A catering wastewater treatment and recycling system for environmental protection

Abstract

This invention provides a wastewater treatment and recycling system for environmental protection, comprising the following functional modules: a data acquisition and preprocessing module for acquiring relevant performance data of activated carbon in the recycling system; a status assessment module for preprocessing the raw performance data and generating a health status index of activated carbon based on the preprocessed performance data; and a risk coefficient assessment module for acquiring influent characteristic data from the recycling system, generating a risk coefficient by combining the influent characteristic data with the health status index; generating a basic urgency index based on the health status index and the risk coefficient, and simultaneously generating decision characteristic factors. The basic urgency index and decision characteristic factors are then combined to construct a two-dimensional decision matrix. This invention relies on the two-dimensional decision matrix to automatically identify the root cause of problems and trigger precise response strategies from operating condition adjustments to emergency replacement, thereby improving the reliability and economy of system operation.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, specifically to a system for treating and recycling catering wastewater for environmental protection. Background Technology

[0002] Catering wastewater contains a large amount of grease, organic matter, and suspended solids. If discharged directly without effective treatment, it will cause serious pollution to water bodies. Activated carbon adsorption technology is widely used in catering wastewater deep treatment and reuse systems due to its high efficiency and economy. However, the performance evaluation of activated carbon materials during use often relies on periodic manual testing or fixed-cycle replacement, lacking real-time and quantitative status monitoring methods. By collecting and integrating activated carbon performance data and influent characteristic information in real time, continuous assessment of material health and system operation risks can be achieved, realizing the transformation from passive response to proactive early warning. It can also intelligently diagnose the root cause of faults and accurately trigger differentiated responses from operating condition adjustment to material replacement, thereby improving operational reliability.

[0003] The prior art, disclosed in CN116903064A, discloses a method and system for treating wastewater resources from the catering industry. This technology includes: real-time monitoring of kitchen wastewater at preset locations to obtain dynamic data; extracting concentration change curves of each monitoring parameter from the dynamic data; analyzing the concentration change curves of all preset location parameters; and determining a treatment strategy based on the analysis results. The preset locations are set at the discharge points of the areas involved in each treatment device. By obtaining dynamic data of the kitchen wastewater and extracting the concentration change curves of each monitoring parameter, the state of the kitchen wastewater can be judged more accurately and comprehensively. Then, based on the analysis results, a treatment strategy can be determined, thereby improving the accuracy of adjusting the process parameters in the catering wastewater treatment process.

[0004] However, the aforementioned existing technologies often only monitor a single parameter and fail to systematically integrate multi-dimensional data such as performance, operating conditions, load, and historical cumulative degradation. Therefore, they cannot accurately determine the health status of materials, nor can they provide early warning of the risk of excessive effluent quality in the early stages of sudden changes in influent load or material performance deterioration. This leads to delayed maintenance decisions, high costs, and an inability to distinguish whether the root cause of the risk is external shock or internal aging, making it difficult to trigger accurate and adaptive response measures. This affects the reliability, economy, and environmental performance of the treatment system.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a system for treating and recycling catering wastewater for environmental protection, thereby addressing the problems mentioned in the background section. This invention utilizes real-time quantitative assessment and proactive risk warning of activated carbon performance. By fusing multi-dimensional data to generate a health status index and risk coefficient, it constructs an intelligent diagnosis and response mechanism based on a two-dimensional decision matrix. This mechanism can accurately distinguish between external load impacts and internal material deterioration, automatically triggering a tiered maintenance strategy from operational condition adjustment to emergency replacement. This significantly improves the operational reliability, maintenance accuracy, and environmental compliance of the treatment system, achieving a fundamental shift from passive treatment to proactive predictive maintenance.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A system for treating and recycling catering wastewater for environmental protection includes the following functional modules: Data acquisition and preprocessing module: Collects relevant performance data of activated carbon in the recycling system. The relevant performance data includes: efficiency coefficient, which is the real-time treatment efficiency obtained by the ratio of effluent concentration to the design standard; operating condition coefficient, which is obtained by taking the difference between the bed pressure drop and temperature deviation from the optimal value; load adaptability coefficient, which is obtained by the ratio of the current pollution load to the system design load; and historical decay coefficient, which is obtained by the ratio of cumulative replenishment to initial loading and by introducing a decay factor. The status assessment module preprocesses the raw data of relevant performance data and performs integrated data normalization and standardization. Based on the preprocessed relevant performance data, it generates a health status index for activated carbon, quantifying the status characteristics of activated carbon into a health score, which is used to quantitatively assess the real-time performance status and aging degree of activated carbon materials. Risk coefficient assessment module: Collects influent characteristic data from the recycling system, including influent concentration and influent concentration limit; performs time-series normalization and feature enhancement preprocessing on real-time influent concentration data; performs parameter serialization and time-series synchronization preprocessing on the designed influent concentration limit data; generates a risk coefficient from the preprocessed influent characteristic data and health status index, which is used to quantitatively assess the probability of the effluent exceeding the standard in a specific future period of the treatment system; Decision and Response Trigger Module: Based on the health status index and risk coefficient, a basic urgency index is generated to characterize the degree to which the overall system status deviates from the ideal state. At the same time, decision feature factors are generated to diagnose whether the current risk is mainly caused by external load impact or by the deterioration of the material's own performance. The basic urgency and decision feature factors are combined to construct a two-dimensional decision matrix. According to the combination of the intervals to which the values ​​of the basic urgency index and the decision feature factors belong, a preset response action is triggered.

[0008] Furthermore, the relevant performance data specifically include: efficiency coefficient, which is the percentage of instantaneous treatment efficiency obtained by comparing and normalizing the measured pollutant concentration with the preset design effluent standard concentration; operating condition coefficient, which is obtained by comparing real-time pressure drop and temperature data with the preset maximum allowable pressure drop and optimal operating temperature, respectively, to obtain pressure drop deviation and temperature deviation, and convert them into scores, with the lowest score among the deviations being taken as the comprehensive operating condition index characterizing the physical smoothness of operation; load adaptability coefficient, which is obtained by calculating the real-time influent pollution load based on influent flow and water quality data, and then performing a ratio calculation and normalization of this load value with the initial design treatment load of the system to quantify the margin and overload degree of the current operating pressure of the system relative to its design capacity; and historical decay coefficient, which is calculated by proportionally comparing the total mass of activated carbon added in the maintenance record database with the initial loading mass of the system, and multiplying it by an empirical decay enhancement factor greater than 1 to quantify the cumulative degradation of material performance caused by long-term use, irreversible adsorption, and structural aging.

[0009] Furthermore, the attenuation enhancement factor is an empirical correction coefficient greater than 1, used to nonlinearly amplify the simple ratio of cumulative replenishment to initial loading. It corrects the idealized assumptions of the linear cumulative model to more accurately quantify the performance of activated carbon in actual operation. The attenuation enhancement factor reflects the following engineering realities: the adsorption performance of newly replenished activated carbon is usually lower than that of the initially loaded activated carbon; the system suffers from irreversible pore blockage and structural fatigue; and the overall efficiency of the mixed bed of new and old activated carbon is reduced.

[0010] Furthermore, the preprocessing first cleans and repairs the original data, including integrated data normalization, which aligns time-series data of different frequencies and physical units by using a unified timestamp and fixed-frequency resampling, and completes logical aggregation through feature calculation; and implements standardization processing, which normalizes each normalized data sequence to a unified, dimensionless scaling range by using their respective defined linear and nonlinear functions, thereby outputting a set of time-synchronized and scale-consistent standardized performance coefficients.

[0011] Furthermore, the health status index is calculated using the following formula: Where: CHI is the health status index of activated carbon; E is the efficiency coefficient; C is the operating condition coefficient; L is the load adaptability coefficient; β is the historical decay coefficient; W1, W2, W3, and W4 are the weighting coefficients for the efficiency coefficient, operating condition coefficient, load adaptability coefficient, and historical decay coefficient, respectively, where W1>W2>W3>W4, and W1+W2+W3+W4=1. Further, the real-time influent concentration data preprocessing includes: performing time-series normalization and feature enhancement preprocessing; achieving time-series alignment through data cleaning and repair, and fixed-frequency resampling; then coupling it with synchronous flow data to calculate the instantaneous pollution load; and applying a moving average filter to extract stable trend features. For influent concentration limit data, parameter serialization and time-series synchronization preprocessing are performed, i.e., ensuring parameter validity through logical verification, expanding it into a constant value sequence of the same length as the real-time data, and performing precise timestamp matching.

[0012] Furthermore, the risk coefficient is calculated using the following formula: The risk factor is calculated using the following formula: Where: R is the risk coefficient; CI represents the influent concentration; CS is the influent concentration limit; The health decay coefficient, Let a1 and a2 be the weighting coefficients for the health degradation coefficient and the load impact coefficient, respectively, where a1 > a2 and a1 + a2 = 1. Further, the basic urgency index is calculated using the following formula:

[0013] Wherein: U is the basic urgency index; 1-R is the risk discount factor. When the risk coefficient R increases, the risk discount factor decreases, indicating that the discounting effect of future risks on the current asset value is enhanced. CHI·(1-R) ​​combines the static health status index of the system with the dynamic risk coefficient, reflecting the estimated processing capacity that activated carbon materials can currently provide under the background of foreseeable risks. Furthermore, the decision characteristic factors are calculated using the following formula: Where: D is the decision characteristic factor; Set a preset diagnostic threshold θ. If D>+θ, it is judged as a health-dominant mode, indicating that the system itself is healthy and the emergency is mainly caused by the impact of external water inflow. If D<-θ, it is judged as a risk-dominant mode, indicating that the system itself has been severely depleted and the root cause of the emergency lies in the internal failure of the activated carbon material. If -θ≤D≤+θ, it is judged as a balanced mode.

[0014] Further, the system sets emergency thresholds Ut and diagnostic thresholds θ for U and D respectively, compares the values of U and D with the preset emergency thresholds and diagnostic thresholds, and locates them in specific response quadrants of the two-dimensional decision matrix. The two-dimensional decision matrix defines four response areas: when U≥Ut and D>+θ, an immediate operating condition adjustment instruction for external shock is triggered; when U≥Ut and D<-θ, an asset emergency replacement instruction for internal failure is triggered; when U<Ut, corresponding optimization monitoring and planned maintenance instructions are triggered according to the positive or negative value of D.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: It quantifies the health status of activated carbon in real time and predicts the risk of exceeding the water quality standard, achieving a fundamental transformation from passive response to active early warning. The system can intelligently diagnose the root cause of faults and accurately trigger differentiated responses from operating condition adjustment to material replacement, thus significantly improving the operating reliability. This not only reduces the maintenance cost, extends the service life of activated carbon, but also effectively ensures the stable compliance of the effluent, enhancing the adaptability and overall economy of the treatment system to cope with the complex load fluctuations of catering wastewater. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 It is a system block diagram of a catering wastewater treatment and recycling system for environmental protection according to the present invention; Figure 2 It is a schematic diagram of the operation process of a catering wastewater treatment and recycling system for environmental protection according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with specific embodiments.

[0017] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar terms used in the present invention do not denote any order, quantity or importance, but are only used to distinguish different components. The terms such as "comprising" or "including" mean that the elements or objects appearing before this term cover the elements or objects listed after this term and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left" and "right" are only used to represent relative positional relationships, and when the absolute position of the described object changes, the relative positional relationship may also change accordingly. Embodiment

[0018] Please refer to Figures 1-2 , the present invention provides a technical solution: Data acquisition and preprocessing module: Collects relevant performance data of high-efficiency activated carbon in the recycling system. The relevant performance data includes: efficiency coefficient directly quantifies the instantaneous purification capacity of high-efficiency activated carbon; the system obtains the real-time concentration of pollutants through online water quality analysis at the outlet and compares it with the preset design discharge standard; calculates the instantaneous removal rate through a specific algorithm and normalizes it to output a percentage value, which dynamically reflects the completion of the core function. The operating condition coefficient is used to evaluate the physical working environment of the high-efficiency activated carbon bed. The system synchronously monitors the pressure drop and reaction temperature flowing through the bed and compares them with the preset maximum allowable pressure drop and optimal operating temperature range, respectively. The two are converted into independent scores through a preset scoring function, and the lower score is taken as a comprehensive index to provide a keen warning of the deterioration of the operating status caused by blockage or abnormal temperature. The load adaptability coefficient aims to quantify the current operating pressure level of the system. The system calculates the instantaneous pollution load by coupling the influent flow rate and influent concentration signals, and obtains a representative load value after averaging over a sliding time window. This load value is compared with the system's rated design treatment load to generate this coefficient, which intuitively reveals the system status and is a key forward-looking indicator for predicting the rate of adsorption capacity consumption. The historical decay coefficient is used to quantify the cumulative performance degradation of high-efficiency activated carbon materials. The system calculates the basic replenishment ratio based on the cumulative total replenishment mass in the maintenance log and the initial total filling mass. To correct the gap between the linear mass replenishment model and the actual nonlinear performance degradation, the system introduces a decay enhancement factor greater than 1 to multiply it. The factor is essentially a performance recovery discount coefficient, which aims to more realistically reflect the inherent performance loss caused by irreversible adsorption, permanent pore blockage, and material structural fatigue. As an important component of water pollution control agents and materials for environmental pollution treatment, the accurate evaluation of the performance of high-efficiency activated carbon is crucial to ensuring the stable operation of wastewater treatment and reuse systems.

[0019] The attenuation enhancement factor is an empirical correction coefficient greater than 1, determined through regression analysis of long-term operating data. It introduces a non-linear amplification effect into a linear cumulative consumption model based on a simple ratio of cumulative replenishment mass to initial loading mass, thereby correcting the idealized assumption implicit in this linear model that "each unit of replenished carbon can completely restore the initial performance of the system." The introduction of this factor aims to more accurately quantify the complex performance attenuation behavior of activated carbon in long-term actual operation through mathematical means. The design of the attenuation enhancement factor specifically reflects the following three key engineering realities: First, the initial adsorption performance of newly added batches of activated carbon is usually slightly lower than that of the original, carefully calibrated activated carbon in the system due to factors such as manufacturing process fluctuations, differences in transportation and storage conditions, and initial moisture content. Second, the system will experience irreversible performance losses during long-term operation, including permanent micropore blockage caused by colloidal substances and microbial metabolites, as well as structural fatigue of the carbon skeleton caused by repeated adsorption-desorption cycles and hydraulic shear. Finally, the fluid distribution and adsorption kinetics of the bed formed by the mixture of new and old activated carbon differ from those of a uniform new bed, resulting in a loss of mass transfer efficiency and disordered adsorption front, which leads to the overall treatment efficiency failing to reach the level corresponding to the theoretical replenishment amount. These considerations are typical engineering challenges in the application of water pollution control agents and materials for environmental pollution treatment, further highlighting the importance of refined state management of environmental pollution treatment agents and materials in water pollution control engineering.

[0020] The status assessment module preprocesses the raw data collected for relevant performance data, including an integrated data regularization stage. First, the system cleans the diverse and heterogeneous time-series data from different sensors with varying sampling frequencies and physical units, including outlier removal based on process rules and statistical models, and imputation of missing values. The system assigns a unified high-precision timestamp to all data streams and resamples and interpolates all data sequences at a preset fixed frequency, thereby achieving precise alignment in the time dimension. After time-series alignment, the system performs logical aggregation and feature calculations, such as vector multiplication of the aligned instantaneous flow rate and concentration sequence to generate a new instantaneous pollution load time-series sequence, completing the transformation from raw data to engineering characteristic signals. During the standardization phase, the system inputs each of the normalized data sequences into its predefined transformation function. These functions include both linear and nonlinear functions, with the common goal of normalizing and mapping the original values ​​with different dimensions to a unified, dimensionless scaling range. Ultimately, the system outputs a set of standardized performance coefficients that are fully synchronized in time and consistent in scale, providing a direct, reliable, and comparable input data foundation for the calculation of subsequent advanced indicators.

[0021] The health status index of activated carbon is generated based on the relevant performance data after pretreatment. The health status index is calculated using the following formula: Where: CHI is the health status index of activated carbon; E is the efficiency coefficient; C is the operating condition coefficient; L is the load adaptability coefficient; β is the historical decay coefficient; W1, W2, W3, and W4 are the weighting coefficients for the efficiency coefficient, operating condition coefficient, load adaptability coefficient, and historical degradation coefficient, respectively, with W1 > W2 > W3 > W4, and W1 + W2 + W3 + W4 = 1. The efficiency coefficient is given the highest weight because it quantifies the real-time pollutant removal efficiency of the system and is a key indicator for assessing whether water pollution prevention and control targets have been achieved and ensuring the quality of wastewater treatment and reuse. The operating condition coefficient receives the second highest weight because it monitors the key physical conditions affecting treatment efficiency and material lifespan; its deterioration will directly and rapidly lead to performance degradation or failure. The index emphasizes the importance placed on the operational health of high-efficiency activated carbon, a core material for environmental pollution treatment. The load adaptability coefficient reflects the recent load pressure on the system. Although it does not directly determine the instantaneous effluent quality, long-term overload is the main external factor driving accelerated performance degradation. This weight reflects a forward-looking warning of potential risks. The negative weight design with a relatively low historical degradation coefficient aims to reflect the impact of irreversible and cumulative performance depreciation of the material on the current health status in a balanced way. This avoids the excessive dominance of historical factors in the instantaneous status assessment and ensures that the long-term aging trend is reflected in the index.

[0022] Risk coefficient assessment module: This module preprocesses real-time influent concentration data, performing time-series normalization and feature enhancement preprocessing. The process begins with data cleaning and repair, identifying and removing outliers from raw sensor readings based on a pre-defined physical range and process knowledge base. Time-series interpolation algorithms are then used to fill in missing data caused by communication interruptions or maintenance. Time-series normalization then normalizes the cleaned, potentially non-uniformly spaced data sequences into normalized time-series data using a fixed-frequency resampling algorithm, ensuring strict synchronization with the system's master clock. After time-series alignment, the system enters the feature enhancement stage: the normalized concentration time-series data is vector-multiplied with the similarly preprocessed and fully synchronized instantaneous influent flow time-series data to calculate the instantaneous pollution load sequence in real time. To further suppress short-term, drastic fluctuations caused by instantaneous dumping and equipment start-up / shutdown and to extract trend signals reflecting the true system pressure, a sliding time window averaging filter is applied to the load sequence. The window length can be configured according to the fluctuation characteristics of the water quality. For static design influent concentration limit data retrieved from the process parameter library, the system performs parameter serialization and time-series synchronization preprocessing. First, the static value undergoes logical verification and version management to ensure it is greater than zero and exceeds the design effluent standard, and the currently effective version is invoked. Then, through parameter serialization, this single scalar value is copied and expanded into a time series with the same length as the preprocessed real-time data series, and all data points have constant values. Finally, through time-series synchronization, the timestamp of each data point in this constant value series is precisely matched and bound to the corresponding timestamp in the real-time data series, thus forming a strictly time-synchronized feature data pair within each calculation cycle. The risk factor is calculated using the following formula: Where: R is the risk coefficient; CI represents the influent concentration; CS is the influent concentration limit; The health decay coefficient, The load impact coefficient is defined as follows: a1 and a2 are the weighting coefficients of the health degradation coefficient and the load impact coefficient, respectively, with a1 > a2 and a1 + a2 = 1. The health degradation term is given a dominant weight because the irreversible degradation of activated carbon's performance is the fundamental and gradual internal cause of the system's reduced treatment capacity, determining the system's basic ability to withstand risks and forming the foundation of long-term risk. The load impact term is given a secondary weight because excessive influent load is a key and sudden external cause triggering instantaneous failure, and its actual severity highly depends on the system's current health status. This weighting structure reflects a risk composition model where internal factors are primary and external factors are secondary. This allows the risk coefficient output to be both robustly dominated by the health status in the long term and sensitively modulated by significant impact events in the short term, thus providing a high-quality probabilistic prediction signal with both stability and sensitivity for downstream intelligent diagnosis and graded response. This is crucial for achieving reliable wastewater treatment and its reuse. Decision and Response Trigger Module: A basic urgency index is generated based on the health status index and the risk coefficient. The basic urgency index is calculated using the following formula: Wherein: U is the basic urgency index; 1-R is defined as a risk discount factor, which is essentially a dynamic discount coefficient that quantifies future uncertainty. When the risk coefficient R increases, indicating a higher probability of future effluent exceeding standards, the risk discount factor decreases accordingly. This represents the strong expected discount effect of future high-risk states on the current asset value of the system. That is, due to the anticipated functional failure or environmental violations, the credibility and usability of the system's current health status in decision-making assessment are significantly reduced. The calculation of CHI·(1-R) ​​completes the key step of coupling the static health status index CHI, which represents the inherent performance of the system, with the dynamic risk coefficient R, which represents the probability of external threats. This achieves a risk-adjusted valuation, the physical meaning of which is that it reflects the effective and timely treatment capacity of activated carbon materials based on their current health status under a foreseeable risk background within a specific time window. This valuation abandons the idealized perspective that only focuses on the material's own state, but instead evaluates it in a real, uncertain operating environment, thus providing a more objective and forward-looking decision-making basis for predictive maintenance and emergency resource scheduling.

[0023] The decision characteristic factor is calculated using the following formula: Where: D is the decision characteristic factor; Set a preset diagnostic threshold θ. If D > +θ, it is determined as the health-dominated mode, indicating that the system itself is healthy and emergencies are mainly caused by external influent load shocks. If D < -θ, it is determined as the risk-dominated mode, indicating that the system itself has severely failed and the root cause of the emergency lies in the internal failure of the activated carbon material performance. If -θ ≤ D ≤ +θ, it is determined as the balance mode.

[0024] The decision logic of the system is represented by a predefined two-dimensional decision matrix with U and D as the coordinate axes and divided into multiple response regions with clear operation definitions by the above thresholds. The core operating mechanism of the two-dimensional decision matrix is as follows: The system continuously compares the calculated U and D value pairs with the thresholds Ut and θ of the matrix to accurately locate them in the corresponding response quadrants.

[0025] The two-dimensional decision matrix defines four typical response regions: High Urgency - External Shock Quadrant: When U ≥ Ut and D > +θ, the system determines that the healthy system is suffering from a severe external shock. At this time, an immediate operating condition adjustment instruction package will be triggered, and the system will automatically perform operations: start the emergency influent buffer tank, adjust the diversion valve, increase the load of the pretreatment unit, and issue an alarm to prompt for checking the abnormal influent source.

[0026] High Urgency - Internal Failure Quadrant: When U ≥ Ut and D < -θ, the system determines that the activated carbon material itself has failed, constituting a fundamental risk. At this time, an asset emergency replacement instruction package will be triggered, and the system will automatically generate a work order with the highest priority, prepare spare materials and a shutdown window, and start the process bypass to ensure a safe transition during the replacement.

[0027] Low Urgency - Optimization Monitoring Area: When U < Ut and D > 0, it indicates that the system is healthy and the operating pressure is controllable. The system will enter the optimization monitoring mode, automatically fine-tune the operating parameters to improve energy efficiency, and maintain the regular monitoring frequency.

[0028] Low Urgency - Planned Maintenance Area: When U < Ut and D < 0, it indicates that although the system has not reached an emergency state, it has shown a trend of performance degradation. The system will trigger a planned maintenance instruction, generate predictive maintenance suggestions, arrange for activated carbon regeneration or replenishment during the next low-load window, list the relevant spare part requirements in the procurement plan, and manage the preventive assets of the special pharmaceutical materials for environmental pollution treatment.

[0029] In addition, the system can also define transitional response strategies such as enhanced monitoring or manual review for the risk root cause fuzzy area where ∣D∣ ≤ θ and the critical warning area where U is close to Ut. Through the mapping mechanism based on the two-dimensional matrix, the system realizes the accurate and automatic jump from continuous state perception to discrete optimal actions, forming a complete intelligent decision-making closed loop.

[0030] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0031] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0032] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0033] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A system for treating and recycling catering wastewater for environmental protection, characterized in that, Includes the following functional modules: Data acquisition and preprocessing module: Collects relevant performance data of activated carbon in the recycling system. The relevant performance data includes: efficiency coefficient, which is the real-time treatment efficiency obtained by the ratio of effluent concentration to the design standard; operating condition coefficient, which is obtained by taking the difference between the bed pressure drop and temperature deviation from the optimal value; load adaptability coefficient, which is obtained by the ratio of the current pollution load to the system design load; and historical decay coefficient, which is obtained by the ratio of cumulative replenishment to initial loading and by introducing a decay factor. The status assessment module preprocesses the raw data of relevant performance data and performs integrated data normalization and standardization. Based on the preprocessed relevant performance data, it generates a health status index for activated carbon, quantifying the status characteristics of activated carbon into a health score, which is used to quantitatively assess the real-time performance status and aging degree of activated carbon materials. Risk coefficient assessment module: Collects influent characteristic data from the recycling system, including influent concentration and influent concentration limit; performs time-series normalization and feature enhancement preprocessing on real-time influent concentration data; performs parameter serialization and time-series synchronization preprocessing on the designed influent concentration limit data; generates a risk coefficient from the preprocessed influent characteristic data and health status index, which is used to quantitatively assess the probability of the effluent exceeding the standard in a specific future period of the treatment system; Decision and Response Trigger Module: Based on the health status index and risk coefficient, a basic urgency index is generated to characterize the degree to which the overall system status deviates from the ideal state. At the same time, decision feature factors are generated to diagnose whether the current risk is mainly caused by external load impact or by the deterioration of the material's own performance. The basic urgency and decision feature factors are combined to construct a two-dimensional decision matrix. According to the combination of the intervals to which the values ​​of the basic urgency index and the decision feature factors belong, a preset response action is triggered.

2. The catering wastewater treatment and recycling system for environmental protection according to claim 1, characterized in that: The relevant performance data specifically include: efficiency coefficient, which is the percentage of instantaneous treatment efficiency obtained by comparing and normalizing the measured pollutant concentration with the preset design effluent standard concentration; operating condition coefficient, which is obtained by comparing real-time pressure drop and temperature data with the preset maximum allowable pressure drop and optimal operating temperature, respectively, to obtain pressure drop deviation and temperature deviation, and convert them into scores. The lowest score among the deviations is taken as the comprehensive operating condition index characterizing the physical smoothness of operation; load adaptability coefficient, which is obtained by calculating the real-time influent pollution load based on the influent flow and water quality data, and then performing a ratio calculation and normalization of this load value with the initial design treatment load of the system to quantify the margin and overload degree of the current operating pressure of the system relative to its design capacity; and historical decay coefficient, which is calculated by proportionally comparing the total mass of activated carbon added in the maintenance record database with the initial loading mass of the system, and multiplying it by an empirical decay enhancement factor greater than 1 to quantify the cumulative degradation of material performance caused by long-term use, irreversible adsorption, and structural aging.

3. A catering wastewater treatment and recycling system for environmental protection according to claim 2, characterized in that: The attenuation enhancement factor is an empirical correction coefficient greater than 1, used to nonlinearly amplify the simple ratio of cumulative replenishment to initial loading. It corrects the idealized assumptions of the linear cumulative model to more accurately quantify the performance of activated carbon in actual operation. The attenuation enhancement factor is used to reflect the following engineering realities: the adsorption performance of newly added activated carbon is usually lower than that of the initially loaded activated carbon; the system suffers from irreversible pore blockage and structural fatigue; and the overall efficiency of the mixed bed of new and old activated carbon is reduced.

4. A catering wastewater treatment and recycling system for environmental protection according to claim 1, characterized in that: The preprocessing first cleans and repairs the original data, including integrated data normalization, which aligns time-series data of different frequencies and physical units by using a unified timestamp and fixed-frequency resampling, and completes logical aggregation through feature calculation; and implements standardization processing, which normalizes each normalized data sequence to a unified, dimensionless scaling range by using their respective defined linear and nonlinear functions, thereby outputting a set of time-synchronized and scale-consistent standardized performance coefficients.

5. A catering wastewater treatment and recycling system for environmental protection according to claim 1, characterized in that: The health status index is calculated using the following formula: Where: CHI is the health status index of activated carbon; E is the efficiency coefficient; C is the operating condition coefficient; L is the load adaptability coefficient; β is the historical decay coefficient; W1, W2, W3, and W4 are the weighting coefficients for the efficiency coefficient, operating condition coefficient, load adaptability coefficient, and historical decay coefficient, respectively, where W1>W2>W3>W4, and W1+W2+W3+W4=1.

6. A catering wastewater treatment and recycling system for environmental protection according to claim 1, characterized in that: The real-time influent concentration data preprocessing includes: performing time-series normalization and feature enhancement preprocessing, achieving time-series alignment through data cleaning and repair and fixed-frequency resampling, and then coupling it with synchronous flow data to calculate instantaneous pollution load, and applying moving average filtering to extract stable trend features; for influent concentration limit data, parameter serialization and time-series synchronization preprocessing are performed, that is, ensuring the validity of parameters through logical verification, expanding it into a constant value sequence of the same length as the real-time data, and performing precise timestamp matching.

7. A catering wastewater treatment and recycling system for environmental protection according to claim 6, characterized in that: The risk coefficient is calculated using the following formula: Where: R is the risk coefficient; CI represents the influent concentration; CS is the influent concentration limit; The health decay coefficient, Let a1 be the load impact coefficient, and a2 be the weighting coefficients of the health decay coefficient and the load impact coefficient, respectively. a1 > a2 and a1 + a2 = 1.

8. A catering wastewater treatment and recycling system for environmental protection according to claim 7, characterized in that: The basic urgency index is calculated using the following formula: Wherein: U is the basic urgency index; 1-R is the risk discount factor. When the risk coefficient R increases, the risk discount factor decreases, indicating that the discount effect of future risks on the current asset value is enhanced. CHI·(1-R) ​​combines the static health status index of the system with the dynamic risk coefficient, reflecting the current processing capacity that activated carbon materials can provide under the background of foreseeable risks.

9. A catering wastewater treatment and recycling system for environmental protection according to claim 7, characterized in that: The decision characteristic factor is calculated using the following formula: Where: D is the decision characteristic factor; Set a preset diagnostic threshold θ. If D>+θ, it is judged as a health-dominant mode, indicating that the system itself is healthy and the emergency is mainly caused by the impact of external water inflow. If D<-θ, it is judged as a risk-dominant mode, indicating that the system itself has been severely depleted and the root cause of the emergency lies in the internal failure of the activated carbon material. If -θ≤D≤+θ, it is judged as a balance mode.

10. A catering wastewater treatment and recycling system for environmental protection according to claim 9, characterized in that: The system sets emergency thresholds Ut and diagnostic thresholds θ for U and D respectively, compares the values of U and D with the preset emergency and diagnostic thresholds, and locates them in specific response quadrants in the two-dimensional decision matrix. The two-dimensional decision matrix defines four response areas: when U≥Ut and D>+θ, an immediate operating condition adjustment instruction for external shock is triggered; when U≥Ut and D<-θ, an emergency asset replacement instruction for internal failure is triggered; when U<Ut, corresponding optimization monitoring and planned maintenance instructions are triggered according to the positive or negative value of D.

Images