Oxidation ditch operation observation credibility evaluation method, device, equipment and medium

Abstract

The invention discloses an oxidation ditch operation observation credibility evaluation method and device, equipment and a medium, and relates to the technical field of oxidation ditch operation monitoring, and the method comprises the steps: collecting an image, carrying out the difference elimination processing to obtain a standardized image, extracting each index to form an image quality feature vector, carrying out the truncation constraint processing of the vector, obtaining a standardized quality vector, and carrying out the observation credibility evaluation of the oxidation ditch operation. Environment disturbance vectors are extracted from the continuous image sequence, smooth disturbance vectors are obtained through time window weighted smoothing processing, and the standardized quality vectors are subjected to weighted fusion based on preset weights to obtain image quality scores; determining an environment modulation factor in combination with the smooth disturbance vector, calculating the observation credibility of the current moment by fusing the image quality score and the environment modulation factor, generating a credibility difference value through time sequence analysis, and obtaining the target observation credibility through an average value and a standard deviation in a sliding window. The efficiency of evaluating the observation credibility of the operation monitoring process of the oxidation ditch is improved.

Description

Technical Field

[0001] This invention relates to the field of oxidation ditch operation monitoring technology, and in particular to a method, apparatus, equipment and medium for evaluating the reliability of oxidation ditch operation observations. Background Technology

[0002] Currently, during the long-term continuous operation of oxidation ditches, image data acquired at the same observation location at different times exhibit significant differences in information integrity, stability, and usability due to factors such as changes in lighting conditions, aeration bubble disturbance, sludge suspended particle movement, and the status of data acquisition equipment. Without a reliability assessment mechanism for the observation process itself, low-quality or interfered observation data entering subsequent analysis and control processes can easily reduce the overall stability and safety of the system.

[0003] In existing oxidation ditch operation monitoring systems, image or sensor data are usually used directly for operation analysis or control decisions after acquisition, but the environmental disturbances and equipment status changes encountered during the acquisition process lack systematic quantitative assessment. Due to the continuous, frequent, and highly uncertain nature of the oxidation ditch operating environment, observation results often fluctuate significantly across different time scales, and this uncertainty is easily amplified in subsequent analysis and control processes.

[0004] As wastewater treatment systems develop towards automation and intelligence, the role of monitoring data in operation and control is becoming increasingly important, and the reliability of observation data has become a key factor affecting the effectiveness of control strategies and system safety.

[0005] As can be seen from the above, how to improve the efficiency of assessing the reliability of oxidation ditch operation monitoring is an urgent problem to be solved. Summary of the Invention

[0006] In view of this, the purpose of this invention is to provide a method, apparatus, equipment, and medium for assessing the reliability of observations during oxidation ditch operation, which can improve the efficiency of assessing the reliability of observations during oxidation ditch operation monitoring. The specific solution is as follows: Firstly, this application provides a method for assessing the observation reliability of oxidation ditch operation, including: Images are acquired at preset observation locations in the oxidation ditch based on a preset image acquisition cycle. The acquired images are then processed to eliminate acquisition differences, resulting in standardized images. The image quality feature vector of the standardized images is then determined. The image quality feature vector includes sharpness index, contrast index, brightness uniformity index, and noise intensity index. Each of the image quality feature vectors is normalized and subjected to truncation constraints to obtain a corresponding standardized quality vector. Then, environmental disturbance vectors are extracted from the image sequence including consecutive frames, and time-window weighted smoothing is applied to the environmental disturbance vectors to obtain smoothed disturbance vectors. The environmental disturbance vectors include bubble disturbance intensity, liquid surface ripple degree, and suspended particle disturbance level. The standardized quality vector is weighted and fused based on preset weight coefficients to obtain an image quality score, and then the environmental modulation factor is determined based on the smooth perturbation vector. The current observation confidence level is determined based on the image quality score and the environmental modulation factor. Time series analysis is then performed on the current observation confidence level to obtain the confidence difference between adjacent time points, as well as the confidence average and confidence standard deviation within the sliding window. The target observation confidence level is then generated based on the confidence difference, the confidence average, and the confidence standard deviation.

[0007] Optionally, the step of acquiring images at a preset observation location in the oxidation ditch based on a preset image acquisition cycle, performing acquisition difference elimination processing on the currently acquired images to obtain standardized images, and then determining the image quality feature vector of the standardized images includes: Based on the target requirements, the preset observation position and preset image acquisition cycle are determined for the oxidation ditch. Then, based on the preset image acquisition cycle, the oxidation ditch is image acquired at the preset observation position to obtain the corresponding current acquired image. The scale normalization operation and brightness linear normalization operation are performed on the currently acquired image to obtain a normalized image. Then, the invalid regions in the normalized image are identified and cropped to obtain a standardized image. The standardized image is processed using the Laplacian operator to obtain the results. Then, the second-order gradient response of the results over the entire image is statistically analyzed to obtain the corresponding sharpness index. The variance of the standardized image in grayscale distribution is determined, and the corresponding contrast index is determined based on the variance. The standardized image is divided into several sub-regions, and the dispersion of the average brightness of each sub-region relative to the average brightness of the whole image is determined, and the brightness uniformity index is determined based on the dispersion. The residual information of the standardized image that meets the preset high-frequency conditions is analyzed to obtain the corresponding noise intensity index, and an image quality feature vector is constructed based on the sharpness index, the contrast index, the brightness uniformity index and the noise intensity index.

[0008] Optionally, the step of normalizing and applying truncation constraints to each of the image quality feature vectors to obtain the corresponding standardized quality vector includes: Determine the preset lower threshold and preset upper threshold corresponding to the sharpness index, the contrast index, the brightness uniformity index and the noise intensity index respectively; The image quality feature vectors are normalized to obtain corresponding normalization results. Then, the current value corresponding to each normalization result is determined, and it is determined whether each current value is less than the corresponding preset lower threshold. If the current value is less than the corresponding preset lower threshold, then the corresponding current value is set as the preset lower threshold. Determine whether each current value is greater than the corresponding preset upper limit threshold. If the current value is greater than the corresponding preset upper limit threshold, set the corresponding current value to the preset upper limit threshold to obtain the truncated standardized quality vector.

[0009] Optionally, the step of extracting environmental perturbation vectors from an image sequence including consecutive frames and performing time-window weighted smoothing on the environmental perturbation vectors to obtain smoothed perturbation vectors includes: An image sequence is constructed based on consecutive frames of images, and the corresponding environmental disturbance vector is extracted from the image sequence. The environmental disturbance vector includes the bubble disturbance intensity determined based on the area ratio of the bright discontinuous region in each image and the temporal change rate, the liquid surface fluctuation degree determined based on the positional offset of the liquid surface boundary in consecutive frames, and the suspended particle disturbance level determined based on the change amplitude of the high-frequency texture region within the time window. A time window of a preset length is constructed based on the target requirements, and a weight coefficient that decreases with time is assigned to the environmental disturbance vector of each historical moment within the time window. The bubble disturbance intensity, the liquid surface ripple degree, and the suspended particle disturbance level, along with their respective weighting coefficients, are weighted and accumulated to obtain the smoothed bubble disturbance intensity, the smoothed liquid surface ripple degree, and the smoothed suspended particle disturbance level. A smoothed perturbation vector is constructed based on the smoothed bubble disturbance intensity, the smoothed liquid surface fluctuation degree, and the smoothed suspended particle disturbance level.

[0010] Optionally, the step of weighting and fusing the standardized quality vector based on preset weight coefficients to obtain an image quality score, and then determining the environmental modulation factor based on the smoothed perturbation vector, includes: An observation confidence calculation model is constructed, and the standardized quality vectors are weighted and fused based on a preset weight coefficient to obtain the corresponding image quality score. The preset weight coefficient is used to reflect the importance of each standardized quality vector in the confidence assessment. The value of the preset weight coefficient is positively correlated with the importance of the vector. The comprehensive environmental disturbance value is determined based on the smoothed disturbance vector and the corresponding preset modulation coefficient. A negative correlation function is constructed based on the comprehensive environmental disturbance value, and then the environmental modulation factor within a preset value range is determined using the negative correlation function. The numerical value corresponding to the comprehensive environmental disturbance value is negatively correlated with the numerical value corresponding to the environmental modulation factor.

[0011] Optionally, the step of determining the current observation confidence level based on the image quality score and the environmental modulation factor, and performing time series analysis on the current observation confidence level to obtain the confidence level difference between adjacent time points, as well as the confidence level average and confidence level standard deviation within the sliding window, includes: The image quality score is corrected using the environmental modulation factor to obtain the current observation confidence level at the current moment, and the confidence level difference between the current observation confidence level at the current moment and the historical observation confidence level at the previous moment is determined. Within a sliding window of a preset length, determine each consecutive historical moment preceding the current moment, and calculate the mean and standard deviation of the historical credibility corresponding to each historical moment to obtain the corresponding average credibility and standard deviation of credibility.

[0012] Optionally, the step of determining the current observation confidence level based on the image quality score and the environmental modulation factor, and performing time series analysis on the current observation confidence level to obtain the confidence level difference between adjacent time points, as well as the confidence level average and confidence level standard deviation within the sliding window, and generating the target observation confidence level based on the confidence level difference, the confidence level average, and the confidence level standard deviation, includes: Determine the trend of the confidence difference value. If the trend indicates that the confidence difference value is negative at each consecutive acquisition time, set the current observation environment state as having a decreasing stability and generate a corresponding prompt message. Determine whether the average confidence level and the standard deviation of confidence level are greater than a preset reliability threshold and a preset fluctuation threshold, respectively. If the average confidence level is greater than the preset reliability threshold and the standard deviation of confidence level is greater than the preset fluctuation threshold, then generate a state identifier that characterizes the current observation environment as unstable but temporarily usable. The target observation confidence level is generated based on the current observation confidence level, the confidence level difference value, the prompt information, and the status identifier.

[0013] Secondly, this application provides a device for evaluating the reliability of observations of oxidation ditch operation, comprising: The image quality feature vector generation module is used to acquire images at preset observation positions in the oxidation ditch based on a preset image acquisition cycle, perform acquisition difference elimination processing on the currently acquired images to obtain standardized images, and then determine the image quality feature vector of the standardized images; the image quality feature vector includes sharpness index, contrast index, brightness uniformity index, and noise intensity index; The smooth perturbation vector determination module is used to normalize and truncate each of the image quality feature vectors to obtain the corresponding standardized quality vectors. Then, it extracts the environmental perturbation vector from the image sequence including consecutive frames and performs time window weighted smoothing on the environmental perturbation vector to obtain the smooth perturbation vector. The environmental perturbation vector includes the bubble perturbation intensity, the liquid surface ripple degree, and the suspended particle perturbation level. An environment modulation factor generation module is used to perform weighted fusion of the standardized quality vector based on preset weight coefficients to obtain an image quality score, and then determine the environment modulation factor based on the smooth perturbation vector. The observation confidence generation module is used to determine the current observation confidence at the current moment based on the image quality score and the environmental modulation factor, and to perform time series analysis on the current observation confidence to obtain the confidence difference value between adjacent moments, as well as the confidence average value and confidence standard deviation within the sliding window, so as to generate the target observation confidence based on the confidence difference value, the confidence average value and the confidence standard deviation.

[0014] Thirdly, this application provides an electronic device, comprising: Memory, used to store computer programs; A processor is used to execute the computer program to implement the aforementioned method for assessing the reliability of observations of oxidation ditch operation.

[0015] Fourthly, this application provides a computer-readable medium for storing a computer program, wherein the computer program, when executed by a processor, implements the aforementioned method for assessing the observation reliability of oxidation ditch operation.

[0016] As can be seen from the above, before conducting the observation reliability assessment of the oxidation ditch operation, this application needs to acquire images at the preset observation location of the oxidation ditch based on a preset image acquisition cycle, and perform acquisition difference elimination processing on the currently acquired images to obtain standardized images. Then, the image quality feature vector of the standardized image is determined. The image quality feature vector includes sharpness index, contrast index, brightness uniformity index, and noise intensity index. Normalization processing and truncation constraint application processing are performed on each image quality feature vector to obtain the corresponding standardized quality vector. Then, environmental disturbance vectors are extracted from the image sequence including consecutive frames, and the environmental disturbance vectors are processed... A time-window weighted smoothing process is used to obtain a smoothed perturbation vector. The environmental perturbation vector includes the intensity of bubble perturbation, the degree of liquid surface fluctuation, and the level of suspended particle perturbation. The standardized quality vector is weighted and fused based on preset weight coefficients to obtain an image quality score. Then, the environmental modulation factor is determined based on the smoothed perturbation vector. The current observation confidence is determined based on the image quality score and the environmental modulation factor. Time series analysis is performed on the current observation confidence to obtain the confidence difference between adjacent time moments, as well as the confidence average and confidence standard deviation within the sliding window. The target observation confidence is generated based on the confidence difference, confidence average, and confidence standard deviation.

[0017] Therefore, this application first requires image acquisition at a preset observation location in the oxidation ditch based on a preset image acquisition cycle, and then performing acquisition difference elimination processing on the currently acquired image to obtain a standardized image. The image quality feature vector of the standardized image is then determined. Secondly, each image quality feature vector is normalized and subject to truncation constraints to obtain a corresponding standardized quality vector. Then, an environmental perturbation vector is extracted from the image sequence including consecutive frames, and the environmental perturbation vector is subjected to time-window weighted smoothing processing to obtain a smoothed perturbation vector. Next, the standardized quality vector is weighted and fused based on preset weight coefficients to obtain an image quality score. Then, an environmental modulation factor is determined based on the smoothed perturbation vector. Finally, the current observation confidence level is determined based on the image quality score and the environmental modulation factor, and a time series analysis is performed on the current observation confidence level to obtain the confidence difference between adjacent time points, as well as the average confidence level and the standard deviation of confidence level within the sliding window. The target observation confidence level is then generated based on the confidence difference, the average confidence level, and the standard deviation of confidence level. This improves the efficiency of assessing the reliability of observations during the operation of oxidation ditches, thereby enhancing the user experience. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 This is a flowchart of a method for assessing the observation reliability of oxidation ditch operation disclosed in this application; Figure 2 This is a schematic diagram of the structure of an observation reliability assessment device for the operation of an oxidation ditch disclosed in this application; Figure 3 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Currently, during the long-term continuous operation of oxidation ditches, image data acquired at the same observation location at different times exhibit significant differences in information integrity, stability, and usability due to factors such as changes in lighting conditions, aeration bubble disturbance, sludge suspended particle movement, and the status of acquisition equipment. Without a reliability assessment mechanism for the observation process itself, low-quality or interfered observation data entering subsequent analysis and control processes can easily reduce the overall stability and safety of the system. Therefore, this application provides a method for assessing the reliability of oxidation ditch operation observations, which improves the efficiency of evaluating the reliability of oxidation ditch operation monitoring processes.

[0022] See Figure 1 As shown, this embodiment of the invention discloses a method for evaluating the observation reliability of oxidation ditch operation, including: Step S11: Based on a preset image acquisition cycle, images are acquired at preset observation positions in the oxidation ditch, and acquisition difference elimination processing is performed on the currently acquired images to obtain standardized images. Then, the image quality feature vector of the standardized images is determined. The image quality feature vector includes sharpness index, contrast index, brightness uniformity index, and noise intensity index.

[0023] In this embodiment, during system operation, fixed or semi-fixed image acquisition devices are first deployed at key observation locations in the oxidation ditch. These observation locations are preferentially selected from aeration areas, reflux confluence areas, or areas with significant sludge concentration changes. The system continuously acquires operational images according to a preset sampling cycle, with each acquisition forming an independent observation. For the first... The system records the images acquired in this acquisition as follows: Simultaneously, the corresponding acquisition time, equipment operating status parameters, aeration intensity set value, and liquid level height are recorded, which together constitute a single observation data unit, providing complete input for subsequent reliability assessment.

[0024] It is worth mentioning that before the images enter the credibility assessment process, the system first performs unified preprocessing on the original acquired images. Specifically, this includes: mapping the original images to a preset resolution scale to eliminate pixel differences caused by different acquisition devices or viewpoints; linearly normalizing the image brightness range to ensure consistency in brightness distribution across images acquired at different time periods; and cropping invalid edge regions or occluded areas in the image to prevent irrelevant regions from interfering with the calculation of quality features. This preprocessing does not alter the spatial structure and texture information of the image; it only normalizes the numerical scale to ensure the comparability of subsequent quality features across different observation times.

[0025] In this embodiment, let the observation image of the oxidation ditch acquired at time t be denoted as . To address issues such as image sharpness degradation, uneven brightness, and noise interference that may occur during image acquisition, this application's embodiments construct an image quality feature vector based on the image's inherent structure. This vector is used to characterize the availability of information in the observed image under current conditions. The vector is defined as: ; in, The image sharpness index is measured by the mean of the Laplacian operator response of the image, and its calculation form is as follows: ; in, It is a set of image pixels.

[0026] In this embodiment, Image contrast features are characterized by the variance of the gray-level distribution: ; in, This represents the average grayscale value of the image.

[0027] Used to describe brightness uniformity, it is calculated by the dispersion of the average brightness of each sub-region after dividing the image into regions: ; in, The number of image blocks. Indicates the first The average brightness of each sub-region. The noise intensity characteristics are represented by the proportion of high-frequency residual energy.

[0028] Furthermore, after the image preprocessing is completed, this embodiment of the application needs to enter the image quality assessment implementation stage, and after the above-mentioned quality features are calculated, they are normalized to form the quality feature vector of the currently observed image.

[0029] Specifically, images are acquired at preset observation locations along the oxidation ditch based on a preset image acquisition cycle. The acquired images undergo acquisition difference elimination processing to obtain standardized images. Then, the image quality feature vector of the standardized image is determined. This process may include: determining the preset observation location and preset image acquisition cycle corresponding to the oxidation ditch based on target requirements; acquiring images of the oxidation ditch at the preset observation locations along the preset image acquisition cycle to obtain the corresponding currently acquired image; performing scale normalization and brightness linear normalization operations on the currently acquired image to obtain a normalized image; identifying invalid regions in the normalized image and cropping these invalid regions to obtain the standardized image. The Laplacian operator is used to perform operations on the standardized image to obtain the results. Then, the second-order gradient response of the results across the entire image is statistically analyzed to obtain the corresponding sharpness index. The variance of the standardized image in the gray-level distribution is determined to determine the corresponding contrast index based on the variance. The standardized image is divided into several sub-regions, and the dispersion of the average brightness of each sub-region relative to the average brightness of the entire image is determined to determine the brightness uniformity index based on the dispersion. The residual information of the standardized image that meets the preset high-frequency conditions is analyzed to obtain the corresponding noise intensity index. Based on the sharpness index, contrast index, brightness uniformity index, and noise intensity index, an image quality feature vector is constructed.

[0030] Step S12: Normalize and truncation constraint are applied to each of the image quality feature vectors to obtain the corresponding standardized quality vectors. Then, environmental disturbance vectors are extracted from the image sequence including consecutive frames, and time window weighted smoothing is applied to the environmental disturbance vectors to obtain smoothed disturbance vectors. The environmental disturbance vectors include bubble disturbance intensity, liquid surface ripple degree and suspended particle disturbance level.

[0031] In this embodiment, to eliminate the influence of different units and value ranges on subsequent fusion calculations, the image quality feature vector needs to be normalized to obtain a standardized quality vector. .

[0032] Specifically, each image quality feature vector is normalized and truncated to obtain a corresponding standardized quality vector. This process may include: determining preset lower and upper threshold values ​​corresponding to sharpness, contrast, brightness uniformity, and noise intensity indices, respectively; normalizing each image quality feature vector to obtain corresponding normalization results; determining the current value corresponding to each normalization result and checking whether each current value is less than the corresponding preset lower threshold value; if the current value is less than the corresponding preset lower threshold value, setting the corresponding current value as the preset lower threshold value; and checking whether each current value is greater than the corresponding preset upper threshold value. If the current value is greater than the corresponding preset upper threshold value, setting the corresponding current value as the preset upper threshold value, thus obtaining the truncated standardized quality vector.

[0033] In this embodiment, besides the image quality itself, factors such as bubble disturbances, liquid surface fluctuations, and suspended particle movement generated by aeration in the oxidation ditch operating environment also significantly affect the observation stability. Therefore, this invention introduces an environmental disturbance vector. This vector is used to characterize the level of uncertainty in environmental conditions at the time of observation. It can be represented as: ; in, The intensity of bubble disturbance is represented by the area ratio of the highlighted discontinuous regions in the image and its temporal rate of change. The degree of liquid surface fluctuation is indicated by the magnitude of the positional offset of the liquid surface boundary in consecutive frames; The level of disturbance of suspended particles is estimated by the magnitude of change of high-frequency texture regions within a time window.

[0034] Considering the potential for localized strong reflections, short-term blurring, or abnormal noise in the actual operating environment, the system incorporates a truncation constraint mechanism for each quality characteristic component during implementation to limit its value range. ; In this way, the above processing method can prevent individual abnormal quality indicators from having a dominant impact on the overall evaluation results during the fusion process, thereby improving the stability and robustness of image quality evaluation during continuous observation.

[0035] In the environmental disturbance analysis implementation phase, this embodiment of the application needs to combine the image sequence of the current moment and several moments before and after it to conduct a comprehensive analysis of the stability of the operating environment. Subsequently, by identifying the spatial distribution characteristics of bubble regions in the image, statistically analyzing their area proportion and changes over time, the bubble disturbance characteristics are calculated. By detecting the magnitude of positional changes at the liquid surface boundary in continuous images, liquid surface undulation features are extracted. Simultaneously, by analyzing the degree of change in high-frequency texture regions within a time window, the perturbation characteristics of suspended particles are estimated. The above characteristics together constitute the environmental disturbance vector, which reflects the current level of disturbance in the operating environment. .

[0036] Specifically, environmental perturbation vectors are extracted from an image sequence including consecutive frames, and time-window weighted smoothing is applied to these vectors to obtain smoothed perturbation vectors. This process includes: constructing an image sequence based on consecutive frames and extracting corresponding environmental perturbation vectors from the image sequence; the environmental perturbation vectors include bubble perturbation intensity determined by the proportion of bright discontinuous regions in each image and the temporal rate of change, liquid surface fluctuation degree determined by the positional offset of the liquid surface boundary in consecutive frames, and suspended particle perturbation level determined by the change amplitude of high-frequency texture regions within the time window; constructing a time window of a preset length based on target requirements and assigning weight coefficients that decrease over time to the environmental perturbation vectors at each historical moment within the time window; weighted summing of bubble perturbation intensity, liquid surface fluctuation degree, and suspended particle perturbation level, along with their respective weight coefficients, to obtain smoothed bubble perturbation intensity, smoothed liquid surface fluctuation degree, and smoothed suspended particle perturbation level; and constructing a smoothed perturbation vector based on the smoothed bubble perturbation intensity, smoothed liquid surface fluctuation degree, and smoothed suspended particle perturbation level.

[0037] Step S13: The standardized quality vector is weighted and fused based on preset weight coefficients to obtain an image quality score, and then the environmental modulation factor is determined based on the smooth perturbation vector.

[0038] In this embodiment, the application requires the construction of an observation confidence calculation model. First, standardized image quality features are weighted and fused to obtain a basic image quality score: ; Among them, weight This is used to reflect the relative importance of different quality characteristics in credibility assessment.

[0039] Furthermore, to reduce the impact of sudden disturbances in a single frame on the reliability calculation results, this application embodiment introduces a length of... A short time window is used to perform weighted smoothing of various environmental disturbance characteristics: ; Among them, the weighting coefficient The weight of the current operating state in the reliability assessment is reduced over time. This smoothing process effectively suppresses the interference of instantaneous bubble bursts or short-term violent fluctuations in the liquid level on the reliability calculation results.

[0040] Furthermore, during the credibility calculation process, the system first performs weighted fusion of the truncated image quality features based on preset weight parameters to obtain a basic image quality score. Specifically, the system performs weighted fusion of standardized quality vectors based on preset weight coefficients to obtain an image quality score. Then, it determines the environmental modulation factor based on the smoothed perturbation vector. This can include: constructing an observation credibility calculation model, using the observation credibility calculation model and weighted fusion of standardized quality vectors based on preset weight coefficients to obtain the corresponding image quality score; the preset weight coefficients are used to reflect the importance of each standardized quality vector in the credibility assessment; the magnitude of the preset weight coefficients is positively correlated with the importance of the vectors; the system determines the comprehensive environmental perturbation value based on the smoothed perturbation vector and the corresponding preset modulation coefficient, constructs a negative correlation function based on the comprehensive environmental perturbation value, and then uses the negative correlation function to determine the environmental modulation factor within a preset value range; the magnitude of the comprehensive environmental perturbation value is negatively correlated with the magnitude of the environmental modulation factor.

[0041] Step S14: Determine the current observation confidence level at the current moment based on the image quality score and the environmental modulation factor, and perform time series analysis on the current observation confidence level to obtain the confidence difference value between adjacent moments, as well as the confidence average value and confidence standard deviation within the sliding window, so as to generate the target observation confidence level based on the confidence difference value, the confidence average value and the confidence standard deviation.

[0042] In this embodiment, the present application requires the introduction of an environmental disturbance modulation term to correct the basic quality score: ; Subsequently, the stable environmental disturbance characteristics are introduced, and the baseline quality score is corrected through modulation relationships to calculate the confidence score for the current observation time. .

[0043] Specifically, the current observation confidence level is determined based on the image quality score and environmental modulation factor, and time series analysis is performed on the current observation confidence level to obtain the confidence difference between adjacent time points, as well as the confidence average and confidence standard deviation within the sliding window. This can include: correcting the image quality score using the environmental modulation factor to obtain the current observation confidence level corresponding to the current time point, and determining the confidence difference between the current observation confidence level and the historical observation confidence level of the previous time point; determining each consecutive historical time point before the current time point within a sliding window of a preset length, and calculating the mean and standard deviation of the historical confidence level corresponding to each historical time point to obtain the corresponding confidence average and confidence standard deviation.

[0044] Finally, the confidence level of the observation at the current moment can be defined as: ; The credibility score is between 0 and 1, with a higher value indicating greater reliability in terms of information integrity and environmental stability.

[0045] Furthermore, to enhance the interpretability of the reliability results during continuous observation, the system needs to perform differential analysis on the reliability results at adjacent time points: ; When credibility shows a continuous downward trend over multiple consecutive moments, that is... When the value remains negative for several consecutive periods, the system recognizes that the stability of the current observation environment is declining. Even if the current confidence level has not yet fallen below the preset threshold, it can generate a prompt message in advance to assist operators in making a comprehensive judgment.

[0046] During the credibility output phase, the system jointly processes the single credibility result with historical statistical information. This is achieved by calculating the average credibility value and its fluctuation level within a time window. ; in, Used to reflect the overall reliability level during the current observation phase. This is used to characterize the stability of confidence over time. When the average confidence level is high but the fluctuation is large, the system can mark this state as "unstable but temporarily available" to prompt operators to pay attention to the changing trend of the current observation conditions.

[0047] Specifically, the current observation confidence level is determined based on image quality score and environmental modulation factor. Time series analysis is then performed on the current observation confidence level to obtain confidence difference values ​​between adjacent time points, as well as the average confidence level and standard deviation within a sliding window. The target observation confidence level is then generated based on the confidence difference values, average confidence level, and standard deviation. This process may include: determining the trend of the confidence difference values; if the trend indicates that the confidence difference values ​​are negative at each consecutive acquisition time, the current observation environment is set to a state of decreasing stability, and a corresponding prompt message is generated; determining whether the average confidence level and standard deviation are greater than a preset reliability threshold and a preset fluctuation threshold, respectively; if the average confidence level is greater than the preset reliability threshold and the standard deviation is greater than the preset fluctuation threshold, a state identifier representing an unstable but temporarily usable condition for the current observation environment is generated; and finally, the target observation confidence level is generated based on the current observation confidence level, confidence difference values, prompt message, and state identifier.

[0048] As can be seen from the above, the embodiments of this application first need to acquire images at preset observation positions in the oxidation ditch based on a preset image acquisition cycle, and perform acquisition difference elimination processing on the currently acquired images to obtain standardized images, and then determine the image quality feature vector of the standardized images; secondly, normalization processing and truncation constraint application processing are performed on each image quality feature vector to obtain the corresponding standardized quality vector, and then environmental perturbation vectors are extracted from the image sequence including consecutive frames of images, and time window weighted smoothing processing is performed on the environmental perturbation vectors to obtain smooth perturbation vectors; then, the standardized quality vectors are weighted and fused based on preset weight coefficients to obtain image quality scores, and then environmental modulation factors are determined based on smooth perturbation vectors; finally, the current observation confidence is determined based on the image quality score and environmental modulation factors, and time series analysis is performed on the current observation confidence to obtain the confidence difference values ​​of adjacent time moments, as well as the confidence average value and confidence standard deviation within the sliding window, so as to generate the target observation confidence based on the confidence difference value, confidence average value, and confidence standard deviation. This improves the efficiency of assessing the reliability of observations during the operation of oxidation ditches, thereby enhancing the user experience.

[0049] Accordingly, see Figure 2 As shown, this application also provides a device for evaluating the observation reliability of oxidation ditch operation, comprising: The image quality feature vector generation module 11 is used to acquire images at a preset observation position in the oxidation ditch based on a preset image acquisition cycle, perform acquisition difference elimination processing on the currently acquired image to obtain a standardized image, and then determine the image quality feature vector of the standardized image; the image quality feature vector includes sharpness index, contrast index, brightness uniformity index and noise intensity index; The smooth perturbation vector determination module 12 is used to normalize and truncate each of the image quality feature vectors to obtain the corresponding standardized quality vectors. Then, it extracts the environmental perturbation vector from the image sequence including consecutive frames and performs time window weighted smoothing on the environmental perturbation vector to obtain the smooth perturbation vector. The environmental perturbation vector includes the bubble perturbation intensity, the liquid surface ripple degree, and the suspended particle perturbation level. The environmental modulation factor generation module 13 is used to perform weighted fusion on the standardized quality vector based on preset weight coefficients to obtain an image quality score, and then determine the environmental modulation factor based on the smooth perturbation vector. The observation confidence generation module 14 is used to determine the current observation confidence at the current moment based on the image quality score and the environmental modulation factor, and to perform time series analysis on the current observation confidence to obtain the confidence difference value between adjacent moments, as well as the confidence average value and confidence standard deviation within the sliding window, so as to generate the target observation confidence based on the confidence difference value, the confidence average value and the confidence standard deviation.

[0050] In some specific embodiments, the image quality feature vector generation module 11 may specifically include: The image acquisition unit is used to determine the preset observation position and preset image acquisition period corresponding to the oxidation ditch based on the target requirements, and then perform image acquisition on the oxidation ditch at the preset observation position based on the preset image acquisition period to obtain the corresponding currently acquired image. The region cropping unit is used to perform scale normalization and brightness linear normalization operations on the currently acquired image to obtain a normalized image. Then, it identifies invalid regions in the normalized image and crops the invalid regions in the normalized image to obtain a standardized image. The sharpness index determination unit is used to perform operations on the standardized image using the Laplacian operator to obtain the operation result, and then to statistically analyze the second-order gradient response of the operation result over the entire image range to obtain the corresponding sharpness index. A contrast index determination unit is used to determine the variance of the standardized image in grayscale distribution, so as to determine the corresponding contrast index based on the variance, and to divide the standardized image into several sub-regions, and then determine the degree of dispersion of the average brightness of each sub-region relative to the average brightness of the whole image, so as to determine the brightness uniformity index based on the degree of dispersion. The noise intensity index determination unit is used to analyze the residual information of the standardized image that meets the preset high-frequency conditions, obtain the corresponding noise intensity index, and construct an image quality feature vector based on the sharpness index, the contrast index, the brightness uniformity index and the noise intensity index.

[0051] In some specific embodiments, the smoothing perturbation vector determination module 12 may specifically include: A threshold determination unit is used to determine a preset lower threshold and a preset upper threshold corresponding to the sharpness index, the contrast index, the brightness uniformity index and the noise intensity index, respectively. The normalization processing result determination unit is used to normalize each of the image quality feature vectors to obtain the corresponding normalization processing results, then determine the current value corresponding to each of the normalization processing results, and determine whether each of the current values ​​is less than the corresponding preset lower limit threshold. The current value setting unit is used to set the corresponding current value to the preset lower limit threshold if the current value is less than the corresponding preset lower limit threshold. The standardized quality vector determination unit is used to determine whether each current value is greater than the corresponding preset upper limit threshold. If the current value is greater than the corresponding preset upper limit threshold, the corresponding current value is set as the preset upper limit threshold to obtain the truncated standardized quality vector.

[0052] In some specific embodiments, the smoothing perturbation vector determination module 12 may specifically include: An environmental disturbance vector determination unit is used to construct an image sequence based on consecutive frames of images and extract the corresponding environmental disturbance vector from the image sequence. The environmental disturbance vector includes bubble disturbance intensity determined based on the area ratio and temporal change rate of the bright discontinuous region in each image, liquid surface fluctuation degree determined based on the positional offset of the liquid surface boundary in consecutive frames, and suspended particle disturbance level determined based on the change amplitude of the high-frequency texture region within the time window. The weight coefficient determination unit is used to construct a time window of a preset length based on the target requirements, and to assign a weight coefficient that decreases with time to the environmental disturbance vector of each historical moment within the time window. The bubble disturbance intensity determination unit is used to weight and accumulate the bubble disturbance intensity, the liquid surface fluctuation degree, and the suspended particle disturbance level, as well as their respective weighting coefficients, to obtain the smoothed bubble disturbance intensity, the smoothed liquid surface fluctuation degree, and the smoothed suspended particle disturbance level. A smoothing perturbation vector construction unit is used to construct a smoothing perturbation vector based on the smoothed bubble perturbation intensity, the smoothed liquid surface ripple degree, and the smoothed suspended particle perturbation level.

[0053] In some specific embodiments, the environmental modulation factor generation module 13 may specifically include: An image quality scoring unit is used to construct an observation credibility calculation model, and to use the observation credibility calculation model to perform weighted fusion of the standardized quality vectors based on preset weight coefficients to obtain the corresponding image quality score; the preset weight coefficients are used to reflect the importance of each of the standardized quality vectors in the credibility assessment; the magnitude of the preset weight coefficients is positively correlated with the importance of the vectors. An environmental modulation factor generation unit is used to determine a comprehensive environmental disturbance value based on the smoothed disturbance vector and the corresponding preset modulation coefficient, construct a negative correlation function based on the comprehensive environmental disturbance value, and then use the negative correlation function to determine an environmental modulation factor within a preset value range; the numerical value corresponding to the comprehensive environmental disturbance value is negatively correlated with the numerical value corresponding to the environmental modulation factor.

[0054] In some specific embodiments, the observation confidence generation module 14 may specifically include: The credibility difference determination unit is used to correct the image quality score using the environmental modulation factor, obtain the current observation credibility at the current moment, and determine the credibility difference between the current observation credibility at the current moment and the historical observation credibility at the previous moment. The credibility average value determination unit is used to determine each consecutive historical moment before the current moment within a sliding window of a preset length, and to calculate the mean and standard deviation of the historical credibility corresponding to each historical moment to obtain the corresponding credibility average value and credibility standard deviation.

[0055] In some specific embodiments, the observation confidence generation module 14 may specifically include: The prompt information generation unit is used to determine the changing trend of the confidence difference value. If the changing trend indicates that the confidence difference value is negative at each consecutive acquisition time, the state corresponding to the current observation environment is set to a decreasing trend in stability, and a corresponding prompt information is generated. A status identifier generation unit is used to determine whether the average confidence level and the standard deviation of confidence level are greater than a preset reliability threshold and a preset fluctuation threshold, respectively. If the average confidence level is greater than the preset reliability threshold and the standard deviation of confidence level is greater than the preset fluctuation threshold, a status identifier is generated to characterize the current observation environment as unstable but temporarily usable. An observation confidence generation subunit is used to generate target observation confidence based on the current observation confidence, the confidence difference value, the prompt information, and the status identifier.

[0056] Furthermore, embodiments of this application also disclose an electronic device, Figure 3 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the observation reliability assessment method for oxidation ditch operation disclosed in any of the foregoing embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0057] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0058] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0059] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the observation reliability assessment method for oxidation ditch operation executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0060] Furthermore, this application also discloses a computer-readable medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned method for evaluating the observation reliability of oxidation ditch operation. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0061] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0062] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0063] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of media known in the art.

[0064] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0065] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for evaluating the reliability of observations during oxidation ditch operation, characterized in that, include: Images are acquired at preset observation locations in the oxidation ditch based on a preset image acquisition cycle. The acquired images are then processed to eliminate acquisition differences, resulting in standardized images. The image quality feature vector of the standardized images is then determined. The image quality feature vector includes sharpness index, contrast index, brightness uniformity index, and noise intensity index. Each of the image quality feature vectors is normalized and subjected to truncation constraints to obtain a corresponding standardized quality vector. Then, environmental disturbance vectors are extracted from the image sequence including consecutive frames, and time-window weighted smoothing is applied to the environmental disturbance vectors to obtain smoothed disturbance vectors. The environmental disturbance vectors include bubble disturbance intensity, liquid surface ripple degree, and suspended particle disturbance level. The standardized quality vector is weighted and fused based on preset weight coefficients to obtain an image quality score, and then the environmental modulation factor is determined based on the smooth perturbation vector. The current observation confidence level is determined based on the image quality score and the environmental modulation factor. Time series analysis is then performed on the current observation confidence level to obtain the confidence difference between adjacent time points, as well as the confidence average and confidence standard deviation within the sliding window. The target observation confidence level is then generated based on the confidence difference, the confidence average, and the confidence standard deviation.

2. The method for evaluating the reliability of observations of oxidation ditch operation according to claim 1, characterized in that, The process involves acquiring images at preset observation locations in the oxidation ditch based on a preset image acquisition cycle, performing acquisition difference elimination processing on the acquired images to obtain standardized images, and then determining the image quality feature vector of the standardized images, including: Based on the target requirements, the preset observation position and preset image acquisition cycle are determined for the oxidation ditch. Then, based on the preset image acquisition cycle, the oxidation ditch is image acquired at the preset observation position to obtain the corresponding current acquired image. The scale normalization operation and brightness linear normalization operation are performed on the currently acquired image to obtain a normalized image. Then, the invalid regions in the normalized image are identified and cropped to obtain a standardized image. The standardized image is processed using the Laplacian operator to obtain the results. Then, the second-order gradient response of the results over the entire image is statistically analyzed to obtain the corresponding sharpness index. The variance of the standardized image in grayscale distribution is determined, and the corresponding contrast index is determined based on the variance. The standardized image is divided into several sub-regions, and the dispersion of the average brightness of each sub-region relative to the average brightness of the whole image is determined, and the brightness uniformity index is determined based on the dispersion. The residual information of the standardized image that meets the preset high-frequency conditions is analyzed to obtain the corresponding noise intensity index, and an image quality feature vector is constructed based on the sharpness index, the contrast index, the brightness uniformity index and the noise intensity index.

3. The method for evaluating the reliability of observations of oxidation ditch operation according to claim 1, characterized in that, The process of normalizing and truncating each of the image quality feature vectors to obtain the corresponding standardized quality vector includes: Determine the preset lower threshold and preset upper threshold corresponding to the sharpness index, the contrast index, the brightness uniformity index and the noise intensity index respectively; The image quality feature vectors are normalized to obtain corresponding normalization results. Then, the current value corresponding to each normalization result is determined, and it is determined whether each current value is less than the corresponding preset lower threshold. If the current value is less than the corresponding preset lower threshold, then the corresponding current value is set as the preset lower threshold. Determine whether each current value is greater than the corresponding preset upper limit threshold. If the current value is greater than the corresponding preset upper limit threshold, set the corresponding current value to the preset upper limit threshold to obtain the truncated standardized quality vector.

4. The method for evaluating the reliability of observations of oxidation ditch operation according to claim 1, characterized in that, The step of extracting environmental perturbation vectors from an image sequence including consecutive frames and performing time-window weighted smoothing on the environmental perturbation vectors to obtain smoothed perturbation vectors includes: An image sequence is constructed based on consecutive frames of images, and the corresponding environmental disturbance vector is extracted from the image sequence. The environmental disturbance vector includes the bubble disturbance intensity determined based on the area ratio of the bright discontinuous region in each image and the temporal change rate, the liquid surface fluctuation degree determined based on the positional offset of the liquid surface boundary in consecutive frames, and the suspended particle disturbance level determined based on the change amplitude of the high-frequency texture region within the time window. A time window of a preset length is constructed based on the target requirements, and a weight coefficient that decreases with time is assigned to the environmental disturbance vector of each historical moment within the time window. The bubble disturbance intensity, the liquid surface ripple degree, and the suspended particle disturbance level, along with their respective weighting coefficients, are weighted and accumulated to obtain the smoothed bubble disturbance intensity, the smoothed liquid surface ripple degree, and the smoothed suspended particle disturbance level. A smoothed perturbation vector is constructed based on the smoothed bubble disturbance intensity, the smoothed liquid surface fluctuation degree, and the smoothed suspended particle disturbance level.

5. The method for evaluating the reliability of observations of oxidation ditch operation according to claim 1, characterized in that, The step of weighting and fusing the standardized quality vector based on preset weight coefficients to obtain an image quality score, and then determining the environmental modulation factor based on the smoothed perturbation vector, includes: An observation confidence calculation model is constructed, and the standardized quality vectors are weighted and fused based on a preset weight coefficient to obtain the corresponding image quality score. The preset weight coefficient is used to reflect the importance of each standardized quality vector in the confidence assessment. The value of the preset weight coefficient is positively correlated with the importance of the vector. The comprehensive environmental disturbance value is determined based on the smoothed disturbance vector and the corresponding preset modulation coefficient. A negative correlation function is constructed based on the comprehensive environmental disturbance value, and then the environmental modulation factor within a preset range is determined using the negative correlation function. The numerical value corresponding to the comprehensive environmental disturbance value is negatively correlated with the numerical value corresponding to the environmental modulation factor.

6. The method for evaluating the reliability of observations of oxidation ditch operation according to claim 1, characterized in that, The process of determining the current observation confidence level based on the image quality score and the environmental modulation factor, and performing time series analysis on the current observation confidence level to obtain the confidence difference between adjacent time points, the average confidence level and the confidence standard deviation within the sliding window, includes: The image quality score is corrected using the environmental modulation factor to obtain the current observation confidence level at the current moment, and the confidence level difference between the current observation confidence level at the current moment and the historical observation confidence level at the previous moment is determined. Within a sliding window of a preset length, determine each consecutive historical moment preceding the current moment, and calculate the mean and standard deviation of the historical credibility corresponding to each historical moment to obtain the corresponding average credibility and standard deviation of credibility.

7. The method for assessing the observation reliability of oxidation ditch operation according to any one of claims 1 to 6, characterized in that, The process of determining the current observation confidence level based on the image quality score and the environmental modulation factor, performing time series analysis on the current observation confidence level to obtain the confidence difference between adjacent time points, the average confidence level and the standard deviation of confidence level within a sliding window, and generating the target observation confidence level based on the confidence difference, the average confidence level and the standard deviation of confidence level, includes: Determine the changing trend of the confidence difference value. If the changing trend indicates that the confidence difference value is negative at each consecutive acquisition time, set the state corresponding to the current observation environment as having a decreasing trend in stability and generate corresponding prompt information. Determine whether the average confidence level and the standard deviation of confidence level are greater than a preset reliability threshold and a preset fluctuation threshold, respectively. If the average confidence level is greater than the preset reliability threshold and the standard deviation of confidence level is greater than the preset fluctuation threshold, then generate a state identifier that characterizes the current observation environment as unstable but temporarily usable. The target observation confidence level is generated based on the current observation confidence level, the confidence level difference value, the prompt information, and the status identifier.

8. A device for evaluating the reliability of observations during oxidation ditch operation, characterized in that, include: The image quality feature vector generation module is used to acquire images at preset observation positions in the oxidation ditch based on a preset image acquisition cycle, perform acquisition difference elimination processing on the currently acquired images to obtain standardized images, and then determine the image quality feature vector of the standardized images; the image quality feature vector includes sharpness index, contrast index, brightness uniformity index, and noise intensity index; The smooth perturbation vector determination module is used to normalize and truncate each of the image quality feature vectors to obtain the corresponding standardized quality vectors. Then, it extracts the environmental perturbation vector from the image sequence including consecutive frames and performs time window weighted smoothing on the environmental perturbation vector to obtain the smooth perturbation vector. The environmental perturbation vector includes the bubble perturbation intensity, the liquid surface ripple degree, and the suspended particle perturbation level. An environment modulation factor generation module is used to perform weighted fusion of the standardized quality vector based on preset weight coefficients to obtain an image quality score, and then determine the environment modulation factor based on the smooth perturbation vector. The observation confidence generation module is used to determine the current observation confidence at the current moment based on the image quality score and the environmental modulation factor, and to perform time series analysis on the current observation confidence to obtain the confidence difference value between adjacent moments, as well as the confidence average value and confidence standard deviation within the sliding window, so as to generate the target observation confidence based on the confidence difference value, the confidence average value and the confidence standard deviation.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the observation reliability assessment method for oxidation ditch operation as described in any one of claims 1 to 7.

10. A computer-readable medium, characterized in that, Used to store a computer program, wherein the computer program, when executed by a processor, implements the observation reliability assessment method for oxidation ditch operation as described in any one of claims 1 to 7.

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