Water gauge liquid level detection method and system for low-temperature frozen water body

Abstract

The invention belongs to the technical field of water level detection control, and provides a water gauge liquid level detection method and system for a low-temperature frozen water body, and the method comprises the steps: obtaining environment parameters of the low-temperature frozen water body; identifying the environment type of the low-temperature frozen water body based on the acquired environment parameters; when the environment type is an icing mode, the thickness of an ice layer is calculated, and water gauge liquid level detection of the low-temperature icing water body is completed; wherein ultrasonic sound velocity temperature drift and water turbidity attenuation are considered, the ice water sound velocity is corrected based on the temperature correction parameter, the ice water sonic path distance is corrected based on the sonic path distance correction parameter, adaptive fusion of the temperature correction parameter and the sonic path distance correction parameter is carried out in combination with the dynamic weight factor, and the ice layer thickness is obtained in combination with the reflection characteristic of the ultrasonic signal on the ice-water interface. Based on self-adaptive processing logic of environmental parameter acquisition, model self-adaptive switching, ice layer interference elimination, low-temperature performance compensation and accurate liquid level calculation, accurate monitoring of the liquid level of the pool in the full temperature range of the low-temperature frozen water body is achieved.

Description

Technical Field

[0001] This invention belongs to the field of water level detection and control technology, specifically relating to a method and system for detecting the water level in low-temperature frozen water bodies. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] In current open-type water level detection, the pressure-capacitance combination detection technology for normal temperature and non-icing scenarios uses a combination of upper and lower dual pressure sensors and a capacitance measurement matrix, and solves the measurement error caused by density differences in different water bodies through density adaptive correction. For existing processing technologies for low temperature and icing scenarios, the traditional pressure detection technology without ice layer compensation (directly using the normal temperature pressure-level calculation formula) and the ice layer melting technology that relies on electric heating wires (removing the ice layer on the sensor surface by heating to maintain the detection function) are used.

[0004] Existing technologies for level detection in low-temperature freezing water bodies (typically referring to ambient temperatures ≤2℃) have the following drawbacks: (1) The pressure measurement is distorted. The extra pressure generated by the ice layer is included in the water pressure, resulting in the liquid level measurement value being 50% to 200% higher than the actual value; (2) Capacitive positioning fails. The dielectric constants of ice and water are very different. The capacitance measurement matrix misjudges "ice coverage" as "liquid level reached", and cannot achieve preliminary liquid level positioning. (3) The low temperature performance of the sensor is degraded. The sensitivity of the pressure sensor decreases by 20% to 30% below -10℃, and the capacitance value of the capacitor sensor drifts by more than 15%, which further amplifies the measurement error. (4) The heating and ice melting technology has a contradiction between energy consumption and maintenance. Its daily energy consumption is 3 to 5 times that of the normal temperature scenario, and the heating wire is prone to breakage due to water corrosion, increasing the maintenance frequency by 50%.

[0005] The root cause of the defects in low-temperature freezing water level detection lies in the lack of adaptation design for the physical characteristics of low-temperature freezing scenarios and the working mechanism of the sensor. Specifically: (1) The root cause of pressure distortion is that the existing technology has not established a "dual medium identification mechanism of ice layer and water body", has not distinguished between ice layer pressure and water body pressure, directly applied the pressure-liquid level formula of a single medium, and ignored the additional pressure generated by the ice layer as an independent medium. (2) The root cause of the capacitor positioning failure is that the existing technology does not take into account the significant difference in dielectric constant between ice and water, and does not adjust the capacitance value change threshold for low temperature environment, which leads to misjudgment caused by changes in dielectric environment; (3) The root cause of the sensor’s low-temperature performance degradation is that the existing technology lacks a “temperature-performance compensation model” and does not calibrate for changes in physical properties such as sensor sensitivity, zero-point drift, and solubility drift at low temperatures; (4) The root cause of the contradiction in the heating and melting technology is that it adopts the idea of ​​"passive physical melting", which does not eliminate the interference of ice layer from the level of detection principle. It only relies on high energy consumption to temporarily alleviate the problem, and does not solve the essential contradiction of ice layer interference.

[0006] Therefore, existing low-temperature freezing water level detection methods urgently need to solve the following problems: the room temperature pressure-capacitor combination scheme, due to the lack of an ice layer recognition mechanism, cannot distinguish between ice layer and water pressure, ignores the difference in dielectric constant between ice and water, and lacks low-temperature sensor compensation, resulting in measurement distortion and capacitor positioning failure; in addition, the heating and ice melting scheme relies on high-power heating to melt the ice layer instead of eliminating interference from the detection principle, and the heating wire is easily corroded by water quality, resulting in high energy consumption and frequent maintenance. Summary of the Invention

[0007] To address the aforementioned issues, this invention proposes a method and system for detecting water level in low-temperature frozen water bodies. Based on an adaptive processing logic of environmental parameter acquisition, model adaptive switching, ice layer interference elimination, low-temperature performance compensation, and accurate water level calculation, it solves the problems of water level measurement distortion caused by ice layers, sensor performance degradation, high energy consumption of traditional heating and ice melting technologies, and capacitor matrix positioning failure in low-temperature freezing scenarios. This enables accurate monitoring of water level in pools across the entire temperature range of low-temperature frozen water bodies.

[0008] According to some embodiments, the first aspect of the present invention provides a method for detecting the water level in a low-temperature frozen water body, employing the following technical solution: A method for detecting the water level in a low-temperature freezing water body, comprising: To obtain environmental parameters of low-temperature freezing water bodies; Based on the acquired environmental parameters, identify the environmental type of the low-temperature freezing water body; When the identified environment type is freezing mode, calculate the ice layer thickness and complete the water level detection of the low-temperature freezing water body; In calculating the ice thickness, the ultrasonic velocity temperature drift and water turbidity attenuation are considered. The ice-water sound velocity is corrected based on the temperature correction parameter, and the ice-water sound path is corrected based on the sound path correction parameter. The temperature correction parameter and the sound path correction parameter are adaptively fused by combining the dynamic weighting factor. The ice thickness is obtained by combining the reflection characteristics of the ultrasonic signal at the ice-water interface.

[0009] As a further technical limitation, the environmental type of the low-temperature freezing water body includes at least an environment with freezing and an environment without freezing.

[0010] Furthermore, in the process of identifying the environmental type of low-temperature freezing water, a dual-parameter intelligent identification logic of temperature-predicted risk and ultrasonic verification of ice layer is adopted. The average environmental temperature in the acquired environmental parameters is compared with the environmental temperature threshold to determine whether the low-temperature freezing water has a freezing risk. When there is a freezing risk, the ultrasonic reflection time difference in the acquired environmental parameters is compared with the time difference threshold to identify whether the low-temperature freezing water with freezing risk is a freezing environment.

[0011] As a further technical limitation, the ultrasonic velocity varies with temperature, and the sound velocity correction in ice is described. for underwater sound velocity correction for ;in, The ambient average temperature.

[0012] Furthermore, the ultrasonic path experiences signal attenuation during propagation in water due to suspended particles. The ultrasonic path is corrected for attenuation based on an attenuation coefficient. ;in, This is the corrected actual sound path in the ice. For the uncorrected theoretical sound path in ice, This is the attenuation coefficient.

[0013] Furthermore, the thickness of the ice layer for ;in, The time of the second ultrasonic reflection. The time of the first ultrasonic reflection. For ultrasound signal weights, Temperature correction weights.

[0014] As a further technical limitation, considering sensor performance degradation, the correlation between temperature and sensor performance is quantified, and a sensor performance degradation prediction model based on an LSTM neural network is used to predict the sensor performance degradation trend, thus avoiding the hysteresis error of passive correction.

[0015] Furthermore, the sensor includes a pressure sensor, and the pressure sensor compensation includes sensitivity compensation and zero-point drift compensation; that is... , ;in, This refers to the temperature-corrected sensitivity of the pressure sensor. The standard sensitivity at room temperature, This is the sensitivity attenuation coefficient. The ambient average temperature This represents the zero-point drift of the pressure sensor after temperature correction.

[0016] Furthermore, the sensor also includes a capacitive sensor, and the capacitive sensor compensation includes capacitance drift compensation and threshold adjustment; , ;in, For the first i The compensated capacitance value of a capacitive sensor. For the first i The measured capacitance value of each capacitive sensor. The ambient average temperature This is the threshold value for capacitance change after temperature correction. This is the standard threshold at room temperature.

[0017] According to some embodiments, the second aspect of the present invention provides a water level detection system for low-temperature frozen water bodies, employing the following technical solution: A water level detection system for low-temperature freezing water bodies includes: The acquisition module is configured to acquire environmental parameters of low-temperature freezing water bodies; The identification module is configured to identify the environmental type of low-temperature freezing water bodies based on the acquired environmental parameters. The detection module is configured to calculate the ice thickness and complete the water level detection of the low-temperature frozen water body when the identified environment type is freezing mode. In calculating the ice thickness, the ultrasonic velocity temperature drift and water turbidity attenuation are considered. The ice-water sound velocity is corrected based on the temperature correction parameter, and the ice-water sound path is corrected based on the sound path correction parameter. The temperature correction parameter and the sound path correction parameter are adaptively fused by combining the dynamic weighting factor. The ice thickness is obtained by combining the reflection characteristics of the ultrasonic signal at the ice-water interface.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention, based on an adaptive processing logic of environmental parameter acquisition, model adaptive switching, ice layer interference elimination, low temperature performance compensation, and accurate liquid level calculation, solves the problems of liquid level measurement distortion caused by ice layer in low temperature freezing scenarios, sensor performance degradation, high energy consumption of traditional heating and ice melting technology, and capacitor matrix positioning failure. It achieves accurate monitoring of water pool liquid level across the entire temperature range of low temperature freezing water. Attached Figure Description

[0019] The accompanying drawings, which form part of this embodiment, are used to provide a further understanding of this embodiment. The illustrative embodiments and their descriptions are used to explain this embodiment and do not constitute an improper limitation of this embodiment.

[0020] Figure 1 This is a flowchart of a method for detecting the water level in a low-temperature freezing water body according to Embodiment 1 of the present invention; Figure 2This is a schematic diagram of the steps of the intelligent water level detection method for low-temperature freezing water bodies in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the overall structure of the intelligent water level detection system in Embodiment 1 of the present invention; Figure 4 This is a schematic diagram illustrating the liquid level detection principle under freezing mode in Embodiment 1 of the present invention; Figure 5 This is the capacitor drift coefficient compensation curve in Embodiment 1 of the present invention; Figure 6 This is the pressure sensitivity coefficient compensation curve in Embodiment 1 of the present invention; Figure 7 This is a structural block diagram of a water level detection system for low-temperature frozen water in Embodiment 2 of the present invention. Detailed Implementation

[0021] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0022] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0023] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0024] In this invention, terms such as "upper," "lower," "left," "right," "front," "back," "vertical," "horizontal," "side," and "bottom" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used only to facilitate the description of the structural relationships of the various components or elements of this invention and do not specifically refer to any component or element in this invention. They should not be construed as limiting the invention.

[0025] In this invention, terms such as "fixed connection," "connected," and "linked" should be interpreted broadly, indicating a fixed connection, an integral connection, or a detachable connection; a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can determine the specific meaning of these terms in this invention based on the specific circumstances, and they should not be construed as limitations on the invention.

[0026] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0027] Example 1 Embodiment 1 of the present invention introduces a method for detecting the water level in a low-temperature frozen water body.

[0028] like Figure 1 The method for detecting the water level in a low-temperature freezing water body, as shown, includes: To obtain environmental parameters of low-temperature freezing water bodies; Based on the acquired environmental parameters, identify the environmental type of the low-temperature freezing water body; When the identified environment type is freezing mode, calculate the ice layer thickness and complete the water level detection of the low-temperature freezing water body; In calculating the ice thickness, the ultrasonic velocity temperature drift and water turbidity attenuation are considered. The ice-water sound velocity is corrected based on the temperature correction parameter, and the ice-water sound path is corrected based on the sound path correction parameter. The temperature correction parameter and the sound path correction parameter are adaptively fused by combining the dynamic weighting factor. The ice thickness is obtained by combining the reflection characteristics of the ultrasonic signal at the ice-water interface.

[0029] This embodiment is based on a dedicated hardware structure (including a capacitance measurement matrix with a low freezing point and low ice adhesion coating, an integrated device that combines an ultrasonic ice sensor, dual pressure sensors, and low-power heat tracing and vacuum insulation modules), supplemented by core software methods (ice thickness calculation and pressure stripping methods based on temperature correction and turbidity iteration, sensor low-temperature performance compensation algorithms, and adaptive switching logic for icing / normal modes), forming a complete technical system; it is achieved through five steps: "environmental parameter acquisition - model adaptive switching - ice interference elimination - low-temperature performance compensation - accurate liquid level calculation".

[0030] As one or more implementation methods, this embodiment first performs environmental parameter acquisition and filtering as the basic data input link for the entire liquid level detection process. The control module periodically collects key environmental and physical parameters and uses a moving average filtering algorithm to eliminate instantaneous noise and interference signals in the raw data. This provides accurate and stable basic data for subsequent "adaptive switching between icing / normal modes", "ice thickness calculation" and "sensor performance compensation", avoiding subsequent detection errors caused by fluctuations in the raw data.

[0031] In this embodiment, three types of core parameters are collected synchronously at a preset period. For the collected raw data, a moving average filtering algorithm is used to suppress noise. The temperature parameter T is collected by a temperature sensor installed on the outer wall of the vacuum insulation chamber, which monitors the ambient temperature and the temperature around the sensor. The sampling period is set to 100ms. A period shorter than 100ms will increase sensor power consumption, while a period longer than 100ms will fail to capture sudden temperature changes in low-temperature environments in a timely manner. The temperature filtering formula is as follows: Where n=5, five consecutive sampling values ​​are taken to avoid misjudgment caused by instantaneous temperature fluctuations.

[0032] The turbidity parameter NTU is obtained by collecting the actual turbidity of the water body through a turbidity sensor installed 10cm below the pressure sensor. The sampling period is consistent with the temperature parameter to ensure that the data of both are synchronized. Calculate the attenuation coefficient of ultrasonic signals in water. This coefficient can accurately correct the deviation of ultrasonic path under different turbidity, avoiding errors in ice thickness calculation caused by turbidity differences.

[0033] Raw ultrasound data t 1 / t 2. The reflection time is collected by an ultrasonic ice layer sensor installed vertically downwards 5cm above the upper pressure sensor, i.e. ;in, t 1 is defined as the time it takes for an ultrasonic signal to travel from the transmitter, be reflected off the surface of the ice layer, and return to the receiver. t 2 is defined as the time it takes for the ultrasonic signal to travel from the transmitter, through the ice layer, and back to the receiver via the ice-water interface. The sampling period is set to 200ms. If the period is too short, the power consumption of the ultrasonic module will increase significantly. If the period is too long, the dynamic changes in the ice layer thickness cannot be captured in time. Where m=3, the ultrasonic reflection interference can be effectively reduced.

[0034] Therefore, the output of this embodiment The four types of data can be directly used as input for subsequent steps, laying the foundation for the accuracy of the entire detection process.

[0035] As one or more implementation methods, the adaptive switching between the icing mode and the normal mode in this embodiment is the core control link for adapting to low-temperature icing scenarios and normal-temperature scenarios. By establishing objective and quantifiable mode switching judgment rules, the system can identify "icing risk environment" and "non-icing environment" without manual intervention, thereby achieving synergistic optimization of accuracy and energy saving.

[0036] This embodiment utilizes a dual-parameter intelligent identification logic of "temperature-based risk prediction + ultrasonic verification of ice layers" to accurately distinguish environmental types without human intervention. Its core identification parameter is the average ambient temperature. and ultrasonic reflection time difference .when When the system detects a "risk of icing," it automatically activates the ultrasonic ice sensor for further verification; subsequent detections... (If the ice layer thickness exceeds 0.1cm, measurement interference may occur), it is directly identified as an "icing environment". When It is directly judged as "no risk of icing" and identified as a "non-icing environment"; if when and Even at this time, it is still identified as a "non-icing environment," avoiding the activation of high-energy-consuming modules. The core guarantee for this identification process that does not require manual intervention lies in the following: at the hardware level, the sensor automatically collects parameters according to a preset cycle; the vacuum insulation chamber ensures that the sensor works normally in a low-temperature environment; at the algorithm level, the built-in filtering process eliminates data fluctuations; the judgment threshold (2℃, 5μs) is an industry-recognized standard that does not require manual adjustment; at the control level, the entire process of environmental identification, mode switching, and module start-up and shutdown is automatically executed by the control module, and abnormal situations trigger self-check prompts without manual intervention.

[0037] This embodiment achieves fully automatic and seamless switching between two modes through a two-layer logic of "dual-condition joint judgment + module adaptive start / stop". The core switching condition adopts a dual-condition joint judgment of "temperature threshold + ultrasonic time difference threshold" to avoid erroneous switching. The switching logic formula realizes mode switching through dual-condition joint judgment, and its judgment logic is as follows: ; generally T 0 = 2℃ The former ensures that the environment has the risk of freezing (temperature conditions), while the latter ensures that an ice layer that can cause measurement interference has already formed. The combined determination of the two can avoid misjudgment.

[0038] Once the system detects an icing mode, it activates the ultrasonic ice sensor, continuously outputting a 2MHz high-frequency ultrasonic signal. This signal, combined with collected data, provides the raw data for subsequent ice thickness calculation and pressure stripping. Simultaneously, it activates the low-power heating element. When the system detects a normal mode, it deactivates both the ultrasonic ice sensor and the low-power heating element.

[0039] It should be noted that when calculating the ice thickness in the freezing mode, the existing technology does not consider two major interference factors: ultrasonic velocity temperature drift and water turbidity attenuation, which leads to an error of more than 0.5 cm in the calculation of ice thickness. This embodiment eliminates the interference of ice layer on liquid level detection from the root by using a five-level processing method: "sonic velocity temperature control correction - sound path turbidity correction - ultrasonic signal intensity detection - dynamic weight adaptive fusion - iterative thickness calculation".

[0040] Ultrasonic velocity varies significantly with temperature. First, temperature correction is applied to the sound velocity in ice / water before calculating the ice thickness. ; ; in, Correction for sound velocity in ice. Correction for sound velocity in water.

[0041] When ultrasonic signals propagate through water, they are attenuated due to scattering and absorption by suspended particles in the water. Therefore, it is necessary to determine the signal based on the attenuation coefficient. The ultrasonic path attenuation is corrected using the following formula: ; in, This is the corrected actual sound path in the ice. It is the theoretical sound path in the uncorrected ice.

[0042] To address the varying impacts of temperature stability, turbidity levels, and ultrasonic signal reliability on computational accuracy under different scenarios, this embodiment introduces a dynamic weighting factor to adaptively fuse the aforementioned correction and adjustment parameters. The specific steps are as follows: The input parameters for weight calculation are the temperature fluctuation range. ( The average temperature of the previous cycle reflects the stability of the temperature environment; water turbidity NTU reflects the degree of ultrasonic signal attenuation; and ultrasonic signal intensity S reflects the reliability of ultrasonic signal reflection data.

[0043] To achieve accurate dynamic weight allocation, it is necessary to simultaneously acquire the ultrasonic signal intensity S. The ultrasonic ice layer sensor receiver must acquire the signal during the acquisition time. t 1. t Simultaneously, the raw voltage data of the echo signal (sampling frequency 5MHz, matched signal ultrasonic period) is acquired to obtain the raw voltage time sequence. Noise is eliminated using a moving average filtering algorithm (m=3, consistent with the filtering window of the ultrasonic time t1 / t2). The filtering formula is as follows: Where k is the current sampling point, The filtered voltage value is then used to extract the maximum peak voltage within the effective echo interval, which is the ultrasonic signal intensity S. This ensures that when the effective threshold S is reached... min =5mV, when S min If the ultrasound data is deemed unreliable, its original weight is set to 0.

[0044] The original weights are calculated based on the degree of influence of the parameters on the measurement accuracy. (The smaller the temperature fluctuation, the higher the temperature correction weight). (The lower the turbidity, the higher the turbidity correction weight). (The stronger the signal, the higher the weight of the ultrasound data).​

[0045] The weights are normalized to ensure that the sum of all weights is 1, thereby achieving a reasonable distribution of parameter influence. (Temperature weighting correction) (Turbidity weighting correction) (Ultrasound signal weighting).

[0046] Based on the above-mentioned "sound velocity temperature correction", "sound path turbidity correction", "ultrasonic signal intensity detection" and "dynamic weight allocation", combined with the reflection characteristics of ultrasonic signals at the ice-water interface, the formula for calculating ice layer thickness is derived as follows: ; To address the issue of "sensor performance degradation leading to amplified measurement errors" in low-temperature icing scenarios, this embodiment quantifies the correlation between temperature and sensor performance to achieve detection accuracy calibration across the entire temperature range of -30℃ to 50℃, ensuring the accuracy of sensor output data and providing reliable input for subsequent precise liquid level calculations.

[0047] Existing compensation methods are mostly "passive real-time correction," meaning adjustments are made based on the current temperature parameters after an error is detected. This approach suffers from lag and fails to consider the cumulative effects of long-term sensor wear and tear, water quality changes, etc., thus failing to prevent sudden errors at their source. Therefore, this embodiment employs a sensor performance degradation prediction model based on an LSTM neural network, achieving a core upgrade from "passive real-time compensation" to "active predictive compensation." This model anticipates sensor performance change trends, providing accurate predictive parameters for compensation calculations. Specifically: The model takes "time-series multi-dimensional features" as input and "performance degradation parameters" as output. Input features include the historical average temperature and fluctuation range over the past 72 hours, the cumulative sensor usage time, the average water turbidity and pH value over the past 72 hours, and the average ultrasonic signal intensity (a total of 6 core features). Output predictions include the pressure sensor sensitivity degradation rate, the pressure sensor zero-point drift coefficient, and the capacitance drift of the capacitance sensor. The model structure is simplified to "input layer - dual LSTM layers - fully connected output layer." It is iteratively trained and optimized using historical monitoring data, updating model parameters with the latest data every 24 hours to ensure prediction accuracy. Its core function is to predict sensor performance degradation trends 24 hours in advance, avoiding the lag error of passive correction.

[0048] Pressure sensor compensation is mainly divided into sensitivity compensation and zero-point drift compensation. Sensitivity compensation addresses the issue that pressure sensor sensitivity decreases with temperature; the compensation formula is as follows: ;in, This refers to the temperature-corrected sensitivity of the pressure sensor. This is the standard sensitivity at room temperature. It is the sensitivity attenuation coefficient; ;in, It is the zero-point drift of the pressure sensor after temperature correction.

[0049] The absolute pressure measured by the pressure sensor includes water pressure, ice layer pressure, atmospheric pressure, sensor drift, and atmospheric pressure drift. To eliminate interference, the following formula is used to obtain a calibrated gauge pressure that reflects only the actual water pressure: ; ; and These are the measured values ​​from the pressure sensors at the bottom and top, respectively, and are directly output by the sensors. and These are the zero-point drift values ​​of the lower and upper pressure sensors, respectively. It is standard atmospheric pressure; It is the temperature shift of atmospheric pressure; It is the extra pressure generated by the ice layer.

[0050] Capacitive sensor compensation is mainly divided into capacitance drift compensation and threshold adjustment. ;in, It is the first i The compensated capacitance value of a capacitive sensor. It is the first i The measured capacitance value of each capacitive sensor.

[0051] By collecting the adjacent capacitance differences in three scenarios—liquid level coverage, ice layer coverage, and no coverage—at different temperatures, a threshold adjustment formula is obtained, namely... ;in, It is the threshold value for capacitance change after temperature correction. It is the standard threshold at room temperature.

[0052] Based on the above compensation, the initial liquid level positioning logic of the capacitor matrix is ​​as follows: If The capacitance matrix then initially locates the liquid level height. ,in This represents the distance from the pressure sensor to the ground. =0.2 cm It refers to the spacing between capacitive sensors.

[0053] This embodiment calculates the final liquid level height in three scenarios: Scenario 1: The liquid level is higher than the upper pressure sensor. At that time, using the corrected and The height of the liquid level from the upper pressure sensor is calculated using the pressure difference. Final liquid level:

[0054] Scenario 2 is where the liquid level is between the upper and lower pressure sensors. First, use capacitors to initially measure the height. Calibrate water density In calculating precise height The final liquid level is .

[0055] Scenario 3 is when the liquid level is lower than the pressure sensor. ,when At this moment, at this moment This pressure may originate from sensor circuit noise or minor airflow disturbances, rather than the static pressure generated by the water body; when ,at this time, .

[0056] This embodiment uses, as follows: Figure 2 The flowchart shown illustrates the logical execution of the liquid level detection method, providing a clear visual representation of the complete steps involved in accurately calculating the liquid level within a full temperature range of -30℃ to 50℃ and a measurement range of 0 to 5m. Starting with environmental parameter acquisition and filtering, the method adaptively switches to either icing mode (activating the ultrasonic ice sensor and calculating ice thickness and additional pressure) or normal mode (disabling redundant equipment to reduce power consumption) based on whether the temperature is below the freezing threshold and the ultrasonic time difference. Subsequently, it performs temperature compensation on the pressure sensor and capacitor matrix to eliminate drift effects. Then, based on the liquid level range (high / medium / low) determined by the capacitor, it calls the corresponding calculation formula to calculate the liquid level value according to the specific scenario, ultimately outputting a precise liquid level value. This comprehensively demonstrates the adaptive processing logic and correction mechanism of the detection method under different temperature and liquid level scenarios.

[0057] This embodiment uses, as follows: Figure 3 The overall structure shown in the figure is as follows: S1 is the lower pressure sensor, located below the water gauge rod; S2 is the upper pressure sensor, located above the water gauge rod; U1 is the ultrasonic ice layer sensor, located above the upper pressure sensor and on the outside of the rod; C1~Cn are capacitor matrices, evenly arranged along the water gauge rod and between S1~S2, with adjacent rectangles spaced 2mm apart; V1 is a vacuum insulation chamber, enclosing S1, S2 and the capacitor matrices; H1 and H2 are heat tracing plates, attached to the inner wall of V1 near the sides of S1 and S2 respectively, to avoid obstructing the core components.

[0058] The liquid level detection principle in the freezing mode of this embodiment is as follows: Figure 4As shown in the diagram, B1 represents the ground, B2 represents the water surface, and B3 represents the ice layer. It can be seen that the ultrasonic ice sensor U1 is located above the pressure sensor on S2. Reflection path 1 is the solid black line below U1, starting from U1, reaching the ice surface, and then returning to U1; reflection path 2 is the dashed black line below U1, starting from U1, penetrating the ice layer to the ice-water interface, and then returning to U1.

[0059] The capacitance drift compensation curve obtained in this embodiment is as follows: Figure 5 As shown in the figure, the effect of temperature on the drift characteristics of a capacitive sensor is clearly demonstrated. It can be seen that low temperatures cause significant capacitance drift, and the drift is greater at lower temperatures. This curve allows for accurate calculation of the drift amount at different temperatures, thereby eliminating its interference with liquid level detection. The pressure sensitivity coefficient compensation curve obtained in this embodiment is shown below. Figure 6 As shown, this curve visually demonstrates that the decrease in sensor sensitivity at low temperatures leads to a smaller pressure measurement. The sensitivity ratio at any temperature can be calculated using this curve, thereby correcting the measurement value and ensuring that the pressure detection error at low temperatures remains within the permissible range.

[0060] This embodiment is based on an adaptive processing logic of environmental parameter acquisition, model adaptive switching, ice layer interference elimination, low temperature performance compensation, and accurate liquid level calculation. It solves the problems of liquid level measurement distortion caused by ice layer in low temperature freezing scenarios, sensor performance degradation, high energy consumption of traditional heating and ice melting technology, and failure of capacitor matrix positioning. It realizes accurate monitoring of water pool liquid level in the whole temperature range of low temperature freezing water.

[0061] Example 2 Embodiment 2 of the present invention introduces a water level detection system for low-temperature frozen water bodies.

[0062] like Figure 7 The water level detection system for low-temperature freezing water bodies shown includes: The acquisition module is configured to acquire environmental parameters of low-temperature freezing water bodies; The identification module is configured to identify the environmental type of low-temperature freezing water bodies based on the acquired environmental parameters. The detection module is configured to calculate the ice thickness and complete the water level detection of the low-temperature frozen water body when the identified environment type is freezing mode. In calculating the ice thickness, the ultrasonic velocity temperature drift and water turbidity attenuation are considered. The ice-water sound velocity is corrected based on the temperature correction parameter, and the ice-water sound path is corrected based on the sound path correction parameter. The temperature correction parameter and the sound path correction parameter are adaptively fused by combining the dynamic weighting factor. The ice thickness is obtained by combining the reflection characteristics of the ultrasonic signal at the ice-water interface.

[0063] The detailed steps are the same as those provided in Example 1 for detecting the water level in a low-temperature frozen water body, and will not be repeated here.

[0064] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0065] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0066] The above description is merely a preferred embodiment of this practice and is not intended to limit the scope of this practice. Various modifications and variations can be made to this practice by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this practice should be included within the protection scope of this practice.

Claims

1. A method for detecting the water level in a low-temperature freezing water body, characterized in that, include: To obtain environmental parameters of low-temperature freezing water bodies; Based on the acquired environmental parameters, identify the environmental type of the low-temperature freezing water body; When the identified environment type is freezing mode, calculate the ice layer thickness and complete the water level detection of the low-temperature freezing water body; In calculating the ice thickness, the ultrasonic velocity temperature drift and water turbidity attenuation are considered. The ice-water sound velocity is corrected based on the temperature correction parameter, and the ice-water sound path is corrected based on the sound path correction parameter. The temperature correction parameter and the sound path correction parameter are adaptively fused by combining the dynamic weighting factor. The ice thickness is obtained by combining the reflection characteristics of the ultrasonic signal at the ice-water interface.

2. The method for detecting the water level in a low-temperature frozen water body as described in claim 1, characterized in that, The environmental types of the low-temperature freezing water body include at least freezing environments and non-freezing environments.

3. The method for detecting the water level in a low-temperature frozen water body as described in claim 2, characterized in that, In the process of identifying the environmental type of low-temperature freezing water, a dual-parameter intelligent identification logic of temperature-predicted risk and ultrasonic verification of ice layer is adopted. The average environmental temperature in the acquired environmental parameters is compared with the environmental temperature threshold to determine whether the low-temperature freezing water has a freezing risk. When there is a freezing risk, the ultrasonic reflection time difference in the acquired environmental parameters is compared with the time difference threshold to identify whether the low-temperature freezing water with freezing risk is a freezing environment.

4. The method for detecting the water level in a low-temperature frozen water body as described in claim 1, characterized in that, The ultrasonic velocity varies with temperature, and the sound velocity in ice is corrected. for underwater sound velocity correction for ;in, The ambient average temperature.

5. The method for detecting the water level in a low-temperature freezing water body as described in claim 4, characterized in that, The ultrasonic path experiences signal attenuation during propagation in water due to suspended particles. Attenuation correction is applied to the ultrasonic path based on an attenuation coefficient. ;in, This is the corrected actual sound path in the ice. For the uncorrected theoretical sound path in ice, This is the attenuation coefficient.

6. The method for detecting the water level in a low-temperature frozen water body as described in claim 5, characterized in that, The thickness of the ice layer for ;in, The time of the second ultrasonic reflection. The time of the first ultrasonic reflection. For ultrasound signal weights, Temperature correction weights.

7. The method for detecting the water level in a low-temperature frozen water body as described in claim 1, characterized in that, Considering sensor performance degradation, by quantifying the correlation between temperature and sensor performance, and combining it with an LSTM neural network sensor performance degradation prediction model, the performance degradation trend of the sensor is predicted, avoiding the hysteresis error of passive correction.

8. The method for detecting the water level in a low-temperature frozen water body as described in claim 7, characterized in that, The sensor includes a pressure sensor, and pressure sensor compensation includes sensitivity compensation and zero-point drift compensation; that is... , ;in, This refers to the temperature-corrected sensitivity of the pressure sensor. The standard sensitivity at room temperature, This is the sensitivity attenuation coefficient. The ambient average temperature This represents the zero-point drift of the pressure sensor after temperature correction.

9. The method for detecting the water level in a low-temperature freezing water body as described in claim 7, characterized in that, The sensor also includes a capacitance sensor, and capacitance sensor compensation includes capacitance drift compensation and threshold adjustment; , ;in, For the first i The compensated capacitance value of a capacitive sensor. For the first i The measured capacitance value of each capacitive sensor. The ambient average temperature This is the threshold value for capacitance change after temperature correction. This is the standard threshold at room temperature.

10. A water level detection system for low-temperature freezing water bodies, characterized in that, include: The acquisition module is configured to acquire environmental parameters of low-temperature freezing water bodies; The identification module is configured to identify the environmental type of low-temperature freezing water bodies based on the acquired environmental parameters. The detection module is configured to calculate the ice thickness and complete the water level detection of the low-temperature frozen water body when the identified environment type is freezing mode. In calculating the ice thickness, the ultrasonic velocity temperature drift and water turbidity attenuation are considered. The ice-water sound velocity is corrected based on the temperature correction parameter, and the ice-water sound path is corrected based on the sound path correction parameter. The temperature correction parameter and the sound path correction parameter are adaptively fused by combining the dynamic weighting factor. The ice thickness is obtained by combining the reflection characteristics of the ultrasonic signal at the ice-water interface.

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