Calibration methods, devices, equipment and media for pneumatic level gauges in wastewater environments

Abstract

This application discloses a calibration method, apparatus, equipment, and medium for a pneumatic level gauge in a wastewater environment, belonging to the field of instrument calibration. The method involves: collecting environmental data from the pneumatic level gauge and measured pneumatic pressure values ​​at multiple preset range points; wherein the preset range points include: a zero-point point, a full-range point, and several intermediate points; each preset range point corresponds to a standard pneumatic pressure value; calculating calibration parameters based on the measured pneumatic pressure values ​​at the zero-point point and the full-range point and the standard pneumatic pressure value; the calibration parameters include: zero-point offset and range correction coefficient; correcting the calibration parameters using a backpropagation neural network based on the first difference between the measured pneumatic pressure values ​​and the standard pneumatic pressure values ​​at each intermediate point and the environmental data; and calibrating the pneumatic level gauge based on the corrected calibration parameters. This application can solve the problem of low calibration accuracy of pneumatic level gauges caused by neglecting environmental factors.

Description

Technical Field

[0001] This application relates to the field of instrument calibration, and in particular to a method, apparatus, equipment and medium for calibrating a pneumatic level gauge in a wastewater environment. Background Technology

[0002] Pneumatic level gauges determine liquid level by measuring gas pressure. They are widely used in sewage lifting systems due to their simple structure and easy maintenance. However, in practical applications, pneumatic level gauges are susceptible to factors such as impurities in sewage, temperature changes, and pipe blockages, leading to a decrease in measurement accuracy.

[0003] Traditional calibration methods require manual operation, which is not only time-consuming and labor-intensive but also unable to respond to real-time environmental changes, making it difficult to meet the automation requirements of wastewater treatment systems. Some existing technologies measure the accuracy of level gauge readings by setting different range points; however, environmental factors can affect air pressure, resulting in different level gauge readings under different environmental conditions for the same liquid level. If the influence of environmental factors is ignored during the level gauge calibration process, the final calibration result will still be affected.

[0004] Therefore, ensuring the calibration efficiency of pneumatic level gauges while improving their calibration accuracy is a technical problem that needs to be solved. Summary of the Invention

[0005] This application provides a method, apparatus, equipment, and medium for calibrating a pneumatic level gauge in a wastewater environment, which can solve the problem of low calibration accuracy of pneumatic level gauges caused by ignoring environmental factors in the prior art.

[0006] Some embodiments of this application provide a method for calibrating a pneumatic level gauge in a wastewater environment, including: The system collects environmental data from a pneumatic level gauge and measured air pressure values ​​at multiple preset range points. The preset range points include: a zero point, a full range point, and several intermediate points. Each preset range point corresponds to a standard air pressure value. Based on the measured air pressure values ​​and standard air pressure values ​​at the zero point and the full range points, the correction parameters are calculated; the correction parameters include: zero point offset and range correction coefficient. The correction parameters are corrected using a backpropagation neural network based on the first difference between the measured air pressure value and the standard air pressure value at each intermediate point, as well as the environmental data. The pneumatic level gauge is calibrated according to the corrected calibration parameters.

[0007] Compared to existing technologies, the above embodiments have the following beneficial effects: Environmental factors can affect the expansion volume of gas, thereby affecting the gas pressure in a closed environment. When calibrating a pneumatic level gauge using a preset range point, the measured gas pressure at this preset range point is affected by environmental factors and the gauge's own accuracy loss, while the standard gas pressure value corresponding to the preset range point is the data under ideal conditions without any influence. Therefore, the correction parameters initially calculated based on the measured gas pressure value and the standard gas pressure value do not consider the environmental factor differences between the measured and standard gas pressure values. Only when the standard gas pressure value is also affected by the same environmental factors can the calculated calibration parameters correct for the influence of the gauge's own accuracy loss. Based on the above deficiencies, this application, after initially calculating the correction parameters, further learns the influence of environmental data on the correction parameters through the nonlinear fitting relationship of the backpropagation neural network, thereby correcting the initially calculated correction parameters and improving the subsequent calibration accuracy of the pneumatic level gauge. In addition, since only zero-point and full-range pressure data were used when initially calculating calibration parameters, the first difference was introduced simultaneously when correcting the calibration parameters, so as to take into account the influence of intermediate range deviation on the range correction coefficient, and further improve the calibration accuracy of the subsequent pneumatic level gauge.

[0008] Further, the step of calculating the correction parameters based on the measured air pressure values ​​and the standard air pressure values ​​at the zero point and the full range points includes: The second difference between the standard value of air pressure at the zero point and the measured value of air pressure is taken as the zero point offset. The range correction coefficient is determined based on the measured air pressure values ​​at the zero point and the full range points, and the standard air pressure values.

[0009] Compared to existing technologies, the above embodiments have the following advantages: Since the zero point is usually unaffected by the liquid level, the zero point offset can accurately reflect the overall offset error of the level gauge sensing cavity caused by changes in temperature, humidity, or gas density; while the range correction coefficient depends on the span difference between two points, effectively reflecting range compression or stretching caused by changes in sensor sensitivity or pipeline blockage. By splitting the parameter calculation into two types of errors—offset and proportional—the coupling error caused by direct regression based on single-point or multi-point data in traditional methods is avoided. This allows the subsequent neural network to correct the two error sources separately when performing environmental compensation, making the model's input distribution clearer and more learnable, ultimately improving the interpretability and stability of the calibration parameter structure, and providing an accurate initial benchmark for subsequent compensation based on intermediate points.

[0010] Further, determining the range correction coefficient based on the measured air pressure values ​​and the standard air pressure values ​​at the zero point and the full range points includes: The measured range is determined based on the third difference between the measured air pressure values ​​at the zero point and the full range points. The standard range is determined based on the fourth difference between the standard air pressure values ​​between the zero point and the full range point. The range correction coefficient is determined based on the ratio of the standard range to the actual measured range.

[0011] Compared to existing technologies, the above embodiments have the following advantages: By directly calculating based on the span between two points, the reliability of range error judgment is improved. Compared to directly fitting multiple discrete points, using the difference between two points can minimize the disturbance to range determination caused by pressure pipeline blockage, bubble adhesion, or local fluctuations in the wastewater environment, because the stability of the two endpoints is usually the strongest and least affected by random noise. This not only improves the physical rationality of parameter calculation but also reduces the information dimension that the neural network needs to learn in the subsequent correction stage, allowing the network to focus more on learning the disturbance law of environmental factors on the proportional error.

[0012] Furthermore, the backpropagation neural network is used to map the nonlinear relationship between the correction value of the zero-point offset and the environmental data.

[0013] Compared to existing technologies, the above embodiments have the following advantages: In wastewater environments, factors such as temperature, gas solubility, sludge adhesion, and gas density changes can nonlinearly alter the initial pressure of the sensing cavity, causing zero-point offset to exhibit characteristics that are difficult to capture by traditional linear models. By learning the implicit relationship between environmental variables and offset errors through neural networks, the system can maintain zero-point stability under different pollution levels, temperature fluctuation ranges, and pipeline humidity conditions. This structure separates zero-point offset modeling from range error modeling, making the learning objective of the neural network more focused and reducing the training difficulty caused by multi-parameter coupling.

[0014] Further, the step of correcting the calibration parameters using a backpropagation neural network based on the first difference between the measured air pressure value and the standard air pressure value at each intermediate point, and the environmental data, includes: The environmental data is input into the backpropagation neural network to obtain the correction values ​​corresponding to the zero-point offset and the range correction coefficient, respectively. The zero-point offset is corrected based on the correction value of the zero-point offset; The range correction coefficient is corrected based on the correction value of the range correction coefficient and the first difference between each of the intermediate points.

[0015] Compared to existing technologies, the above embodiments have the following advantages: Since zero-point offset is more strongly correlated with environmental changes, and range error is more susceptible to proportional distortion revealed by intermediate point deviations, this application effectively avoids insufficient compensation caused by a single feature-driven approach through environment-driven offset correction and a proportional correction strategy jointly driven by the environment and intermediate points. The introduction of intermediate points allows the system to capture nonlinear range changes during calibration, such as nonlinear pressure drop distribution caused by localized pipe blockage, making range correction effective not only at the endpoints but also covering the entire range, thus improving linearization. This hierarchical correction method ensures that calibration parameters gradually approach optimal values ​​under multi-source information fusion, significantly improving the full-range measurement accuracy of the level gauge in complex wastewater environments.

[0016] Further, the step of calibrating the pneumatic level gauge according to the corrected calibration parameters includes: adding the zero-point offset to the reading of the pneumatic level gauge and multiplying it by the range correction coefficient to obtain the corrected reading.

[0017] Compared to existing technologies, the above embodiments offer the following advantages: Zero-point error typically originates from baseline drift caused by residual gas pressure within the sensing cavity or environmental changes; while range error reflects variations in sensor sensitivity, pipeline patency, and overall pressure drop ratio. By compensating for both sequentially, the calibration process better aligns with the measurement mechanism of pneumatic level gauges, avoiding the mutual masking or amplification of errors caused by directly fitting linear functions. Simultaneously, this structure ensures that the calibrated readings maintain linear consistency across the entire range, enabling the level gauge to maintain good response stability when handling sudden changes in sewage level, foam disturbances, or temperature gradients. This improves the reliability of long-term online operation and reduces maintenance costs caused by accuracy drift.

[0018] Furthermore, before collecting environmental data from the pneumatic level gauge and the measured pneumatic pressure values ​​at multiple preset range points, the method further includes cleaning impurities from the inner wall of the pneumatic level gauge.

[0019] Compared to existing technologies, the above embodiments have the following beneficial effects: Wastewater environments often experience phenomena such as silt deposition, grease adhesion, and microbial coagulation, which alter the effective space inside the sensing pipeline, causing changes in gas compression and resulting in random or cumulative deviations in the measured gas pressure. If cleaning is not completed before data acquisition, the calibration model will be calculated based on data affected by contamination, leading to zero-point offset and range correction coefficients failing to accurately reflect the actual performance of the level gauge. By pre-cleaning the pipeline, the initial state of the sensing channel can be restored, allowing gas pressure changes to more accurately correspond to the true liquid level, ensuring that subsequent calculations and compensations are based on repeatable and reliable fundamental data.

[0020] Another embodiment of this application provides a pneumatic liquid level gauge calibration device in a wastewater environment, including: a data acquisition module, a correction coefficient calculation module, a correction coefficient correction module, and a liquid level gauge calibration module; The data acquisition module is used to acquire environmental data from the pneumatic level gauge and measured air pressure values ​​at multiple preset range points; wherein, the preset range points include: a zero point, a full range point, and several intermediate points; each preset range point corresponds to a standard air pressure value. The correction coefficient calculation module is used to calculate correction parameters based on the measured air pressure values ​​and the standard air pressure values ​​at the zero point and the full range points; the correction parameters include: zero point offset and range correction coefficient. The correction coefficient correction module is used to correct the correction parameters by means of a backpropagation neural network, based on the first difference between the measured air pressure value and the standard air pressure value at each intermediate point and the environmental data. The liquid level gauge calibration module is used to calibrate the pneumatic liquid level gauge according to the corrected calibration parameters.

[0021] Another embodiment of this application also provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of the pneumatic level gauge calibration method in a wastewater environment as described in this application.

[0022] Another embodiment of this application also provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the pneumatic level gauge calibration method in a wastewater environment as described in this application. Attached Figure Description

[0023] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating a method for calibrating a pneumatic level gauge in a wastewater environment, as provided in some embodiments of this application. Figure 2 This is a schematic diagram of the structure of a pneumatic level gauge calibration device in a wastewater environment provided in some embodiments of this application. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] Unless otherwise defined, 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 application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0027] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0028] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0029] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0030] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0031] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0032] Traditional calibration methods require manual operation, which is not only time-consuming and labor-intensive but also unable to respond to real-time environmental changes, making it difficult to meet the automation requirements of wastewater treatment systems. Some existing technologies measure the accuracy of level gauge readings by setting different range points; however, environmental factors can affect air pressure, resulting in different level gauge readings under different environmental conditions for the same liquid level. If the influence of environmental factors is ignored during the level gauge calibration process, the final calibration result will still be affected.

[0033] Please refer to Figure 1 To address the problem of low calibration accuracy of pneumatic level gauges in existing technologies due to neglecting environmental factors, this application provides a calibration method for pneumatic level gauges in a wastewater environment, comprising steps S101 to S104, specifically: S101: Collect environmental data from the pneumatic level gauge and measured air pressure values ​​at multiple preset range points; wherein, the preset range points include: a zero point, a full range point, and several intermediate points; each preset range point corresponds to a standard air pressure value.

[0034] Preferably, in some embodiments of this application, the calibration method for a pneumatic level gauge in a wastewater environment is triggered in the following ways: Comprehensive and real-time monitoring of the operating status of the wastewater lifting device, including its working mode (e.g., running, idle), the stability of the level gauge output signal, and whether external control commands are received. When no calibration is currently being performed, a timer is used to keep track of the time. Once the duration exceeds a preset threshold (which can be set according to the actual working conditions of different wastewater lifting devices, e.g., 24 hours), automatic calibration is triggered. Real-time analysis of the fluctuations in the level gauge output signal is also performed. When the fluctuation amplitude exceeds a preset error range (e.g., ±5% FS), it indicates a possible measurement deviation in the level gauge, and calibration is also triggered. Additionally, receiving remote control commands, such as calibration commands issued by operators through a remote monitoring platform, or reaching a preset scheduled maintenance time, will also automatically initiate the level gauge calibration process. This multi-condition triggering mechanism ensures timely calibration under various possible level gauge malfunctions, guaranteeing measurement accuracy.

[0035] Furthermore, in some embodiments of this application, before collecting environmental data from the pneumatic level gauge and the measured pneumatic pressure values ​​at multiple preset range points, the method further includes cleaning impurities from the inner wall of the pneumatic level gauge.

[0036] Preferably, in some embodiments of this application, cleaning the inner wall of the pneumatic level gauge includes: using a backflushing device to input gas at a preset pressure value into the pipe of the pneumatic level gauge for a certain period of time (e.g., 5 seconds), thereby effectively removing dirt and impurities adhering to the pipe wall and discharging them with the wastewater. The backflushing process lasts for 30 seconds to ensure the cleanliness of the inside of the pipe, providing a clean and undisturbed environment for subsequent calibration work.

[0037] Preferably, in some embodiments of this application, the cleaning of impurities on the inner wall of the pneumatic level gauge can be performed before each calibration of the pneumatic level gauge, or a preset cycle can be set to perform the operation at regular intervals.

[0038] Preferably, in some embodiments of this application, the preset measurement range points can be understood as: setting the preset measurement range points to 0%, 25%, 50%, 75%, and 100%. For each preset measurement range point, a gas source storing standard gas with stable pressure is connected to the measuring pipeline through a gas injection device, and then standard gas at 0 kPa, 25 kPa, 50 kPa, 75 kPa, and 100 kPa is injected respectively. However, since the standard gas here is actually a gas under ideal conditions, for example, the volume of gas injected for 100 kPa standard gas is different at different temperatures. At this time, the injected gas is the gas volume corresponding to the ideal environment, so there are still certain environmental factors affecting it, and further environmental compensation is needed through a neural network.

[0039] Preferably, in some embodiments of this application, after each injection of standard gas into the pipeline of the pneumatic level gauge, a 30-second stabilization period is maintained. This is to ensure that the gas pressure in the pipeline is sufficiently stable, avoiding pressure fluctuations caused by factors such as gas flow, thereby ensuring the accuracy of subsequent data acquisition.

[0040] S102: Calculate correction parameters based on the measured air pressure values ​​and standard air pressure values ​​at the zero point and the full range points; the correction parameters include: zero point offset and range correction coefficient.

[0041] Preferably, in some embodiments of this application, after knowing the measured air pressure value of each preset range point, the measured air pressure value is subtracted from the corresponding standard air pressure value to obtain an error value. Based on the magnitude of the error value, it is determined whether the air pressure data collected at the preset range point is invalid data. Specifically, when the ratio of the error value to the standard air pressure value exceeds ±10% FS, the air pressure data collected at the preset range point is considered invalid data, and the measured air pressure value at the preset range point is collected again.

[0042] Furthermore, in some embodiments of this application, the step of calculating the correction parameter based on the measured air pressure values ​​and the standard air pressure values ​​at the zero-point and full-range points includes: The second difference between the standard value of air pressure at the zero point and the measured value of air pressure is taken as the zero point offset. The range correction coefficient is determined based on the measured air pressure values ​​at the zero point and the full range points, and the standard air pressure values.

[0043] Preferably, in some embodiments of this application, the formula for calculating the zero-point offset is: zero-point offset = standard value of air pressure at the zero point - measured value of air pressure at the zero point.

[0044] Since the zero point is typically unaffected by liquid level, the zero-point offset accurately reflects the overall offset error of the level gauge's sensing cavity caused by changes in temperature, humidity, or gas density. Meanwhile, the range correction coefficient, dependent on the span difference between two points, effectively reflects range compression or expansion caused by changes in sensor sensitivity or pipeline blockage. By splitting parameter calculation into offset and proportional errors, the coupling error caused by direct regression based on single or multi-point data in traditional methods is avoided. This allows the subsequent neural network to correct for both error sources separately during environmental compensation, resulting in a clearer and more learnable input distribution for the model. Ultimately, this improves the interpretability and stability of the calibration parameter structure and provides an accurate initial benchmark for subsequent compensation based on intermediate points.

[0045] Furthermore, in some embodiments of this application, determining the range correction coefficient based on the measured air pressure values ​​and the standard air pressure values ​​at the zero-point and full-range points includes: The measured range is determined based on the third difference between the measured air pressure values ​​at the zero point and the full range points. The standard range is determined based on the fourth difference between the standard air pressure values ​​between the zero point and the full range point. The range correction coefficient is determined based on the ratio of the standard range to the actual measured range.

[0046] Preferably, in some embodiments of this application, the calculation formula for the range correction coefficient is: Range correction coefficient = (standard air pressure value at the full range point - standard air pressure value at the zero point point) / (measured air pressure value at the full range point - measured air pressure value at the zero point point).

[0047] By directly calculating the span based on two points, the reliability of range error judgment is improved. Compared to fitting multiple discrete points directly, using the difference between two points minimizes the disturbances to range determination caused by pressure pipeline blockage, bubble adhesion, or local fluctuations in the wastewater environment, as the stability of the two endpoints is usually the strongest and least affected by random noise. This not only improves the physical rationality of parameter calculations but also reduces the information dimension that the neural network needs to learn in the subsequent correction stage, allowing the network to focus more on learning the perturbation patterns of environmental factors on the proportional error.

[0048] S103: The correction parameters are corrected by using a backpropagation neural network based on the first difference between the measured air pressure value and the standard air pressure value at each intermediate point and the environmental data.

[0049] Furthermore, in some embodiments of this application, the backpropagation neural network is used to map the nonlinear relationship between the correction value of the zero-point offset and the environmental data.

[0050] Preferably, in some embodiments of this application, the backpropagation neural network learns the correlation between "error between measured value and standard value – environmental factors – correction parameters" through nonlinear mapping capabilities: for example, it identifies that "the increase in ambient temperature leads to a higher range measurement value, and the range correction coefficient needs to be slightly reduced."

[0051] Preferably, in some embodiments of this application, the environmental parameters include: the current ambient temperature and the current atmospheric pressure.

[0052] Preferably, in some embodiments of this application, the backpropagation neural network is trained and acquired using historical calibration parameters, historical environmental parameters, and the error between historical measured values ​​and standard values.

[0053] It should be noted that backpropagation neural networks are a general existing technology, and this application does not limit the specific structure of the backpropagation neural network used.

[0054] In wastewater environments, factors such as temperature, gas solubility, sludge adhesion, and changes in gas density can nonlinearly alter the initial pressure of the sensing cavity, causing zero-point offset to exhibit characteristics that are difficult to capture using traditional linear models. By learning the implicit relationship between environmental variables and offset error through a neural network, the system can maintain zero-point stability under varying pollution levels, temperature fluctuations, and pipeline humidity conditions. This structure separates zero-point offset modeling from range error modeling, making the neural network's learning objective more focused and reducing the training difficulty caused by multi-parameter coupling.

[0055] Furthermore, in some embodiments of this application, the step of correcting the correction parameters using a backpropagation neural network based on the first difference between the measured air pressure value and the standard air pressure value at each intermediate point and the environmental data includes: The environmental data is input into the backpropagation neural network to obtain the correction values ​​corresponding to the zero-point offset and the range correction coefficient, respectively. The zero-point offset is corrected based on the correction value of the zero-point offset; The range correction coefficient is corrected based on the correction value of the range correction coefficient and the first difference between each of the intermediate points.

[0056] Preferably, in some embodiments of this application, the step of inputting the environmental data into the backpropagation neural network to obtain the correction values ​​corresponding to the zero-point offset and the range correction coefficient can be specifically understood as follows: For the zero-point offset, assuming its initial value is -0.35 kPa, combined with a temperature increase of 4°C (compared to 25°C in historical data), it is further fine-tuned to -0.37 kPa (i.e., for every 1°C increase, the absolute value of the zero-point offset increases by 0.005 kPa). For the range correction coefficient, assuming its initial value is 0.9838, combined with the temperature effect and the range error of the intermediate point (i.e., the first difference), it is fine-tuned to 0.9825.

[0057] It is understood that this application does not limit the process of correcting the range correction coefficient based on the correction value of the range correction coefficient and the first difference of each intermediate point. The process can be to combine the correction value of the neural network and the average proportional deviation of the first difference of each intermediate point into the final correction amount by weighted linear method; or to linearly fit the correction value of the full range points output by the linear model with the first difference of each intermediate point by least squares method to obtain the relationship between the degree of error deviation of each range and the range.

[0058] Since zero-point offset is more strongly correlated with environmental changes, and range error is more susceptible to proportional distortion revealed by intermediate point deviation, this application effectively avoids insufficient compensation caused by a single feature-driven approach by employing an environment-driven offset correction strategy and a proportional correction strategy jointly driven by the environment and intermediate points. The introduction of intermediate points allows the system to capture nonlinear range changes during calibration, such as nonlinear pressure drop distribution caused by localized pipe blockage, making range correction effective not only at the endpoints but also covering the entire range, thus improving linearization. This hierarchical correction method ensures that calibration parameters gradually approach optimal values ​​under multi-source information fusion, significantly improving the full-range measurement accuracy of the level gauge in complex wastewater environments.

[0059] S104: Calibrate the pneumatic level gauge according to the corrected calibration parameters.

[0060] Furthermore, in some embodiments of this application, the step of correcting the pneumatic level gauge according to the corrected correction parameters includes: adding the zero-point offset to the reading of the pneumatic level gauge and multiplying it by the range correction coefficient to obtain the corrected reading.

[0061] Preferably, in some embodiments of this application, after calibrating the pneumatic level gauge according to the corrected calibration parameters, the calibration result is further verified, including: calculating the error between the corrected reading and the standard pneumatic pressure value at the corresponding range point. For example, the correction value for a 50% range point is: Corrected measured value = (Reading of pneumatic level gauge + Zero offset) × Range correction coefficient = (50.5 - 0.37) × 0.9825 ≈ 50.13 × 0.9825 ≈ 49.25 kPa. Further, the standard pneumatic pressure value is subtracted from the corrected measured value and divided by the standard pneumatic pressure value to obtain the error ratio (49.25 - 50) / 50 × 100% = -1.5%. If this ratio is within a preset range, the verification is considered successful; otherwise, the verification is considered unsuccessful, and the pneumatic level gauge calibration method of this application is called again.

[0062] Preferably, in some embodiments of this application, after obtaining the zero-point offset and the range correction coefficient, the method further includes: to eliminate extreme correction parameters caused by backpropagation neural network anomalies or data interference, to ensure that the correction parameters conform to the hardware performance and actual working conditions of the level gauge, and to avoid invalid parameters being used for level gauge correction, this application sets a reasonable range for the parameters (based on the level gauge's range and hardware characteristics). For example, the reasonable range for the zero-point offset is ±2 kPa (exceeding this indicates a zero-point fault in the level gauge, which cannot be resolved by parameter adjustment); the reasonable range for the range correction coefficient is 0.9~1.1 (exceeding this indicates a serious abnormality in the linearity of the level gauge, requiring hardware investigation). The difference between the obtained correction parameters and the reasonable range is further compared. For example, if the zero-point offset is -0.37 kPa, within ±2 kPa, and the range correction coefficient is 0.9825, within 0.9~1.1, this is considered reasonable; if it exceeds the range, the optimization of the backpropagation neural network is re-executed, and abnormal data filtering logic is added. Once the zero-point offset is verified against the range correction coefficient, a final calibration parameter report is generated, recording the calibration parameter values, calculation basis, and environmental conditions for subsequent traceability.

[0063] Preferably, in some embodiments of this application, after calibration, the calibration effect is further verified by injecting standard gas at different pressures into the pipeline, including the standard gas pressure values ​​corresponding to the 0%, 25%, 50%, 75%, and 100% range points. The level gauge then collects the measured gas pressure value again and compares it with the standard gas pressure value to calculate the error. If the error corresponding to each preset range point is within the preset range, the calibration is considered successful; otherwise, the pneumatic level gauge calibration method of this application is called again. This mechanism of multiple verifications and adjustments ensures that the level gauge achieves high measurement accuracy after calibration.

[0064] Zero-point error typically originates from baseline drift caused by residual gas pressure within the sensing chamber or environmental changes; while range error reflects variations in sensor sensitivity, pipeline patency, and overall pressure drop ratio. By compensating for both sequentially, the calibration process better aligns with the measurement mechanism of pneumatic level gauges, avoiding the mutual masking or amplification of errors caused by directly fitting linear functions. Simultaneously, this structure ensures that the calibrated readings maintain linear consistency across the entire range, enabling the level gauge to maintain good response stability when handling sudden changes in wastewater level, foam disturbances, or temperature gradients. This improves the reliability of long-term online operation and reduces maintenance costs caused by accuracy drift.

[0065] In summary, the pneumatic level gauge calibration method for wastewater environments provided in this application has the following advantages compared to existing technologies: Environmental factors affect the expansion volume of gas, thus affecting the gas pressure in a closed environment. When calibrating a pneumatic level gauge using a preset range point, the measured gas pressure at this point is affected by environmental factors and the gauge's own accuracy loss, while the standard gas pressure value corresponding to the preset range point is the data under ideal conditions without any influence. Therefore, the correction parameters initially calculated based on the measured and standard gas pressure values ​​do not consider the environmental factor differences between them. Only when the standard gas pressure value is also affected by the same environmental factors can the calculated calibration parameters correct for the influence of the gauge's own accuracy loss. Based on these shortcomings, after initially calculating the correction parameters, this application further learns the influence of environmental data on the correction parameters through the nonlinear fitting relationship of a backpropagation neural network, thereby correcting the initially calculated correction parameters and improving the subsequent calibration accuracy of the pneumatic level gauge. In addition, since only zero-point and full-range pressure data were used when initially calculating calibration parameters, the first difference was introduced simultaneously when correcting the calibration parameters, so as to take into account the influence of intermediate range deviation on the range correction coefficient, and further improve the calibration accuracy of the subsequent pneumatic level gauge.

[0066] like Figure 2 As shown, based on the above method embodiments, an embodiment of this application provides a pneumatic liquid level gauge calibration device in a wastewater environment, including: a data acquisition module 201, a correction coefficient calculation module 202, a correction coefficient correction module 203, and a liquid level gauge calibration module 204. Further, in some embodiments of this application, the data acquisition module 201 is used to acquire environmental data of the pneumatic level gauge and measured air pressure values ​​at multiple preset range points; wherein, the preset range points include: a zero point, a full range point, and several intermediate points; each preset range point corresponds to a standard air pressure value; the correction coefficient calculation module 202 is used to calculate correction parameters based on the measured air pressure values ​​at the zero point and the full range points and the standard air pressure value; the correction parameters include: zero offset and range correction coefficient; the correction coefficient correction module 203 is used to correct the correction parameters using a backpropagation neural network based on the first difference between the measured air pressure value at each intermediate point and the standard air pressure value, and the environmental data; the level gauge correction module 204 is used to correct the pneumatic level gauge based on the corrected correction parameters.

[0067] Further, in some embodiments of this application, the correction coefficient calculation module 202 includes: a first calculation unit and a second calculation unit; the correction coefficient calculation module 202 is used to calculate correction parameters based on the measured air pressure values ​​and the standard air pressure values ​​at the zero point and the full range points, including: the first calculation unit is used to use the second difference between the standard air pressure value and the measured air pressure value at the zero point as the zero point offset; the second calculation unit is used to determine the range correction coefficient based on the measured air pressure values ​​and the standard air pressure values ​​at the zero point and the full range points.

[0068] Furthermore, in some embodiments of this application, the second calculation unit is used to determine the range correction coefficient based on the measured air pressure values ​​and the standard air pressure values ​​at the zero point and the full range points, including: determining the measured range based on a third difference between the measured air pressure values ​​at the zero point and the full range points; determining the standard range based on a fourth difference between the standard air pressure values ​​at the zero point and the full range points; and determining the range correction coefficient based on the ratio of the standard range to the measured range.

[0069] Furthermore, in some embodiments of this application, the backpropagation neural network is used to map the nonlinear relationship between the correction value of the zero-point offset and the environmental data.

[0070] Further, in some embodiments of this application, the correction coefficient correction module 203 comprises a neural network calling unit, a first correction unit, and a second correction unit; the correction coefficient correction module 203 is used to correct the correction parameters by means of a backpropagation neural network based on the first difference between the measured air pressure value and the standard air pressure value at each intermediate point and the environmental data, including: the neural network calling unit is used to input the environmental data into the backpropagation neural network to obtain the correction values ​​corresponding to the zero-point offset and the range correction coefficient respectively; the first correction unit is used to correct the zero-point offset based on the correction value of the zero-point offset; the second correction unit is used to correct the range correction coefficient based on the correction value of the range correction coefficient and the first difference at each intermediate point.

[0071] Furthermore, in some embodiments of this application, the liquid level gauge calibration module 204 is used to calibrate the pneumatic liquid level gauge according to the corrected calibration parameters, including: adding the zero-point offset to the reading of the pneumatic liquid level gauge and multiplying it by the range correction coefficient to obtain the corrected reading.

[0072] Furthermore, in some embodiments of this application, before collecting environmental data from the pneumatic level gauge and the measured pneumatic pressure values ​​at multiple preset range points, the method further includes cleaning impurities from the inner wall of the pneumatic level gauge.

[0073] It is understood that the above-described device embodiments correspond to the method embodiments of this application, and can implement the pneumatic level gauge calibration method in a wastewater environment provided by any of the above-described method embodiments of this application.

[0074] In summary, the pneumatic level gauge calibration device for wastewater environments provided in this application has the following advantages compared to existing technologies: Environmental factors affect the expansion volume of gas, thus affecting the gas pressure in a closed environment. When calibrating a pneumatic level gauge using a preset range point, the measured gas pressure at this preset range point is affected by environmental factors and the gauge's own accuracy loss. The standard gas pressure value corresponding to the preset range point is the data under ideal conditions without any influence. Therefore, the correction parameters initially calculated based on the measured and standard gas pressure values ​​do not consider the environmental factor differences between them. Only when the standard gas pressure value is also affected by the same environmental factors can the calculated calibration parameters correct for the influence of the gauge's own accuracy loss. Based on the above deficiencies, after initially calculating the correction parameters, this application further learns the influence of environmental data on the correction parameters through the nonlinear fitting relationship of the backpropagation neural network, thereby correcting the initially calculated correction parameters and improving the subsequent calibration accuracy of the pneumatic level gauge. In addition, since only zero-point and full-range pressure data were used when initially calculating calibration parameters, the first difference was introduced simultaneously when correcting the calibration parameters, so as to take into account the influence of intermediate range deviation on the range correction coefficient, and further improve the calibration accuracy of the subsequent pneumatic level gauge.

[0075] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided in this application, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0076] Based on the above embodiments of the pneumatic level gauge calibration method in a wastewater environment, another embodiment of this application provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the pneumatic level gauge calibration method in a wastewater environment according to any embodiment of this application.

[0077] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete this application. The one or more module units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0078] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0079] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0080] Based on the above-described method embodiments, another embodiment of this application provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the pneumatic level gauge calibration method in a wastewater environment as described in any of the above-described method embodiments of this application.

[0081] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

Claims

1. A calibration method for a pneumatic level gauge in a wastewater environment, characterized in that, include: The system collects environmental data from a pneumatic level gauge and measured air pressure values ​​at multiple preset range points. The preset range points include: a zero point, a full range point, and several intermediate points. Each preset range point corresponds to a standard air pressure value. Based on the measured air pressure values ​​and standard air pressure values ​​at the zero point and the full range points, correction parameters are calculated; the correction parameters include: zero point offset and range correction coefficient. The correction parameters are corrected using a backpropagation neural network based on the first difference between the measured air pressure value and the standard air pressure value at each intermediate point, as well as the environmental data. The pneumatic level gauge is calibrated according to the corrected calibration parameters; The step of calculating correction parameters based on the measured air pressure values ​​and standard air pressure values ​​at the zero point and the full range points includes: The second difference between the standard value of air pressure at the zero point and the measured value of air pressure is taken as the zero point offset. The range correction coefficient is determined based on the measured air pressure values ​​and the standard air pressure values ​​at the zero point and the full range points. The step of determining the range correction coefficient based on the measured air pressure values ​​and the standard air pressure values ​​at the zero point and the full range points includes: The measured range is determined based on the third difference between the measured air pressure values ​​at the zero point and the full range points. The standard range is determined based on the fourth difference between the standard air pressure values ​​between the zero point and the full range point. The range correction coefficient is determined based on the ratio of the standard range to the actual measured range. The backpropagation neural network is used to map the nonlinear relationship between the correction value of the zero-point offset and the environmental data. The step of correcting the calibration parameters using a backpropagation neural network based on the first difference between the measured air pressure value and the standard air pressure value at each intermediate point, and the environmental data, includes: The environmental data is input into the backpropagation neural network to obtain the correction values ​​corresponding to the zero-point offset and the range correction coefficient, respectively. The zero-point offset is corrected based on the correction value of the zero-point offset; The range correction coefficient is corrected based on the correction value of the range correction coefficient and the first difference between each of the intermediate points.

2. The calibration method for a pneumatic level gauge in a wastewater environment as described in claim 1, characterized in that, The step of calibrating the pneumatic level gauge according to the corrected calibration parameters includes: adding the zero-point offset to the reading of the pneumatic level gauge and multiplying it by the range correction coefficient to obtain the corrected reading.

3. The calibration method for a pneumatic level gauge in a wastewater environment as described in any one of claims 1 to 2, characterized in that, Before collecting environmental data from the pneumatic level gauge and the measured air pressure values ​​at multiple preset range points, the process also includes cleaning impurities from the inner wall of the pneumatic level gauge.

4. A pneumatic level gauge calibration device for use in wastewater environments, characterized in that, include: Data acquisition module, correction coefficient calculation module, correction coefficient correction module, and level gauge calibration module; The data acquisition module is used to acquire environmental data from the pneumatic level gauge and measured air pressure values ​​at multiple preset range points; wherein, the preset range points include: a zero point, a full range point, and several intermediate points; each preset range point corresponds to a standard air pressure value. The correction coefficient calculation module is used to calculate correction parameters based on the measured air pressure values ​​and standard air pressure values ​​at the zero point and the full range points; the correction parameters include: zero point offset and range correction coefficient; The correction coefficient correction module is used to correct the correction parameters by means of a backpropagation neural network, based on the first difference between the measured air pressure value and the standard air pressure value at each intermediate point and the environmental data. The liquid level gauge calibration module is used to calibrate the pneumatic liquid level gauge according to the corrected calibration parameters; The correction coefficient calculation module includes a first calculation unit and a second calculation unit. The correction coefficient calculation module is used to calculate correction parameters based on the measured air pressure values ​​and standard air pressure values ​​at the zero point and the full range points. Specifically, the first calculation unit uses the second difference between the standard air pressure value and the measured air pressure value at the zero point as the zero-point offset. The second calculation unit determines the range correction coefficient based on the measured air pressure values ​​and standard air pressure values ​​at the zero point and the full range points. The second calculation unit is used to determine the range correction coefficient based on the measured air pressure values ​​and the standard air pressure values ​​at the zero point and the full range points, including: determining the actual measurement range based on a third difference between the measured air pressure values ​​at the zero point and the full range points; determining the standard range based on a fourth difference between the standard air pressure values ​​at the zero point and the full range points; and determining the range correction coefficient based on the ratio of the standard range to the actual measurement range. The backpropagation neural network is used to map the nonlinear relationship between the correction value of the zero-point offset and the environmental data. The correction coefficient correction module includes: a neural network calling unit, a first correction unit, and a second correction unit. The correction coefficient correction module is used to correct the correction parameters using a backpropagation neural network based on the first difference between the measured air pressure value and the standard air pressure value at each intermediate point, and the environmental data. This includes: the neural network calling unit inputting the environmental data into the backpropagation neural network to obtain the correction values ​​corresponding to the zero-point offset and the range correction coefficient; the first correction unit correcting the zero-point offset based on its correction value; and the second correction unit correcting the range correction coefficient based on its correction value and the first difference between each intermediate point.

5. A terminal device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement a method for calibrating a pneumatic level gauge in a wastewater environment as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform a method for calibrating a pneumatic level gauge in a wastewater environment as described in any one of claims 1 to 3.

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