Intelligent prediction and regulation method, device and equipment for PAHs-O3 combined pollution in coffee planting area and storage medium

Abstract

The application provides a coffee planting area PAHs-O3 composite pollution intelligent prediction and control method, device, equipment and storage medium, relating to the technical field of environmental pollution control. The method comprises the following steps: acquiring real-time or historical multi-source data of a target planting area, including meteorological data, atmospheric environmental data and plant physiological parameters; through a prediction model fusing atmospheric chemical mechanism and plant metabolism dynamics, the enrichment amount of benzo[a]pyrene and its oxidation products in coffee leaves and coffee beans is dynamically calculated, and the incremental carcinogenic risk ΔILCR caused by the enrichment of the oxidation products is quantified; based on the enrichment amount and ΔILCR, decision support information including pollution early warning, picking time suggestion and control measures is generated and output. The application can make up for the omission of existing detection standards for oxidized PAHs, provide data support for atmospheric pollution prevention and control in high-altitude production areas, and promote the sustainable development of the coffee industry.

Description

Technical Field

[0001] This application relates to the field of environmental pollution control technology, and in particular to a method, device, equipment and storage medium for intelligent prediction and control of PAHs-O3 compound pollution in coffee growing areas. Background Technology

[0002] The hazards of polycyclic aromatic hydrocarbons (PAHs) in coffee beans involve food safety and human health. Benzo[a]pyrene (BaP) is the most representative monomeric compound in the PAH family, possessing strong carcinogenicity. It is metabolized in the human body to 7,8-dihydrodiol-9,10-epoxide (BPDE), which forms adducts with DNA, inducing p53 gene mutations, a major cause of lung and stomach cancer. Daily intake of 1 ng of BaP per kg of body weight increases the lifetime risk of cancer by 1 × 10⁻⁶. -6 (US EPA model).

[0003] When the BaP content in roasted coffee beans is ≥2 μg / kg, the ILCR (increased risk of cancer) of brewed coffee exceeds 10. -5 (EU safety threshold). Currently, the EU (NO. 835 / 2011) sets the limit for BaP content in roasted coffee beans at 2.0 μg / kg, while China (GB 2762-2022) sets it at 5.0 μg / kg. BaP is a typical product of incomplete combustion, and its generation increases significantly during dark roasts (>200℃), reaching 3-5 times higher than light roasts. Therefore, it is generally believed that BaP in coffee beans mainly originates from roasting fumes and diesel dryers during the deep processing stage. However, the Japanese JAS believes that PAHs generated during roasting may be partially removed by subsequent processes (such as hulling and washing), while air pollution near the planting area and direct smoke drying during the initial processing stage can introduce BaP into green coffee beans. Therefore, the limit for BaP in green coffee beans is set at 1.0 μg / kg. Currently, the detection of PAHs content in both roasted coffee and green coffee beans is a sample-based experimental method that requires sample pretreatment (including -80℃ quick-freezing, vacuum drying, Soxhlet extraction, solid-phase extraction purification) and instrumental analysis (GC-MS, mass spectrometry). The process is complicated and the results are delayed.

[0004] AI prediction technology offers advantages in process simplification and efficiency improvement. Currently, the technology for predicting PAH content in green coffee beans involves the development and application of statistical regression analysis of processing and drying processes, machine learning modeling for supply chain traceability, and risk assessment models that combine drying methods and transportation conditions.

[0005] AI has also been applied to predict the accumulation of PAHs in organisms. For example, existing predictive models use soil PAH concentration, organic carbon content, and biogenic lipid content as core independent variables, and the accumulation concentration in invertebrates (such as earthworms) as the dependent variable. The coefficient of determination (R²) is then optimized through model fitting. 2 This modeling method achieves the highest quantitative relationship, enabling direct prediction of PAH enrichment concentrations in organisms. It is used to assess ecological risks, overcoming the limitations of traditional assessments that rely solely on environmental concentrations.

[0006] Currently, LISA kits can be used to detect PAHs content in coffee pods during the planting and growth process. They can produce results in 15 minutes and are suitable for rapid detection in the field for initial screening. However, the results are only for initial screening and need to be confirmed by GC-MS. Surface-enhanced Raman spectroscopy (SERS) has a detection capability of 0.1 μg / kg for crude coffee pod extract, but the analysis process is complex and the equipment is expensive. Portable SPI-MS (single-photon ionization mass spectrometry) real-time mass spectrometry technology is only seen in theoretical feasibility analysis and has not yet been applied in practice. The accuracy of portable GC-MS is only 1.0 µg / kg.

[0007] BaP is a core monitoring indicator for PAHs in coffee beans, but it is not the only one. EU No. 835 / 2011 uses BaP as a reference, while also requiring the total amount of PAH4 (BaP + benzo[a]anthracene + β-benzo[b]fluoranthracene) to be ≤10.0 μg / kg. It should be noted that BaP usually accounts for only 5-20% of the total PAHs detected in coffee beans. Oxidized PAHs contain hydroxyl / carbonyl groups and are far more toxic than parent PAHs. However, due to high polarity, low extraction efficiency, easy volatility or thermal decomposition when detected by GC-MS, weak fluorescence in high performance liquid chromatography-fluorescence detection (HPLC-UV / FLD), and the fact that most oxidized PAHs are not included in the IARC assessment list and that no tolerable daily intake (TDI) or food limit standards for oxidized PAHs have been established globally, they are not found in food testing standards.

[0008] O3 attacks the benzene ring double bonds of gaseous BaP, generating oxidized BaP (BaP-Ozonide) such as benzo[a]pyrene-7,8-dione. In vitro cell experiments show that the health risk level is increased by 300% compared to maternal BaP. Particulate PAHs react with O3 on the PM surface to generate hydroxy-BaP products with stronger mutagenicity, and the health risk level is increased by 500% compared to maternal products. Nitro-BaP products are generated that are more likely to cross the placental barrier and interfere with the endocrine system. Animal developmental toxicity experiments show that the health risk level is increased by 200% compared to maternal products.

[0009] The coexistence of O3 and BaP in the atmospheric environment of coffee plantations leads to the toxicity of oxidized BaP accumulated in coffee bean pods.

[0010] Current technologies have significant shortcomings in addressing PAHs-O3 compound pollution in high-altitude coffee. First, there are gaps in monitoring standards and regulatory systems. Current international and Chinese standards only cover parent PAHs (such as BaP or PAH4), excluding oxidized PAHs with toxicity 300%–500% higher (such as BaP-Ozonide), and failing to adequately consider the unique environmental risks posed by high concentrations of ozone and cross-border biomass burning in the Yunnan plateau. Second, detection methods are outdated. Traditional GC-MS methods are time-consuming and costly, making them unsuitable for real-time field monitoring. Existing rapid detection technologies (such as LISA kits and portable GC-MS) can only initially screen parent PAHs, failing to accurately quantify oxidized PAHs, and the equipment is expensive or lacks practical validation. Third, existing AI prediction models have significant limitations, mostly relying on static parameters and failing to integrate dynamic O3 exposure data and atmospheric chemical mechanisms. They cannot effectively predict the formation trend of oxidized derivatives, and the scarcity of prior data leads to insufficient reliability of Bayesian predictions. Summary of the Invention

[0011] This application provides a method, device, equipment, and storage medium for intelligent prediction and control of PAHs-O3 compound pollution in coffee-growing areas. It aims to establish an artificial intelligence-based dynamic prediction and control system for pollutant health risks, specifically addressing the unique ozone and polycyclic aromatic hydrocarbon (PAHs) compound pollution problem in high-altitude coffee-growing regions. By integrating atmospheric chemistry, plant physiology, and machine learning, a cross-predictive model is created to predict the content of BaP in coffee leaves and the enrichment amount in coffee pods. Policy directives are proposed based on the incremental values ​​of BaP-Ozonide toxicity, enabling timely reduction of BaP toxicity progression through control measures. Simultaneously, a lightweight decision-making terminal suitable for farmers and a decision-making terminal for government-formulated regional economic control policies are developed.

[0012] Firstly, this application provides a method for intelligent prediction and control of PAHs-O3 compound pollution in coffee-growing areas, including: Acquire real-time or historical multi-source data of the target planting area, including meteorological data, atmospheric environmental data, and plant physiological parameters; Based on the multi-source data, the enrichment of benzo[a]pyrene BaP and its oxidation product BaP-Ozonide in coffee leaves and coffee pods was dynamically calculated by using a prediction model that integrates atmospheric chemical mechanisms and plant metabolic dynamics, and the incremental carcinogenic risk ΔILCR caused by the enrichment of the oxidation product was quantified. Based on the enrichment amount and ΔILCR, decision support information including pollution warnings, harvesting timing suggestions, and control measures is generated and output.

[0013] In one possible design, based on the enrichment level and ΔILCR, decision support information including pollution warnings, harvesting timing suggestions, and control measures is generated and output, including: Set decision thresholds, including an early warning threshold for the proportion of oxidation products. T ox Incremental carcinogenic risk safety threshold T ILCR And the threshold for the lowest ozone concentration in meteorological data; The calculated enrichment percentage of BaP-Ozonide in coffee bean pods R ox = [ BaP-Ozonide ] pod / ([ BaP ] pod +[ BaP-Ozonide ] pod The warning threshold for the proportion of oxidation products. T ox Compare, and / or compare the calculated ΔILCR with the incremental carcinogenic risk safety threshold. T ILCR Compare the results and obtain the comparison results; Based on the comparison results and real-time ozone concentration data, at least one of the following decision support information is generated and output: a) when R ox > T ox At the same time, it outputs early warning information suggesting the initiation of regional ozone emission reduction and control measures; b) When the real-time or predicted ozone concentration is below the low-period threshold, output information suggesting the timing of coffee harvesting during that period; c) When ΔILCR> T ILCR At that time, high-risk warning information will be output.

[0014] In one possible design, based on the aforementioned multi-source data, the enrichment of benzo[a]pyrene (BaP) and its oxidation product BaP-Ozonide in coffee leaves and pods is dynamically calculated by integrating a predictive model of atmospheric chemical mechanisms and plant metabolic kinetics. The incremental carcinogenic risk ΔILCR resulting from the enrichment of the oxidation product is then quantified, including: Calculate the amount of BaP-Ozonide produced by the reaction of gaseous BaP and particulate BaP with O3; Calculate the permeation of BaP-Ozonide in coffee leaves and the enrichment of BaP and BaP-Ozonide in coffee pods. Based on the calculated enrichment amount, the lifetime carcinogenic risk model was used to calculate ΔILCR.

[0015] In one possible design, the calculation of the amounts of gaseous BaP and particulate BaP reacting with O3 to form BaP-Ozonide includes: The amount of BaP-Ozonide generated from gaseous BaP was calculated based on gas-phase reaction kinetic equations. : In the formula, The rate constant for the O3 reaction is [ BaP g [ represents the concentration of gaseous BaP,] O [3] represents the O3 concentration, and t represents time; The amount of BaP-Ozonide generated from particulate BaP was calculated based on the particulate phase oxidation equation. : In the formula, [ BaP p [This represents the concentration of particulate BaP.] k surf The oxidation rate coefficient of particulate matter surface. S This is the reaction surface area parameter.

[0016] In one possible design, the permeation of BaP-Ozonide by coffee leaves and the enrichment of BaP and BaP-Ozonide by coffee pods are calculated, including: Calculate the flux of BaP-Ozonide permeating the cuticle of coffee leaves and predict the content of BaP-Ozonide in the leaves over the next 72 hours. : In the formula, A leaf Leaf area P c For the stratum corneum penetration efficiency, The O3 inhibition coefficient is... BaP-Ozonide content in leaves; Calculate the enrichment of BaP in coffee bean pods. BaP ] pod : In the formula, k met This refers to the plant's metabolic rate. Lipid The percentage represents the lipid content of coffee beans. e It is a natural constant; Calculate the enrichment amount of BaP-Ozonide in coffee bean pods. BaP - Ozonide ] pod :

[0017] In one possible design, based on the calculated enrichment level, the formula for calculating ΔILCR using a lifetime carcinogenic risk model is as follows: In the formula, IR For daily coffee intake, EF For exposure frequency, ED For exposure years, CSF It is the oral carcinogenicity slope factor of benzo[a]pyrene. BW The average weight of consumers.

[0018] In one possible design, after acquiring real-time or historical multi-source data of the target planting area, the method further includes: Obtain historical environmental data and corresponding measured values ​​of BaP concentration in coffee beans; Define the BaP oxidation rate coefficient on particulate surfaces. k surf Plant metabolic rate k met stratum corneum penetration efficiency P c O3 inhibition coefficient Parameter space; Using the Bayesian optimization algorithm, with the historical environmental data as input and the measured values ​​as the objective, iterative optimization is performed in the parameter space to find the optimal parameter combination θ* that minimizes the prediction model error; The enrichment amount and ΔILCR are calculated based on the optimal parameter combination θ*.

[0019] Secondly, this application provides an intelligent prediction and control device for PAHs-O3 compound pollution in coffee-growing areas, the device comprising: The data acquisition module is configured to acquire real-time or historical multi-source data of the target planting area, including meteorological data, atmospheric environmental data and plant physiological parameters. The parameter prediction module is configured to dynamically calculate the enrichment of benzo[a]pyrene BaP and its oxidation product BaP-Ozonide in coffee leaves and coffee pods based on the multi-source data by using a prediction model that integrates atmospheric chemical mechanisms and plant metabolic dynamics, and to quantify the incremental carcinogenic risk ΔILCR caused by the enrichment of the oxidation product. The decision support module is configured to generate and output decision support information, including pollution warnings, harvesting timing suggestions, and control measures, based on the enrichment amount and ΔILCR.

[0020] Thirdly, embodiments of this application provide an electronic device, including: at least one processor and a memory; the memory stores computer execution instructions; the at least one processor executes the computer execution instructions stored in the memory, causing the at least one processor to perform the intelligent prediction and control method for PAHs-O3 compound pollution in coffee-growing areas as described in the first aspect and various possible designs of the first aspect.

[0021] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the intelligent prediction and control method for PAHs-O3 compound pollution in coffee-growing areas as described in the first aspect and various possible designs of the first aspect.

[0022] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the intelligent prediction and control method for PAHs-O3 compound pollution in coffee-growing areas as described in the first aspect and various possible designs of the first aspect.

[0023] The intelligent prediction and control method, device, equipment, and storage medium for PAHs-O3 compound pollution in coffee-growing areas provided in this application have at least the following beneficial effects: This application realizes dynamic prediction based on the synergistic pollution mechanism of ozone and polycyclic aromatic hydrocarbons. By coupling real-time monitoring data of the plateau environment (O3 concentration, meteorological parameters, and cross-border pollution index) with Bayesian algorithms and toxicokinetics, it achieves accurate prediction of the generation potential and enrichment concentration of highly toxic oxidized PAHs (such as BaP-Ozonide) in coffee beans. Furthermore, it constructs an integrated closed-loop solution of intelligent monitoring, AI early warning, and field regulation. Through portable detection equipment and cloud AI platform linkage, it can provide farmers with real-time decision support such as avoiding harvesting during the pollution window and optimizing primary processing technology. Attached Figure Description

[0024] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0025] Figure 1 A flowchart illustrating an intelligent prediction and control method for PAHs-O3 compound pollution in coffee-growing areas, provided for embodiments of this application; Figure 2A flowchart illustrating the calculation of enrichment amount and ΔILCR provided for embodiments of this application; Figure 3 A flowchart for generating and outputting decision support information, including pollution warning, harvesting timing suggestions and control measures, based on enrichment amount and ΔILCR, is provided for embodiments of this application. Figure 4 A structural diagram of the intelligent prediction and control device for PAHs-O3 compound pollution in coffee-growing areas provided in this application embodiment.

[0026] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0027] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0028] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0029] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0030] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0031] This application provides an intelligent prediction and control method for PAHs-O3 compound pollution in coffee-growing areas, such as... Figure 1 As shown, the intelligent prediction and control method for PAHs-O3 compound pollution in this coffee-growing area includes the following steps S10-S30.

[0032] S10: Obtain real-time or historical multi-source data for the target planting area. The multi-source data includes meteorological data, atmospheric environmental data, and plant physiological parameters.

[0033] In this embodiment, the multi-source data specifically includes data obtained through collection and research: ① Meteorological data, including ultraviolet radiation intensity, temperature, relative humidity, wind speed and direction, precipitation, etc. These data directly affect the atmospheric chemical behavior of pollutants (such as photochemical reaction rate) and the physiological activities of plants (such as stomatal opening and closing).

[0034] ② Atmospheric environmental data, including pollution source data, ozone concentration, and transboundary pollution index. Among them, pollution source data are derived from the emission inventories of local environmental protection bureaus, obtaining regional emission source intensity and concentration data of PAHs (especially benzo[a]pyrene, BaP); ozone concentration: real-time or predicted O3 concentration data, which is a key independent variable that triggers oxidation reactions; transboundary pollution index reflects the degree of transboundary pollution impact from biomass burning in Southeast Asia.

[0035] ③ Plant physiological parameters: including leaf characteristics, pod characteristics, and growth cycle data. Leaf characteristics include leaf wax thickness, stomatal conductance, and leaf area index, which affect the adsorption and penetration of pollutants on the leaf surface; pod characteristics refer to the lipid content of coffee pods, which is a key factor determining the lipophilic enrichment of PAHs; growth cycle data: this pertains to different growth stages of coffee beans, as their ability to accumulate pollutants and their metabolic rates vary.

[0036] S20: Based on multi-source data, by integrating atmospheric chemical mechanisms and plant metabolic kinetics prediction models, the enrichment of benzo[a]pyrene BaP and its oxidation product BaP-Ozonide in coffee leaves and coffee pods is dynamically calculated, and the incremental carcinogenic risk ΔILCR caused by the enrichment of oxidation products is quantified.

[0037] In some embodiments, step S20 can be implemented through a core algorithm module. This core algorithm module includes three modules: module 1, module 2, and module 3, each implementing the following... Figure 2 The steps S201-S203 are shown.

[0038] S201: Calculate the amount of BaP-Ozonide produced by the reaction of gaseous BaP and particulate BaP with O3.

[0039] Step S201 can be configured to be implemented in module 1, which describes the amount of BaP-Ozonide generated from gaseous BaP based on the atmospheric chemical kinetics of O3 and BaP.

[0040] Specifically, the amount of BaP-Ozonide generated from gaseous BaP is calculated based on the gas-phase reaction kinetic equation. : In the formula, The rate constant for the O3 reaction is [ BaP g [ represents the concentration of gaseous BaP,] O [3] represents the O3 concentration, and t represents time; The amount of BaP-Ozonide generated from particulate BaP was calculated based on the particulate phase oxidation equation. : In the formula, [ BaP p [This represents the concentration of particulate BaP.] k surf The oxidation rate coefficient of particulate matter surface. S This is the reaction surface area parameter.

[0041] S202: Calculate the permeation of BaP-Ozonide in coffee leaves and the enrichment of BaP and BaP-Ozonide in coffee pods.

[0042] Step S202 can be configured to be implemented in module 2, which calculates the BaP content of coffee leaves and coffee pods as a basis for decision-making for short-term early warning and long-term risk assessment.

[0043] Specifically, the flux of BaP-Ozonide permeating the cuticle of coffee leaves was calculated, and the BaP-Ozonide content in the leaves was predicted over the next 72 hours. : In the formula, A leaf Leaf area P c For the stratum corneum penetration efficiency, The O3 inhibition coefficient is... BaP-Ozonide content in leaves; Calculate the enrichment of BaP in coffee bean pods. BaP ] pod : In the formula, k met This refers to the plant's metabolic rate. Lipid The percentage represents the lipid content of coffee beans. e It is a natural constant; Calculate the enrichment amount of BaP-Ozonide in coffee bean pods. BaP - Ozonide ] pod :

[0044] S203: Based on the calculated enrichment amount, the lifetime carcinogenic risk model is used to calculate ΔILCR.

[0045] Step S203 can be configured to be implemented in module 3, which uses the lifetime carcinogenic risk (ILCR) model recommended by the U.S. Environmental Protection Agency to quantitatively analyze the health risk increment of BaP-Ozonide enriched in coffee pods.

[0046] Specifically, based on the calculated enrichment amount, the formula for calculating ΔILCR using the lifetime carcinogenic risk model is as follows: In the formula, IR For daily coffee intake, EF For exposure frequency, ED For exposure years, CSF It is the oral carcinogenicity slope factor of benzo[a]pyrene. BW The average weight of consumers.

[0047] The prerequisite for making a prediction in step S20 is to determine the calculation parameters. Based on the determined calculation parameters and the relevant parameters obtained in step S10, the enrichment amount and ΔILCR can be predicted or calculated.

[0048] In some embodiments, the range of values ​​for parameters that need to be calibrated locally is shown in Table 1.

[0049] Table 1. Range of parameters requiring local calibration

[0050] Specifically, this embodiment achieves parameter calibration through machine learning-assisted optimization, which includes the following four steps.

[0051] Step 1: Prepare input data.

[0052] Fixed historical input data, including background concentrations of gaseous and particulate BaP in the atmosphere, average ozone concentration during the monitoring period, average solar radiation intensity, average temperature and humidity, coffee bean growth period length, and coffee bean oil content; The target real data includes the real concentration value of BaP in coffee beans measured by the GC-MS standard method during the same period as the above environmental data; The parameter space to be optimized includes the BaP oxidation rate coefficient on the particulate surface.k surf The rate constant of BaP metabolism in coffee beans k met Coffee bean skin permeability coefficient Pc, and the inhibition threshold of O3 on gaseous BaP permeation. KO 3.

[0053] Step 2: Definition of the mechanism model.

[0054] Based on chemical kinetics and biological enrichment theory, the above mathematical and physical model was constructed. The atmospheric chemical model was used to analyze how ozone oxidizes gaseous and particulate BaP in the atmosphere, with the rate being determined by... k surf Parameter control; The bioaccumulation model is based on the absorption of coffee leaves by the leaves and pods through osmosis and metabolism. P c , k met Parameters such as these are controlled.

[0055] Step 3: Iterative calibration.

[0056] First, multiple sets of initial parameters are randomly selected within the parameter space. Then, each set of parameters is substituted into the mechanistic model to calculate the predicted concentration, which is compared with the true value to calculate the loss function. Finally, a surrogate model is constructed based on historical results using a Bayesian optimizer to intelligently find the next set of parameters that minimizes the prediction error.

[0057] Step 4: Convergence and Consolidation.

[0058] After multiple iterations, the optimal parameter combination θ* is output. θ* is then incorporated into the mechanistic model, forming a high-precision prediction model tailored for the Yunnan production area (the target region in this example). The target calibration accuracy is defined by the statistical index R. 2 >0.85.

[0059] S30: Based on enrichment amount and ΔILCR, generate and output decision support information including pollution warning, harvesting timing suggestions and control measures.

[0060] In some embodiments, such as Figure 3 As shown, S30 can be implemented through the following steps S301-S303.

[0061] S301: Set decision thresholds, including an early warning threshold for the proportion of oxidation products. T ox Incremental carcinogenic risk safety threshold T ILCR And the threshold for the lowest ozone concentration in meteorological data; S302: Calculate the percentage of BaP-Ozonide enrichment in coffee bean pods. R ox = [ BaP-Ozonide ] pod / ([ BaP ] pod +[ BaP-Ozonide ] pod Warning threshold for the proportion of oxidation products T ox Compare and / or compare the calculated ΔILCR with the incremental carcinogenic risk safety threshold. T ILCR Compare the results and obtain the comparison results; S303: Based on the comparison results and real-time ozone concentration data, generate and output at least one of the following decision support information: a) when R ox > T ox At the same time, it outputs early warning information suggesting the initiation of regional ozone emission reduction and control measures; b) When the real-time or predicted ozone concentration is below the low-temperature threshold, output information suggesting the timing of coffee harvesting during that period; c) When ΔILCR> T ILCR At that time, high-risk warning information will be output.

[0062] This application also provides an intelligent prediction and control device for PAHs-O3 compound pollution in coffee-growing areas, such as... Figure 4 As shown, the intelligent prediction and control device for PAHs-O3 compound pollution in this coffee-growing area includes: The data acquisition module 401 is configured to acquire real-time or historical multi-source data of the target planting area, including meteorological data, atmospheric environmental data and plant physiological parameters. The parameter prediction module 402 is configured to dynamically calculate the enrichment of benzo[a]pyrene BaP and its oxidation product BaP-Ozonide in coffee leaves and coffee pods based on the multi-source data by using a prediction model that integrates atmospheric chemical mechanisms and plant metabolic dynamics, and to quantify the incremental carcinogenic risk ΔILCR caused by the enrichment of the oxidation product. The decision support module 403 is configured to generate and output decision support information, including pollution warnings, harvesting timing suggestions, and control measures, based on the enrichment amount and ΔILCR.

[0063] This application provides an electronic device. The electronic device may include a processor and a memory, wherein the processor and the memory can communicate; exemplarily, the processor and the memory communicate via a communication bus.

[0064] The processor executes computer execution instructions stored in memory, causing the processor to perform the scheme in the above embodiments. The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0065] The communication bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. Transceivers are used to enable communication between database access devices and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) and may also include non-volatile memory.

[0066] The electronic device provided in this application embodiment can be the terminal device described in the above embodiments.

[0067] This application also provides a computer-readable storage medium storing computer instructions. When the computer instructions are executed on a computer, the computer performs the technical solution of the intelligent prediction and control method for PAHs-O3 compound pollution in coffee-growing areas described in the above embodiment.

[0068] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, it can implement the technical solution of the intelligent prediction and control method for PAHs-O3 compound pollution in coffee planting areas described in the above embodiments.

[0069] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0070] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0071] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0072] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0073] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0074] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0075] Buses can be Industry Standard Architecture (ISA) buses, Peripheral Component Interconnect (PCI) buses, or Extended Industry Standard Architecture (EISA) buses, etc. Buses can be categorized into address buses, data buses, control buses, etc.

[0076] The aforementioned storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0077] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. The processor and storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic control unit or main control device.

[0078] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for intelligent prediction and control of PAHs-O3 complex pollution in coffee-growing areas, characterized in that, The method includes: Acquire real-time or historical multi-source data of the target planting area, including meteorological data, atmospheric environmental data, and plant physiological parameters; Based on the multi-source data, the enrichment of benzo[a]pyrene BaP and its oxidation product BaP-Ozonide in coffee leaves and coffee pods was dynamically calculated by using a prediction model that integrates atmospheric chemical mechanisms and plant metabolic dynamics, and the incremental carcinogenic risk ΔILCR caused by the enrichment of the oxidation product was quantified. Based on the enrichment amount and ΔILCR, decision support information including pollution warnings, harvesting timing suggestions, and control measures is generated and output.

2. The intelligent prediction and control method for PAHs-O3 compound pollution in coffee-growing areas according to claim 1, characterized in that, Based on the enrichment amount and ΔILCR, decision support information including pollution warnings, harvesting timing suggestions, and control measures is generated and output, including: Set decision thresholds, including an early warning threshold for the proportion of oxidation products. T ox Incremental carcinogenic risk safety threshold T ILCR And the threshold for the lowest ozone concentration in meteorological data; The calculated enrichment percentage of BaP-Ozonide in coffee bean pods R ox = [ BaP-Ozonide ] pod / ([ BaP ] pod +[ BaP-Ozonide ] pod The warning threshold for the proportion of oxidation products. T ox Compare, and / or compare the calculated ΔILCR with the incremental carcinogenic risk safety threshold. T ILCR Compare the results and obtain the comparison results; Based on the comparison results and real-time ozone concentration data, at least one of the following decision support information is generated and output: a) when R ox > T ox At the same time, it outputs early warning information suggesting the initiation of regional ozone emission reduction and control measures; b) When the real-time or predicted ozone concentration is below the low-period threshold, output information suggesting the timing of coffee harvesting during that period; c) When ΔILCR> T ILCR At that time, high-risk warning information will be output.

3. The intelligent prediction and control method for PAHs-O3 compound pollution in coffee-growing areas according to claim 1, characterized in that, Based on the aforementioned multi-source data, a predictive model integrating atmospheric chemical mechanisms and plant metabolic kinetics was used to dynamically calculate the enrichment of benzo[a]pyrene (BaP) and its oxidation product BaP-Ozonide in coffee leaves and pods. The incremental carcinogenic risk ΔILCR resulting from the enrichment of these oxidation products was quantified, including: Calculate the amount of BaP-Ozonide produced by the reaction of gaseous BaP and particulate BaP with O3; Calculate the permeation of BaP-Ozonide in coffee leaves and the enrichment of BaP and BaP-Ozonide in coffee pods. Based on the calculated enrichment amount, the lifetime carcinogenic risk model was used to calculate ΔILCR.

4. The intelligent prediction and control method for PAHs-O3 compound pollution in coffee-growing areas according to claim 3, characterized in that, Calculate the amount of BaP-Ozonide produced by the reaction of gaseous BaP and particulate BaP with O3, including: The amount of BaP-Ozonide generated from gaseous BaP was calculated based on gas-phase reaction kinetic equations. : In the formula, The rate constant for the O3 reaction is [ BaP g [ represents the concentration of gaseous BaP,] O [3] represents the O3 concentration, and t represents time; The amount of BaP-Ozonide generated from particulate BaP was calculated based on the particulate phase oxidation equation. : In the formula, [ BaP p [This represents the concentration of particulate BaP.] k surf The oxidation rate coefficient of particulate matter surface. S This is the reaction surface area parameter.

5. The intelligent prediction and control method for PAHs-O3 compound pollution in coffee-growing areas according to claim 4, characterized in that, The permeation of BaP-Ozonide by coffee leaves and the enrichment of BaP and BaP-Ozonide by coffee pods were calculated, including: Calculate the flux of BaP-Ozonide permeating the cuticle of coffee leaves and predict the content of BaP-Ozonide in the leaves over the next 72 hours. : In the formula, A leaf Leaf area P c For the stratum corneum penetration efficiency, The O3 inhibition coefficient is... BaP-Ozonide content in leaves; Calculate the enrichment of BaP in coffee bean pods. BaP ] pod : In the formula, k met This refers to the plant's metabolic rate. Lipid The percentage represents the lipid content of coffee beans. e It is a natural constant; Calculate the enrichment amount of BaP-Ozonide in coffee bean pods. BaP - Ozonide ] pod : 。 6. The intelligent prediction and control method for PAHs-O3 compound pollution in coffee-growing areas according to claim 4, characterized in that, Based on the calculated enrichment level, the formula for calculating ΔILCR using the lifetime carcinogenic risk model is as follows: In the formula, IR For daily coffee intake, EF For exposure frequency, ED For exposure years, CSF It is the oral carcinogenicity slope factor of benzo[a]pyrene. BW The average weight of consumers.

7. The intelligent prediction and control method for PAHs-O3 compound pollution in coffee-growing areas according to claim 1, characterized in that, After acquiring real-time or historical multi-source data of the target planting area, the method further includes: Obtain historical environmental data and corresponding measured values ​​of BaP concentration in coffee beans; Define the BaP oxidation rate coefficient on particulate surfaces. k surf Plant metabolic rate k met stratum corneum penetration efficiency P c O3 inhibition coefficient Parameter space; Using the Bayesian optimization algorithm, with the historical environmental data as input and the measured values ​​as the objective, iterative optimization is performed in the parameter space to find the optimal parameter combination θ* that minimizes the prediction model error; The enrichment amount and ΔILCR are calculated based on the optimal parameter combination θ*.

8. A smart prediction and control device for PAHs-O3 complex pollution in coffee-growing areas, characterized in that, The device includes: The data acquisition module is configured to acquire real-time or historical multi-source data of the target planting area, including meteorological data, atmospheric environmental data and plant physiological parameters. The parameter prediction module is configured to dynamically calculate the enrichment of benzo[a]pyrene BaP and its oxidation product BaP-Ozonide in coffee leaves and coffee pods based on the multi-source data by using a prediction model that integrates atmospheric chemical mechanisms and plant metabolic dynamics, and to quantify the incremental carcinogenic risk ΔILCR caused by the enrichment of the oxidation product. The decision support module is configured to generate and output decision support information, including pollution warnings, harvesting timing suggestions, and control measures, based on the enrichment amount and ΔILCR.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes the computer execution instructions stored in the memory to implement the intelligent prediction and control method for PAHs-O3 compound pollution in coffee growing areas as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the intelligent prediction and control method for PAHs-O3 compound pollution in coffee-growing areas as described in any one of claims 1-7.

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