Landfill leachate treatment method and device based on biology-ozone oxidation technology

Abstract

The invention discloses a landfill leachate treatment method and device based on a biological-ozone oxidation technology, and relates to the technical field of wastewater treatment. The method comprises the following steps: acquiring historical parameter sequences of various parameter types of landfill leachate equipment in multi-batch historical landfill leachate treatment; for each parameter type, based on each historical parameter sequence, determining the component influence expression degree of the parameter type at each sampling moment; aiming at each parameter type, based on the component influence representation degree of the parameter type at each sampling moment, determining the structural anomaly significance of the parameter type at each sampling moment; on the basis of the structural anomaly significance of each parameter type at each sampling moment, determining the parameter adjustment amount of each parameter type at each sampling moment in the current landfill leachate treatment, so that the parameter value of the parameter type of the landfill leachate equipment is adjusted on the basis of the parameter adjustment amount, and the treatment of the current landfill leachate is realized. According to the invention, the effectiveness of landfill leachate treatment can be improved.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, specifically to a method and apparatus for treating landfill leachate based on biological-ozone oxidation technology. Background Technology

[0002] Landfill leachate often contains a significant amount of waste oil, originating from the decomposition of organic matter and oily pollutants from some industrial waste. It is characterized by high concentration and complex composition. Ozone oxidation technology is frequently combined with biological treatment and membrane filtration to form a combined process for landfill leachate treatment, aiming to decolorize and deodorize waste oil. This process is widely used in the field of waste oil recycling.

[0003] Currently, when using ozone oxidation technology to treat landfill leachate, the ozone reacts with the oil through an aeration structure. However, the waste oil mixture has a complex composition, and some oils have low flash points, which can easily affect the mixing effect and even cause explosions that damage the aeration structure. Furthermore, the effect of changes in the waste oil mixture on the aeration structure is difficult to quantify effectively.

[0004] Thus, existing technologies struggle to quantify the impact of leachate treatment equipment parameter settings on aeration efficiency based on actual reaction process data, resulting in poor parameter settings for leachate treatment equipment and consequently, poor effectiveness of leachate treatment. Summary of the Invention

[0005] This invention provides a method and apparatus for treating landfill leachate based on biological-ozone oxidation technology, which can improve the effectiveness of landfill leachate treatment.

[0006] A first aspect of this invention provides a method for treating landfill leachate based on biological-ozone oxidation technology, comprising: acquiring historical parameter sequences of various parameter types from multiple batches of historical landfill leachate treatment; the historical parameter sequences including parameter values ​​of parameter types at each sampling time during historical landfill leachate treatment; for each parameter type, determining the component influence performance degree of the parameter type at each sampling time based on each historical parameter sequence; the component influence performance degree is used to measure the degree to which the parameter type exhibits different performances due to the influence of waste oil components in the landfill leachate; for each parameter type, determining the structural anomaly significance of the parameter type at each sampling time based on the component influence performance degree of the parameter type at each sampling time; the structural anomaly significance is used to measure the degree of influence of aeration structural anomalies caused by the parameter type on the biological-ozone oxidation reaction effect; and determining the parameter adjustment amount of each parameter type at each sampling time during current landfill leachate treatment based on the structural anomaly significance of each parameter type at each sampling time, so as to adjust the parameter values ​​of the parameter types of the landfill leachate treatment equipment based on the parameter adjustment amount, thereby achieving the treatment of the current landfill leachate.

[0007] Furthermore, this invention proposes, for each parameter type, to determine the component influence performance of the parameter type at each sampling time based on each historical parameter sequence, including: determining the type difference coefficient of the target parameter type at the target sampling time based on the parameter values ​​at the target sampling time in each historical parameter sequence of the target parameter type; the target sampling time is any sampling time, the target parameter type is any parameter type, and the type difference coefficient is used to measure the degree of difference exhibited by the target parameter type due to the influence of waste oil components in landfill leachate; and determining the component influence performance of the target parameter type at the target sampling time based on the type difference coefficient of each parameter type at the target sampling time.

[0008] Furthermore, the present invention also proposes to determine the type difference coefficient of the target parameter type at the target sampling time based on the parameter values ​​at the target sampling time in each historical parameter sequence of the target parameter type, including: obtaining the absolute value of the parameter difference between the parameter values ​​at the target sampling time in adjacent historical parameter sequences of the target parameter type; and averaging the absolute values ​​of the parameter differences to obtain the type difference coefficient of the target parameter type at the target sampling time.

[0009] Furthermore, the present invention proposes to determine the component influence performance of the target parameter type at the target sampling time based on the type difference coefficient of each parameter type at the target sampling time, including: averaging the type difference coefficients of each parameter type at the target sampling time to obtain the average type difference coefficient; dividing the type difference coefficient of the target parameter type at the target sampling time by the average type difference coefficient to obtain the type difference coefficient deviation of the target parameter type at the target sampling time; and using the type difference coefficient deviation to determine the component influence performance of the target parameter type at the target sampling time.

[0010] Furthermore, this invention proposes, for each parameter type, determining the structural anomaly significance at each sampling time based on the component influence performance of the parameter type at each sampling time, including: sorting the component influence performance of each parameter type at each sampling time according to time order to construct a component influence performance sequence for each parameter type; dividing each component influence performance sequence from the target sampling time to obtain a first subsequence and a second subsequence corresponding to each component influence performance sequence; the target sampling time is any sampling time; based on each first subsequence, determining the first aeration influence coefficient of the target parameter type at the target sampling time, and based on each second subsequence, determining the second aeration influence coefficient of the target parameter type at the target sampling time; the target parameter type is any parameter type; determining the structural anomaly significance of the target parameter type at the target sampling time based on the target first subsequence, the target second subsequence, the first aeration influence coefficient, and the second aeration influence coefficient; the target first subsequence and the target second subsequence are the first and second subsequences obtained by dividing the component influence performance sequence of the target parameter type from the target sampling time.

[0011] Furthermore, the present invention also proposes to determine the first aeration influence coefficient of the target parameter type at the target sampling time based on each first subsequence, including: obtaining the subsequence difference degree between the target first subsequence and each reference first subsequence; the reference first subsequence is each first subsequence other than the target first subsequence; and averaging the subsequence difference degree to obtain the first aeration influence coefficient of the target parameter type at the target sampling time.

[0012] Furthermore, the present invention proposes a method for determining the structural anomaly significance of a target parameter type at the target sampling time based on a target first subsequence, a target second subsequence, a first aeration influence coefficient, and a second aeration influence coefficient. This method includes: obtaining the first mean of the influence of each component in the target first subsequence and obtaining the second mean of the influence of each component in the target second subsequence; dividing the first aeration influence coefficient by the first mean of the first mean of the first mean of the first mean of the first mean of the second ...

[0013] Furthermore, the present invention proposes to determine the parameter adjustment amount of each parameter type at each sampling time in the current landfill leachate treatment based on the structural anomaly significance of each parameter type at each sampling time, including: obtaining the abnormal parameter types among each parameter type that have abnormal change direction at the target sampling time; the target sampling time is any sampling time, and the abnormal change direction is used to characterize the actual change trend of the parameter type as being far from the standard change trend; and determining the parameter adjustment amount of the target parameter type at the target sampling time based on the first structural anomaly significance of the target parameter type at the target sampling time and the second structural anomaly significance of each abnormal parameter type at the target sampling time.

[0014] Furthermore, the present invention proposes a method for determining the parameter adjustment amount of a target parameter type at the target sampling time in the current landfill leachate treatment based on the first structural anomaly significance of the target parameter type at the target sampling time and the second structural anomaly significance of each anomaly parameter type at the target sampling time. This method includes: averaging the second structural anomaly significance of each anomaly parameter type at the target sampling time to obtain the average structural anomaly significance; determining the step size adjustment value using the difference between the first structural anomaly significance and the average structural anomaly significance; and adding the preset step size value of the target parameter type to the step size adjustment value to obtain the parameter adjustment amount of the target parameter type at the target sampling time in the current landfill leachate treatment.

[0015] A second aspect of this invention provides a landfill leachate treatment device based on biological-ozone oxidation technology, comprising: a parameter acquisition module for acquiring historical parameter sequences of various parameter types in multiple batches of historical landfill leachate treatment; the historical parameter sequences include parameter values ​​of each parameter type at each sampling time in the historical landfill leachate treatment; and a first analysis module for determining the component influence performance of each parameter type at each sampling time based on each historical parameter sequence; the component influence performance is used to measure the degree to which the parameter type exhibits different performances due to the influence of waste oil components in the landfill leachate. The second analysis module is used to determine the structural anomaly significance of each parameter type at each sampling time based on the component influence performance of the parameter type at each sampling time. The structural anomaly significance is used to measure the degree of influence of the aeration structure anomaly caused by the parameter type on the biological-ozone oxidation reaction effect. The weight determination module is used to determine the parameter adjustment amount of each parameter type at each sampling time in the current landfill leachate treatment based on the structural anomaly significance of each parameter type at each sampling time, so as to adjust the parameter values ​​of the parameter type of the landfill leachate equipment based on the parameter adjustment amount, thereby realizing the treatment of the current landfill leachate.

[0016] The present invention has the following beneficial effects: The landfill leachate treatment method based on biological-ozone oxidation technology provided in this invention first obtains historical parameter sequences of various parameter types from multiple batches of historical landfill leachate treatment. This sequence covers information such as changes in waste oil composition under different conditions, providing a comprehensive foundation for subsequent analysis. Next, the component influence performance of each parameter type at each sampling time is determined. Since waste oil composition is complex and variable, different parameter types are affected to varying degrees. Determining the component influence performance reveals the extent to which each parameter type exhibits different behaviors due to oil composition influence, providing a basis for quantifying the impact. Then, the structural anomaly significance is determined based on the component influence performance. Because parameter types may cause aeration structure anomalies, thereby affecting the biological-ozone oxidation reaction effect, determining the structural anomaly significance can measure the degree of this impact. Finally, the parameter adjustment amount is determined based on the structural anomaly significance. Adjusting the parameter type accordingly allows for optimization of the landfill leachate treatment equipment parameters to adapt to different oil compositions, thereby improving aeration efficiency and enhancing the effectiveness of landfill leachate treatment. Attached Figure Description

[0017] To more clearly illustrate the technical solutions and advantages 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic flowchart of a landfill leachate treatment method based on biological-ozone oxidation technology provided in one embodiment of the present invention; Figure 2 This is a schematic flowchart of S200 provided in one embodiment of the present invention; Figure 3 This is a schematic flowchart of S300 provided in one embodiment of the present invention; Figure 4 This is a schematic flowchart of S400 provided in one embodiment of the present invention; Figure 5 This is a schematic diagram of the results of a landfill leachate treatment device based on biological-ozone oxidation technology provided in one embodiment of the present invention. Detailed Implementation

[0019] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a landfill leachate treatment method and apparatus based on biological-ozone oxidation technology proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] In traditional landfill leachate treatment systems, ozone oxidation technology relies on aeration structures to achieve gas-liquid mixing. However, the complexity and dynamic changes in waste oil composition make it difficult to quantify the correlation between parameter types and aeration effects. Due to the lack of a dynamic assessment mechanism for the impact of oil composition on parameter types during historical treatment processes, the operating parameters of landfill leachate treatment equipment cannot be adaptively adjusted according to real-time operating conditions. This results in uneven gas-liquid distribution in localized areas of the aeration structure, reduced mass transfer efficiency at the reaction interface, and significant fluctuations in ozone utilization.

[0022] Faced with the aforementioned problems, this invention first considers how to establish a dynamic correlation mechanism between parameter types and aeration structure anomalies. Traditional methods, relying on fixed parameter thresholds, cannot adapt to fluctuations in oil composition and require mining the implicit relationship between parameter changes and aeration effects from historical data. To address this, this invention attempts to construct time-series features of parameter types using multiple batches of historical data to analyze the dynamic performance of parameters affected by oil composition at different sampling times. Furthermore, it is necessary to quantify the actual impact of parameter anomalies on the aeration structure, thereby establishing a dynamic weight allocation mechanism for parameter adjustment. By comparing the differences in component sensitivity of different parameter types in historical sequences, the invention ultimately determines the component impact performance as the core indicator, combining it with the significance of structural anomalies to achieve real-time correction of parameter weights.

[0023] In this regard, such as Figure 1 As shown, the present invention provides a schematic flow diagram of a landfill leachate treatment method based on biological-ozone oxidation technology. This landfill leachate treatment method based on biological-ozone oxidation technology can be applied to electronic devices. The landfill leachate treatment method based on biological-ozone oxidation technology may include the following steps S100 to S400: S100, acquire historical parameter sequences of various parameter types in multiple batches of historical landfill leachate treatment; the historical parameter sequences include parameter values ​​of each parameter type at each sampling time in the historical landfill leachate treatment.

[0024] In this step, the landfill leachate treatment equipment refers to a specialized device used to treat landfill leachate. It integrates the facilities and components required for related treatment technologies such as biological treatment and ozone oxidation, including reaction vessels, ozone generation systems, and aeration structures. It is the core hardware carrier for realizing the landfill leachate treatment process.

[0025] Multiple parameter types refer to the numerous measurable and monitorable parameters involved in the treatment of landfill leachate, such as ozone concentration, reaction temperature, and reaction pressure. These parameters all affect the treatment effect and are therefore objects that need to be analyzed and adjusted.

[0026] A historical parameter sequence refers to a sequence recording the parameter values ​​of a parameter type at various sampling times during historical landfill leachate treatment. Arranged chronologically, it sequentially displays the parameter values ​​corresponding to each sampling time, providing a clear visual representation of parameter type changes during the treatment process and offering a data foundation for subsequent analysis. Specifically, it can be implemented by using sensors to collect parameter values ​​at different time points and storing them as time-series data to reflect the dynamic changes of parameter types throughout historical treatment processes.

[0027] The sampling time is a specific point in time set during the landfill leachate treatment process to measure and record various parameters. The sampling time must be determined based on the characteristics and requirements of the treatment process to ensure accurate capture of parameter changes.

[0028] Specifically, in multiple historical treatment batches of the landfill leachate treatment plant, various sensors (such as ozone concentration sensors, temperature sensors, and pressure sensors) were used to measure multiple parameter types (such as ozone concentration, reaction temperature, and reaction pressure) in real time. The parameter values ​​collected by these sensors were recorded at pre-set sampling times and organized into a historical parameter sequence in chronological order. Accurate parameter measurement via sensors allows for timely acquisition of real-time data on the plant's operation. Setting sampling times ensures that parameter information is captured at appropriate points in time to reflect dynamic changes during the treatment process. Organizing the data into a historical parameter sequence provides a comprehensive and orderly data foundation for subsequent analysis of parameter variation patterns and influencing factors, facilitating the extraction of valuable information from a large amount of historical data.

[0029] S200, for each parameter type, based on each historical parameter sequence, determines the component influence performance of the parameter type at each sampling time; the component influence performance is used to measure the degree to which the parameter type exhibits different performance due to the influence of waste oil components in landfill leachate.

[0030] In this step, the component impact performance measure is used to assess the degree to which different parameter types exhibit different performances due to the influence of waste oil components in landfill leachate. Because the waste oil components in landfill leachate are complex and variable, different parameter types are affected to varying degrees; the component impact performance measure quantifies these differences in influence.

[0031] Specifically, for each parameter type, data analysis methods (such as statistical analysis and correlation analysis) are used to calculate the degree to which the parameter type exhibits different behaviors due to the influence of waste oil composition at different sampling times, based on the parameter values ​​of that parameter type in historical parameter sequences at different sampling times. This is known as the component influence performance. Since the composition of waste oil in landfill leachate is complex and variable, different parameter types are affected differently. Data analysis methods can uncover the intrinsic relationship between parameter types and waste oil composition, quantify the degree to which each parameter type is affected by components, and thus clarify the differences in the impact of oil composition changes on different parameters at different times.

[0032] S300, for each parameter type, determines the structural anomaly significance of the parameter type at each sampling time based on the component influence performance of the parameter type at each sampling time; the structural anomaly significance is used to measure the degree of influence of the aeration structural anomaly caused by the parameter type on the biological-ozone oxidation reaction effect.

[0033] In this step, structural anomaly significance aims to measure the impact of aeration structure anomalies caused by parameter type on the effectiveness of the biological-ozone oxidation reaction. The aeration structure plays a crucial role in the treatment process; anomalies in parameter type can lead to problems with the aeration structure, thereby affecting the overall effectiveness of the biological-ozone oxidation reaction. Structural anomaly significance assesses the degree of this impact.

[0034] Specifically, for each parameter type, based on its component influence at each sampling time, we further analyze the potential abnormalities in the aeration structure (such as uneven aeration, insufficient aeration volume, etc.) caused by anomalies in that parameter type, and the extent to which these abnormalities affect the biological-ozone oxidation reaction (such as reaction rate, pollutant removal rate, etc.). We determine the significance of structural anomalies by establishing corresponding evaluation models or empirical formulas. Parameter type anomalies may trigger problems in the aeration structure, and the state of the aeration structure directly affects the effectiveness of the biological-ozone oxidation reaction. By analyzing the logical relationship between component influence, aeration structure anomalies, and reaction effects, we establish an evaluation method that can accurately measure the impact of parameter type-induced aeration structure anomalies on the reaction effect, providing crucial basis for subsequent parameter adjustments.

[0035] S400, based on the structural anomaly significance of each parameter type at each sampling time, determines the parameter adjustment amount of each parameter type at each sampling time in the current landfill leachate treatment, so as to adjust the parameter values ​​of the parameter type of the landfill leachate equipment based on the parameter adjustment amount, thereby realizing the treatment of the current landfill leachate.

[0036] In this step, the parameter adjustment amount is determined based on the significance of structural anomalies for each parameter type at each sampling time. This reflects the importance of adjusting the parameter values ​​for different parameter types at each sampling time during the current landfill leachate treatment process. Adjusting the parameter values ​​of the landfill leachate treatment equipment based on the parameter adjustment amount can make the equipment parameters more aligned with actual treatment needs and improve treatment efficiency.

[0037] Specifically, based on the significance of structural anomalies for each parameter type at each sampling time, weighted calculations and other methods are used to determine the parameter adjustment amount for each parameter type at each sampling time in the current landfill leachate treatment process. Then, the corresponding parameter types of the landfill leachate treatment equipment are adjusted in real time according to these weights to optimize the treatment process. The significance of structural anomalies reflects the importance of parameter types to the treatment effect. By determining the parameter adjustment amount, the priority and magnitude of adjustment for each parameter type in the current treatment process can be clearly identified. Adjustments based on weights allow equipment parameters to better align with actual treatment needs, improving the efficiency and quality of the biological-ozone oxidation reaction, thereby enhancing the overall effect of landfill leachate treatment.

[0038] To clearly illustrate how to adjust specific parameter values ​​based on parameter adjustment amounts, a concrete implementation method is provided here: For different parameter types, such as ozone concentration, reaction temperature, and reaction pressure, the calculated parameter adjustment amount can be added to the parameter value before adjustment for that parameter type to obtain the adjusted parameter value. For example, for the parameter type of ozone concentration, after calculating the parameter adjustment amount corresponding to the ozone concentration at that moment, the parameter adjustment amount is added to the original ozone concentration to obtain the final ozone concentration.

[0039] As an example, a landfill leachate treatment plant employs an integrated biological treatment and ozone oxidation system. First, various sensors collect data on ozone concentration, reaction temperature, and other parameters across multiple historical treatment batches at predetermined sampling times, creating a historical parameter sequence. Next, data analysis methods are used to calculate the degree of influence of waste oil components on each parameter type at each time point, based on historical data. Then, based on this degree of influence, the impact of parameter anomalies on the aeration structure and the effectiveness of the biological-ozone oxidation reaction is analyzed, and the significance of structural anomalies is determined using an evaluation model. Finally, based on the significance of structural anomalies, a weighted calculation is used to determine the adjustment weight of each parameter type at each time point in the current treatment process. The equipment parameters of the landfill leachate treatment system are adjusted in real time according to these weights, optimizing the treatment process and improving the treatment efficiency.

[0040] This embodiment first obtains historical parameter sequences for various parameter types from multiple batches of landfill leachate treatment equipment, covering information such as changes in waste oil composition under different conditions, providing a comprehensive foundation for subsequent analysis. Next, the component impact performance of each parameter type at each sampling time is determined. Since waste oil composition is complex and variable, different parameter types are affected to varying degrees. Determining the component impact performance reveals the extent to which each parameter type exhibits different behaviors due to oil composition, providing a basis for quantifying the impact. Then, the structural anomaly significance is determined based on the component impact performance. Because parameter types may cause aeration structure anomalies, thereby affecting the biological-ozone oxidation reaction effect, determining the structural anomaly significance can measure the degree of this impact. Finally, the parameter adjustment amount is determined based on the structural anomaly significance. Adjusting the parameter type accordingly allows for optimization of the landfill leachate treatment equipment parameters to adapt to different oil compositions, thereby improving aeration efficiency and enhancing the effectiveness of landfill leachate treatment.

[0041] In some of the solutions described above in this invention, the degree of influence of parameter types on components at each sampling time is determined based on historical parameter sequences. However, when measuring the degree of difference in the influence of waste oil components on parameter types, since historical parameter sequences may have random fluctuations or batch-to-batch differences, directly calculating the difference based on the original parameter values ​​can easily introduce noise interference, resulting in insufficient accuracy of the degree of influence of components.

[0042] In this regard, such as Figure 2 As shown, the present invention further proposes that S200 includes the following S210 to S220: S210, Based on the parameter values ​​at the target sampling time in each historical parameter sequence of the target parameter type, determine the type difference coefficient of the target parameter type at the target sampling time; the target sampling time is any sampling time, the target parameter type is any parameter type, and the type difference coefficient is used to measure the degree of difference of the target parameter type due to the influence of waste oil components in landfill leachate; S220, based on the type difference coefficient of each parameter type at the target sampling time, determine the component influence performance of the target parameter type at the target sampling time.

[0043] In this embodiment, the type difference coefficient focuses on a single parameter type at a specific sampling time, measuring the degree of difference in that parameter type due to the influence of waste oil components in landfill leachate. For example, the ozone concentration parameter may fluctuate significantly at the same sampling time under the influence of different waste oil components, and the type difference coefficient quantifies the degree of such fluctuation.

[0044] Specifically, assuming the target parameter type is reaction temperature and the target sampling time is a specific point in time, the reaction temperature value corresponding to the target sampling time is extracted from the historical parameter sequences of reaction temperature. Then, the reaction temperature values ​​at the same time in these different batches of historical treatments are analyzed. By calculating statistics such as variance, standard deviation, and mean, or by using a specific difference calculation model, the degree of difference in the influence of waste oil components on the reaction temperature at that time is determined, i.e., the type difference coefficient. Next, the type difference coefficients of all parameter types at the target sampling time are collected. Based on these data, mathematical methods such as weighted averaging and normalization are used to determine the degree of influence of the composition on the reaction temperature at the target sampling time.

[0045] In particular, the composition of waste oil in landfill leachate is complex and variable, and different waste oil components have different effects on parameter types, resulting in differences in parameter types across different batches processed at the same time. These differences can be captured by calculating the type difference coefficient. The component influence performance is determined based on the type difference coefficient of each parameter type, which comprehensively measures the differences in the impact of components on each parameter type, thus providing a more comprehensive assessment of the degree to which the target parameter type is significantly affected by waste oil components.

[0046] As an example, firstly, the parameter values ​​at the target sampling time are obtained from the historical parameter sequences of each target parameter type. Here, the target sampling time is any sampling time, and the target parameter type is any parameter type. Then, based on these parameter values, the type difference coefficient of the target parameter type at the target sampling time is determined. The type difference coefficient measures the degree of difference exhibited by the target parameter type due to the influence of waste oil components in landfill leachate. Further, based on the type difference coefficient of each parameter type at the target sampling time, the degree of influence of component composition on the target parameter type at the target sampling time is determined.

[0047] For example, for the parameter type of reaction temperature, its value at a specific sampling time (e.g., 1 hour after the start of treatment) across multiple historical batches can be obtained. The type difference coefficient of reaction temperature at that sampling time can then be calculated, thereby determining its influence on the composition. Similar processing can be performed on other parameter types. In this way, the degree to which each parameter type exhibits different characteristics due to the influence of waste oil composition at different times can be quantified.

[0048] This embodiment enables quantitative analysis of the varying degrees to which different parameter types are affected by waste oil composition based on historical data, providing a basis for subsequently determining the significance of structural anomalies. This allows for a more accurate assessment of the impact of parameter settings on aeration efficiency, thereby optimizing parameter adjustments in landfill leachate treatment equipment and improving treatment effectiveness.

[0049] In some of the solutions described above in this invention, the type difference coefficient of the parameter type at the sampling time needs to measure the degree of difference in the influence of waste oil components on the parameter type. However, directly using the cumulative absolute value of the parameter difference between each historical parameter sequence may result in large fluctuations in a single difference, which cannot stably reflect the overall trend of the parameter type being affected by the components.

[0050] In this regard, the present invention further proposes that S210 includes: Obtain the absolute value of the parameter difference between the parameter values ​​at the target sampling time in adjacent historical parameter sequences of the target parameter type; The absolute values ​​of the differences between each parameter are averaged to obtain the type difference coefficient of the target parameter type at the target sampling time.

[0051] In this embodiment, adjacent historical parameter sequences refer to data sequences of the same parameter type that are sequentially adjacent in time across different processing batches. The absolute value of the parameter difference is calculated by subtracting the values ​​at the same sampling time from those of adjacent sequences, reflecting the fluctuation range of the parameter type between different batches at the same time. The mean processing uses an arithmetic mean method to aggregate the difference calculation results of multiple adjacent sequences, eliminating the random bias that may exist in a single difference. For example, in five adjacent batches, the absolute values ​​of the parameter differences at the target sampling time are 0.8, 1.2, 0.5, 1.0, and 0.7, respectively, and the type difference coefficient obtained after mean processing is 0.84.

[0052] Specifically, in the process of landfill leachate treatment, historical data for parameter types includes time series data from multiple treatment batches. By extracting parameter values ​​from adjacent batches at the same sampling time and calculating the differences between each pair, the strength of the impact of waste oil composition variations on parameter types can be reflected. Since there may be slight differences in operating conditions or raw material composition between different batches, averaging the differences from multiple adjacent batches can effectively suppress the interference of individual outlier differences on the overall assessment. This averaged type difference coefficient serves as a stable input for subsequent calculations of the influence of components, ensuring that the determination of parameter adjustment amounts has higher noise resistance and reliability.

[0053] The type difference coefficient of the target parameter type at the target sampling time can be determined using the following formula 1: Formula 1 In formula 1, The type difference coefficient used to characterize the type of the i-th parameter at the t-th sampling time. The parameter value used to characterize the i-th parameter type at the t-th sampling time in the j-th historical parameter sequence. J is used to characterize the parameter value of the i-th parameter type at the t-th sampling time in the (j-1)-th historical parameter sequence, and J is used to characterize the total number of historical parameter sequences.

[0054] In Formula 1 above, when the fluctuation range of the parameter value of that parameter type increases at the same sampling time in adjacent historical parameter sequences, that is... The larger the value, the greater the difference between the parameter values ​​of this parameter type at the same time between different batches. Therefore, the type difference coefficient obtained by summing and averaging will also be larger, indicating that the target parameter type exhibits greater differences due to factors such as waste oil composition at the target sampling time. Thus, the degree of difference of the target parameter type affected by composition is determined in this way, i.e., the type difference coefficient.

[0055] Furthermore, considering that inconsistent dimensions can easily lead to inaccurate comparisons later, the type difference coefficient of the target parameter type at the target sampling time can be normalized to overcome the problem of inconsistent dimensions.

[0056] As an example, first obtain the absolute value of the parameter difference between the parameter values ​​at the target sampling time in adjacent historical parameter sequences of the target parameter type. For example, for the parameter type of dissolved oxygen concentration, obtain the difference in dissolved oxygen concentration values ​​at the same sampling time in two adjacent batches of historical landfill leachate treatment.

[0057] The absolute values ​​of the differences between each parameter are then averaged to obtain the type difference coefficient of the target parameter type at the target sampling time. Specifically, the average difference is calculated by adding the absolute values ​​of the differences between multiple parameters and dividing by the number of absolute values ​​of the parameter differences. This average difference is the type difference coefficient.

[0058] Furthermore, the above processing can be performed separately for multiple sampling times to obtain the type difference coefficient of the target parameter type at each sampling time. This allows for a comprehensive reflection of the degree of variation in the target parameter type due to the influence of waste oil composition throughout the entire processing process.

[0059] This embodiment enables an objective quantification of the degree to which parameter types are affected by waste oil components, based on the absolute value of the parameter difference between adjacent batches in a historical parameter sequence. This provides a reliable data foundation for subsequent analysis of the component influence of parameter types, helping to more accurately assess the impact of waste oil components in landfill leachate on the treatment process, thereby optimizing equipment parameter settings and improving the effectiveness of landfill leachate treatment.

[0060] In some of the above-mentioned solutions of the present invention, the degree of difference in the influence of waste oil components on parameter types is measured by calculating the type difference coefficient. However, the type difference coefficients of different parameter types may be at different levels. Directly using the type difference coefficient cannot accurately reflect the relative degree of influence of each parameter type at the same sampling time, resulting in deviations in the subsequent allocation of parameter adjustment amounts.

[0061] In this regard, the present invention further proposes that S220 includes: The average type difference coefficient of each parameter type at the target sampling time is obtained by averaging the type difference coefficients. Divide the type difference coefficient of the target parameter type at the target sampling time by the average type difference coefficient to obtain the deviation of the type difference coefficient of the target parameter type at the target sampling time; By using the type difference coefficient deviation, the component influence of the target parameter type at the target sampling time is determined.

[0062] In this embodiment, the mean processing can employ an arithmetic mean or a weighted average. The deviation of the type difference coefficient is calculated as the ratio of the type difference coefficient of the target parameter type to the average of the type difference coefficients of all parameter types. The performance of the component's influence is determined by the standardized result of the deviation of the type difference coefficient. For example, the average type difference coefficient is the sum of the type difference coefficients of all parameter types divided by the number of parameter types, and the deviation of the type difference coefficient is the quotient of the type difference coefficient of a single parameter type and this average.

[0063] Specifically, by calculating the average of the type difference coefficients for each parameter type, a unified benchmark value is established to eliminate the influence of orders of magnitude differences in the type difference coefficients of different parameter types. The type difference coefficient of a single parameter type is compared with the benchmark value to obtain the type difference coefficient deviation, reflecting the degree to which that parameter type is significantly affected by waste oil components relative to other parameter types at the target sampling time. When using the type difference coefficient deviation to determine the component influence performance, further standardization can be performed to convert the type difference coefficient deviation into a standardized value that can be compared horizontally. For example, the min-max normalization method can be used to linearly transform the type difference coefficient deviation to a preset interval, or a non-linear transformation can be performed using the sigmoid function. Therefore, the component influence performance can accurately characterize the relative significance of different parameter types being affected by waste oil components at the same sampling time, providing a consistent data basis for subsequent calculations of structural anomalies and ensuring the accuracy of parameter adjustment allocation.

[0064] The influence of the target parameter type on the component at the target sampling time can be determined using the following formula 2: Formula 2 In formula 2, Used to characterize the component influence of the i-th parameter type at the t-th sampling time. The type difference coefficient used to characterize the type of the i-th parameter at the t-th sampling time. The average type difference coefficient is used to characterize the type difference coefficient of each parameter type at the t-th sampling time, and norm is used to characterize the standardization process. It should be noted that, in order to ensure that the calculation results are meaningful, when performing fractional operations in this embodiment of the invention, if the denominator is 0, a very small positive number needs to be added to the denominator before addition to prevent the denominator from being 0. The value of the parameter tuning factor can be 0.01, which can be set by the implementer according to the actual situation. This application does not impose any special restrictions.

[0065] In Formula 2 above, a benchmark is first established by calculating the average value of the type difference coefficients of each parameter type to eliminate the influence of magnitude differences. Then, the type difference coefficient of a single parameter type is compared with the benchmark value to obtain the type difference coefficient deviation, which reflects the relative prominence of the influence of waste oil components on the parameter type. Then, the type difference coefficient deviation is converted to a preset range through standardization, so that the component influence performance of the target parameter type at the target sampling time can accurately and consistently measure the relative significance of the influence of waste oil components on different parameter types at the same time, providing a reliable data basis for subsequent structural anomaly significance calculation.

[0066] As an example, we first average the type difference coefficients of each parameter type at the target sampling time to obtain the average type difference coefficient. Specifically, we can add up the type difference coefficients of all parameter types at the target sampling time and then divide by the total number of parameter types to obtain the average type difference coefficient.

[0067] Next, divide the type difference coefficient of the target parameter type at the target sampling time by the average type difference coefficient to obtain the type difference coefficient deviation of the target parameter type at the target sampling time. For example, if the type difference coefficient of the target parameter type is 0.8, and the average type difference coefficient is 0.5, then the type difference coefficient deviation is 1.6.

[0068] Finally, using the type difference coefficient deviation, the component influence performance of the target parameter type at the target sampling time is determined by Formula 2 above. Therefore, the component influence performance can more accurately reflect the degree to which the parameter type is affected by the waste oil components in landfill leachate.

[0069] This embodiment accurately quantifies the degree to which each parameter type is affected by the waste oil components in landfill leachate. This effectively identifies parameter types that significantly impact aeration structures, providing a reliable basis for subsequent parameter adjustments. Furthermore, by introducing the average value of the type difference coefficient as a benchmark, this scheme eliminates the influence of differences in the absolute values ​​of parameter types across different batches of landfill leachate treatment, improving the comparability and accuracy of the component impact performance.

[0070] In some of the solutions described above in this invention, when determining the significance of structural anomalies, it may be impossible to effectively distinguish the changes in the performance of component influence over different time periods, resulting in an inaccurate assessment of aeration structural anomalies and affecting the accuracy of parameter adjustments.

[0071] In this regard, such as Figure 3 As shown, the present invention further proposes that S300 includes the following S310 to S340: S310, Sort the component influence performance of each parameter type at each sampling time according to the time order to construct the component influence performance sequence of each parameter type; S320, the influence performance sequence of each component is divided from the target sampling time to obtain the first subsequence and the second subsequence corresponding to the influence performance sequence of each component; the target sampling time is any sampling time; S330, based on each first subsequence, determine the first aeration influence coefficient of the target parameter type at the target sampling time, and based on each second subsequence, determine the second aeration influence coefficient of the target parameter type at the target sampling time; the target parameter type can be any parameter type; S340, based on the first subsequence of the target, the second subsequence of the target, the first aeration influence coefficient, and the second aeration influence coefficient, determine the structural anomaly significance of the target parameter type at the target sampling time; the first subsequence of the target and the second subsequence of the target are the first and second subsequences obtained by dividing the component influence performance sequence of the target parameter type from the target sampling time.

[0072] In this embodiment, the component influence performance sequence is constructed by chronological ordering, with the target sampling time serving as the dividing point to divide the sequence into a first subsequence and a second subsequence. The first aeration influence coefficient is obtained by comparing the mean of the subsequence differences between the target first subsequence and each reference first subsequence. The target first subsequence is the first subsequence of the component influence performance sequence of the target parameter type, divided from the target sampling time. The reference first subsequences are all first subsequences other than the target first subsequence. The second aeration influence coefficient is calculated in the same way as the first aeration influence coefficient. The structural anomaly significance can be calculated based on the target first subsequence, the target second subsequence, the first aeration influence coefficient, and the second aeration influence coefficient using a pre-trained mathematical model.

[0073] Specifically, the ranking of the component influence performance sequence reflects the trend of parameter type changes over time, and the division of the target sampling time separates the data from the historical processing stage and the current processing stage. The first subsequence represents the historical component influence performance data before the target sampling time, and the second subsequence represents the component influence performance data after the target sampling time. By calculating the subsequence difference between the target first subsequence and the reference first subsequence corresponding to other parameter types, the first aeration influence coefficient is obtained, reflecting the correlation of historical data; the second aeration influence coefficient is calculated by the subsequence difference between the target second subsequence and the reference second subsequence corresponding to other parameter types, reflecting the correlation of subsequent data. The target second subsequence is the second subsequence of the component influence performance sequence of the target parameter type divided from the target sampling time, and the reference second subsequence is all the second subsequences other than the target second subsequence. By inputting the target first subsequence, the target second subsequence, the first aeration influence coefficient, and the second aeration influence coefficient into a pre-trained mathematical model, the structural anomaly significance of the target parameter type at the target sampling time can be calculated.

[0074] As an example, the component influence performance of each parameter type at each sampling time is sorted in chronological order to construct a component influence performance sequence for each parameter type. For example, for the chemical oxygen demand index, its component influence performance sequence can be obtained as [0.8, 0.9, 1.2, 1.5, 1.3, 1.1, 0.7].

[0075] Dividing the data from the target sampling time, we obtain the first and second subsequences corresponding to the influence performance sequence of each component. Assuming the target sampling time is the 4th sampling point, the first subsequence of the chemical oxygen demand index is [0.8, 0.9, 1.2, 1.5], and the second subsequence is [1.3, 1.1, 0.7].

[0076] Then, based on each first subsequence, the first aeration influence coefficient of the target parameter type at the target sampling time is determined. Specifically, this can be obtained by calculating the subsequence difference between the target first subsequence and the reference first subsequence of other parameter types, and taking the average value. Then, based on each second subsequence, the second aeration influence coefficient of the target parameter type at the target sampling time is determined. This is also obtained by calculating the subsequence difference and taking the average value.

[0077] Finally, based on the first subsequence of the target, the second subsequence of the target, the first aeration influence coefficient, and the second aeration influence coefficient, the structural anomaly significance of the target parameter type at the target sampling time is determined by a pre-trained mathematical model.

[0078] This embodiment enables the analysis of historical data to determine the changing trends of parameter types at different times, thereby quantifying the impact of aeration structure anomalies on the effectiveness of the biological-ozone oxidation reaction. This method considers the dynamic changes in landfill leachate composition, more accurately reflecting the relationship between parameter types and aeration effects, providing a reliable basis for subsequent parameter adjustments, and contributing to improving the effectiveness and stability of landfill leachate treatment.

[0079] In some of the above-mentioned solutions of the present invention, a first aeration influence coefficient for determining the target parameter type at the target sampling time based on each first subsequence is proposed. However, when calculating the first aeration influence coefficient, the synergistic influence between different parameter types is not effectively quantified, resulting in insufficient accuracy in judging aeration structure anomalies.

[0080] In this regard, the present invention further proposes that S330 includes: Obtain the subsequence difference between the target first subsequence and each reference first subsequence; the reference first subsequences are each first subsequence other than the target first subsequence. The difference between each subsequence is averaged to obtain the first aeration influence coefficient of the target parameter type at the target sampling time.

[0081] In this embodiment, the target first subsequence is the first subsequence obtained from the component influence performance sequence of the target parameter type, and the reference first subsequence is the first subsequence corresponding to other parameter types. The subsequence difference can be calculated using a dynamic time warping algorithm, and the mean can be processed using an arithmetic mean or a weighted average. The difference calculation between the target first subsequence and each reference first subsequence can reflect the correlation of the component influence performance change trend of different parameter types before the target sampling time.

[0082] Specifically, after determining the target first subsequence corresponding to the target parameter type, the first subsequences corresponding to other parameter types are traversed as reference first subsequences. A dynamic time warping algorithm is used to calculate the DTW distance between the target first subsequence and each reference first subsequence to obtain the subsequence difference. All subsequence differences are input into the arithmetic mean calculation module, and the output mean is used as the first aeration influence coefficient. By introducing the mean of subsequence differences for different parameter types, the influence of multiple parameters' coordinated changes on the aeration structure can be comprehensively considered, avoiding misjudgments caused by single parameter bias. For example, when the dynamic time warping algorithm calculation results for the target first subsequence and multiple reference first subsequences are all large, it indicates that the trend of the target parameter type's component influence before the target sampling time differs significantly from other parameters. In this case, the first aeration influence coefficient increases, suggesting that the target parameter type may cause aeration structure abnormalities.

[0083] The first aeration influence coefficient of the target parameter type at the target sampling time can be determined using the following formula 3: Formula 3 In formula 3, The first aeration influence coefficient used to characterize the i-th parameter type at the t-th sampling time. Used to characterize the first subsequence of the target corresponding to the i-th parameter type The dtw is used to characterize the k-th reference first subsequence, the k-th reference first subsequence is used to characterize the dynamic time warping algorithm, and the k is used to characterize the total number of reference first subsequences.

[0084] In Formula 3 above, the target first subsequence and each reference first subsequence are first obtained, and the subsequence difference degree between them is calculated using the dynamic time warping algorithm. This reflects the correlation of the changing trend of the component influence performance of different parameter types before the target sampling time. Then, the difference degree of all subsequences is averaged to comprehensively consider the influence of the coordinated changes of multiple parameters on the aeration structure and avoid misjudgment caused by the deviation of a single parameter. The final average value is the first aeration influence coefficient of the target parameter type at the target sampling time, which can more accurately measure the possibility of the parameter type causing aeration structure abnormality and provide a reliable basis for judging aeration structure abnormality.

[0085] As an example, when determining the first aeration influence coefficient of the target parameter type at the target sampling time based on each first subsequence, the subsequence difference between the target first subsequence and each reference first subsequence is first obtained. The reference first subsequences are all first subsequences other than the target first subsequence. Specifically, a dynamic time warping algorithm can be used to calculate the subsequence difference. For example, for the target first subsequence A and the reference first subsequence B, a distance matrix D is constructed, where D(i,j) represents the Euclidean distance between the i-th element of A and the j-th element of B. Then, the optimal alignment path is found through dynamic programming, and the sum of the distances along the path is calculated as the subsequence difference.

[0086] Furthermore, the differences between each subsequence are averaged to obtain the first aeration influence coefficient of the target parameter type at the target sampling time. Therefore, the impact of the target parameter type on the aeration effect can be quantified by comprehensively considering the differences between the target parameter type and other parameter types before the target sampling time.

[0087] This embodiment enables the calculation of the impact of parameter types on aeration effects based on historical data, avoiding the subjectivity and inaccuracy of human experience-based judgment. Simultaneously, subsequence difference analysis captures the dynamic characteristics of parameter type changes over time, improving the accuracy and representativeness of the aeration influence coefficient. This provides a reliable basis for subsequent adjustments to landfill leachate treatment equipment parameters, contributing to improved effectiveness of landfill leachate treatment.

[0088] In some of the solutions described above in this invention, by dividing the component influence performance sequence into a first subsequence and a second subsequence, and calculating the first aeration influence coefficient and the second aeration influence coefficient, the differences in the influence of parameter types on the aeration structure at different time periods can be reflected. However, in the dynamic processing, the changing trends of parameter types at different time periods may exhibit nonlinear correlations, and it is difficult to accurately quantify the degree of dynamic influence of parameter types on aeration structure anomalies solely through subsequence division and aeration influence coefficients.

[0089] In this regard, the present invention further proposes that S340 includes: Obtain the first mean of the influence of each component on the performance of the target first subsequence, and obtain the second mean of the influence of each component on the performance of the target second subsequence; Divide the first aeration influence coefficient by the mean value of the first performance to obtain the first ratio, and divide the second aeration influence coefficient by the mean value of the second performance to obtain the second ratio; The absolute value of the difference between the first ratio and the second ratio is standardized to obtain the structural anomaly significance of the target parameter type at the target sampling time.

[0090] In this embodiment, the first mean of the influence of each component on the performance of the target first subsequence is obtained, and the second mean of the influence of each component on the performance of the target second subsequence is obtained; the first aeration influence coefficient is divided by the first mean of the performance to obtain a first ratio, and the second aeration influence coefficient is divided by the second mean of the performance to obtain a second ratio; the absolute value of the difference between the first ratio and the second ratio is standardized to obtain the structural anomaly significance of the target parameter type at the target sampling time.

[0091] Specifically, by calculating the mean of the first and second expressiveness, the overall change level of the target parameter type before and after the division can be reflected. The first and second ratios characterize the relative strength of the aeration influence coefficient relative to the component influence expressiveness in the first and second subsequences, respectively, and the absolute value of the difference reflects the dynamic difference between the two subsequences. Standardization eliminates the dimensional differences between different parameter types, making the significance of structural anomalies comparable. For example, when the first ratio is 0.8 and the second ratio is 1.2, the absolute value of the difference is 0.4, which, after normalization, yields a standardized value of 0.32. This method can dynamically assess the degree of change in the aeration influence of parameter types before and after the time division point, thereby accurately quantifying the impact of aeration structural anomalies on the biological-ozone oxidation reaction effect.

[0092] The significance of structural anomalies in the target parameter type at the target sampling time can be determined using the following formula 4: Formula 4 In formula 4, Used to characterize the significance of structural anomalies of the i-th parameter type at the t-th sampling time. The first aeration influence coefficient used to characterize the i-th parameter type at the t-th sampling time. The mean first expressiveness value used to characterize the influence of each component on the expressiveness of the i-th parameter type in the first subsequence of the target at the t-th sampling time. The second aeration influence coefficient is used to characterize the type of parameter i at the t-th sampling time. The mean of the second expressiveness is used to characterize the influence of each component on the expressiveness of the i-th parameter type in the target second subsequence at the t-th sampling time, and norm is used to characterize the normalization process.

[0093] In Formula 4 above, the mean first performance of the influence of each component in the first subsequence of the target and the mean second performance of the influence of each component in the second subsequence of the target are first obtained to reflect the overall change level of the target parameter type before and after the subsequence division. Then, the first aeration influence coefficient is divided by the mean first performance to obtain the first ratio, and the second aeration influence coefficient is divided by the mean second performance to obtain the second ratio. These two ratios respectively characterize the relative strength of the aeration influence coefficient relative to the component influence performance in the first and second subsequences. Finally, the absolute value of the difference between the first ratio and the second ratio is standardized to eliminate the dimensional differences between different parameter types, making the obtained structural anomalies comparable. This allows for an accurate measurement of the influence of aeration structural anomalies on the biological-ozone oxidation reaction effect before and after a certain time point, providing a reliable basis for judging the dynamic correlation of aeration structural anomalies.

[0094] As an example, the mean of the first expressiveness of each component in the first target subsequence is obtained, and the mean of the second expressiveness of each component in the second target subsequence is obtained. For example, for the first target subsequence [0.8,0.9,1.1,1.2], the mean of the first expressiveness is calculated to be 1.0; for the second target subsequence [1.3,1.4,1.5,1.6], the mean of the second expressiveness is calculated to be 1.45.

[0095] The first ratio is obtained by dividing the first aeration influence coefficient by the mean of the first performance, and the second ratio is obtained by dividing the second aeration influence coefficient by the mean of the second performance. Specifically, assuming the first aeration influence coefficient is 1.2 and the second aeration influence coefficient is 1.6, then the first ratio is 1.2 / 1.0=1.2 and the second ratio is 1.6 / 1.45=1.1.

[0096] The absolute value of the difference between the first ratio and the second ratio is standardized to obtain the structural anomaly significance of the target parameter type at the target sampling time. Furthermore, the min-max standardization method can be used to map the absolute value of the difference (0.1) to the 0-1 interval, obtaining the standardized structural anomaly significance value.

[0097] This embodiment enables the calculation of structural anomaly significance based on the mean performance of the first and second target subsequences, combined with the first and second aeration influence coefficients. This allows for the quantitative assessment of the performance differences of parameter types across different time periods, thereby determining the degree of influence of aeration structural anomalies on the biological-ozone oxidation reaction. This method considers the time-series variation characteristics of parameter types, improving the accuracy and rationality of structural anomaly significance calculation.

[0098] In some of the solutions described above in this invention, the influence of abnormal changes in other parameter types on the current parameter type is not considered when determining the parameter adjustment amount. This results in the parameter adjustment amount failing to accurately reflect the dynamic changes in the abnormal state of the aeration structure, affecting the pertinence and effectiveness of the parameter adjustment.

[0099] In this regard, such as Figure 4 As shown, the present invention further proposes that S400 includes the following S410 to S420: S410, Obtain the abnormal parameter types among all parameter types that have abnormal change directions at the target sampling time; the target sampling time is any sampling time, and the abnormal change direction is used to characterize the actual change trend of the parameter type as being far away from the standard change trend; S420, based on the first structural anomaly significance of the target parameter type at the target sampling time and the second structural anomaly significance of each anomaly parameter type at the target sampling time, determine the parameter adjustment amount of the target parameter type at the target sampling time in the current landfill leachate treatment.

[0100] In this embodiment, the direction of abnormal changes is determined by the degree of deviation between the actual change trend of a parameter type and the standard change trend, which can be quantified by the difference in the slope of the trend line. Specifically, firstly, for each parameter type, a trend line of its actual change trend is drawn based on its parameter value, and the slope of the trend line is calculated; at the same time, the slope of the trend line of the theoretical standard change trend of that parameter type is determined.

[0101] Next, the difference between the actual trend line slope and the standard trend line slope is calculated. If the difference exceeds the preset slope difference threshold (this slope difference threshold is determined based on historical data statistical analysis and process requirements. For example, for dissolved oxygen concentration, after a large amount of historical data fitting analysis, it is determined that when the slope difference exceeds 0.2, it is considered that there is an abnormal change direction), then it is determined that the parameter type has an abnormal change direction at the target sampling time.

[0102] The standard trend is an idealized parameter variation pattern derived from historical data accumulated during the long-term stable and normal operation of landfill leachate treatment equipment, after professional analysis and processing. Specifically, the process involves collecting normal operation data of the landfill leachate treatment equipment over a relatively long period, covering parameter values ​​for each type at different times. Then, data fitting techniques, such as polynomial fitting and spline interpolation, are used to fit this historical data, resulting in a trend line that reflects the normal variation pattern of the parameters. The trend presented by this trend line is the standard trend. It represents the expected change direction of each parameter type under the condition of no abnormal interference.

[0103] Subsequently, the average significance of the second structural anomaly across all anomalous parameter types is calculated and used as the baseline reference value for the current time step. The difference between the significance of the first structural anomaly of the target parameter type and this baseline reference value is calculated; a larger absolute value of the difference indicates a higher degree of influence of the anomalous parameter type on the target parameter type. Based on this difference, a step size adjustment value is determined and added to a preset step size value to obtain the final parameter adjustment amount.

[0104] As an example, in the process of landfill leachate treatment, the direction of change of each parameter type at the target sampling time is monitored in real time. By comparing the actual change trend with the preset standard change trend curve, abnormal parameter types whose actual change trend deviates from the standard trend are screened out. Subsequently, the first structural anomaly significance data of the target parameter type at the target sampling time is extracted, and at the same time, the second structural anomaly significance data set of all abnormal parameter types at the target sampling time is obtained. The second structural anomaly significance data is input into the mean calculation model, and the mean of structural anomaly significance is output. Further, the difference between the first structural anomaly significance and the mean of structural anomaly significance is calculated, and then the step size adjustment value is determined based on the difference. This step size adjustment value is superimposed with the preset fixed step size value to generate the dynamic adjustment weight of the target parameter type at the target sampling time. For example, if the preset step size value is 0.5 and the step size adjustment value is 0.3, then the final parameter adjustment amount is 0.8, which is used to guide the equipment's control range for this parameter type.

[0105] This embodiment enables precise quantification of the adjustment weights for target parameter types based on the dynamic correlation between abnormal parameter types and their impact on the aeration structure. This avoids parameter control lags or over-adjustments caused by fluctuations in waste oil composition. By dynamically adjusting the weights, the equipment can prioritize correcting parameter types that significantly affect the aeration structure, thereby maintaining the stability of the ozone-oil reaction, reducing the risk of explosions caused by parameter mismatch in the aeration structure, and improving the efficiency of landfill leachate treatment.

[0106] In some of the solutions described above in this invention, when determining the parameter adjustment amount, only the structural anomaly of a single parameter is considered, without considering the synergistic effect of other abnormal parameters, resulting in insufficient accuracy and adaptability of parameter adjustment.

[0107] In this regard, the present invention further proposes S420 including: The significance of the second structural anomaly at the target sampling time for each anomaly parameter type is averaged to obtain the average significance of the structural anomaly. The step size adjustment value is determined by using the difference between the significance of the first structural anomaly and the mean significance of the structural anomalies; Add the preset step size value and the step size adjustment value of the target parameter type to obtain the parameter adjustment amount of the target parameter type at the target sampling time in the current landfill leachate treatment.

[0108] In this embodiment, the abnormal parameter type is the parameter type that exhibits an abnormal direction of change at the target sampling time. The abnormal direction of change is determined by the degree of deviation between the actual change trend and the standard change trend. The mean structural anomaly significance is obtained by taking the arithmetic mean or weighted average of the second structural anomaly significance of each abnormal parameter type. The step size adjustment value is calculated by the difference between the first structural anomaly significance and the mean structural anomaly significance; the ratio of the difference to the step size adjustment value is determined by the actual application scenario. The parameter adjustment amount is generated by linearly superimposing a preset step size value and the step size adjustment value; the preset step size value is pre-set according to the parameter type.

[0109] Specifically, at the target sampling time, parameter types exhibiting abnormal change directions are identified as an abnormal parameter type set. For each target parameter type, its first structural anomaly salience at the target sampling time is extracted, along with the second structural anomaly salience of each parameter in the abnormal parameter type set at the target sampling time. The average of each second structural anomaly salience is calculated to obtain the mean structural anomaly salience reflecting the overall influence of the abnormal parameters. By calculating the difference between the first structural anomaly salience and the mean structural anomaly salience, a preset step size is dynamically adjusted. The sign of the difference determines the direction of the step size adjustment, and the magnitude of the difference determines the adjustment magnitude. The final parameter adjustment amount is determined by the adjusted step size. For example, when the first structural anomaly salience is higher than the mean, the weight is increased to enhance the adjustment; when it is lower than the mean, the weight is decreased to reduce interference. This method allows the parameter adjustment amount to be dynamically optimized based on the group characteristics of abnormal parameters, improving the adaptability of parameter adjustment to changes in complex components.

[0110] The parameter adjustment amount at the target sampling time in the current landfill leachate treatment can be determined using the following formula 5: Formula 5 In formula 5, This is used to characterize the parameter adjustment amount of the i-th parameter type at the t-th sampling time in the current landfill leachate treatment. A preset step size value used to characterize the type of the i-th parameter. Used to characterize the significance of structural anomalies of the i-th parameter type at the t-th sampling time. The parameter is used to characterize the structural anomaly significance of the nth anomaly parameter type at the tth sampling time, where N represents the total number of anomaly parameter types. Used to characterize the floor operation.

[0111] x represents a pre-set non-zero adjustment ratio coefficient, used to represent the proportional conversion relationship between the difference between the first structural anomaly significance and the mean structural anomaly significance, and the step size adjustment value. Its value is determined by the actual application scenario and can be pre-set to values ​​such as 0.1 or 0.2. When the adjustment ratio coefficient is 0.1, it means that the difference between the first structural anomaly significance and the mean structural anomaly significance, and the step size adjustment value, are converted at a ratio of 1:0.1.

[0112] In Formula 5 above, the significance of the second structural anomaly at the target sampling time for each anomaly parameter type is first averaged to obtain the average significance of structural anomalies reflecting the overall influence of the anomaly parameters. Then, the difference between the significance of the first structural anomaly of the target parameter type and this average is calculated to determine the step size adjustment value. The sign of the difference determines the direction of the step size adjustment, and the magnitude of the difference determines the adjustment range. Finally, the preset step size value and the step size adjustment value for the target parameter type are added to obtain the parameter adjustment amount at the target sampling time in the current landfill leachate treatment. In this way, the parameter adjustment amount can be dynamically optimized based on the group characteristics of the anomaly parameters, improving the adaptability to changes in complex components. For example, when the significance of the first structural anomaly is higher than the average, the weight is increased to enhance the adjustment strength; when it is lower than the average, the weight is decreased to reduce interference.

[0113] As an example, in the leachate treatment process, dissolved oxygen concentration and pH value were selected as the parameter types to be monitored. When the target sampling time was 30 minutes, the dissolved oxygen concentration was detected to deviate from the standard curve and was marked as an abnormal parameter type. At this time, the first structural anomaly significance of the target parameter type pH value at 30 minutes was calculated to be 0.75. The second structural anomaly significance of the abnormal parameter type dissolved oxygen concentration at the same sampling time was 0.92. Further, the mean of the second structural anomaly significance of dissolved oxygen concentration was calculated to be 0.92. Subsequently, the difference between the first structural anomaly significance of pH value (0.75) and the mean (0.92) was calculated to obtain a step size adjustment value of -1. The preset step size value of 5 was invoked, and after adding it to the step size adjustment value, the final parameter adjustment amount of pH value at the current sampling time was determined to be 4.

[0114] This embodiment effectively solves the problem of parameter adjustment lag caused by the complex composition of waste oil. By dynamically quantifying the impact of abnormal parameters on the aeration structure, precise correction of parameter weights is achieved. Specifically, the step size adjustment value calculated based on the mean difference of structural anomalies can specifically suppress the interference of abnormal parameters on weight allocation, thereby improving the timeliness and accuracy of parameter adjustment during the biological-ozone oxidation reaction and avoiding a decrease in aeration efficiency or equipment malfunction due to sudden changes in mixed components.

[0115] Based on the above embodiments of the present invention, a landfill leachate treatment method based on biological-ozone oxidation technology is provided. Accordingly, the present invention also provides a specific embodiment of a landfill leachate treatment device based on biological-ozone oxidation technology.

[0116] like Figure 5 As shown, the landfill leachate treatment device 500 based on biological-ozone oxidation technology provided in this embodiment of the invention includes a parameter acquisition module 510, a first analysis module 520, a second analysis module 530, and a weight determination module 540.

[0117] The parameter acquisition module 510 is used to acquire historical parameter sequences of various parameter types in multiple batches of historical landfill leachate treatment by the landfill leachate equipment; the historical parameter sequence includes the parameter values ​​of the parameter type at each sampling time in the historical landfill leachate treatment; The first analysis module 520 is used to determine the component influence performance of each parameter type at each sampling time based on each historical parameter sequence; the component influence performance is used to measure the degree to which the parameter type exhibits different performances due to the influence of waste oil components in landfill leachate. The second analysis module 530 is used to determine the structural anomaly significance of each parameter type at each sampling time based on the component influence performance of the parameter type at each sampling time. The structural anomaly significance is used to measure the degree of influence of the aeration structural anomaly caused by the parameter type on the biological-ozone oxidation reaction effect. The weight determination module 540 is used to determine the parameter adjustment amount of each parameter type at each sampling time based on the structural anomaly significance of each parameter type at each sampling time in the current landfill leachate treatment, so as to adjust the parameter values ​​of the parameter type of the landfill leachate equipment based on the parameter adjustment amount, thereby realizing the treatment of the current landfill leachate.

[0118] In the landfill leachate treatment device based on biological-ozone oxidation technology provided in this invention embodiment, firstly, historical parameter sequences of various parameter types are obtained from multiple batches of historical landfill leachate treatment. This sequence covers information such as changes in waste oil composition under different conditions, providing a comprehensive foundation for subsequent analysis. Next, the component influence performance of each parameter type at each sampling time is determined. Since the composition of waste oil is complex and variable, different parameter types are affected to varying degrees. Determining the component influence performance reveals the degree to which each parameter type exhibits different behaviors due to the influence of oil composition, providing a basis for quantifying the impact. Then, the structural anomaly significance is determined based on the component influence performance. Because parameter types may cause aeration structure anomalies, thereby affecting the biological-ozone oxidation reaction effect, determining the structural anomaly significance can measure the degree of this impact. Finally, the parameter adjustment amount is determined based on the structural anomaly significance. Adjusting the parameter type accordingly allows for optimization of the landfill leachate equipment parameters to adapt to different oil compositions, thereby improving aeration efficiency and enhancing the effectiveness of landfill leachate treatment.

[0119] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0120] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for treating landfill leachate based on biological-ozone oxidation technology, characterized in that, The method includes: Obtain historical parameter sequences of various parameter types from landfill leachate treatment equipment in multiple batches of historical landfill leachate treatment; the historical parameter sequences include parameter values ​​of the parameter types at each sampling time in the historical landfill leachate treatment; For each parameter type, based on each historical parameter sequence, the component influence performance of the parameter type at each sampling time is determined; the component influence performance is used to measure the degree to which the parameter type exhibits different performances due to the influence of waste oil components in the landfill leachate. For each parameter type, the structural anomaly significance of the parameter type at each sampling time is determined based on the component influence performance of the parameter type at each sampling time; the structural anomaly significance is used to measure the degree of influence of the aeration structural anomaly caused by the parameter type on the biological-ozone oxidation reaction effect; Based on the structural anomaly significance of each parameter type at each sampling time, the parameter adjustment amount for each parameter type at each sampling time in the current landfill leachate treatment is determined, so that the parameter values ​​of the parameter types of the landfill leachate equipment are adjusted based on the parameter adjustment amount, thereby achieving the treatment of the current landfill leachate.

2. The landfill leachate treatment method based on biological-ozone oxidation technology according to claim 1, characterized in that, The determination of the component influence of each parameter type at each sampling time, based on each historical parameter sequence, includes: Based on the parameter values ​​at the target sampling time in each of the historical parameter sequences of the target parameter type, a type difference coefficient for the target parameter type at the target sampling time is determined; the target sampling time is any one of the sampling times, the target parameter type is any one of the parameter types, and the type difference coefficient is used to measure the degree of difference exhibited by the target parameter type due to the influence of waste oil components in the landfill leachate; Based on the type difference coefficient of each parameter type at the target sampling time, the component influence performance of the target parameter type at the target sampling time is determined.

3. The landfill leachate treatment method based on biological-ozone oxidation technology according to claim 2, characterized in that, The determination of the type difference coefficient of the target parameter type at the target sampling time based on the parameter values ​​of each of the historical parameter sequences based on the target parameter type includes: Obtain the absolute value of the parameter difference between the parameter values ​​at the target sampling time in adjacent historical parameter sequences of the target parameter type; The absolute values ​​of the differences between the parameters are averaged to obtain the type difference coefficient of the target parameter type at the target sampling time.

4. The landfill leachate treatment method based on biological-ozone oxidation technology according to claim 2, characterized in that, The determination of the component influence performance of the target parameter type at the target sampling time based on the type difference coefficient of each of the parameter types at the target sampling time includes: The average type difference coefficient of each parameter type at the target sampling time is obtained by averaging the type difference coefficients. The deviation of the type difference coefficient of the target parameter type at the target sampling time is obtained by dividing the type difference coefficient of the target parameter type by the average type difference coefficient at the target sampling time. By utilizing the deviation of the type difference coefficient, the component influence of the target parameter type at the target sampling time is determined.

5. The landfill leachate treatment method based on biological-ozone oxidation technology according to claim 1, characterized in that, For each parameter type, determining the structural anomaly significance of the parameter type at each sampling time based on the component influence performance of the parameter type at each sampling time includes: The component influence performance of each parameter type at each sampling time is sorted in chronological order to construct a component influence performance sequence for each parameter type. The influence performance sequence of each component is divided from the target sampling time to obtain the first subsequence and the second subsequence corresponding to each component influence performance sequence; the target sampling time is any one of the sampling times. Based on each of the first subsequences, a first aeration influence coefficient of the target parameter type at the target sampling time is determined, and based on each of the second subsequences, a second aeration influence coefficient of the target parameter type at the target sampling time is determined; the target parameter type is any one of the parameter types. Based on the first subsequence of the target, the second subsequence of the target, the first aeration influence coefficient, and the second aeration influence coefficient, the structural anomaly significance of the target parameter type at the target sampling time is determined; the first subsequence of the target and the second subsequence of the target are the first and second subsequences obtained by dividing the component influence performance sequence of the target parameter type from the target sampling time.

6. The landfill leachate treatment method based on biological-ozone oxidation technology according to claim 5, characterized in that, The determination of the first aeration influence coefficient of the target parameter type at the target sampling time based on each of the first sub-sequences includes: Obtain the subsequence difference between the target first subsequence and each reference first subsequence; the reference first subsequences are each of the first subsequences other than the target first subsequence. The difference between each subsequence is averaged to obtain the first aeration influence coefficient of the target parameter type at the target sampling time.

7. The landfill leachate treatment method based on biological-ozone oxidation technology according to claim 5, characterized in that, The determination of the structural anomaly significance of the target parameter type at the target sampling time based on the target first subsequence, the target second subsequence, the first aeration influence coefficient, and the second aeration influence coefficient includes: Obtain the first mean of the influence of each component on the performance of the target first subsequence, and obtain the second mean of the influence of each component on the performance of the target second subsequence; Divide the first aeration influence coefficient by the average value of the first performance to obtain the first ratio, and divide the second aeration influence coefficient by the average value of the second performance to obtain the second ratio; The absolute value of the difference between the first ratio and the second ratio is standardized to obtain the structural anomaly significance of the target parameter type at the target sampling time.

8. The landfill leachate treatment method based on biological-ozone oxidation technology according to claim 1, characterized in that, The determination of the parameter adjustment amount for each parameter type at each sampling time based on the structural anomaly significance of each parameter type at each sampling time in the current landfill leachate treatment includes: Identify the abnormal parameter types among the parameter types that exhibit abnormal change directions at the target sampling time; the target sampling time is any of the sampling times, and the abnormal change direction is used to characterize the actual change trend of the parameter type as being far from the standard change trend. Based on the significance of the first structural anomaly of the target parameter type at the target sampling time, and the significance of the second structural anomaly of each of the anomalous parameter types at the target sampling time, the parameter adjustment amount of the target parameter type at the target sampling time in the current landfill leachate treatment is determined.

9. The landfill leachate treatment method based on biological-ozone oxidation technology according to claim 8, characterized in that, The determination of the parameter adjustment amount of the target parameter type at the target sampling time based on the first structural anomaly significance of the target parameter type at the target sampling time, and the second structural anomaly significance of each of the anomalous parameter types at the target sampling time, includes: The average value of the second structural anomaly significance of each of the aforementioned anomaly parameter types at the target sampling time is processed to obtain the average value of structural anomaly significance; The step size adjustment value is determined by using the difference between the first structural anomaly significance and the mean of the structural anomaly significance. The preset step size value of the target parameter type is added to the step size adjustment value to obtain the parameter adjustment amount of the target parameter type at the target sampling time in the current landfill leachate treatment.

10. A landfill leachate treatment device based on biological-ozone oxidation technology, characterized in that, The device includes: The parameter acquisition module is used to acquire historical parameter sequences of various parameter types in multiple batches of historical landfill leachate treatment by the landfill leachate equipment; the historical parameter sequence includes the parameter values ​​of the parameter type at each sampling time in the historical landfill leachate treatment; The first analysis module is used to determine the component influence performance of each parameter type at each sampling time based on each historical parameter sequence; the component influence performance is used to measure the degree to which the parameter type exhibits different performances due to the influence of waste oil components in the landfill leachate. The second analysis module is used to determine the structural anomaly significance of each parameter type at each sampling time based on the component influence performance of the parameter type at each sampling time; the structural anomaly significance is used to measure the degree of influence of the aeration structural anomaly caused by the parameter type on the biological-ozone oxidation reaction effect. The weight determination module is used to determine the parameter adjustment amount for each parameter type at each sampling time in the current landfill leachate treatment based on the structural anomaly significance of each parameter type at each sampling time, so as to adjust the parameter value of the parameter type of the landfill leachate equipment based on the parameter adjustment amount, thereby realizing the treatment of the current landfill leachate.

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