Method, system and equipment for predicting quarterly discharge amount of perfluorononanoic acid in tail water of sewage plant

Abstract

The invention provides a sewage plant tail water perfluorononanoic acid quarterly discharge prediction method, system and equipment, and the method comprises the following steps: obtaining basic data, the basic data including the monitoring concentration of perfluorononanoic acid in tail water of a town sewage treatment plant and the daily sewage treatment capability data of a town resident sewage treatment plant; calculating the monthly discharge amount of perfluorononanoic acid in the tail water of the urban sewage treatment plant based on the basic data; performing correlation fitting on the basis of the monthly discharge amount of perfluorononanoic acid in the tail water of the urban sewage treatment plant and target statistical data to obtain a fitting relationship; and constructing a prediction model of the target statistical data, and predicting the monthly discharge amount of perfluorononanoic acid in the tail water of the urban sewage treatment plant by combining the fitting relationship. According to the method, the system and the equipment for predicting the quarterly discharge amount of the perfluorononanoic acid in the tail water of the sewage plant, optimization suggestions can be provided for a sewage treatment process and front-end management and control measures under the condition of ensuring that the concentration of the perfluorononanoic acid in the tail water of the sewage plant reaches the standard, so that the environmental restoration efficiency is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of environmental engineering and data processing, and in particular relates to a method, system and equipment for predicting the quarterly discharge of perfluorononanoic acid in tail water of a sewage plant. Background Art

[0002] The current sewage treatment process in urban sewage treatment plants is difficult to completely remove perfluorinated and polyfluoroalkyl compounds. Due to the transformation of precursor substances, sludge desorption, treatment process limitations and the influence of industrial wastewater, the effluent concentration of sewage treatment plants may even be higher than the inlet concentration. Therefore, the tailwater of urban sewage treatment plants is a potential source of pollution of perfluorinated and polyfluoroalkyl compounds in urban receiving water bodies. By predicting the quarterly emissions of perfluorinated and polyfluoroalkyl compounds in the tailwater of urban sewage treatment plants, its distribution and degree of pollution in the natural environment can be more accurately analyzed, so as to take targeted control measures. It is also beneficial for scientists and environmental managers to understand the migration, transformation and bioaccumulation process of perfluorinated and polyfluoroalkyl compounds in ecosystems, so as to propose more effective technical solutions to protect ecological balance and biodiversity.

[0003] Existing prediction methods for the discharge of pollutants from wastewater treatment plant tailwater include prediction methods based on monitoring and models, prediction methods based on process simulation, prediction methods based on emission factors and material balance, prediction methods based on environmental exposure assessment, and prediction methods based on machine learning. These methods may require the collection of a large amount of experimental and monitoring data, or require full familiarity with the wastewater treatment process, or require the analysis of a variety of environmental and human health variables. They usually rely on a large amount of data resources and have a short prediction period. Considering the difficulty of detecting perfluorinated and polyfluoroalkyl compounds, high performance liquid chromatography, gas chromatography, capillary electrophoresis and other methods are usually required for detection, which have high requirements for laboratory environment. Usually, the monitoring data of various process links and tailwater of sewage treatment plants are relatively missing, so it is difficult to predict emissions through traditional prediction methods. At present, there is a lack of methods and systems that rely on a small number of variables and make long-term emission predictions. Summary of the invention

[0004] In view of the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a method, system and equipment for predicting the quarterly discharge of perfluorononanoic acid in the tail water of a sewage plant, so as to solve the above-mentioned problems.

[0005] In a first aspect, the present invention provides a method for predicting the quarterly discharge of perfluorononanoic acid in tail water of a sewage plant, the method comprising the following steps:

[0006] Obtaining basic data, including the monitoring concentration of perfluorononanoic acid in tail water of urban sewage treatment plants and the daily sewage treatment capacity data of urban residential sewage treatment plants;

[0007] Calculate the quarterly discharge of perfluorononanoic acid in tail water of urban sewage treatment plants based on the basic data;

[0008] A fitting relationship is obtained by performing correlation fitting based on the quarterly perfluorononanoic acid discharge in the tail water of the urban sewage treatment plant and the target statistical data;

[0009] A prediction model for target statistical data is constructed, and the quarterly emission of perfluorononanoic acid in tail water of urban sewage treatment plants is predicted in combination with the fitting relationship.

[0010] In a possible implementation of the present application, the obtaining of basic data specifically includes:

[0011] Collect the monitoring concentration of perfluorononanoic acid in the tail water of urban sewage treatment plants based on preset conditions, wherein the preset conditions at least include quarterly and provincial divisions;

[0012] Collecting daily sewage treatment capacity data of urban sewage treatment plants based on the towns in the preset conditions;

[0013] The basic data are obtained based on the monitoring concentration of perfluorononanoic acid in the effluent of urban sewage treatment plants and the daily sewage treatment capacity data of urban sewage treatment plants.

[0014] In a possible implementation of the present application, the method further includes: preprocessing the collected basic data, specifically including eliminating the tail water concentration of the sewage treatment plant and monitoring abnormal values.

[0015] In a possible implementation of the present application, the calculation of the quarterly discharge of perfluorononanoic acid in tail water of a municipal sewage treatment plant based on the basic data specifically includes:

[0016] Based on the monitoring concentration of perfluorononanoic acid in the tail water of urban sewage treatment plants and the daily sewage treatment capacity data of urban sewage treatment plants, a weighted calculation is performed to obtain the quarterly discharge of perfluorononanoic acid in the tail water of urban sewage treatment plants. The formula is as follows:

[0017]

[0018] Among them, D qua is the quarterly discharge of perfluorononanoic acid in the tail water of the urban sewage treatment plant, c i is the monitoring concentration of perfluorononanoic acid in the tail water of the municipal sewage treatment plant, Q i is the daily sewage treatment capacity data of the urban residential sewage treatment plant, and i represents the number of sewage treatment plants.

[0019] In a possible implementation of the present application, the quarterly discharge of perfluorononanoic acid in tail water of a municipal sewage treatment plant is calculated, which specifically includes:

[0020] The quarterly mean value of perfluorononanoic acid in tail water of urban sewage treatment plants was calculated;

[0021] Collecting urban sewage daily treatment capacity data based on the preset conditions;

[0022] Based on the quarterly average perfluorononanoic acid value of the tail water of the urban sewage treatment plant and the daily treatment capacity data of urban sewage, the quarterly perfluorononanoic acid discharge of the tail water of the urban sewage treatment plant is calculated, and the formula is as follows:

[0023]

[0024] Among them, D qua is the quarterly discharge of perfluorononanoic acid in tail water from the urban sewage treatment plant, is the quarterly average perfluorononanoic acid value of the tail water of the urban sewage treatment plant, Q qua This is the daily treatment capacity data of urban sewage.

[0025] In a possible implementation of the present application, the fitting relationship is obtained by performing correlation fitting based on the quarterly perfluorononanoic acid discharge in the tail water of the urban sewage treatment plant and the target statistical data, specifically including:

[0026] Collecting target statistical data based on the preset conditions, wherein the target statistical data includes GDP data;

[0027] Based on the preset fitting formula, a logarithmic fitting relationship is established between the quarterly perfluorononanoic acid discharge in the tail water of the urban sewage treatment plant and the target statistical data, and the formula is as follows:

[0028] D qua =α·In(GDP qua )+b;

[0029] Among them, D qua is the quarterly discharge of perfluorononanoic acid in tail water of the urban sewage treatment plant, GDP qua is the target statistical data, a and b are fitting parameter factors.

[0030] In a possible implementation of the present application, the prediction model of the target statistical data is constructed, and the quarterly emission of perfluorononanoic acid in the tail water of the urban sewage treatment plant is predicted in combination with the fitting relationship, specifically including:

[0031] Construct a prediction model for target statistical data, the formula is as follows:

[0032] SARIMA(p,d,q,)(sp,sd,sq);

[0033] Among them, p is the order of the non-seasonal autoregressive term, d is the order of the non-seasonal difference, q represents the order of the non-seasonal moving average term, sp is the order of the seasonal autoregressive term, sd is the order of the seasonal autoregressive difference, and sq is the order of the seasonal moving average term;

[0034] Based on the fitting relationship and the target statistical data prediction model, the quarterly emission of perfluorononanoic acid in the tail water of urban sewage treatment plants is obtained, and the formula is as follows:

[0035] D f =a·ln(SARIMA(p,d,q)(sp,sd,sq)+b;

[0036] Among them, D f is the predicted quarterly emission of perfluorononanoic acid in the tail water of urban sewage treatment plants, p is the order of the non-seasonal autoregressive term, d is the order of the non-seasonal difference, q represents the order of the non-seasonal moving average term, sp is the order of the seasonal autoregressive term, sd is the order of the seasonal autoregressive difference, sq is the order of the seasonal moving average term, and a and b are fitting parameter factors.

[0037] In a second aspect, the present invention provides a system for predicting the quarterly discharge of perfluorononanoic acid in tail water of a sewage plant, the system comprising:

[0038] An acquisition module, used to acquire basic data, the basic data including the monitoring concentration of perfluorononanoic acid in tail water of urban sewage treatment plants and the daily sewage treatment capacity data of urban residential sewage treatment plants;

[0039] A calculation module, used for calculating the quarterly discharge of perfluorononanoic acid in tail water of urban sewage treatment plants based on the basic data;

[0040] A fitting module, used for performing correlation fitting based on the quarterly perfluorononanoic acid discharge in tail water of the urban sewage treatment plant and target statistical data to obtain a fitting relationship;

[0041] The prediction module is used to construct a prediction model for target statistical data, and to predict the quarterly emission of perfluorononanoic acid in the tail water of urban sewage treatment plants in combination with the fitting relationship.

[0042] In a third aspect, the present invention provides an electronic device, the electronic device comprising: a processor and a memory;

[0043] The memory is used to store computer programs;

[0044] The processor is used to execute the computer program stored in the memory so that the electronic device executes the above-mentioned method for predicting the quarterly emission of perfluorononanoic acid in the tail water of a sewage plant.

[0045] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by an electronic device, implements the above-mentioned method for predicting the quarterly emission of perfluorononanoic acid in the tail water of a sewage plant.

[0046] As described above, the method, system and equipment for predicting the quarterly emission of perfluorononanoic acid in tail water of sewage treatment plants described in the present invention utilize the strong correlation between the emission of perfluorononanoic acid in tail water of sewage treatment plants and GDP to establish a perfluorononanoic acid emission prediction model. When it is necessary to predict the long-term change of perfluorononanoic acid emission in tail water of sewage treatment plants in the whole province / autonomous region / municipality directly under the central government, a small amount of sewage treatment plant tail water monitoring data is collected, and a logarithmic relationship is established with the GDP of the province / autonomous region / municipality directly under the central government. Then, based on the GDP prediction model, a long-term prediction of perfluorononanoic acid emission can be achieved, wherein the prediction result is relatively objective, accurate and fast; compared with the monitoring method used in the prior art, the perfluorononanoic acid emission prediction model is more accurate and faster. The prediction methods based on the prediction method based on the model, the prediction method based on process simulation, the prediction method based on emission factors and material balance, the prediction method based on environmental exposure assessment and the prediction method based on machine learning can achieve long-term prediction with a small amount of data, and effectively avoid the difficulties in long-term and high-frequency prediction of perfluorononanoic acid in sewage plant effluent caused by the need for a large amount of monitoring data, reliance on subjective consciousness, excessive professional input, etc., and provide technical support for the decision-making of environmental managers. In addition, while ensuring that the concentration of perfluorononanoic acid in the effluent meets the standard, it can intelligently provide optimization suggestions for the control measures of various provinces / autonomous regions / municipalities, thereby improving the efficiency of environmental remediation. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 Shown is a schematic diagram of a scene of an electronic device in an embodiment of the present invention;

[0048] Figure 2 Shown is a flow chart of a method for predicting the quarterly discharge of perfluorononanoic acid in tail water of a sewage plant according to an embodiment of the present invention;

[0049] Figure 3 It is a schematic diagram showing the calculation of the quarterly perfluorononanoic acid discharge in tail water of a municipal sewage treatment plant in one embodiment of the method for predicting the quarterly perfluorononanoic acid discharge in tail water of a sewage treatment plant of the present invention;

[0050] Figure 4 It is a schematic diagram showing the correlation fitting calculation of the method for predicting the quarterly emission of perfluorononanoic acid in tail water of a sewage plant in one embodiment of the present invention;

[0051] Figure 5 It is a schematic diagram showing GDP prediction of a method for predicting quarterly perfluorononanoic acid discharge in tail water of a sewage plant according to one embodiment of the present invention;

[0052] Figure 6 Shown is a schematic structural diagram of a system for predicting the quarterly discharge of perfluorononanoic acid in tail water of a sewage plant according to an embodiment of the present invention;

[0053] Figure 7 It is a schematic structural diagram of an electronic device of the present invention in one embodiment. DETAILED DESCRIPTION

[0054] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0055] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0056] The following embodiments of the present invention provide a method for predicting the quarterly discharge of perfluorononanoic acid in tail water of a sewage plant, which can be applied to Figure 1 The electronic device shown. The electronic device described in the present invention may include a mobile phone 11 with a wireless charging function, a tablet computer 12, a laptop computer 13, a wearable device, a vehicle-mounted device, an augmented reality (AR) / virtual reality (VR) device, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), etc. The embodiment of the present invention does not impose any restrictions on the specific type of the electronic device.

[0057] For example, the electronic device may be a station (STAION, ST) in a WLAN with a wireless charging function, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with a wireless charging function, a computing device or other processing device, a computer, a laptop computer, a handheld communication device, a handheld computing device, and / or other devices for communicating on a wireless system and a next generation communication system, such as a mobile terminal in a 5G network, a mobile terminal in a future evolved Public Land Mobile Network (PLMN), or a mobile terminal in a future evolved Non-terrestrial Network (NTN), etc.

[0058] Perfluorononanoic acid is a typical perfluorinated and polyfluoroalkyl compound, which can usually be used as a drug carrier to assist in targeted drug therapy. In addition, due to its extremely low surface tension and chemical stability, it can also be used as an anti-adhesive agent and lubricant. Perfluorononanoic acid has strong persistence and bioaccumulation, which may cause growth inhibition, reproductive disorders and behavioral changes in aquatic organisms. For human health, perfluorononanoic acid may cause great harm to the stomach, intestines, skin, etc., and may also cause drug resistance and inhibit the action of drugs. Considering its high ecological risk, it is necessary to accurately and widely predict the quarterly discharge of perfluorononanoic acid in the tail water of urban sewage treatment plants, and deploy corresponding control measures based on the prediction results.

[0059] In order to overcome the above-mentioned defects, the purpose of the present invention is to provide a method for urban sewage treatment plants to make a large-scale prediction of the quarterly discharge of perfluorononanoic acid in tail water, and adjust the corresponding perfluorononanoic acid pollution control measures according to the prediction of the quarterly discharge. To achieve the above-mentioned purpose, the present invention is based on the GDP prediction model and the nonlinear fitting method for predicting the effluent discharge of sewage treatment plants. First, the monitoring concentration data of perfluorononanoic acid in the tail water of urban sewage treatment plants and the corresponding daily sewage treatment capacity are collected by quarter and by province / autonomous region / municipality, and the quarterly discharge of perfluorononanoic acid in the tail water of urban sewage treatment plants is calculated. Then, the relationship between the quarterly discharge of perfluorononanoic acid in the tail water of urban sewage treatment plants and the GDP of provinces / autonomous regions / municipalities is established, and the GDP prediction model of provinces / autonomous regions / municipalities is established. Finally, the quarterly discharge of perfluorononanoic acid in the tail water of urban sewage treatment plants in future provinces / autonomous regions / municipalities is predicted.

[0060] The invention relates to a large-scale long-term prediction method for the quarterly perfluorononanoic acid emissions from tail water of urban sewage treatment plants across the country. The invention utilizes the strong correlation between the perfluorononanoic acid emissions from tail water of sewage treatment plants and GDP to establish a perfluorononanoic acid emissions prediction model. When it is necessary to predict the long-term changes in the perfluorononanoic acid emissions from tail water of sewage treatment plants across the province / autonomous region / municipality directly under the central government, a small amount of tail water monitoring data of sewage treatment plants is collected, a logarithmic relationship is established with the GDP of the province / autonomous region / municipality directly under the central government, and then the long-term prediction of perfluorononanoic acid emissions can be achieved based on the GDP prediction model. The prediction results are relatively objective, accurate and fast. Compared with the conventional technology using monitoring data, the perfluorononanoic acid emissions are more accurate and faster. The prediction methods based on the prediction method based on the model, the prediction method based on process simulation, the prediction method based on emission factors and material balance, the prediction method based on environmental exposure assessment and the prediction method based on machine learning can achieve long-term prediction with a small amount of data, and effectively avoid the difficulties in long-term and high-frequency prediction of perfluorononanoic acid in sewage plant effluent caused by the need for a large amount of monitoring data, reliance on subjective consciousness, excessive professional input, etc., and provide technical support for the decision-making of environmental managers. In addition, it can also intelligently provide optimization suggestions for the control measures of various provinces / autonomous regions / municipalities while ensuring that the perfluorononanoic acid concentration in the effluent meets the standard, thereby improving the efficiency of environmental remediation.

[0061] The technical solutions in the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0062] Specifically, see Figure 2 In one embodiment of the invention, the method for predicting the quarterly discharge of perfluorononanoic acid in tail water of a sewage plant of the present invention comprises the following steps:

[0063] Step S202, obtaining basic data, wherein the basic data includes the monitoring concentration of perfluorononanoic acid in tail water of urban sewage treatment plants and the daily sewage treatment capacity data of urban sewage treatment plants;

[0064] Step S204, calculating the quarterly discharge of perfluorononanoic acid in tail water of urban sewage treatment plants based on the basic data;

[0065] Step S206, performing correlation fitting based on the quarterly perfluorononanoic acid discharge in tail water of the urban sewage treatment plant and target statistical data to obtain a fitting relationship;

[0066] Step S208, constructing a target statistical data prediction model, and combining the fitting relationship to predict the quarterly emission of perfluorononanoic acid in the tail water of urban sewage treatment plants.

[0067] It should be noted that in this embodiment, the basic data must first be obtained, that is, the monitoring concentration of perfluorononanoic acid in the effluent of urban sewage treatment plants and the daily sewage treatment capacity data of urban sewage treatment plants, so as to calculate the quarterly discharge of perfluorononanoic acid in the effluent of urban sewage treatment plants based on the basic data.

[0068] Specifically, obtaining basic data includes: collecting the monitoring concentration of perfluorononanoic acid in the effluent of urban sewage treatment plants based on preset conditions, wherein the preset conditions at least include quarterly and provincial divisions, wherein the provincial divisions specifically include provinces / autonomous regions / municipalities directly under the central government; collecting the daily sewage treatment capacity data of urban sewage treatment plants based on the towns in the preset conditions; and obtaining the basic data based on the monitoring concentration of perfluorononanoic acid in the effluent of urban sewage treatment plants and the daily sewage treatment capacity data of urban sewage treatment plants.

[0069] It should be noted that, in this embodiment, Figure 3 As shown, it is a schematic diagram of the calculation of the quarterly emission of perfluorononanoic acid in the tail water of urban sewage treatment plants. After obtaining the basic data, the method also includes preprocessing the collected basic data, specifically including eliminating the tail water concentration of the sewage treatment plant and monitoring abnormal values.

[0070] Furthermore, if Figure 3 As shown, there are two ways to calculate the quarterly emission of perfluorononanoic acid in the tail water of urban sewage treatment plants. One is to obtain the quarterly emission of perfluorononanoic acid in the tail water of urban sewage treatment plants by weighted calculation based on the monitoring concentration of perfluorononanoic acid in the tail water of urban residential sewage treatment plants and the daily sewage treatment capacity data of urban residential sewage treatment plants. The formula is as follows:

[0071]

[0072] Among them, D qua is the quarterly discharge of perfluorononanoic acid in the tail water of the urban sewage treatment plant, c i is the monitoring concentration of perfluorononanoic acid in the tail water of the municipal sewage treatment plant, Q i is the daily sewage treatment capacity data of the urban residential sewage treatment plant, i represents the number of sewage treatment plants; and, the second is to calculate the quarterly perfluorononanoic acid mean value of the tail water of the urban sewage treatment plant, and then collect the daily treatment capacity data of urban sewage based on the preset conditions, and finally calculate the quarterly perfluorononanoic acid discharge of the tail water of the urban sewage treatment plant based on the quarterly perfluorononanoic acid mean value of the tail water of the urban sewage treatment plant and the daily treatment capacity data of urban sewage, the formula is as follows:

[0073]

[0074] Among them, D qua is the quarterly discharge of perfluorononanoic acid in tail water from the urban sewage treatment plant, is the quarterly average perfluorononanoic acid value of the tail water of the urban sewage treatment plant, Q qua The daily treatment capacity data of urban sewage is shown in Figure 2, among which the first method is more accurate than the second method.

[0075] Further, in one embodiment of the invention, the correlation fitting based on the quarterly perfluorononanoic acid discharge of the tail water of the urban sewage treatment plant and the target statistical data to obtain a fitting relationship specifically includes: collecting the target statistical data based on the preset conditions, and then establishing a logarithmic fitting relationship between the quarterly perfluorononanoic acid discharge of the tail water of the urban sewage treatment plant and the target statistical data based on a preset fitting formula, and the formula is as follows: D qua =a·ln(GDP qua )+b; where D qua is the quarterly discharge of perfluorononanoic acid in tail water of the urban sewage treatment plant, GDP qua is the target statistical data, and accordingly, the target statistical data includes GDP data, and a and b are fitting parameter factors.

[0076] It should be noted that, in this embodiment, after obtaining the quarterly perfluorononanoic acid discharge of the tail water of the urban sewage treatment plant, it is necessary to perform correlation fitting with the target statistical data to obtain a fitting relationship, specifically, to establish a logarithmic fitting relationship. In order to ensure the accuracy of the fitting, at least 10 groups of quarterly perfluorononanoic acid discharges of tail water of urban sewage treatment plants and target statistical data (GDP data) of provinces / autonomous regions / municipalities directly under the Central Government are collected. According to the quarterly perfluorononanoic acid discharges D of sewage treatment plants in effluent by quarter and province / autonomous region / municipality directly under the Central Government, the target statistical data (GDP data) of the tail water of urban sewage treatment plants in 10 groups are collected. qua and GDP qua Calculate the parameter factors a and b in the logarithmic relationship and determine D qua and GDP qua The nonlinear fitting formula is as follows: Figure 4 As shown, in one embodiment of the invention, the correlation fitting calculation formula between the quarterly perfluorononanoic acid discharge of the tail water of the urban sewage treatment plant and the GDP data is specifically D qua =1.5641×ln(GDP qua )-10.729, accordingly, the fitting parameter factor a is "1.5641" and b is "10.729". Further, refer to Figure 4 , linear fit R 2 The higher the value, the better the fit. The linear fit R 2 The value is "0.9751", indicating that the correlation fitting degree in this embodiment is very good.

[0077] Further, in one embodiment of the invention, the target statistical data prediction model is constructed, and the quarterly emission of perfluorononanoic acid in the tail water of urban sewage treatment plants is predicted in combination with the fitting relationship, specifically including: constructing a prediction model for target statistical data, the formula is as follows: SARIMA (p, d, q,) (sp, sd, sp); wherein p is the order of the non-seasonal autoregressive term, d is the non-seasonal difference order, q represents the non-seasonal moving average term order, sp is the seasonal autoregressive order, sd is the seasonal autoregressive difference order, and sq is the seasonal moving average term order; based on the fitting relationship combined with the target statistical data prediction model, the quarterly emission of perfluorononanoic acid in the tail water of urban sewage treatment plants is obtained, the formula is as follows:

[0078] D f =a·ln(SARIMA(p,d,q)(sp,sd,sq)+b;

[0079] Among them, D f is the predicted quarterly emission of perfluorononanoic acid in the tail water of urban sewage treatment plants, p is the order of the non-seasonal autoregressive term, d is the order of the non-seasonal difference, q represents the order of the non-seasonal moving average term, sp is the order of the seasonal autoregressive term, sd is the order of the seasonal autoregressive difference, sq is the order of the seasonal moving average term, and a and b are fitting parameter factors.

[0080] It should be noted that the SARIMA model is an analytical method for seasonal time series prediction. It is based on the ARIMA model and adds consideration of seasonal factors, so that data with periodic changes can be predicted more accurately. The full name of the SARIMA model is the Seasonal Autoregressive Integrated Moving Average model. It is applied in this embodiment, so the specific details are not described. Specifically, the SARIMA model includes a time variable, so the quarterly discharge of perfluorononanoic acid in the tail water of urban sewage treatment plants in the future can be predicted, such as Figure 5 As shown in the figure, a forecast diagram for target statistical data (GDP data) is shown, with the specific forecast period being "2030", where: Figure 5 The "UCL" in the table stands for Upper Control Limit. Figure 5 The "LCL" in it stands for lower control limit, specifically Lower Control Limit.

[0081] Furthermore, based on the fitting relationship and the target statistical data prediction model, the quarterly emission of perfluorononanoic acid in the tail water of urban sewage treatment plants is obtained, and the specific prediction year is "2030".f The prediction formula is used to calculate the emission of perfluorononanoic acid, thereby realizing the prediction of the emission of perfluorononanoic acid. In the actual calculation, the predicted quarterly emission of perfluorononanoic acid in the tail water of urban sewage treatment plants is D f The specific parameter values ​​in are: a is "1.5641", b is "10.729", (p, d, q) is "(1, 1, 0)", and (sp, sd, sq) is "(0, 1, 2)". Accordingly, the specific calculation formula is: f =1.5641·ln(SARIMA(1,1,0)(0,1,2)+10.729, and the emission of perfluorononanoic acid in 2030 is predicted to be 4.41 kg.

[0082] An embodiment of the present application also provides a system for predicting the quarterly emissions of perfluorononanoic acid from sewage plant tail water. The system for predicting the quarterly emissions of perfluorononanoic acid from sewage plant tail water can implement the method for predicting the quarterly emissions of perfluorononanoic acid from sewage plant tail water described in the present application. However, the device for implementing the method for predicting the quarterly emissions of perfluorononanoic acid from sewage plant tail water described in the present application includes but is not limited to the structure of the system for predicting the quarterly emissions of perfluorononanoic acid from sewage plant tail water listed in the present embodiment. All structural deformations and replacements of the prior art made according to the principles of the present application are included in the protection scope of the present application.

[0083] See also Figure 6 In one embodiment, the present embodiment provides a system 60 for predicting the quarterly emission of perfluorononanoic acid in tail water of a sewage plant, the system comprising:

[0084] An acquisition module 61 is used to acquire basic data, wherein the basic data includes the monitoring concentration of perfluorononanoic acid in tail water of urban sewage treatment plants and the daily sewage treatment capacity data of urban residential sewage treatment plants;

[0085] A calculation module 62, for calculating the quarterly discharge of perfluorononanoic acid in tail water of urban sewage treatment plants based on the basic data;

[0086] A fitting module 63 is used to obtain a fitting relationship by performing correlation fitting based on the quarterly perfluorononanoic acid discharge in tail water of the urban sewage treatment plant and target statistical data;

[0087] The prediction module 64 is used to construct a prediction model for target statistical data, and predict the quarterly emission of perfluorononanoic acid in the tail water of urban sewage treatment plants in combination with the fitting relationship.

[0088] Since the specific implementation of this embodiment corresponds to the aforementioned method embodiment, the same details will not be repeated here, and those skilled in the art should also understand that Figure 6The division of the modules in the embodiment is merely a division of logical functions, and in actual implementation, all or part of them can be integrated into one or more physical entities, and these modules can be implemented entirely in the form of software called by processing elements, or entirely in the form of hardware, or some modules can be implemented in the form of software called by processing elements, and some modules can be implemented in the form of hardware.

[0089] In the several embodiments provided by the present invention, it should be understood that the disclosed system, device or method can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules / units is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules or units, which can be electrical, mechanical or other forms.

[0090] The modules / units described as separate components may or may not be physically separated, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention. For example, the functional modules / units in the various embodiments of the present invention may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0091] Those of ordinary skill in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0092] The embodiment of the present invention also provides a computer-readable storage medium. A person of ordinary skill in the art can understand that all or part of the steps in the method for implementing the above embodiment can be completed by instructing the processor through a program, and the program can be stored in a computer-readable storage medium, and the storage medium is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state hard disk, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium (e.g., a solid-state disk (SSD)), etc.

[0093] An embodiment of the present invention further provides an electronic device, which includes a processor and a memory.

[0094] The memory is used to store computer programs.

[0095] The memory includes: ROM, RAM, disk, USB flash drive, memory card or CD and other media that can store program codes.

[0096] The processor is connected to the memory and is used to execute the computer program stored in the memory so that the electronic device executes the above-mentioned method for predicting the quarterly emission of perfluorononanoic acid in the tail water of a sewage plant.

[0097] Preferably, 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 gates or transistor logic devices, discrete hardware components.

[0098] like Figure 7As shown, the electronic device of the present invention is in the form of a general computing device. The components of the electronic device may include but are not limited to: one or more processors or processing units 71, a memory 72, and a bus 73 connecting different system components (including the memory 72 and the processing unit 71).

[0099] Bus 73 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0100] Electronic devices typically include a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, removable and non-removable media.

[0101] The memory 72 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 721 and / or cache memory 722. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 723 may be used to read and write non-removable, non-volatile magnetic media ( Figure 7 not shown, usually called a "hard drive"). Although Figure 7 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 73 via one or more data medium interfaces. The memory 72 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0102] A program / utility 724 having a set (at least one) of program modules 7241 may be stored, for example, in the memory 72, such program modules 7241 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 7241 generally perform the functions and / or methods of the embodiments described herein.

[0103] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, displays, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via input / output (I / O) interface 74. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapter 75. Figure 7 As shown, the network adapter 75 communicates with other modules of the electronic device via the bus 73. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0104] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A method for predicting the quarterly discharge of perfluorononanoic acid from tail water of a sewage plant, characterized in that: include: Obtaining basic data, including the monitoring concentration of perfluorononanoic acid in tail water of urban sewage treatment plants and the daily sewage treatment capacity data of urban residential sewage treatment plants; Calculate the quarterly discharge of perfluorononanoic acid in tail water of urban sewage treatment plants based on the basic data; A fitting relationship is obtained by performing correlation fitting based on the quarterly perfluorononanoic acid discharge in the tail water of the urban sewage treatment plant and the target statistical data; A prediction model for target statistical data is constructed, and the quarterly emission of perfluorononanoic acid in tail water of urban sewage treatment plants is predicted in combination with the fitting relationship.

2. The method for predicting the quarterly discharge of perfluorononanoic acid in tail water of a sewage treatment plant according to claim 1, characterized in that: The obtaining of basic data specifically includes: Collect the monitoring concentration of perfluorononanoic acid in tail water of urban sewage treatment plants based on preset conditions, wherein the preset conditions at least include quarterly and provincial divisions; Collecting daily sewage treatment capacity data of urban sewage treatment plants based on the provincial divisions in the preset conditions; The basic data are obtained based on the monitoring concentration of perfluorononanoic acid in the tail water of urban sewage treatment plants and the daily sewage treatment capacity data of urban residential sewage treatment plants.

3. The method for predicting the quarterly discharge of perfluorononanoic acid in tail water of a sewage plant according to claim 2, characterized in that: The method also includes: preprocessing the collected basic data, specifically including eliminating the tail water concentration of the sewage treatment plant and monitoring abnormal values.

4. The method for predicting the quarterly discharge of perfluorononanoic acid in tail water of a sewage plant according to claim 2, characterized in that: The calculation of the quarterly discharge of perfluorononanoic acid in tail water of urban sewage treatment plants based on the basic data specifically includes: Based on the monitoring concentration of perfluorononanoic acid in the tail water of urban sewage treatment plants and the daily sewage treatment capacity data of urban residential sewage treatment plants, a weighted calculation is performed to obtain the quarterly discharge of perfluorononanoic acid in the tail water of the urban sewage treatment plants. The formula is as follows: Among them, D qua is the quarterly discharge of perfluorononanoic acid in the tail water of the urban sewage treatment plant, c i is the monitoring concentration of perfluorononanoic acid in the tail water of the municipal sewage treatment plant, Q i is the daily sewage treatment capacity data of the urban residential sewage treatment plant, and i represents the number of sewage treatment plants.

5. The method for predicting the quarterly discharge of perfluorononanoic acid in tail water of a sewage plant according to claim 4, characterized in that: Calculate the quarterly discharge of perfluorononanoic acid in the tail water of urban sewage treatment plants, including: The quarterly mean value of perfluorononanoic acid in tail water of urban sewage treatment plants was calculated; Collecting urban sewage daily treatment capacity data based on the preset conditions; Based on the quarterly average perfluorononanoic acid value of the tail water of the urban sewage treatment plant and the daily treatment capacity data of urban sewage, the quarterly perfluorononanoic acid discharge of the tail water of the urban sewage treatment plant is calculated, and the formula is as follows: Among them, D qua is the quarterly discharge of perfluorononanoic acid in tail water from the urban sewage treatment plant, is the quarterly average perfluorononanoic acid value of the tail water of the urban sewage treatment plant, Q qua This is the daily treatment capacity data of urban sewage.

6. The method for predicting the quarterly discharge of perfluorononanoic acid in tail water of a sewage plant according to claim 4, characterized in that: The fitting relationship obtained by performing correlation fitting based on the quarterly perfluorononanoic acid discharge in the tail water of the urban sewage treatment plant and the target statistical data specifically includes: Collecting target statistical data based on the preset conditions, wherein the target statistical data includes GDP data; Based on the preset fitting formula, a logarithmic fitting relationship is established between the quarterly perfluorononanoic acid discharge in the tail water of the urban sewage treatment plant and the target statistical data, and the formula is as follows: D qua =a·ln(GDP qua )+b; Among them, D qua is the quarterly discharge of perfluorononanoic acid in tail water of the urban sewage treatment plant, GDP qua is the target statistical data, a and b are fitting parameter factors.

7. The method for predicting the quarterly discharge of perfluorononanoic acid in tail water of a sewage plant according to claim 6, characterized in that: The prediction model of the target statistical data is constructed, and the quarterly emission of perfluorononanoic acid in tail water of urban sewage treatment plants is predicted in combination with the fitting relationship, specifically including: Construct a prediction model for target statistical data, the formula is as follows: SARIMA(p,d,q,)(sp,sd,sq); Among them, p is the order of the non-seasonal autoregressive term, d is the order of the non-seasonal difference, q represents the order of the non-seasonal moving average term, sp is the order of the seasonal autoregressive term, sd is the order of the seasonal autoregressive difference, and sq is the order of the seasonal moving average term; Based on the fitting relationship and the target statistical data prediction model, the quarterly emission of perfluorononanoic acid in the tail water of urban sewage treatment plants is obtained, and the formula is as follows: D f =a·ln(SARIMA(p,d,q)(sp,sd,sq)+b; Among them, D f is the predicted quarterly emission of perfluorononanoic acid in the tail water of urban sewage treatment plants, p is the order of the non-seasonal autoregressive term, d is the order of the non-seasonal difference, q represents the order of the non-seasonal moving average term, sp is the order of the seasonal autoregressive term, sd is the order of the seasonal autoregressive difference, sq is the order of the seasonal moving average term, and a and b are fitting parameter factors.

8. A system for predicting the quarterly discharge of perfluorononanoic acid from tail water of a sewage plant, characterized in that: include: An acquisition module is used to acquire basic data, wherein the basic data includes the monitoring concentration of perfluorononanoic acid in tail water of urban sewage treatment plants and the daily sewage treatment capacity data of urban residential sewage treatment plants; A calculation module, used for calculating the quarterly discharge of perfluorononanoic acid in tail water of urban sewage treatment plants based on the basic data; A fitting module, used for performing correlation fitting based on the quarterly perfluorononanoic acid discharge in tail water of the urban sewage treatment plant and target statistical data to obtain a fitting relationship; The prediction module is used to construct a prediction model for target statistical data, and to predict the quarterly emission of perfluorononanoic acid in the tail water of urban sewage treatment plants in combination with the fitting relationship.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for predicting the quarterly emission of perfluorononanoic acid in the tail water of a sewage plant as described in any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: The electronic device comprises: a processor and a memory; wherein the memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory, so that the electronic device executes the method for predicting the quarterly emission of perfluorononanoic acid in the tail water of a sewage treatment plant as described in any one of claims 1 to 7.

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