Method and system for accurately measuring liquid level of printing and dyeing sewage

Abstract

The invention relates to a method and system for accurately measuring the liquid level of printing and dyeing sewage. The method comprises the steps that heat distribution information and environment temperature detection data are obtained; correcting the thermodynamic distribution information by using the environment temperature detection data, splitting the corrected thermodynamic distribution information based on a preset splitting and combining rule, and recombining to obtain a plurality of pieces of dispersed thermodynamic distribution information; inputting the dispersed thermal distribution information into a preset rationality analysis model to obtain thermal distribution rationality information; and obtaining a measurement result according to the thermodynamic distribution rationality information. The device has the effect of improving the accuracy of printing and dyeing sewage liquid level measurement.

Description

Technical Field

[0001] The present invention relates to the technical field of measuring the liquid level of printing and dyeing wastewater, and particularly to a method and system for accurately measuring the liquid level of printing and dyeing wastewater. Background Art

[0002] At present, in the technical field of liquid level measurement, traditional liquid level gauges are widely used, and these liquid level gauges can generally meet the liquid level detection requirements in general liquid environments. However, in special environments such as printing and dyeing wastewater, the high-temperature characteristics of printing and dyeing wastewater and the problem of easy formation of condensed water droplets greatly increase the difficulty of ensuring measurement accuracy. For example, due to the formation of condensed water droplets and the high temperature of the wastewater, these devices often have inaccurate judgments, mistaking the condensed water droplets for the actual liquid level, resulting in large measurement errors. This not only affects the precise control of the process flow, but also increases the frequency of manual intervention, reducing the overall operation efficiency and reliability.

[0003] To address the difficulties in measuring the liquid level in printing and dyeing wastewater, there are some improvement schemes in the existing technologies. These schemes mainly reduce the influence of high-temperature steam and condensed water droplets on the liquid level reading by adding specific filters, optimizing algorithms, or making certain improvements to traditional liquid level gauges. However, these existing technical means are still inadequate in practice and cannot meet the high requirements for accurate measurement in the printing and dyeing wastewater environment. Summary of the Invention

[0004] To improve the accuracy of measuring the liquid level of printing and dyeing wastewater, the present application provides a method and system for accurately measuring the liquid level of printing and dyeing wastewater.

[0005] In a first aspect, the above-mentioned invention object of the present application is achieved through the following technical solutions: A method for accurately measuring the liquid level of printing and dyeing wastewater, which is applied to a thermal induction type liquid level measurement system. The thermal induction type liquid level measurement system includes a thermal imager for capturing the thermal distribution, and one or more temperature sensors connected to the thermal imager for providing ambient temperature data to correct measurement errors. The method for accurately measuring the liquid level of printing and dyeing wastewater includes: Obtaining thermal distribution information and ambient temperature detection data; Using the ambient temperature detection data to correct the thermal distribution information, and then based on a preset splitting and combining rule, splitting and recombining the corrected thermal distribution information to obtain a number of dispersed thermal distribution information; Inputting the dispersed thermal distribution information into a preset rationality analysis model to obtain thermal distribution rationality information; Obtaining a measurement result according to the thermal distribution rationality information.

[0006] By adopting the above technical solution, interference factors such as steam and water droplets that may exist on the surface of printing and dyeing wastewater will affect the accuracy of traditional liquid level measurement methods. Therefore, by introducing a thermal imager to capture the thermal distribution and combining it with the environmental temperature detection data for correction, the actual temperature distribution on the wastewater surface can be more accurately reflected. The real-time correction combined with the environmental temperature reduces the measurement error caused by environmental temperature changes, thereby improving the accuracy of liquid level measurement. In addition, by splitting and recombining the corrected thermal distribution information and using a rationality analysis model for screening, interference factors such as steam and water droplets can be effectively excluded, improving the stability and reliability of the measurement results. For example, by comparing multiple thermal distribution information, areas with unreasonable temperatures are identified, and some thermal images considered to be steam and water droplets are removed, resulting in a cleaner measurement result. Thus, by combining thermal imaging technology and an intelligent analysis model, the intelligent measurement of the liquid level of printing and dyeing wastewater is achieved. Since the liquid level of printing and dyeing wastewater may be affected by various factors such as wastewater temperature, flow rate, and concentration, traditional measurement methods often have difficulty adapting to these complex environments. By comprehensively considering multiple factors such as thermal distribution and environmental temperature, the actual measurement requirements of the liquid level of printing and dyeing wastewater can be better met, improving the adaptability and flexibility of the measurement.

[0007] In a preferred example of the present application, it can be further configured as follows: when splitting the corrected thermal distribution information, at least two different sets of thermal distribution information are selected for splitting, and the same thermal distribution information is split into at least two different parts.

[0008] By adopting the above technical solution, by selecting at least two different sets of thermal distribution information for splitting, more diverse data samples can be obtained, more comprehensively reflecting the thermal distribution on the surface of printing and dyeing wastewater, providing a richer information basis for subsequent analysis and measurement. Splitting the same thermal distribution information into at least two different parts can make the analysis process more detailed and in-depth. Different splitting methods may represent different characteristics or patterns in the thermal distribution. These characteristics or patterns may not be obvious when analyzed separately, but when combined, they can provide a more accurate measurement result. At the same time, the diverse splitting methods also enhance the robustness to abnormal data or noise, improving the accuracy of the analysis. By splitting the thermal distribution information, the complex thermal distribution problem can be decomposed into multiple relatively simple sub-problems for processing. The strategy of dividing and conquering helps to optimize the calculation process, improve the calculation efficiency. At the same time, since different parts of the thermal distribution information can be processed in parallel, the computing resources can be better utilized, improving the resource utilization rate. In addition, the diverse data splitting methods enable the support of more complex analysis models, which can be constructed based on different splitting results, thereby more accurately describing and predicting the change law of the thermal distribution. By continuously iterating and optimizing these models, the measurement accuracy and adaptability can be further improved.

[0009] In a preferred example, the present application can be further configured as follows: based on a preset splitting and combining rule, splitting and recombining the corrected thermal distribution information to obtain a number of discrete thermal distribution information, including: Intercepting a number of consecutive corrected thermal distribution information based on a preset splitting and combining rule; Splitting a number of consecutive corrected thermal distribution information to obtain a number of optional thermal distribution information; Performing permutation and combination on a number of the optional thermal distribution information to obtain a number of discrete thermal distribution information.

[0010] By adopting the above technical solution, by intercepting a number of consecutive corrected thermal distribution information, the local characteristics of the thermal distribution can be analyzed more carefully, and the minute changes in the thermal distribution can be captured, thereby improving the measurement accuracy. Splitting the consecutive corrected thermal distribution information to obtain a number of optional thermal distribution information provides more possibilities for subsequent permutation and combination, enabling the system to flexibly adjust the data processing strategy according to different analysis requirements or scenarios to meet different measurement requirements. Performing permutation and combination on the optional thermal distribution information can generate a variety of different discrete thermal distribution information. These discrete thermal distribution information not only retain the original thermal distribution characteristics but also have a certain representativeness, and can more comprehensively reflect the thermal distribution on the surface of the printing and dyeing wastewater. Because the process of permutation and combination is actually an introduction of data redundancy and diversity. When a certain set of thermal distribution information is affected by interference or error, the information of other combinations can be used as a supplement or verification, thereby improving the robustness of the entire measurement result.

[0011] In a preferred example, the present application can be further configured as follows: inputting the discrete thermal distribution information into a preset rationality analysis model to obtain thermal distribution rationality information, including: Inputting the discrete thermal distribution information into a preset rationality analysis model, and based on the rationality analysis model, obtaining the basic thermal accuracy and the rationality of thermal laws; Combining the basic thermal accuracy and the rationality of thermal laws to obtain thermal distribution rationality information.

[0012] By adopting the above technical solution, the preset rationality analysis model is constructed based on scientific principles and statistical methods, and can objectively analyze and evaluate the thermal distribution information. For example, it can identify anomalies or unreasonable situations in the thermal distribution, such as mutations of data points, deviations of distribution trends, etc. By using this model, the subjectivity and uncertainty of human judgment can be reduced, and the scientificity and reliability of the evaluation results can be improved; at the same time, it evaluates the accuracy of the thermal basis and the rationality of the thermal law, and can comprehensively examine multiple dimensions of the thermal distribution information. The accuracy of the thermal basis focuses on the accuracy of a single data point, while the rationality of the thermal law focuses on the relevance between data and the rationality of the overall distribution trend. The comprehensive evaluation method helps to more accurately judge the rationality of the thermal distribution.

[0013] In a preferred example of the present application, it can be further configured as follows: obtaining the measurement result according to the thermal distribution rationality information includes: Comparing the thermal basis accuracy and the thermal law rationality with a preset thermal threshold respectively to obtain two different comparison results; When both of the two different comparison results indicate compliance with the preset thermal threshold, obtaining the measurement result based on the corrected thermal distribution information; When it does not occur that both of the two different comparison results indicate compliance with the preset thermal threshold, adjusting the thermal distribution rationality information based on the thermal threshold, and then obtaining the measurement result based on the adjusted thermal distribution rationality information.

[0014] By adopting the above technical solution, comparing the thermal basis accuracy and the thermal law rationality with a preset thermal threshold can ensure that the measurement result meets both the accuracy requirements of single-point data and the rationality of the overall thermal distribution law. The accuracy of the measurement result is effectively improved through a dual verification mechanism, and the error caused by abnormal data or unreasonable distribution is reduced. When the thermal distribution rationality information does not meet the preset thermal threshold, these information can be automatically adjusted, and the measurement result can be re-obtained based on the adjusted result, enabling the system to handle various complex situations and improving the robustness and stability of the system; the conditional judgment logic in the technical solution (that is, obtaining the measurement result based on the corrected thermal distribution information only when both comparison results meet the preset thermal threshold) simplifies the measurement process, avoids unnecessary repeated calculations or incorrect judgments. At the same time, when the conditions are not met, the system can respond quickly and adjust, improving the measurement efficiency.

[0015] In the second aspect, the above object of the present invention of the present application is achieved by the following technical solutions: A system for accurately measuring the liquid level of printing and dyeing wastewater, which is applied to a thermal induction liquid level measurement system. The thermal induction liquid level measurement system includes a thermal imager for capturing the thermal distribution, and one or more temperature sensors connected to the thermal imager for providing ambient temperature data to correct measurement errors. The system for accurately measuring the liquid level of printing and dyeing wastewater includes: A data acquisition module for acquiring thermal distribution information and ambient temperature detection data; A splitting and combining module for correcting the thermal distribution information by using the ambient temperature detection data, and then splitting and recombining the corrected thermal distribution information based on a preset splitting and combining rule to obtain several discrete thermal distribution information; A rationality analysis module for inputting the discrete thermal distribution information into a preset rationality analysis model to obtain thermal distribution rationality information; A measurement result module for obtaining a measurement result according to the thermal distribution rationality information.

[0016] Optionally, when splitting the corrected thermal distribution information, at least two different sets of thermal distribution information are selected for splitting, and the same thermal distribution information is split into at least two different parts.

[0017] Optionally, the splitting and combining module includes: An interception sub-module for intercepting several consecutive corrected thermal distribution information based on a preset splitting and combining rule; A splitting sub-module for splitting the several consecutive corrected thermal distribution information to obtain several optional thermal distribution information; A combining sub-module for arranging and combining the several optional thermal distribution information to obtain several discrete thermal distribution information.

[0018] In a third aspect, the above-mentioned invention object of the present application is achieved by the following technical solution: A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for accurately measuring the liquid level of printing and dyeing wastewater are implemented.

[0019] In a fourth aspect, the above-mentioned invention object of the present application is achieved by the following technical solution: A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for accurately measuring the liquid level of printing and dyeing wastewater are implemented.

[0020] In summary, the present application includes at least one of the following beneficial technical effects: 1. Interference factors such as steam and water droplets that may exist on the surface of printing and dyeing wastewater can affect the accuracy of traditional liquid level measurement methods. Therefore, by introducing a thermal imager to capture the thermal distribution and combining it with environmental temperature detection data for calibration, the actual temperature distribution on the wastewater surface can be more accurately reflected. The real-time calibration combined with the environmental temperature reduces the measurement error caused by environmental temperature changes, thereby improving the accuracy of liquid level measurement. In addition, by splitting and recombining the calibrated thermal distribution information and using a rationality analysis model for screening, interference factors such as steam and water droplets can be effectively excluded, improving the stability and reliability of the measurement results. For example, by comparing multiple thermal distribution information, areas with unreasonable temperatures are judged, and some thermal images considered to be steam and water droplets are removed, resulting in a cleaner measurement result. Thus, through the combination of thermal imaging technology and an intelligent analysis model, the intelligent measurement of the liquid level of printing and dyeing wastewater is achieved. Since the liquid level of printing and dyeing wastewater may be affected by various factors such as wastewater temperature, flow rate, and concentration, traditional measurement methods often struggle to adapt to these complex environments. By comprehensively considering multiple factors such as thermal distribution and environmental temperature, the actual measurement requirements of the liquid level of printing and dyeing wastewater can be better met, improving the adaptability and flexibility of the measurement. 2. By selecting at least two different sets of thermal distribution information for splitting, more diverse data samples can be obtained, more comprehensively reflecting the thermal distribution on the surface of printing and dyeing wastewater and providing a richer information basis for subsequent analysis and measurement. Splitting the same thermal distribution information into at least two different parts can make the analysis process more detailed and in-depth. Different splitting methods may reveal different characteristics or patterns in the thermal distribution. These characteristics or patterns may not be obvious when analyzed individually but can provide a more accurate measurement result when combined. At the same time, diverse splitting methods also enhance the robustness to abnormal data or noise, improving the accuracy of the analysis. By splitting the thermal distribution information, complex thermal distribution problems can be decomposed into multiple relatively simple sub-problems for processing. The strategy of dividing and conquering helps optimize the calculation process, improve the calculation efficiency. At the same time, since different parts of the thermal distribution information can be processed in parallel, computing resources can be better utilized, improving resource utilization. In addition, diverse data splitting methods enable the support of more complex analysis models, which can be constructed based on different splitting results, thus more accurately describing and predicting the changing rules of thermal distribution. By continuously iterating and optimizing these models, the measurement accuracy and adaptability can be further improved. 3. By intercepting several consecutive pieces of corrected thermal distribution information, the local characteristics of the thermal distribution can be analyzed more meticulously, and the minute changes in the thermal distribution can be captured, thereby improving the measurement accuracy. Splitting the consecutive corrected thermal distribution information to obtain several optional thermal distribution information provides more possibilities for subsequent permutations and combinations, enabling the system to flexibly adjust the data processing strategy according to different analysis requirements or scenarios to adapt to different measurement requirements. Permuting and combining the optional thermal distribution information can generate a variety of different dispersed thermal distribution information. While retaining the original thermal distribution characteristics, these dispersed thermal distribution information also have a certain representativeness and can more comprehensively reflect the thermal distribution on the surface of the printing and dyeing wastewater. Because the process of permutation and combination is actually an introduction of data redundancy and diversity. When a certain set of thermal distribution information is affected by interference or errors, the information of other combinations can be used as a supplement or verification, thereby improving the robustness of the entire measurement result. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is the implementation flowchart of the method for accurately measuring the liquid level of printing and dyeing wastewater in an embodiment of the present application; Figure 2 is the implementation flowchart of S20 of the method for accurately measuring the liquid level of printing and dyeing wastewater in an embodiment of the present application; Figure 3 is the implementation flowchart of S30 of the method for accurately measuring the liquid level of printing and dyeing wastewater in an embodiment of the present application; Figure 4 is the implementation flowchart of S40 of the method for accurately measuring the liquid level of printing and dyeing wastewater in an embodiment of the present application; Figure 5 is a schematic block diagram of the principle of a system for accurately measuring the liquid level of printing and dyeing wastewater in an embodiment of the present application; Figure 6 is the internal structure diagram of a computer device in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The following is a further detailed description of the present application in conjunction with the attached Figure 1-6 drawings.

[0023] In one embodiment, as Figure 1As shown in the figure, this application discloses a method for accurately measuring the liquid level of printing and dyeing wastewater. The method for accurately measuring the liquid level of printing and dyeing wastewater is applied to a thermal induction liquid level measurement system. The thermal induction liquid level measurement system includes a thermal imager for capturing the thermal distribution, and one or more temperature sensors connected to the thermal imager for providing ambient temperature data to correct measurement errors. As the core component of the system, the thermal imager needs to select a model with high sensitivity, high resolution, and good thermal stability. The thermal imager generates a thermal distribution image of the wastewater surface and the air above it by capturing infrared radiation. To ensure the accuracy of the measurement, the thermal imager is carefully installed on a stable and wide-view platform beside the wastewater tank. This position should minimize the interference of surrounding environmental factors (such as direct sunlight, wind speed changes, etc.) on the thermal imaging effect. At the same time, the lens of the thermal imager needs to be accurately calibrated to ensure that its field of view can completely cover the wastewater tank surface and clearly distinguish the temperature difference boundary between the wastewater and the air. To correct the measurement errors caused by environmental temperature changes, the system is equipped with one or more high-precision temperature sensors. These sensors are installed at different positions near the thermal imager to obtain temperature data representing the surrounding environment of the wastewater tank. The temperature sensors need to have the characteristics of fast response, long-term stability, and high accuracy, and can reflect the subtle changes in ambient temperature in real time. By combining these temperature data with the thermal distribution image captured by the thermal imager, the system can automatically perform temperature correction, thereby eliminating the influence of ambient temperature on the liquid level measurement result. The system is built-in with advanced data processing and analysis algorithms for extracting liquid level information from the thermal distribution image obtained by the thermal imager. The algorithm first uses image processing technology to identify the temperature difference boundary between the wastewater surface and the air above it. This boundary usually appears as a clear dividing line in the image. Subsequently, the algorithm calculates the actual liquid level height of the wastewater according to the position of the temperature difference boundary and the preset scale or calibration parameters. To achieve real-time monitoring of the liquid level of printing and dyeing wastewater, the system is also equipped with a data transmission and display module. This module can transmit the processed liquid level data to the monitoring center or display terminal in real time for the operator to view at any time. At the same time, the system also has an automatic alarm function. When the liquid level exceeds the preset safety threshold, it will immediately trigger an alarm signal to remind the operator to take timely measures to prevent wastewater overflow or other safety accidents. The method for accurately measuring the liquid level of printing and dyeing wastewater specifically includes the following steps: S10: Obtain thermal distribution information and ambient temperature detection data.

[0024] In this embodiment, the thermal distribution information refers to the analysis information of the thermal imaging of the thermal imager.

[0025] Specifically, obtain the thermal distribution information and the ambient temperature detection data. The thermal distribution information refers to the analysis result obtained by image recognition processing of the thermal imaging above the printing and dyeing wastewater. For example, it includes specific thermal values, the size and location of the thermal distribution, and the time point of the thermal imaging. The ambient temperature detection data is the data of the ambient temperature values around the printing and dyeing wastewater pool collected by the temperature sensor.

[0026] In this embodiment, the thermal imager and the temperature sensor can continuously monitor in real time or can be periodically monitored according to a predetermined frequency. Obtaining the thermal distribution information and the ambient temperature detection data can also be in real time or can be periodically obtained according to a predetermined frequency. The predetermined frequency of the obtained thermal distribution information and the ambient temperature detection data can be determined according to the detection requirements. For example, the frequency requirements for liquid level measurement, etc.

[0027] S20: Correct the thermal distribution information using the ambient temperature detection data, and then based on the preset splitting and combining rules, split and recombine the corrected thermal distribution information to obtain several discrete thermal distribution information.

[0028] Specifically, match the ambient temperature detection data and the thermal distribution information to ensure that the data collection of the thermal imager and the temperature sensor is carried out synchronously. Preprocess the collected ambient temperature data, including removing noise, filling in missing values, smoothing processing, etc., to improve the reliability and consistency of the data; analyze the ambient temperature data to identify areas with large temperature gradients. These areas may be places where the thermal distribution is greatly affected by the ambient temperature. Based on this, establish a heat conduction model based on physical principles to simulate the diffusion process of heat on the wastewater surface and in the air above, and consider the ambient temperature as one of the boundary conditions. Use statistical analysis methods, such as Pearson correlation coefficient or Spearman rank correlation coefficient, to analyze the correlation between the ambient temperature and the temperature in specific areas of the thermal distribution image to determine which ambient temperature data is the most critical for the correction process; in this embodiment, there are multiple correction methods: For the case where there is a linear relationship between the ambient temperature and the specific temperature in the thermal distribution, a linear regression algorithm can be used for correction. By fitting the linear relationship formula between the ambient temperature and the thermal temperature, perform a linear transformation on the temperature values in the thermal distribution image to eliminate the influence of the ambient temperature. Nonlinear models (such as polynomial regression, neural networks, etc.) and other algorithms can also be used; Spatial interpolation techniques (such as Kriging interpolation, inverse distance weighted interpolation, etc.) can also be used to convert the discrete ambient temperature data into a continuous temperature field and perform a spatial mapping with the thermal distribution image. By comparing the differences between the two, perform local or global correction on the thermal distribution image; The image fusion technology can also be adopted to fuse the corrected ambient temperature data with the original thermal distribution image in the form of an image. Through image fusion algorithms (such as multi-source image fusion, weighted fusion, etc.), the effective information in both can be combined to generate a more accurate and comprehensive thermal distribution image; After the correction of the thermal distribution information is completed in this way, based on the preset splitting and combining rules, the corrected thermal distribution information is split and recombined. For example, according to the boundary between the actual liquid level and water droplets represented by the corrected thermal distribution information, the thermal distribution information can be split into the thermal distribution information representing the actual printing and dyeing wastewater and the thermal distribution information representing water droplets, so as to obtain several discrete thermal distribution information.

[0029] In this embodiment, the splitting and combining rules can be based on the feature differences in some historical thermal distribution images, such as temperature gradient, shape, texture, etc. The main goal is to distinguish the thermal distribution of the actual printing and dyeing wastewater from the thermal distribution of water droplets (which may be formed due to evaporation, splashing, etc.). Therefore, multiple ways can be used to define the splitting and combining rules, including: Temperature threshold method: Set one or more temperature thresholds, and divide the pixel points in the thermal distribution image into different categories according to their temperature values. For example, the area below a certain temperature threshold may be considered as the thermal distribution of water droplets, while the area above this threshold belongs to the thermal distribution of the actual printing and dyeing wastewater; Morphological analysis method: Use morphological operations in image processing (such as erosion, dilation, opening operation, and closing operation, etc.) to identify specific shapes or structures in the thermal distribution image. The thermal distribution of water droplets may present smaller, circular or irregular shapes, while the thermal distribution of the actual printing and dyeing wastewater may be more continuous and extensive; Edge detection method: Identify the boundary lines in the thermal distribution image through edge detection algorithms (such as Canny edge detector, Sobel operator, etc.). The boundary lines represent the dividing lines between the actual printing and dyeing wastewater and water droplets; When applying the splitting and combining rules, the corrected thermal distribution image can be analyzed and processed pixel by pixel or region by region first. By comparing the temperature values, shape features or edge information of pixel points, they are classified into corresponding categories, and then a unique identifier is assigned to each category and their boundaries are marked in the thermal distribution image. Then, image segmentation techniques (such as region-based segmentation, graph cut-based segmentation, etc.) are used to split the thermal distribution image into multiple independent regions, and each region corresponds to a type of thermal distribution. Among the multiple discrete thermal distribution information obtained by splitting, selective recombination can be carried out according to needs. For example, the thermal distribution information representing the actual printing and dyeing wastewater and the thermal distribution information representing water droplets can be extracted separately for subsequent analysis or processing.

[0030] Furthermore, for the correction algorithm of thermal distribution information, the cross-validation method can be used to evaluate the accuracy and generalization ability of the correction algorithm. That is, the dataset is divided into a training set and a test set. The correction model is trained on the training set, and its performance is evaluated on the test set. Error analysis is also performed on the corrected thermal distribution image, and statistical indicators such as temperature difference and standard deviation before and after correction are calculated to evaluate the quality of the correction effect. In addition, according to the results of the error analysis, the parameters of the correction algorithm are optimized, such as adjusting the coefficients of the regression model, optimizing the structure and parameters of the neural network, etc., to further improve the accuracy and stability of the correction.

[0031] S30: Input the scattered thermal distribution information into a preset rationality analysis model to obtain thermal distribution rationality information.

[0032] Specifically, before inputting the scattered thermal distribution information into the rationality analysis model, it is necessary to ensure that this information is complete and in a unified format. For example, each set of scattered thermal distribution information should include: recording the temperature values of each pixel point at different time points, describing the shape, size, edge contour and other characteristics of the thermal distribution area, and the specific time when the thermal distribution information was collected. The rationality analysis model is used to analyze the rationality of the recombined scattered thermal distribution information, that is, the rationality of the thermal distribution changing over time. The higher the rationality, the higher the measurement accuracy. Conversely, the lower the rationality, the lower the measurement accuracy. The rationality analysis model presets thermal propagation principle analysis rules, that is, according to some thermal theorems, combined with the time interval for collecting different thermal distribution information and the current environmental temperature, to judge the analysis rules of thermal change. For example, the thermal radiation analysis rule (evaluating the heat loss or gain caused by radiation in the thermal distribution according to the thermal radiation law (such as the Stefan-Boltzmann law), considering the radiation coefficients and surface properties of different materials (such as sewage, water droplets, air)), the heat convection analysis rule (analyzing the heat transfer caused by fluid movement (such as natural convection, forced convection) in the thermal distribution based on fluid mechanics and heat transfer theory, considering the physical properties such as the velocity, density, and viscosity of the fluid in the sewage tank, as well as factors such as the wind speed and temperature gradient of the external environment), and the heat conduction analysis rule (evaluating the heat transfer through solid media (such as the sewage tank wall, pipes, etc.) in the thermal distribution using heat conduction theories such as Fourier's law, considering factors such as the thermal conductivity, thickness, and shape of the media). Specific thermal propagation principle analysis rules can be determined according to historical thermal distribution information, the operation processes such as water storage and discharge of the printing and dyeing sewage tank, and the average temperature of the current printing and dyeing sewage tank. Therefore, input the scattered thermal distribution information into a preset rationality analysis model, sort the input thermal distribution information according to the time stamp, construct a time series, and then analyze the change trend of the thermal distribution between different time points, such as temperature rise and fall, shape change, etc. Using the above thermal propagation principle analysis rules, simulate the change of the thermal distribution. The model will consider various factors such as time interval, ambient temperature, and sewage tank operation status, predict the reasonable state of the thermal distribution at different time points, and then compare the simulation results with the actual thermal distribution information to evaluate its rationality. For example, calculate the difference degree between the two (such as mean square error, correlation coefficient, etc.), and judge the rationality of the thermal distribution according to the preset threshold. Finally, for each divided sub-part of the thermal distribution information, give a quantitative index of its rationality size (such as rationality score, percentage of difference degree, etc.), and these indexes reflect the degree of conformity of the thermal distribution information of this part with the actual thermal propagation law; Therefore, through the preset rationality analysis model, judge the rationality of the combination of the temperature values, shape features or edge information of the pixel points of different original thermal distribution information in the combination, and obtain the thermal distribution rationality information. This thermal distribution rationality information includes the data of the rationality size of the thermal distribution information of each divided sub-part. Therefore, it is possible that the rationality sizes of the thermal distribution information of multiple sub-parts divided based on the same original thermal distribution information are different.

[0033] S40: Obtain the measurement result according to the thermal distribution rationality information.

[0034] Specifically, according to the thermal distribution rationality information output by the rationality analysis model, identify the parts with lower rationality. These parts may be manifested as abnormal fluctuations in temperature values, distortions of shape features, or blurring of edge information, etc. Therefore, analyze the possible reasons for the lower rationality. For example, noise data caused by random errors or environmental factors during the measurement process; data of some pixel points may be missing due to transmission errors or recording problems; there may be inconsistencies between data at different time points, and the temperature change trend does not conform to physical laws, etc. Make corresponding adjustments according to the analyzed reasons. For example, for data with noise, use smoothing techniques (such as moving average, median filtering, etc.) to reduce the influence of noise. For missing data, use interpolation methods (such as linear interpolation, bilinear interpolation, etc.) to estimate the missing values according to the data of surrounding pixel points. For data that obviously does not conform to physical laws (such as sudden temperature rise and fall), correct it according to the thermal propagation principle to make it conform to the general law of thermal distribution, and thus obtain the measurement result.

[0035] In one embodiment, an automatic calibration device can be added to the thermal induction type liquid level measurement system. The device includes a calibration mark with a specific thermal signal, and the mark is set within the field of view of the thermal imager. Before starting work every day, the thermal induction type liquid level measurement system will automatically trigger the calibration process: the calibration mark emits a specific thermal signal, and after the thermal imager captures this signal, it automatically adjusts its image processing parameters by comparing with the preset value to correct possible deviations. In this way, even after long-term operation or environmental changes, the system can still maintain the accurate measurement ability of the printing and dyeing sewage liquid level.

[0036] In one embodiment, an imageData big data processing and analysis module and machine learning algorithms can be integrated. The module is used to collect and analyze the thermal imaging map sequence and temperature data, and continuously optimize the recognition model of the liquid level and condensed water droplets through machine learning algorithms to meet the accurate measurement requirements of the liquid level under different climates and working conditions. For example, in summer with high temperature, the system can identify the thermal distribution changes caused by high temperature through learning and accurately judge the liquid level; in winter with low temperature, the system can also identify the difference between the condensed water droplets and the liquid level through learning and achieve accurate measurement.

[0037] In one embodiment, when splitting the corrected thermal distribution information, at least two different sets of thermal distribution information are selected for splitting, and the same thermal distribution information is split into at least two different parts.

[0038] Specifically, when splitting the corrected thermal distribution information, at least two different sets of thermal distribution information are selected for splitting, and more different sets of thermal distribution information can also be split. After splitting, they are combined, and the combination method can be traversal combination. In this way, the more different sets of thermal distribution information are split, the more scattered thermal distribution information is obtained. The parts split from one set of thermal distribution information can be combined with more other thermal distribution information. In this way, the number of analysis and judgment times for the thermal distribution information is more, and the obtained measurement results will be more accurate. Similarly, the same thermal distribution information is split into at least two different parts. The finer the split, the less the content of the split sub-parts. Each split sub-part is combined with more other thermal distribution information, and more scattered thermal distribution information is obtained. In this way, the analysis and judgment of the split sub-parts of the same thermal distribution information will be more accurate.

[0039] In this embodiment, the number of sets of thermal distribution information selected for splitting, as well as the number of sub-parts into which the same thermal distribution information is split, can be set by oneself according to the measurement requirements.

[0040] In one embodiment, as Figure 2As shown, in step S20, based on the preset splitting and combining rules, the corrected thermal distribution information is split and recombined to obtain a number of discrete thermal distribution information, including: S21: Intercept a number of consecutive corrected thermal distribution information based on the preset splitting and combining rules.

[0041] Specifically, based on the preset splitting and combining rules, a number of consecutive corrected thermal distribution information are intercepted. Since the thermal distribution is a dynamically changing process, its changes often show a certain continuity and regularity. Intercepting consecutive thermal distribution information can ensure that the analyzed data remains coherent in the time series, thus more accurately reflecting the true situation of thermal changes, capturing subtle changes in the thermal distribution, such as the gradual change of the temperature gradient, the slow deviation of the heat flow direction, etc., providing a reliable basis for subsequent splitting and combination analysis. Moreover, in the consecutive thermal distribution information, outliers or mutation points are often easier to identify because outliers or mutation points will break the original continuity of the data, forming obvious "breakpoints" or "jumps". By comparing the differences between consecutive data points, these anomalies or mutations can be quickly located. At the same time, the continuous data also helps to identify the long-term trends in the thermal distribution, such as the overall rise or fall of the temperature, the stable change of the heat flow direction, etc., reducing the error accumulation caused by data discontinuity and making the analysis results closer to the real situation.

[0042] S22: Split a number of consecutive corrected thermal distribution information to obtain a number of optional thermal distribution information.

[0043] Specifically, based on the preset splitting and combining rules, a number of consecutive corrected thermal distribution information are split to obtain a number of optional thermal distribution information. Optional thermal distribution information refers to the information of the sub-parts after splitting the thermal distribution information. For example, each piece of optional thermal distribution information may correspond to a specific area or time period of the original thermal imaging map. These areas can be naturally divided based on temperature gradients, heat flow directions or other thermal characteristics, or can be artificially set regions of interest. Each part of the thermal imaging map details key information such as the temperature distribution, hot spot position and heat diffusion path within the region. In addition, matching with part of the thermal imaging map, each piece of optional thermal distribution information also includes the temperature data of the corresponding region, which can be in the form of single-point temperature values, average temperatures or temperature gradient distribution maps, etc. In addition to the direct thermal imaging and temperature data, other additional metadata may include the timestamp of data acquisition, sensor location, environmental conditions (such as humidity, wind speed, etc.) and calibration parameters, etc.

[0044] S23: Arrange and combine a number of optional thermal distribution information to obtain a number of discrete thermal distribution information.

[0045] Specifically, a number of optional thermal distribution information is arranged and combined to obtain a number of dispersed thermal distribution information. For example, combination can be carried out according to specific characteristics of the optional thermal distribution information, including temperature range, heat flow direction, hot spot distribution pattern, etc. By selecting information with similar or complementary characteristics for combination, a series of dispersed thermal distribution information with specific thermal characteristics can be generated; combination of the optional information can be carried out according to the time series based on the dynamics of the thermal distribution information to simulate the change process of the thermal distribution in different time periods; combination of the optional thermal distribution information can be carried out according to the spatial position.

[0046] Furthermore, through arrangement and combination, the obtained dispersed thermal distribution information can be used to simulate and predict the future state of the thermal system. By adjusting the combination parameters and conditions, multiple possible thermal distribution scenarios can be generated; based on the analysis results of the dispersed thermal distribution information, the thermal system can be optimized and adjusted. For example, by identifying inefficient regions or potential risk points in the thermal distribution, the equipment layout can be improved, the operating parameters can be adjusted, or new thermal energy management technologies can be introduced.

[0047] In one embodiment, as Figure 3 shown, in step S30, the dispersed thermal distribution information is input into a preset rationality analysis model to obtain thermal distribution rationality information, including: S31: Input the dispersed thermal distribution information into a preset rationality analysis model. Based on the rationality analysis model, obtain the thermal basic accuracy and the thermal law rationality.

[0048] Specifically, input the dispersed thermal distribution information into a preset rationality analysis model. Based on the rationality analysis model, obtain the thermal basic accuracy and the thermal law rationality. Among them, the thermal basic accuracy refers to the analysis result of the accuracy of the thermal imaging map and temperature data, and the thermal law rationality refers to the analysis result that the thermal distribution situation represented by the dispersed thermal distribution information conforms to the thermal physical law; As an important visual tool for thermal analysis, the accuracy of the thermal imaging map is directly related to the reliability of subsequent analysis. In the analysis process, various methods can be used to verify the accuracy of the imaging map; for example, first, by comparing the thermal imaging maps obtained at different times, different angles or by different sensors, check the consistency and stability of the images. Secondly, use known temperature standards or reference points (such as ambient temperature, known heat source temperature, etc.) to calibrate and verify the imaging map and then re - conduct the accuracy analysis; In addition, for the verification of the thermophysical laws for the dispersed thermal distribution information, it includes checking whether the thermal distribution conforms to the first law of thermodynamics (energy conservation), the second law of thermodynamics (entropy increase principle), and the theoretical predictions of basic thermal processes such as heat conduction, convection, and radiation. Through simulation means, it can be verified whether parameters such as temperature gradient, heat flow direction, and heat exchange rate in the dispersed thermal distribution information conform to the predictions of thermophysical laws; in addition to the direct comparison with thermophysical laws, it is also possible to evaluate the rationality of the thermal distribution pattern presented by the dispersed thermal distribution information, including analyzing the spatial distribution characteristics, temporal variation laws of the thermal distribution, and the correlation with other physical quantities (such as pressure, flow rate, etc.). By constructing a thermal model and conducting numerical simulation verification, etc., the rationality of the thermal distribution pattern can be evaluated.

[0049] S32: Combine the thermal basis accuracy and the rationality of thermal laws to obtain the thermal distribution rationality information.

[0050] Specifically, associate the thermal basis accuracy and the rationality of thermal laws to obtain the thermal distribution rationality information for the thermal distribution information divided into each sub - part in the dispersed thermal distribution information.

[0051] In one embodiment, as Figure 4 shown, in step S40, according to the thermal distribution rationality information, obtain the measurement results, including: S41: Compare the thermal basis accuracy and the rationality of thermal laws with the preset thermal thresholds respectively to obtain two different comparison results.

[0052] Specifically, compare the thermal basis accuracy and the rationality of thermal laws corresponding to the thermal distribution information of each divided sub - part with the preset thermal thresholds respectively to obtain two different comparison results.

[0053] In this embodiment, the preset thermal thresholds for the thermal basis accuracy and the rationality of thermal laws can be the same or different. The way to obtain the preset thermal threshold for the thermal basis accuracy can be determined according to relevant industry standards or specifications, which are usually based on extensive experimental data and experience summaries, providing an acceptable accuracy range for thermal analysis in specific application scenarios; or by analyzing historical thermal data, identifying the temperature fluctuation range, imaging quality indicators, etc. under normal operating conditions, so as to set the corresponding thermal thresholds; or in the case of lack of clear standards or historical data, industry experts can be invited to set the thermal thresholds based on experience. At the same time, numerical simulation tools can also be used for predictive analysis to set a reasonable threshold range according to the simulation results; The method for obtaining the preset thermal threshold for the rationality of thermal laws can be based on thermal physical laws and theoretical models to deduce the specific conditions or ranges that the thermal distribution should satisfy, and the conditions or ranges can be used as the preset thermal thresholds; or through experimental means, measure and record the actual data of the thermal distribution under different conditions, and through the analysis of these data, a reasonable range of thermal thresholds can be set.

[0054] S42: When both of the two different comparison results indicate compliance with the preset thermal threshold, obtain the measurement result based on the corrected thermal distribution information.

[0055] Specifically, when both of the two different comparison results indicate compliance with the preset thermal threshold, that is, both the thermal basic accuracy and the rationality of thermal laws comply with the preset thermal threshold, it means that the current measurement accuracy is relatively high and no adjustment is required. Then, obtain the measurement result based on the corrected thermal distribution information.

[0056] S43: When there are no two different comparison results that both indicate compliance with the preset thermal threshold, adjust the rationality information of the thermal distribution based on the thermal threshold, and then obtain the measurement result based on the adjusted rationality information of the thermal distribution.

[0057] Specifically, when there are no two different comparison results that both indicate compliance with the preset thermal threshold, that is, the thermal basic accuracy does not comply with the preset thermal threshold, or the rationality of thermal laws does not comply with the preset thermal threshold, or both the thermal basic accuracy and the rationality of thermal laws do not comply with the preset thermal threshold, the thermal basic accuracy and / or the rationality of thermal laws can be adjusted to the corresponding preset thermal threshold based on the thermal threshold, and then based on the adjusted rationality information of the thermal distribution, the thermal distribution situation corresponding to the thermal threshold is deduced. In this way, combined with the thermal distribution information of other sub-parts that comply with the preset thermal threshold after correction, the measurement result is obtained.

[0058] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0059] In one embodiment, a system for accurately measuring the liquid level of printing and dyeing wastewater is provided. The system for accurately measuring the liquid level of printing and dyeing wastewater corresponds one-to-one with the method for accurately measuring the liquid level of printing and dyeing wastewater in the above embodiment. As Figure 5 shown, the system for accurately measuring the liquid level of printing and dyeing wastewater includes a data acquisition module, a splitting and combining module, a rationality analysis module, and a measurement result module. The detailed description of each functional module is as follows: The data acquisition module is used to acquire thermal distribution information and environmental temperature detection data; The splitting and combining module is used to correct the thermal distribution information by using the ambient temperature detection data, and then based on the preset splitting and combining rules, split the corrected thermal distribution information and recombine it to obtain several scattered thermal distribution information; The rationality analysis module is used to input the scattered thermal distribution information into a preset rationality analysis model to obtain the thermal distribution rationality information; The measurement result module is used to obtain the measurement result according to the thermal distribution rationality information.

[0060] Optionally, when splitting the corrected thermal distribution information, at least two different groups of thermal distribution information are selected for splitting, and the same thermal distribution information is split into at least two different parts.

[0061] Optionally, the splitting and combining module includes: The interception sub-module is used to intercept several consecutive corrected thermal distribution information based on the preset splitting and combining rules; The splitting sub-module is used to split several consecutive corrected thermal distribution information to obtain several optional thermal distribution information; The combination sub-module is used to arrange and combine several optional thermal distribution information to obtain several scattered thermal distribution information.

[0062] Optionally, the rationality analysis module includes: The model analysis sub-module is used to input the scattered thermal distribution information into a preset rationality analysis model, and based on the rationality analysis model, obtain the thermal basic accuracy and the thermal law rationality; The rationality combination sub-module is used to combine the thermal basic accuracy and the thermal law rationality to obtain the thermal distribution rationality information.

[0063] Optionally, the measurement result module includes: The threshold comparison sub-module is used to compare the thermal basic accuracy and the thermal law rationality with the preset thermal threshold respectively to obtain two different comparison results; The first result sub-module is used to obtain the measurement result based on the corrected thermal distribution information when both of the two different comparison results indicate compliance with the preset thermal threshold; The second result sub-module is used to adjust the thermal distribution rationality information based on the thermal threshold when it does not occur that both of the two different comparison results indicate compliance with the preset thermal threshold, and then obtain the measurement result based on the adjusted thermal distribution rationality information.

[0064] For the specific limitations of the system for accurately measuring the liquid level of printing and dyeing wastewater, reference can be made to the limitations of the method for accurately measuring the liquid level of printing and dyeing wastewater in the foregoing text, which will not be elaborated here. Each module in the above system for accurately measuring the liquid level of printing and dyeing wastewater can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so as to facilitate the processor to call and execute the operations corresponding to the above modules.

[0065] In one embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 6 shown. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used for storage. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it realizes a method for accurately measuring the liquid level of printing and dyeing wastewater.

[0066] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented: Obtain thermal distribution information and ambient temperature detection data; Use the ambient temperature detection data to correct the thermal distribution information, and then based on a preset splitting and combining rule, split and recombine the corrected thermal distribution information to obtain several discrete thermal distribution information; Input the discrete thermal distribution information into a preset rationality analysis model to obtain thermal distribution rationality information; Obtain a measurement result according to the thermal distribution rationality information.

[0067] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, the following steps are implemented: Obtain thermal distribution information and ambient temperature detection data; Use the ambient temperature detection data to correct the thermal distribution information, and then based on a preset splitting and combining rule, split and recombine the corrected thermal distribution information to obtain several discrete thermal distribution information; Input the discrete thermal distribution information into a preset rationality analysis model to obtain thermal distribution rationality information; Obtain the measurement result according to the rationality information of the thermal distribution.

[0068] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0069] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules as needed, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above.

[0070] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for accurately measuring the liquid level of printing and dyeing wastewater, characterized in that: Applied to a thermal induction liquid level measurement system, the thermal induction liquid level measurement system includes a thermal imager for capturing thermal distribution conditions, and one or more temperature sensors connected to the thermal imager for providing ambient temperature data to correct measurement errors. The method for accurately measuring the liquid level of printing and dyeing wastewater includes: Obtain thermal distribution information and ambient temperature detection data; Correcting the thermal distribution information using the ambient temperature detection data, and then splitting and recombining the corrected thermal distribution information based on a preset splitting and combining rule to obtain a plurality of dispersed thermal distribution information; Inputting the dispersed thermal distribution information into a preset rationality analysis model to obtain thermal distribution rationality information; A measurement result is obtained according to the thermal distribution rationality information.

2. The method for accurately measuring the liquid level of printing and dyeing wastewater according to claim 1, characterized in that: When splitting the corrected thermal distribution information, at least two groups of different thermal distribution information are selected for splitting, and the same thermal distribution information is split into at least two groups of different parts.

3. The method for accurately measuring the liquid level of printing and dyeing wastewater according to claim 1, characterized in that: The method of splitting and recombining the corrected thermal distribution information based on the preset splitting and combining rules to obtain a plurality of dispersed thermal distribution information includes: Based on a preset splitting and combining rule, intercepting a plurality of continuous corrected thermal distribution information; Splitting a plurality of continuous corrected thermal distribution information to obtain a plurality of optional thermal distribution information; Arrange and combine some of the optional thermal distribution information to obtain some dispersed thermal distribution information.

4. The method for accurately measuring the liquid level of printing and dyeing wastewater according to claim 1, characterized in that: The step of inputting the dispersed thermal distribution information into a preset rationality analysis model to obtain thermal distribution rationality information includes: Inputting the dispersed thermal distribution information into a preset rationality analysis model, and obtaining the accuracy of the thermal foundation and the rationality of the thermal law based on the rationality analysis model; Combined with the accuracy of the thermal foundation and the rationality of the thermal laws, the rationality information of the thermal distribution is obtained.

5. The method for accurately measuring the liquid level of printing and dyeing wastewater according to claim 4, characterized in that: The obtaining of the measurement result according to the thermal distribution rationality information comprises: The accuracy of the thermal foundation and the rationality of the thermal law are respectively compared with the preset thermal threshold value to obtain two different comparison results; When the two different comparison results both indicate that the preset thermal threshold is met, a measurement result is obtained based on the corrected thermal distribution information; When there are no two different comparison results indicating that both meet the preset thermal threshold, the thermal distribution rationality information is adjusted based on the thermal threshold, and then the measurement result is obtained based on the adjusted thermal distribution rationality information.

6. A system for accurately measuring the level of printing and dyeing wastewater, characterized in that: Applied to a thermal induction liquid level measurement system, the thermal induction liquid level measurement system includes a thermal imager for capturing thermal distribution conditions, and one or more temperature sensors connected to the thermal imager for providing ambient temperature data to correct measurement errors. The system for accurately measuring the liquid level of printing and dyeing wastewater includes: A data acquisition module is used to obtain thermal distribution information and ambient temperature detection data; A splitting and combining module, used to correct the thermal distribution information using the ambient temperature detection data, and then split and recombine the corrected thermal distribution information based on a preset splitting and combining rule to obtain a plurality of dispersed thermal distribution information; A rationality analysis module, used for inputting the dispersed thermal distribution information into a preset rationality analysis model to obtain thermal distribution rationality information; The measurement result module is used to obtain the measurement result according to the thermal distribution rationality information.

7. The system for accurately measuring the level of printing and dyeing wastewater according to claim 6, characterized in that: When splitting the corrected thermal distribution information, at least two groups of different thermal distribution information are selected for splitting, and the same thermal distribution information is split into at least two groups of different parts.

8. The system for accurately measuring the level of printing and dyeing wastewater according to claim 6, characterized in that: The splitting and combining module comprises: An interception submodule, used for intercepting a plurality of continuous corrected thermal distribution information based on a preset splitting and combining rule; A splitting submodule, used for splitting a plurality of continuous corrected thermal distribution information to obtain a plurality of optional thermal distribution information; The combination submodule is used to arrange and combine a plurality of the optional thermal distribution information to obtain a plurality of dispersed thermal distribution information.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for accurately measuring the liquid level of printing and dyeing wastewater as claimed in any one of claims 1 to 5 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, the steps of the method for accurately measuring the liquid level of printing and dyeing wastewater as claimed in any one of claims 1 to 5 are implemented.

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