Radar emission angle adjusting method for radar level meter based on bat algorithm

Abstract

The invention relates to the technical field of bat algorithms, and particularly discloses a radar emission angle adjusting method for a radar level meter based on the bat algorithm, which comprises the following steps: S1, acquiring an emission angle range of the radar level meter, and presetting a bat population and a maximum number of iterations; s2, performing iterative optimization on the bat to adjust the frequency, calculating the fitness value, the updating speed and the angle of the echo signal, and generating a local new solution; s3, issuing the local new solution, collecting echo data, calculating a fitness value corresponding to the local new solution, and calculating the pulse emissivity and loudness of the bats after iteration; and S4, updating the globally optimal solution and the fitness value, outputting the globally optimal solution and setting parameters of the radar level meter. According to the scheme, the optimization model is constructed based on the bat algorithm, the emission angle is converted into the algorithm search space, the search efficiency is balanced by dynamically adjusting the frequency, the emissivity and the loudness, the optimization parameters are verified in combination with a hardware closed loop, and the optimal parameters are solidified to realize stable measurement, so that the precision is improved.

Description

Technical Field

[0001] This invention relates to the field of bat algorithm technology, and specifically to a radar emission angle adjustment method for radar level gauges based on the bat algorithm. Background Technology

[0002] Radar level gauges, with their advantages of non-contact measurement and strong anti-interference capabilities, have become the core equipment for industrial level monitoring, and their market size continues to expand. Among them, guided wave radar level gauges are increasingly widely used in small container and low dielectric constant medium measurement scenarios due to their suitability for complex working conditions. The existing Cro8100 series guided wave radar level gauges have developed into various technical solutions. Some products have improved adaptability to small containers by optimizing probe design to reduce the measurement blind zone and installation opening size. Products using coaxial structure detection components show significant advantages in measuring low dielectric constant and low viscosity liquids and in high-precision operating conditions. Moreover, the measurement results are not affected by the density, temperature, foam, or other characteristics of the medium, demonstrating strong environmental adaptability. Furthermore, for the installation and fixation of the detection components, a technical approach using a bottom counterweight and threaded connection structure for positioning has emerged, providing a foundation for improving measurement stability.

[0003] However, existing guided wave radar level gauges still have key technical defects in practical applications. Limited by the fixed design of the radar emission angle, when the detection cable deviates or touches the container wall due to factors such as material agitation or container vibration, the emission angle cannot be adaptively adjusted, resulting in unstable echo signal reception. Even with a bottom-fixed structure, if the fixing accuracy is insufficient or there is slight swaying of the cable, signal misjudgment will still occur due to the mismatch between the emission angle and the measurement path. In addition, the existing technology has not optimized the coordinated adaptation between the emission angle and the cable fixing state, making it difficult to meet the installation requirements of small containers and the measurement reliability under complex working conditions, thus restricting the application effect of the product in dynamic working conditions. Summary of the Invention

[0004] The purpose of this invention is to provide a radar emission angle adjustment method for radar level gauges based on the bat algorithm, thereby solving the above-mentioned technical problems.

[0005] The objective of this invention can be achieved through the following technical solutions: The radar emission angle adjustment method for radar level gauges based on the bat algorithm includes the following steps: S1: Obtain the emission angle range [θmin, θmax] of the radar level gauge, and construct the angle xi(t), which represents the angle of the i-th bat at iteration t; The bat population n and the maximum number of iterations Tmax are preset, and the angle xi and velocity vi of the i-th bat are preset, θmin≤xi≤θmax; Obtain the radar level gauge frequency range [fmin, fmax], initial loudness A0, and pulse emission rate r0; S2: Perform iterative optimization on the i-th bat. The iterative optimization includes the following steps: Let the number of iterations t ≤ Tmax; The adjustment frequency fi = fmin + (fmax - fmin) × β, where β is the preset aggressive coefficient; Get the current angle xi, calculate the fitness value F(xi) of the echo signal based on the angle xi, get the maximum fitness value of the echo signal after optimization iteration, and record the corresponding angle xi as the global optimal solution xbest; Update speed vi(t) = vi(t-1) + (xi(t-1) - xbest) × fi; Update angle xi_new = xi(t-1) + vi(t); Generate local new solutions Where ε represents a preset dynamic coefficient, -1≤ε≤1, This represents the average loudness of the current bat population; S3: Send the new local solution xlocal to the radar level gauge and collect echo data, and calculate the fitness value F(xlocal) corresponding to the new local solution xlocal. Calculate the pulse emission rate ri(t+1) of the i-th bat in the (t+1)-th iteration = r0 × [1 - exp(-γ × t)], where γ represents the preset pulse attenuation coefficient, 0 < γ < 1; Calculate the loudness Ai(t+1) of the i-th bat in the (t+1)-th iteration = α × Ai(t), where α represents the preset loudness attenuation coefficient, and α > 0; S4: Compare the fitness values ​​of all bats and select the maximum fitness value. Update the global optimal solution and fitness value. When the maximum number of iterations Tmax is reached, output the global optimal solution xbest. Set the radar level gauge to the angle corresponding to the global optimal solution xbest and simultaneously set the corresponding pulse emission rate and loudness. Enter the stable measurement mode.

[0006] As a further aspect of the present invention: in step S2, the method for calculating the fitness value F(xi) of the echo signal based on the angle xi includes: The echo signal amplitude SS is obtained at angle xi. The ratio of echo amplitude to background noise level STR is calculated. The ratio of total energy of interfering echo to main echo energy CR is calculated. The echo position is measured N times continuously at angle xi. The reciprocal of the standard deviation SI of the echo position is calculated. N represents the preset number of measurements. Calculate the fitness value F(xi) = w1×SS + w2×STR - w3×CR - w4×SI, where w1, w2, w3, and w4 represent the preset first, second, third, and fourth weight coefficients, respectively.

[0007] As a further aspect of the present invention, it includes: Obtain the frequency difference δ between the received and transmitted signals of the radar level gauge, obtain the frequency modulation slope K of the radar level gauge, and calculate the distance R = C × δ / 2K between the installation position of the radar level gauge and the material surface, where C represents the speed of light.

[0008] As a further aspect of the present invention: it is prohibited to install the radar level gauge in the center of the tank or directly above the feed inlet.

[0009] As a further aspect of the present invention: ensure that the distance between the installation position of the radar level gauge and the pipe wall is greater than 20cm.

[0010] As a further aspect of the present invention: in step S2, when iteratively optimizing the i-th bat, the radical coefficient after each iteration is made to be different from the current iteration coefficient.

[0011] As a further aspect of the present invention: in step S4, when fitness values ​​are equal, bats with lower pulse emission rates are preferentially selected.

[0012] As a further aspect of the present invention: in step S3, if the global optimal solution xbest remains unchanged after iterative optimization, then the subsequent steps of calculating the local new solution and fitness value are stopped.

[0013] The beneficial effects of this invention are as follows: First, by simulating the echolocation behavior of bats to construct an optimization model, the emission angle range of the radar level gauge is transformed into the algorithm's search space. Population iteration is used to accurately find the optimal measurement angle, effectively solving the measurement error problem caused by material accumulation morphology and interference from the inner wall of the tank in traditional fixed-angle measurements, significantly improving the accuracy and stability of level measurement. Furthermore, through the design of dynamically adjusting the frequency, pulse emission rate, and loudness, the system possesses adaptive optimization capabilities. The frequency is dynamically adjusted with the aggressive coefficient β, balancing the efficiency of global search and local mining. The mechanism of increasing pulse emission rate with the number of iterations and decreasing loudness with the number of iterations allows the algorithm to focus on large-scale exploration in the early stages and then focus on fine optimization of the optimal solution region in the later stages, ensuring both the comprehensiveness of the search and improving the convergence speed, enabling rapid locking of the optimal combination of measurement parameters. In addition, new local solutions are sent to the radar level gauge in real time and echo data is collected for verification, forming a closed-loop control that deeply couples the optimization process with the actual measurement scenario. Ultimately, by outputting the global optimal solution and solidifying the working parameters of the radar level gauge, the equipment enters a stable measurement mode. This not only reduces the complexity of manual debugging but also adapts to dynamic working conditions such as changes in material form and environmental interference. It provides an intelligent and high-precision solution for level measurement in chemical, warehousing and other fields, significantly improving the reliability and automation level of industrial process monitoring. Attached Figure Description

[0014] The invention will now be further described with reference to the accompanying drawings.

[0015] Figure 1 This is a flowchart illustrating the radar emission angle adjustment method for radar level gauges based on the bat algorithm of this invention. Detailed Implementation

[0016] 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.

[0017] Please see Figure 1 As shown, this invention is a radar emission angle adjustment method for radar level gauges based on the bat algorithm, comprising the following steps: S1: Obtain the emission angle range [θmin, θmax] of the radar level gauge, and construct the angle xi(t), which represents the angle of the i-th bat at iteration t; The bat population n and the maximum number of iterations Tmax are preset, and the angle xi and velocity vi of the i-th bat are preset, θmin≤xi≤θmax; Obtain the radar level gauge frequency range [fmin, fmax], initial loudness A0, and pulse emission rate r0; S2: Perform iterative optimization on the i-th bat. The iterative optimization includes the following steps: Let the number of iterations t ≤ Tmax; The adjustment frequency fi = fmin + (fmax - fmin) × β, where β is the preset aggressive coefficient; Get the current angle xi, calculate the fitness value F(xi) of the echo signal based on the angle xi, get the maximum fitness value of the echo signal after optimization iteration, and record the corresponding angle xi as the global optimal solution xbest; Update speed vi(t) = vi(t-1) + (xi(t-1) - xbest) × fi; Update angle xi_new = xi(t-1) + vi(t); Generate local new solutions Where ε represents a preset dynamic coefficient, -1≤ε≤1, This represents the average loudness of the current bat population; S3: Send the new local solution xlocal to the radar level gauge and collect echo data, and calculate the fitness value F(xlocal) corresponding to the new local solution xlocal. Calculate the pulse emission rate ri(t+1) of the i-th bat in the (t+1)-th iteration = r0 × [1 - exp(-γ × t)], where γ represents the preset pulse attenuation coefficient, 0 < γ < 1; Calculate the loudness Ai(t+1) of the i-th bat in the (t+1)-th iteration = α × Ai(t), where α represents the preset loudness attenuation coefficient, and α > 0; S4: Compare the fitness values ​​of all bats and select the maximum fitness value. Update the global optimal solution and fitness value. When the maximum number of iterations Tmax is reached, output the global optimal solution xbest. Set the radar level gauge to the angle corresponding to the global optimal solution xbest and simultaneously set the corresponding pulse emission rate and loudness. Enter the stable measurement mode.

[0018] It should be noted that, firstly, the scheme uses the emission angle range of the radar level gauge as the algorithm search boundary, and achieves dynamic adaptation of the measurement angle through iterative optimization using a bat population. When traditional radar level gauges use a fixed emission angle, they are prone to echo interference due to changes in the material accumulation angle and obstruction by internal tank components, leading to measurement deviations. However, this scheme, through iterative updates of the angle xi(t), can locate the optimal detection angle on the material surface, effectively avoiding interference sources, reducing measurement errors, and improving the accuracy and stability of level measurement under complex working conditions.

[0019] Secondly, the scheme customizes the core parameters of the bat algorithm according to specific scenarios. The frequency fi is dynamically adjusted within the range of [fmin, fmax] by the aggressive coefficient β. In the early stage of iteration, β is set to a larger value to expand the search range and ensure the comprehensiveness of global exploration; in the later stage of iteration, β is set to a smaller value to focus on the optimal solution region and enhance the local mining capability. The design of increasing pulse emission rate ri(t+1) with the number of iterations and decreasing loudness Ai(t+1) with the number of iterations simulates the behavior logic of a bat from a large-scale search to precise localization, which improves the convergence speed of the algorithm and can quickly lock the global optimal solution within a limited number of iterations, balancing optimization efficiency and solution quality.

[0020] Then, iterative optimization is performed on the i-th bat. First, the number of iterations is initialized to t=1. The iteration is started under the condition that t≤Tmax. First, the frequency fi=fmin+(fmax-fmin)×β is adjusted. β is a preset aggressive coefficient that can flexibly adjust the search characteristics. When the value of β is large, the frequency approaches fmax to enhance global exploration. When the value of β is small, the frequency approaches fmin to focus on local mining. This design allows the algorithm to expand the search range in the early stage of iteration to avoid getting trapped in local optima, and then finely optimizes to improve the accuracy of the solution in the later stage. Next, the angle xi(t-1) of the current i-th bat is obtained. Based on this angle, the radar level gauge is controlled to emit a detection signal. After collecting the echo data, the fitness value F(xi(t-1)) is calculated through indicators such as signal strength and signal-to-noise ratio. Then, the fitness values ​​of all bats are traversed to filter out the maximum value. The corresponding angle is marked as the global optimal solution xbest. This fitness calculation method based on the actual echo signal deeply binds the algorithm optimization with the hardware measurement effect. To ensure that the optimization objective aligns with actual application requirements and avoid a disconnect between theoretical optimality and engineering practice, the velocity is updated using the formula vi(t) = vi(t-1) + (xi(t-1) - xbest) × fi. The frequency fi is used to adjust the step size, guiding individual bats towards the global optimum. The velocity update formula incorporates the global optimum xbest as a guide, ensuring that the population iteration always revolves around the optimal direction, significantly improving convergence efficiency. The angle is then updated using xi_new = xi(t-1) + vi(t). If the new angle exceeds the range [θmin, θmax], it is corrected through boundary truncation or reflection. Boundary constraint processing ensures that the angle parameters conform to the hardware physical limits of the radar level gauge, avoiding ineffective optimization. Finally, a local new solution is generated, where ε is a dynamic coefficient of -1 ≤ ε ≤ 1. Local perturbation is used to search for the vicinity of the optimal solution. This local new solution generation mechanism can further explore better parameters near the global optimum, improving the upper limit of measurement accuracy.

[0021] The local novel solution xlocal is sent to the hardware control module of the radar level gauge, driving the device to adjust the emission angle to xlocal in real time and emit a detection signal. Simultaneously, echo data reflected from the material surface is collected. By analyzing key indicators such as the signal-to-noise ratio, amplitude stability, and effective signal ratio of the echo signal, a weighted scoring method is used to calculate the fitness value F(xlocal) corresponding to the local novel solution xlocal. This value directly reflects the effectiveness and accuracy of the measurement at the current angle. Subsequently, the fitness value F(xlocal) of the i-th bat at t+1 is calculated according to the formula ri(t+1) = r0 × [1 - exp(-γ × t)]. The pulse emission rate in one iteration, with γ as the pulse attenuation coefficient, can increase with the number of iterations through the exponential function property. The low emission rate in the early stage ensures the breadth of global exploration, while the high emission rate in the later stage enhances the locking efficiency of the optimal solution. The loudness is then calculated according to Ai(t+1)=α×Ai(t), where α causes the loudness to gradually decrease with iteration. The high loudness in the early stage supports large-scale signal detection, while the low loudness in the later stage focuses on local optimization. This dynamic adjustment mechanism perfectly simulates the behavioral characteristics of bat echolocation, allowing the algorithm to explore the global optimal area in the early stage in a "broad net" mode, and to explore the details of the optimal solution in the later stage in a "precise localization" mode.

[0022] The design of real-time hardware distribution and real echo data acquisition using xlocal constructs a real-time closed loop between algorithm optimization and physical measurement, avoiding the disconnect between pure theoretical optimization and actual working conditions. This ensures that each iteration is based on real measurement feedback, significantly improving the engineering practicality of the optimization results. The incremental design of pulse emission rate allows high-quality solutions to be retained more frequently in the later stages of algorithm iteration, reducing invalid exploration and improving convergence speed. The incremental design of loudness gradually narrows the search range from the signal detection level, complementing the emission rate adjustment. This allows the angle parameters of the radar level gauge to converge to the optimal measurement point during iteration, significantly improving the stability and accuracy of level measurement under complex working conditions.

[0023] The algorithm iterates through the fitness values ​​of all bat individuals in the current iteration, sorts and filters out the maximum value and its corresponding angle parameter, compares it with the historical global optimum, and updates the global optimum xbest and fitness value if the new value is better. It continues to iterate until the number of iterations reaches the maximum number of iterations Tmax. At this point, the final global optimum xbest is output, and the pulse emission rate and loudness parameters of the corresponding iteration are extracted. The radar level gauge's emission angle is automatically calibrated to xbest, and the matching pulse emission rate and loudness parameters are configured synchronously. The iteration optimization module is turned off, the device is switched to stable measurement mode, and level data is continuously collected according to the optimal parameters.

[0024] This process ensures that the algorithm always locks onto the best combination of measurement parameters through a dynamic update mechanism of the global optimal solution, avoiding measurement deviations caused by local optima. The parameter solidification design after iteration termination allows the radar level gauge to enter stable monitoring in the optimal state, preserving the results of algorithm optimization and ensuring the continuity of the measurement process. The linkage scheme of synchronously configuring angle, emissivity and loudness enables the hardware parameters to form a matching optimal combination, maximizing the performance of the equipment and ultimately achieving a dual improvement in the accuracy and stability of level measurement under complex working conditions.

[0025] In another preferred embodiment of the present invention, the method for calculating the fitness value F(xi) of the echo signal based on the angle xi includes: The echo signal amplitude SS is obtained at angle xi. The ratio of echo amplitude to background noise level STR is calculated. The ratio of total energy of interfering echo to main echo energy CR is calculated. The echo position is measured N times continuously at angle xi. The reciprocal of the standard deviation SI of the echo position is calculated. N represents the preset number of measurements. Calculate the fitness value F(xi) = w1×SS + w2×STR - w3×CR - w4×SI, where w1, w2, w3, and w4 represent the preset first, second, third, and fourth weight coefficients, respectively.

[0026] It is worth noting that, firstly, the radar level gauge is driven to emit a detection signal at angle x_i, and the peak amplitude SS of the echo signal is collected and identified. This value directly reflects the intensity of the main echo. Next, the ratio STR of the echo amplitude to the background noise level is calculated. The larger the STR, the more prominent the effective signal. At the same time, the main echo and the interference echo are separated by signal analysis technology, and the ratio CR of the total energy of the interference echo to the energy of the main echo is calculated. The smaller the CR, the lower the interference effect. Then, N measurements are continuously performed at angle xi, and the level data of each measurement is recorded. The standard deviation of the echo position is calculated and the reciprocal is taken to obtain SI. The larger the SI, the more stable the measurement result. Finally, the fitness value is calculated according to the formula, where w1, w2, w3, and w4 are preset weight coefficients that can be dynamically adjusted according to the on-site working conditions. For example, the weight of w3 is increased when the interference is severe.

[0027] The comprehensive consideration of multi-dimensional indicators ensures both the strength and clarity of the echo signal, suppresses interference, and emphasizes measurement stability, avoiding the one-sidedness of evaluation based on a single indicator. The adjustable design of the weight coefficients allows the fitness calculation to adapt to different working conditions, enhancing the algorithm's scenario adaptability. By calculating SI through N consecutive measurements, random errors are effectively filtered out, improving the reliability of the evaluation results. Finally, the constructed fitness function quantifies the measurement effect of angle x_i, providing a basis for algorithm iteration and ensuring that the optimization direction matches the actual measurement requirements.

[0028] In another preferred embodiment of the present invention, it includes: Obtain the frequency difference δ between the received and transmitted signals of the radar level gauge, obtain the frequency modulation slope K of the radar level gauge, and calculate the distance R = C × δ / 2K between the installation position of the radar level gauge and the material surface, where C represents the speed of light.

[0029] It is understandable that the signal processing module of the radar level gauge extracts the frequency difference δ between the received signal and the transmitted linear frequency modulated signal; obtains the preset frequency modulation slope K of the equipment; and calculates the actual distance according to the formula R=C×δ / 2K, with the denominator coefficient 2 originating from the round-trip distance compensation of the signal.

[0030] Frequency difference ranging based on the principle of frequency-modulated continuous wave has strong anti-interference ability and can effectively distinguish between material echo and tank wall reflection interference. By calculating with fixed parameters of light speed and frequency modulation slope, high-precision ranging can be achieved without complex calibration. The formula is simple and has high calculation efficiency, which is suitable for the real-time measurement needs of radar level gauges and ultimately provides stable distance data support for level monitoring.

[0031] In another preferred embodiment of the present invention, it is prohibited to install the radar level gauge in the center of the tank or directly above the feed inlet.

[0032] It is important to note that the large amount of dust and turbulence generated by the material impact during feeding should be avoided to prevent distortion of the echo signal. False reflected waves are easily formed in the center of the tank. Off-center installation can reduce measurement errors caused by multiple reflections from the tank wall, ensuring that the equipment accurately captures the true echo from the material surface, and significantly improving measurement stability and accuracy.

[0033] In another preferred embodiment of the present invention, the distance between the installation position of the radar level gauge and the pipe wall is ensured to be greater than 20cm.

[0034] It should be noted that this avoids interference from the reflection of radar waves by the pipe wall and reduces the generation of false echoes; sufficient spacing ensures that the radar beam covers the effective area of ​​the material surface, prevents the edge of the beam from touching the pipe wall and causing signal distortion, and also reserves operating space for equipment maintenance.

[0035] In another preferred embodiment of the present invention, when iteratively optimizing the i-th bat, the radical coefficient after each iteration is made to be different from the current iteration coefficient.

[0036] Understandably, when iterating and optimizing the i-th bat, making the aggressive coefficient different for each iteration from the current iteration coefficient allows for dynamic adjustment of the algorithm's search characteristics. In the early stages of iteration, a larger aggressive coefficient is used to expand the global search range and avoid getting trapped in local optima; in later stages, a smaller coefficient is used to focus on local exploration and improve convergence accuracy. This differentiated setting allows the algorithm to flexibly switch between exploration and development, significantly improving optimization efficiency and solution quality.

[0037] In another preferred embodiment of the present invention, when fitness values ​​are equal, bats with lower pulse emission rates are preferentially selected.

[0038] It is worth noting that when fitness values ​​are equal, bats with lower pulse emission rates are selected first, thus preserving individuals with global exploration capabilities in the early stages of the algorithm and avoiding premature convergence to local optima. At the same time, the population is guided to continue to expand the search range while maintaining the current optimization effect, balancing the relationship between global exploration and local mining, and ultimately increasing the probability of the algorithm finding the global optimum.

[0039] In another preferred embodiment of the present invention, if the global optimal solution xbest remains unchanged after iterative optimization, the subsequent steps of calculating local new solutions and fitness values ​​are stopped.

[0040] It is worth noting that if the global optimal solution xbest remains unchanged after iterative optimization, the subsequent steps of calculating local new solutions and fitness values ​​should be stopped. This avoids unnecessary computational consumption and hardware interaction, improving the efficiency of algorithm iteration. At the same time, it prevents unnecessary perturbation of the already determined optimal solution, ensuring the stability of the global optimal solution and reducing resource waste caused by redundant calculations.

[0041] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A radar emission angle adjustment method for radar level gauges based on the bat algorithm, characterized in that, Includes the following steps: S1: Obtain the emission angle range [θmin, θmax] of the radar level gauge, and construct the angle xi(t), which represents the angle of the i-th bat at iteration t; The bat population n and the maximum number of iterations Tmax are preset, and the angle xi and velocity vi of the i-th bat are preset, θmin≤xi≤θmax; Obtain the radar level gauge frequency range [fmin, fmax], initial loudness A0, and pulse emission rate r0; S2: Perform iterative optimization on the i-th bat. The iterative optimization includes the following steps: Let the number of iterations t ≤ Tmax; The adjustment frequency fi = fmin + (fmax - fmin) × β, where β is the preset aggressive coefficient; Get the current angle xi, calculate the fitness value F(xi) of the echo signal based on the angle xi, get the maximum fitness value of the echo signal after optimization iteration, and record the corresponding angle xi as the global optimal solution xbest; Update speed vi(t) = vi(t-1) + (xi(t-1) - xbest) × fi; Update angle xi_new = xi(t-1) + vi(t); Generate local new solutions Where ε represents a preset dynamic coefficient, -1≤ε≤1, This represents the average loudness of the current bat population; S3: Send the new local solution xlocal to the radar level gauge and collect echo data, and calculate the fitness value F(xlocal) corresponding to the new local solution xlocal. Calculate the pulse emission rate ri(t+1) of the i-th bat in the (t+1)-th iteration = r0 × [1 - exp(-γ × t)], where γ represents the preset pulse attenuation coefficient, 0 < γ < 1; Calculate the loudness Ai(t+1) of the i-th bat in the (t+1)-th iteration = α × Ai(t), where α represents the preset loudness attenuation coefficient, and α > 0; S4: Compare the fitness values ​​of all bats and select the maximum fitness value. Update the global optimal solution and fitness value. When the maximum number of iterations Tmax is reached, output the global optimal solution xbest. Set the radar level gauge to the angle corresponding to the global optimal solution xbest and simultaneously set the corresponding pulse emission rate and loudness. Enter the stable measurement mode.

2. The radar emission angle adjustment method for radar level gauges based on the bat algorithm according to claim 1, characterized in that, In step S2, the method for calculating the fitness value F(xi) of the echo signal based on the angle xi includes: The echo signal amplitude SS is obtained at angle xi. The ratio of echo amplitude to background noise level STR is calculated. The ratio of total energy of interfering echo to main echo energy CR is calculated. The echo position is measured N times continuously at angle xi. The reciprocal of the standard deviation SI of the echo position is calculated. N represents the preset number of measurements. Calculate the fitness value F(xi) = w1×SS + w2×STR - w3×CR - w4×SI, where w1, w2, w3, and w4 represent the preset first, second, third, and fourth weight coefficients, respectively.

3. The radar emission angle adjustment method for radar level gauges based on the bat algorithm according to claim 1, characterized in that, include: Obtain the frequency difference δ between the received and transmitted signals of the radar level gauge, obtain the frequency modulation slope K of the radar level gauge, and calculate the distance R = C × δ / 2K between the installation position of the radar level gauge and the material surface, where C represents the speed of light.

4. The radar emission angle adjustment method for radar level gauges based on the bat algorithm according to claim 1, characterized in that, Radar level gauges must not be installed in the center of the tank or directly above the feed inlet.

5. The radar emission angle adjustment method for radar level gauges based on the bat algorithm according to claim 1, characterized in that, Ensure that the distance between the radar level gauge installation location and the pipe wall is greater than 20cm.

6. The radar emission angle adjustment method for radar level gauges based on the bat algorithm according to claim 1, characterized in that, In step S2, when iteratively optimizing the i-th bat, the aggressive coefficient after each iteration is made to be different from the current iteration coefficient.

7. The radar emission angle adjustment method for radar level gauges based on the bat algorithm according to claim 1, characterized in that, In step S4, when fitness values ​​are equal, bats with lower pulse emission rates are preferred.

8. The radar emission angle adjustment method for radar level gauges based on the bat algorithm according to claim 1, characterized in that, In step S3, if the global optimal solution xbest remains unchanged after iterative optimization, the subsequent steps of calculating local new solutions and fitness values ​​are stopped.

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