Laser snow depth instrument self-calibration method based on dynamic reference surface fusion compensation, electronic equipment and storage medium
By employing a self-calibration method with dynamic reference surface fusion compensation, the system error of the laser snow depth meter is monitored and compensated in real time, solving the problems of reference surface drift and environmental interference, and realizing efficient and accurate snow depth measurement at unattended stations.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-21
- Publication Date
- 2026-04-03
AI Technical Summary
Existing laser snow depth meters suffer from decreased measurement accuracy in field environments due to reference surface drift, instrument drift, and environmental interference. Furthermore, existing solutions are inefficient and cannot meet the needs of unattended sites.
A self-calibration method based on dynamic reference surface fusion compensation is adopted. Through laser ranging unit, temperature sensing unit and state machine model, system errors are monitored and compensated in real time, and the reference surface is dynamically updated to achieve fully automatic high-precision snow depth measurement.
It enables fully automated, high-precision snow depth measurement in unattended environments, reducing operation and maintenance costs, improving anti-interference capabilities, and ensuring the accuracy and stability of measurement data.
Smart Images
Figure CN121783038A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of meteorological and hydrological monitoring technology, and in particular to a self-calibration method, electronic device and storage medium for laser snow depth meters based on dynamic reference surface fusion compensation. Background Technology
[0002] Laser snow depth gauges are widely used due to their high accuracy and non-contact operation.
[0003] However, in practical applications, long-term accuracy measurements in field environments face the following challenges: Reference surface drift: The installation foundation and reference surface of the laser snow depth meter may change due to soil freeze-thaw cycles and settlement, causing the measurement zero reference to drift. Instrument drift: The laser and electronic components will generate measurement errors due to temperature changes and aging over time. Environmental interference: Phenomena such as hard snow crusts and blowing snow may interfere with automatic judgment. Existing solutions mostly involve periodic manual on-site calibration or manual reference setting during snowless summer months, which is inefficient and cannot meet the needs of unattended sites. Some devices with internal calibration can only correct internal errors and cannot address external errors caused by reference surface changes. Summary of the Invention
[0004] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide a self-calibration method, electronic device and storage medium for laser snow depth meters based on dynamic reference surface fusion compensation, which solves the problems of low efficiency, inability to meet the needs of unattended sites and inability to solve the external errors caused by changes in the reference surface in existing methods.
[0005] To achieve the above objectives, the present invention provides the following solution: A self-calibration method for laser snow depth meters based on dynamic reference surface fusion compensation includes: Data is collected from the target reference surface using a laser ranging unit at a fixed frequency to obtain the original distance measurement value; The original distance measurement value is subjected to median filtering to obtain the filtered distance value; The internal fixed reference optical path is measured using a laser ranging unit to obtain the original value of the internal reference, and the difference between the original value of the internal reference and the actual length of the internal reference optical path is calculated to obtain the basic error component. The internal temperature parameters are read using a temperature sensing unit to obtain the real-time internal temperature, and the temperature compensation component is obtained by calculating the real-time internal temperature using a piecewise linear temperature-error compensation model. The basic error component and the temperature compensation component are calculated using the error calculation formula to obtain the total system error, and the total system error is used to compensate for the filtered distance value to obtain the accurate distance value; The standard deviation of the precise distance values within the past time window is calculated to obtain a stability index, and the stability index is judged using a preset stability threshold to obtain the measurement status. Based on the measured state, a state machine model is used to perform state matching to obtain the working state; When the working state is active with snow or stable with snow, lock the current reference plane distance value; When the working state is a stable, snowless state, the current reference surface distance value is updated using a simple moving average or an exponentially weighted moving average. The snow depth measurement results are obtained by calculating the current distance value to the reference surface and the precise distance value using the snow depth calculation formula.
[0006] Preferably, the expression for the piecewise linear temperature-error compensation model is: Among them, E T The temperature compensation component is denoted as ; a and b are the slope parameter and intercept parameter, respectively; T is the internal real-time temperature.
[0007] Preferably, the error calculation formula is: E=E T +E r Among them, E r =(dE T –d0; E is the total systematic error; E r d represents the residual error; d represents the original value of the internal reference; E T d0 is the temperature compensation component; d0 is the actual length of the internal reference optical path.
[0008] Preferably, the expression for the precise distance value is: D = D f -E; where D is the precise distance value; D f denoted as the filtering distance value; E represents the total system error.
[0009] Preferably, the stability threshold is 1.0 mm; wherein, the measurement state corresponding to the stability index that is less than the stability threshold is a stable state.
[0010] Preferably, the expression for the simple moving average is: ;in, The updated reference plane distance value; The number of the most recent continuous, precise distance values measured over the past 6 hours; This is the i-th precise distance value.
[0011] Preferably, the expression for the exponentially weighted moving average is: S t =α·D t+(1-α)·S t-1 Among them, S t The reference surface value at the current moment; α is the smoothing factor; D t This is the latest measurement; S t-1 This is the reference surface value at the previous moment.
[0012] Preferably, the operating logic of the state machine model includes: If, during the initial working state, the stability index is lower than the first stability threshold and the temporary snow depth is lower than the first snow depth threshold within a first preset time period, the working state is switched to the stable snowless state. When the working state is in the stable snowless state, if the decrease in the precise distance value exceeds the snowfall trigger threshold and the ambient temperature is lower than the preset temperature threshold, the working state will be switched to the active snowy state. If, during the active snowy state, the stability index is lower than the second stability threshold and the snow depth is greater than the second snow depth threshold within a first preset time period, the working state is switched to the stable snowy state. If the snow depth is less than or equal to the regression threshold when the working state is in the active snowy state or the stable snowy state, the working state is switched to the stable snowless state.
[0013] Preferably, an electronic device includes: at least one processor and a memory communicatively connected to the processor; wherein the memory stores instructions executable by the processor, the instructions being executed by the processor to enable the processor to perform the aforementioned self-calibration method for a laser snow depth meter based on dynamic reference surface fusion compensation.
[0014] Preferably, a non-transient computer-readable storage medium stores computer instructions for causing a computer to execute the aforementioned self-calibration method for a laser snow depth meter based on dynamic reference surface fusion compensation.
[0015] The present invention discloses the following technical effects: This invention provides a self-calibration method, electronic device, and storage medium for a laser snow depth meter based on dynamic reference surface fusion compensation. By correcting inherent system errors through an internal fusion calibration compensation module, continuously monitoring laser ranging values and performing stability analysis, using a state machine model to identify environmental states, and adopting corresponding reference surface locking or updating strategies under different states, this invention solves the problems of low efficiency, inability to meet the needs of unattended sites, and inability to solve external errors caused by reference surface changes in existing methods. It achieves fully automatic and high-precision snow depth measurement in unattended environments. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 This is a schematic diagram of the self-calibration process of a laser snow depth meter based on dynamic reference surface fusion compensation, provided in an embodiment of the present invention. Detailed Implementation
[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0019] The purpose of this invention is to provide a self-calibration method, electronic device and storage medium for laser snow depth meters based on dynamic reference surface fusion compensation, which solves the problems of low efficiency, inability to meet the needs of unattended sites and inability to solve the external errors caused by changes in the reference surface in existing methods.
[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0021] Figure 1 This is a schematic diagram of the self-calibration process of a laser snow depth meter based on dynamic reference surface fusion compensation provided in an embodiment of the present invention, as shown below. Figure 1 As shown, this invention provides a self-calibration method for a laser snow depth meter based on dynamic reference surface fusion compensation, comprising: Step 100: Use a laser ranging unit to collect data from the target reference surface at a fixed frequency to obtain the original distance measurement value; Step 200: Perform median filtering on the original distance measurement values to obtain filtered distance values; Step 300: Use the laser ranging unit to measure the internal fixed reference optical path to obtain the original value of the internal reference, and calculate the difference between the original value of the internal reference and the actual length of the internal reference optical path to obtain the basic error component; Step 400: Use the temperature sensing unit to read the internal temperature parameters to obtain the internal real-time temperature, and use the piecewise linear temperature-error compensation model to calculate the internal real-time temperature to obtain the temperature compensation component. Step 500: Calculate the basic error component and the temperature compensation component using the error calculation formula to obtain the total system error, and use the total system error to compensate for the filtered distance value to obtain the accurate distance value; Step 600: Calculate the standard deviation of the precise distance values within the past time window to obtain a stability index, and use a preset stability threshold to determine the stability of the stability index to obtain the measurement status; Step 700: Based on the measured state, use a state machine model to perform state matching to obtain the working state; Step 800: When the working state is active with snow or stable with snow, lock the current reference plane distance value; Step 900: When the working state is a stable, snowless state, update the current reference surface distance value using a simple moving average or an exponentially weighted moving average; Step 1000: Calculate the current distance value to the reference surface and the precise distance value using the snow depth calculation formula to obtain the snow depth measurement result.
[0022] Furthermore, the expression for the piecewise linear temperature-error compensation model is: Among them, E T The temperature compensation component is denoted as ; a and b are the slope parameter and intercept parameter, respectively; T is the internal real-time temperature.
[0023] Preferably, the error calculation formula is: E=E T +E r Among them, E r =(dE T –d0; E is the total systematic error; E r d represents the residual error; d represents the original value of the internal reference; E T d0 is the temperature compensation component; d0 is the actual length of the internal reference optical path.
[0024] Furthermore, the expression for the precise distance value is: D = D f -E; where D is the precise distance value; D f denoted as the filtering distance value; E represents the total system error.
[0025] Specifically, the stability threshold is 1.0 mm; wherein, the measurement state corresponding to the stability index that is less than the stability threshold is a stable state.
[0026] Preferably, the expression for the simple moving average is: ;in, The updated reference plane distance value; The number of the most recent continuous, precise distance values measured over the past 6 hours; This is the i-th precise distance value.
[0027] Specifically, the expression for the exponentially weighted moving average is: S t =α·D t +(1-α)·S t-1 Among them, S t The reference surface value at the current moment; α is the smoothing factor; D t This is the latest measurement; S t-1 This is the reference surface value at the previous moment.
[0028] Furthermore, the operational logic of the state machine model includes: If, during the initial working state, the stability index is lower than the first stability threshold and the temporary snow depth is lower than the first snow depth threshold within a first preset time period, the working state is switched to the stable snowless state. When the working state is in the stable snowless state, if the decrease in the precise distance value exceeds the snowfall trigger threshold and the ambient temperature is lower than the preset temperature threshold, the working state will be switched to the active snowy state. If, during the active snowy state, the stability index is lower than the second stability threshold and the snow depth is greater than the second snow depth threshold within a first preset time period, the working state is switched to the stable snowy state. If the snow depth is less than or equal to the regression threshold when the working state is in the active snowy state or the stable snowy state, the working state is switched to the stable snowless state.
[0029] Specifically, a self-calibration method and system for a laser snow depth meter based on dynamic reference surface fusion compensation is characterized by including the following steps: S1: Continuously acquire the original distance measurement value D of the laser ranging unit to the reference surface. r and D r The filtering process is performed to obtain the filtered distance value D. f ; S2: For the filtered distance value D f System error compensation based on internal reference optical path and temperature is performed to obtain the accurate distance value D; S3: Calculate the stability index of the precise distance value D within the time window, and set the stability threshold according to the resolution and stability of the laser snow depth meter; S4: Based on the stability index, the preset snow depth trigger threshold, and the ambient temperature parameter, a state machine model is constructed to determine the current working state of the system. The working state includes at least the "stable no snow" state, the "active snow" state, and the "stable snow" state. When the system is determined to be in the "stable no snow" state, the reference surface distance value D0 is dynamically updated based on the precise distance value D. When the system is in the "snow" state, D0 is locked. S5: Calculate the snow depth H based on the reference surface distance D0 and the current precise distance value D, where H = D0 - D.
[0030] Furthermore, the state transition logic of the state machine model in step S4 includes: When the system is in the initial state, and the stability index is lower than the first stability threshold and the temporary snow depth value is less than the first snow depth threshold for a continuous first preset time period, it switches to a stable snowless state. When the system is in a stable, snowless state, and the detected continuous decrease in D exceeds the snowfall trigger threshold, and the ambient temperature is lower than the preset temperature threshold, it switches to an active, snowy state. When the system is in an active snowy state, and the stability index is lower than the second stability threshold and the snow depth is greater than the second snow depth threshold for a continuous second preset time period, it switches to a stable snowy state. When the system is in an active snowy state or a stable snowy state, and the snow depth value is less than or equal to the regression threshold, it switches to a stable snowless state.
[0031] Furthermore, the dynamic update of the reference surface distance D0 specifically involves: using a rolling average or exponentially weighted moving average algorithm to process the N most recently acquired D values under the current stable, snowless state, and using the result as the new D0.
[0032] Specifically, the systematic error compensation in step S2 includes an internal calibration step: S2a: Control the laser ranging unit to measure the internal fixed reference optical path and obtain the original value d of the internal reference; S2b: Based on the difference between the original internal reference value d and the actual length d0 of the internal reference optical path, combined with the temperature compensation component E obtained based on the internal temperature T. T The real-time system error E is calculated. S2c: Using the real-time system error E to adjust the filtered distance value D f Compensation is performed, resulting in D.
[0033] Furthermore, in step S2b, the temperature compensation component E is obtained by querying the pre-stored temperature-error compensation model. T The temperature-error compensation model was established through calibration experiments, characterizing the functional relationship E between the system error and the internal temperature T.T = f(T); the real-time system error E at least includes the temperature compensation component E T .
[0034] Optionally, a self-calibration system for a laser snow depth meter includes: a laser ranging unit, a temperature sensing unit, a data processing unit, and a storage unit; the data processing unit is configured to execute the method as described above.
[0035] Specifically, in an embodiment: taking a laser snow depth meter with a resolution of 1.0 mm as an example for demonstration. Specifically as follows: Step S1: Obtain an original distance data D every second r , and perform median filtering using a sliding window with a length of 31 to obtain a filtered value D f .
[0036] Step S2: System error compensation.
[0037] 1) Internal calibration and temperature compensation: Every 6 hours, measure the internal reference optical path to obtain d, and read the internal temperature T. Query the temperature compensation model E T = f(T), calculate the residual error E r = (d - E T ) – d0, then the total system error E = E T + E r ; d in the text is the internal reference original value, and the temperature compensation has actually been included during measurement, so the temperature compensation value E is subtracted in the formula calculation T .
[0038] 2) Real-time compensation: For each main measurement, execute D = D f - E. Subsequent state machine judgment and snow depth calculation are both based on D.
[0039] As a preferred implementation manner of the temperature-error compensation model, the pre-stored temperature-error compensation model is established through a calibration experiment. Specifically, it is a piecewise linear temperature compensation model, and its algorithm formula is: Where: E T is the system error predicted at temperature T; T is the internally read real-time temperature; a and b are model parameters (slope and intercept), which are预先 determined through a calibration experiment before use. Example: Set 4 temperature intervals at [-10, 15, 40]. When T ≤ -10 °C: (a = 0.05, b = 0.5); when - in the text, it should be -10 °C < T ≤ 15 °C: (a = 0.02, b = 0.2); when 15 °C < T ≤ 40 °C: It should be noted that there seems to be an incomplete formula in the original text at the end part. Please check and correct it if necessary. Also, the "预先" in the translation of ID=47 should be the correct Chinese expression for "previously" in English in the context. If there are any other specific requirements or corrections, feel free to let me know.(a=-0.03, b=0.5); When T>40℃: (a=-0.05, b=0.8).
[0040] Step S3: Calculate the standard deviation σ of D over the past 30 minutes (1800 points) as a stability index. Set the stability threshold to 1.0 mm. If σ < 1.0 mm, it is considered "stable".
[0041] Step S4: State machine model.
[0042] Initial state → Stable snowless state: If the snow is stable for 6 consecutive hours and the temporary snow depth is <1mm, then the state enters a stable snowless state and D0 is initialized with the mean of the current D.
[0043] Stable (no snow) state → Active (snowy) state: If D continuously decreases by more than 1 mm within 1 hour and the ambient temperature t < 2℃, the state is switched and D0 is locked.
[0044] Active with snow → Stable with snow: Switch if the snow is stable for 3 consecutive hours and the snow depth H > 10mm.
[0045] Any snowy state → stable snowless state: If H is consistently less than 1mm (regression threshold) and remains stable for 6 hours, switch, and immediately update D0 with the current D.
[0046] Optionally, as an implementation of dynamically updating the reference plane distance value D0, when the system is determined to be in a "stable, snow-free" state, algorithms such as sliding window averaging or exponentially weighted moving average can be used to process the continuously acquired precise distance values D to obtain new, more reliable D0 values.
[0047] Example 1 (Simple Moving Average): Take the arithmetic mean of the latest N D values, that is: This method can effectively smooth random noise and obtain a stable reference surface estimate.
[0048] Example 2 (Exponentially Weighted Moving Average): According to formula S t =α·Dt+(1-α)·S t -1 is calculated recursively. Where S t Let D0 be the new reference surface value at the current moment, Dt be the latest measured value, and S be the reference surface value. t -1 represents the old reference surface value at the previous time step, and α is a smoothing factor between 0 and 1. This method assigns higher weight to recent data, enabling it to more sensitively track slow changes in the reference surface. Technicians can select appropriate algorithms and parameters (N, α) based on the specific application scenario (such as the rate of ground change, measurement noise level, etc.).
[0049] Step S5: Calculate the snow depth H based on the reference surface distance D0 and the current precise distance value D, where H = D0 - D. The final output can be converted to the required unit. The numerical parameters listed above (such as time window, stability threshold, temperature threshold, etc.) are only illustrative examples and are not intended to limit the present invention. They can be adjusted according to specific circumstances in practical applications.
[0050] As an optional implementation, this embodiment also provides an electronic device, including: at least one processor, and a memory communicatively connected to the processor; wherein the memory stores instructions executable by the processor, the instructions being executed by the processor to enable the processor to perform the aforementioned self-calibration method for a laser snow depth meter based on dynamic reference surface fusion compensation.
[0051] As an optional implementation, this embodiment also provides a non-transient computer-readable storage medium storing computer instructions for causing a computer to execute the aforementioned self-calibration method for a laser snow depth meter based on dynamic reference surface fusion compensation.
[0052] The beneficial effects of this invention are as follows: (1) Fully automated and unmanned: It eliminates the reliance on manual on-site calibration of reference surfaces and significantly reduces operation and maintenance costs.
[0053] (2) High precision and long-term reliability: By dynamically updating the reference surface, the measurement error caused by foundation settlement, instrument drift and other factors is effectively compensated, ensuring the accuracy of the measurement data.
[0054] (3) Strong anti-interference capability: Based on the state machine model with multiple parameters (stability, temperature, snow depth threshold), it can effectively distinguish between real snowfall, snow melting process and short-term environmental disturbances (such as snow blowing), and prevent mismeasurement.
[0055] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.
[0056] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.
Claims
1. A self-calibration method for a laser snow depth meter based on dynamic reference surface fusion compensation, characterized in that, include: Data is collected from the target reference surface using a laser ranging unit at a fixed frequency to obtain the original distance measurement value; The original distance measurement value is subjected to median filtering to obtain the filtered distance value; The internal fixed reference optical path is measured using a laser ranging unit to obtain the original value of the internal reference, and the difference between the original value of the internal reference and the actual length of the internal reference optical path is calculated to obtain the basic error component. The internal temperature parameters are read using a temperature sensing unit to obtain the real-time internal temperature, and the temperature compensation component is obtained by calculating the real-time internal temperature using a piecewise linear temperature-error compensation model. The basic error component and the temperature compensation component are calculated using the error calculation formula to obtain the total system error, and the total system error is used to compensate for the filtered distance value to obtain the accurate distance value; The standard deviation of the precise distance values within the past time window is calculated to obtain a stability index, and the stability index is judged using a preset stability threshold to obtain the measurement status. Based on the measured state, a state machine model is used to perform state matching to obtain the working state; When the working state is active with snow or stable with snow, lock the current reference plane distance value; When the working state is a stable, snowless state, the current reference surface distance value is updated using a simple moving average or an exponentially weighted moving average. The snow depth measurement results are obtained by calculating the current distance value to the reference surface and the precise distance value using the snow depth calculation formula.
2. The self-calibration method for a laser snow depth meter based on dynamic reference surface fusion compensation according to claim 1, characterized in that, The expression for the piecewise linear temperature-error compensation model is as follows: Among them, E T The temperature compensation component is denoted as ; a and b are the slope parameter and intercept parameter, respectively; T is the internal real-time temperature.
3. The self-calibration method for a laser snow depth meter based on dynamic reference surface fusion compensation according to claim 1, characterized in that, The error calculation formula is: E=E T +E r ; Among them, E r =(dE T –d0; E is the total systematic error; E r d represents the residual error; d represents the original value of the internal reference; E T d0 is the temperature compensation component; d0 is the actual length of the internal reference optical path.
4. The self-calibration method for a laser snow depth meter based on dynamic reference surface fusion compensation according to claim 1, characterized in that, The expression for the precise distance value is: D = D f -E; where D is the precise distance value; D f denoted as the filtering distance value; E represents the total system error.
5. The self-calibration method for a laser snow depth meter based on dynamic reference surface fusion compensation according to claim 1, characterized in that, The stability threshold is 1.0 mm; wherein, the measurement state corresponding to the stability index that is less than the stability threshold is a stable state.
6. The self-calibration method for a laser snow depth meter based on dynamic reference surface fusion compensation according to claim 1, characterized in that, The expression for the simple moving average is: ;in, The updated reference plane distance value; The number of the most recent continuous, precise distance values measured over the past 6 hours; This is the i-th precise distance value.
7. The self-calibration method for a laser snow depth meter based on dynamic reference surface fusion compensation according to claim 1, characterized in that, The expression for the exponentially weighted moving average is: S t =α·D t +(1-α)·S t-1 ; Among them, S t The reference surface value at the current moment; α is the smoothing factor; D t This is the latest measurement; S t-1 This is the reference surface value at the previous moment.
8. The self-calibration method for a laser snow depth meter based on dynamic reference surface fusion compensation according to claim 1, characterized in that, The operational logic of the state machine model includes: If, during the initial working state, the stability index is lower than the first stability threshold and the temporary snow depth is lower than the first snow depth threshold within a first preset time period, the working state is switched to the stable snowless state. When the working state is in the stable snowless state, if the decrease in the precise distance value exceeds the snowfall trigger threshold and the ambient temperature is lower than the preset temperature threshold, the working state will be switched to the active snowy state. If, during the active snowy state, the stability index is lower than the second stability threshold and the snow depth is greater than the second snow depth threshold within a first preset time period, the working state is switched to the stable snowy state. If the snow depth is less than or equal to the regression threshold when the working state is in the active snowy state or the stable snowy state, the working state is switched to the stable snowless state.
9. An electronic device, characterized in that, include: At least one processor, and a memory communicatively connected to the processor; wherein the memory stores instructions executable by the processor, the instructions being executed by the processor to enable the processor to perform a self-calibration method for a laser snow depth meter based on dynamic reference surface fusion compensation as described in any one of claims 1 to 8.
10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the self-calibration method for a laser snow depth meter based on dynamic reference surface fusion compensation as described in any one of claims 1 to 8.