set threshold (such as 0.1m / s). Analyze the flow rate fluctuation data to find the time period of continuous invalid records. Use data mining technology to calculate the proportion of valid data in each monitoring cycle and identify the loss. Use statistical analysis software (such as SPSS or R language) to generate a detailed report on flow rate monitoring loss data, record the time and frequency of the loss, and provide data support for the stability evaluation of flow rate monitoring.

[0193] Step S446: Calculate the abnormal flow rate monitoring ratio of the monitoring data of the hydrological monitoring instrument according to the flow rate monitoring loss data, so as to obtain abnormal flow rate monitoring data.

[0194] In this embodiment, based on the flow velocity monitoring loss data, the flow velocity monitoring abnormality ratio is calculated for the monitoring data of the hydrological monitoring instrument to obtain the flow velocity monitoring abnormality data. The flow velocity monitoring loss data is compared with the overall monitoring data, and the total duration of the monitoring loss and the total duration of the flow velocity data are calculated. The calculation formula for the abnormality ratio is set: abnormality ratio = (monitoring loss time / total monitoring time) × 100%. Through this formula, the abnormality ratio of flow velocity monitoring is determined, and a flow velocity monitoring abnormality data report is generated to quantify the monitoring efficiency and reliability of the hydrological monitoring instrument and guide subsequent maintenance and improvement measures.

[0195] Optionally, this specification also provides a river hydrological flow velocity rapid measurement method and system based on H-ADCP, which is used to perform the river hydrological flow velocity rapid measurement method based on H-ADCP as described above, and the river hydrological flow velocity rapid measurement system includes:

[0196] Solar installation analysis module: obtains the structural data of hydrological monitoring instruments and solar panel structure data; performs solar installation location analysis based on the solar panel structure data to obtain solar panel installation location data;

[0197] Instrument box heat dissipation analysis module: used to extract the structural features of the instrument box according to the structural data of the hydrological monitoring instrument, so as to obtain the structural data of the instrument box; optimize the battery layout module according to the structure of the instrument box, so as to obtain the battery layout module data; perform heat dissipation design on the instrument box structure according to the battery layout module data, so as to obtain the heat dissipation data of the instrument box structure;

[0198] Hydrological monitoring instrument energy supply analysis module: used to analyze the energy supply stability of the hydrological monitoring instrument based on the solar panel installation position data and the instrument box structure heat dissipation data, so as to obtain the energy supply stability data of the hydrological monitoring instrument;

[0199] River hydrological flow rate monitoring module: used to obtain monitoring data of hydrological monitoring instruments; conduct river hydrological flow rate monitoring continuity analysis on the monitoring data of hydrological monitoring instruments according to the energy supply stability data of hydrological monitoring instruments, so as to obtain river hydrological flow rate monitoring continuity data;

[0200] River hydrological flow velocity correction module: used to perform flow velocity measurement error analysis based on the river hydrological flow velocity monitoring continuity data, so as to obtain flow velocity measurement error data; perform river hydrological flow velocity correction based on the flow velocity measurement error data, so as to obtain river hydrological flow velocity correction data, and upload it to the hydrological engineering management platform to perform river hydrological flow velocity measurement tasks.

[0201] The H-ADCP-based rapid river hydrological flow velocity measurement method and system of the present invention can implement any one of the H-ADCP-based rapid river hydrological flow velocity measurement methods of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the H-ADCP-based rapid river hydrological flow velocity measurement method, and the internal modules of the system cooperate with each other to complete the task of rapid river hydrological flow velocity measurement.

[0202] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0203] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for rapid measurement of river hydrological flow velocity based on H-ADCP, characterized in that: The following steps are involved: Step S1: Acquire hydrological monitoring instrument structure data and solar panel structure data; Perform solar installation location analysis based on solar panel structure data to obtain solar panel installation location data; Step S2: extracting the structural features of the instrument box according to the structural data of the hydrological monitoring instrument, thereby obtaining the structural data of the instrument box; optimizing the battery layout module according to the structure of the instrument box, thereby obtaining the battery layout module data; performing heat dissipation design on the instrument box structure according to the battery layout module data, thereby obtaining the heat dissipation data of the instrument box structure; Step S3: analyzing the energy supply stability of the hydrological monitoring instrument according to the solar panel installation position data and the heat dissipation data of the instrument box structure, thereby obtaining the energy supply stability data of the hydrological monitoring instrument; Step S4: acquiring monitoring data of the hydrological monitoring instrument; performing river hydrological flow rate monitoring continuity analysis on the monitoring data of the hydrological monitoring instrument according to the energy supply stability data of the hydrological monitoring instrument, thereby obtaining river hydrological flow rate monitoring continuity data; Step S5: Perform velocity measurement error analysis based on the river hydrological velocity monitoring continuity data to obtain velocity measurement error data; perform river hydrological velocity correction based on the velocity measurement error data to obtain river hydrological velocity correction data, and upload it to the hydrological engineering management platform to execute the river hydrological velocity measurement task.

2. The method for rapid measurement of river hydrological flow velocity based on H-ADCP according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: Acquire hydrological monitoring instrument structure data and solar panel structure data; Step S12: analyzing the solar panel installation angle according to the solar panel structure data, thereby obtaining the solar panel installation angle data; Step S13: performing solar panel installation light blocking analysis according to the solar panel installation angle data, thereby obtaining solar panel installation light blocking data; Step S14: integrating the solar panel installation positions according to the solar panel installation angle data and the solar panel installation light blocking data, thereby obtaining the solar panel installation position data.

3. The method for rapid measurement of river hydrological flow velocity based on H-ADCP according to claim 2, characterized in that: Step S12 is specifically as follows: Step S121: extracting solar panel support structure features according to the solar panel structure data, thereby obtaining solar panel support structure data; Step S122: Acquire river geographical latitude data and solar altitude angle data; Step S123: performing seasonal solar orbit simulation according to the river geographical latitude data and the solar altitude angle data, thereby obtaining seasonal solar orbit data; Step S124: adjusting the tilt angle of the solar panel support structure data according to the node solar orbit data, thereby obtaining the tilt angle data of the solar panel support; Step S125: performing statistics of the light intensity at the tilt angle of the solar panel support according to the tilt angle data of the solar panel support, thereby obtaining the light intensity data at the tilt angle of the solar panel support; Step S126: setting the solar panel installation angle according to the solar panel support tilt angle light intensity data, thereby obtaining the solar panel installation angle data.

4. The method for rapid measurement of river hydrological flow velocity based on H-ADCP according to claim 2, characterized in that: Step S13 is specifically as follows: Step S131: extracting solar panel installation azimuth angle features according to the solar panel installation angle data, thereby obtaining solar panel installation azimuth angle data; Step S132: acquiring remote sensing data of the river monitoring area, and extracting bridge location features based on the remote sensing data of the river monitoring area, thereby obtaining bridge location data; Step S133: performing light shielding path analysis on the solar panel installation azimuth data according to the bridge position data and the seasonal solar orbit data, thereby obtaining light shielding path data; Step S134: Calculating the solar panel area according to the solar panel structure data, thereby obtaining solar panel area data; Step S135: Calculate the solar panel light blocking ratio for the solar panel area data according to the light blocking path data, so as to obtain the solar panel installation light blocking data.

5. The method for rapid measurement of river hydrological flow velocity based on H-ADCP according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: extracting the structural features of the instrument box according to the hydrological monitoring instrument structural data, thereby obtaining the instrument box structural data; Step S22: constructing a three-dimensional spatial structure model of the instrument box according to the instrument box structure data, thereby obtaining a three-dimensional spatial structure model of the instrument box; Step S23: marking the positions of the instrument components according to the three-dimensional spatial structure model of the instrument box, thereby obtaining the position data of the instrument components; Step S24: simulating the component layout of the three-dimensional spatial structure model of the instrument box according to the instrument component position data, thereby obtaining the instrument component layout data; Step S25: performing battery layout interval evaluation according to the instrument component layout data, thereby obtaining battery layout interval evaluation data; Step S26: performing modular design according to the battery layout interval evaluation data, thereby obtaining battery layout module data; Step S27: Perform heat dissipation analysis on the instrument box structure according to the battery layout module data, thereby obtaining heat dissipation data of the instrument box structure.

6. The method for rapid measurement of river hydrological flow velocity based on H-ADCP according to claim 5, characterized in that: Step S27 is specifically as follows: Step S271: performing battery heating simulation according to the battery layout module data, thereby obtaining battery module heating data; Step S272: drawing a heat distribution map of the battery module according to the heat generation data of the battery module, thereby obtaining a heat distribution map of the battery module; Step S273: designing the air circulation path of the instrument box for the three-dimensional spatial structure model of the instrument box according to the heat distribution diagram of the battery module, thereby obtaining the air circulation path data of the instrument box; Step S274: designing the heat sink of the instrument box for the three-dimensional spatial structure model of the instrument box according to the heat distribution diagram of the battery module, thereby obtaining the heat sink data of the instrument box; Step S275: Perform heat dissipation integration of the instrument box structure according to the instrument box air circulation path data and the instrument box heat sink data, thereby obtaining the instrument box structure heat dissipation data.

7. The method for rapid measurement of river hydrological flow velocity based on H-ADCP according to claim 1, characterized in that: Step S4 is specifically as follows: Step S41: Acquire monitoring data of hydrological monitoring instruments; Step S42: performing energy fluctuation statistics according to the energy supply stability data of the hydrological monitoring instrument, thereby obtaining energy supply fluctuation data of the hydrological monitoring instrument; Step S43: performing a hydrological monitoring instrument equipment load analysis on the monitoring data of the hydrological monitoring instrument according to the energy supply fluctuation data of the hydrological monitoring instrument, thereby obtaining the hydrological monitoring instrument equipment load data; Step S44: performing flow rate monitoring anomaly analysis on the monitoring data of the hydrological monitoring instrument according to the energy supply fluctuation data of the hydrological monitoring instrument, thereby obtaining flow rate monitoring anomaly data; Step S45: Perform river hydrological flow rate monitoring continuity evaluation on the monitoring data of the hydrological monitoring instrument according to the load data of the hydrological monitoring instrument equipment and the flow rate monitoring abnormality data, so as to obtain river hydrological flow rate monitoring continuity data.

8. The method for rapid measurement of river hydrological flow velocity based on H-ADCP according to claim 7, characterized in that: Step S43 is specifically as follows: Step S431: performing fluctuation amplitude statistics according to the energy supply fluctuation data of the hydrological monitoring instrument, thereby obtaining high-amplitude energy supply fluctuation data; Step S432: performing fluctuation time frequency statistics according to the energy supply fluctuation data of the hydrological monitoring instrument, thereby obtaining high-frequency energy supply fluctuation time data; Step S433: performing energy supply instability intersection operation according to the high-amplitude energy supply fluctuation data and the high-frequency energy supply fluctuation time data, thereby obtaining energy supply instability data; Step S434: extracting sensor power consumption characteristics according to the monitoring data of the hydrological monitoring instrument, thereby obtaining sensor power consumption data; Step S435: performing sensor overload operation analysis according to the sensor power consumption data and energy supply instability data, thereby obtaining sensor overload operation data; Step S436: performing overload operation time statistics according to the sensor overload operation data, thereby obtaining the sensor overload operation time data; Step S437: Perform a load analysis of the hydrological monitoring instrument sensor module on the monitoring data of the hydrological monitoring instrument according to the sensor overload operation time data, thereby obtaining the hydrological monitoring instrument equipment load data.

9. The method for rapid measurement of river hydrological flow velocity based on H-ADCP according to claim 7, characterized in that: Step S44 is specifically as follows: Step S441: performing statistics of energy supply fluctuation time periods according to the energy supply fluctuation data of the hydrological monitoring instrument, thereby obtaining data of high fluctuation time periods of energy supply of the hydrological monitoring instrument; Step S442: extracting flow velocity monitoring features according to the monitoring data of the hydrological monitoring instrument, thereby obtaining flow velocity monitoring data; Step S443: The hydrological monitoring instrument energy supply high fluctuation time period data is used to synchronize the flow velocity monitoring data with the flow velocity fluctuation monitoring time, thereby obtaining the flow velocity fluctuation monitoring time data; Step S444: extracting flow velocity fluctuation features according to the flow velocity fluctuation monitoring time data, thereby obtaining flow velocity fluctuation data; Step S445: performing flow rate monitoring loss identification according to the flow rate fluctuation data, thereby obtaining flow rate monitoring loss data; Step S446: Calculate the abnormal flow rate monitoring ratio of the monitoring data of the hydrological monitoring instrument according to the flow rate monitoring loss data, so as to obtain abnormal flow rate monitoring data.

10. A rapid measurement method and system for river hydrological flow velocity based on H-ADCP, characterized in that: Used to perform the river hydrological flow velocity rapid measurement method based on H-ADCP as claimed in claim 1, the river hydrological flow velocity rapid measurement system comprises: Solar installation analysis module: used to obtain the structural data of hydrological monitoring instruments and solar panel structure data; perform solar installation location analysis based on the solar panel structure data to obtain solar panel installation location data; Instrument box heat dissipation analysis module: used to extract the structural features of the instrument box according to the structural data of the hydrological monitoring instrument, so as to obtain the structural data of the instrument box; optimize the battery layout module according to the structure of the instrument box, so as to obtain the battery layout module data; perform heat dissipation design on the instrument box structure according to the battery layout module data, so as to obtain the heat dissipation data of the instrument box structure; Hydrological monitoring instrument energy supply analysis module: used to analyze the energy supply stability of the hydrological monitoring instrument based on the solar panel installation position data and the instrument box structure heat dissipation data, so as to obtain the energy supply stability data of the hydrological monitoring instrument; River hydrological flow rate monitoring module: used to obtain monitoring data of hydrological monitoring instruments; conduct river hydrological flow rate monitoring continuity analysis on the monitoring data of hydrological monitoring instruments according to the energy supply stability data of hydrological monitoring instruments, so as to obtain river hydrological flow rate monitoring continuity data; River hydrological flow velocity correction module: used to perform flow velocity measurement error analysis based on the river hydrological flow velocity monitoring continuity data, so as to obtain flow velocity measurement error data; perform river hydrological flow velocity correction based on the flow velocity measurement error data, so as to obtain river hydrological flow velocity correction data, and upload it to the hydrological engineering management platform to perform river hydrological flow velocity measurement tasks.

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