Urban nitrogen income and expenditure prediction method and device, computer equipment and storage medium

Abstract

The invention relates to the field of data analysis, in particular to an urban nitrogen income and expenditure prediction method and device and computer equipment, and the method comprises the steps: obtaining nitrogen related data of a target region at a plurality of time points in a sample time period and a preset urban nitrogen income and expenditure quantitative model, the nitrogen related data is input into the nitrogen income and expenditure calculation module for nitrogen income and expenditure calculation to obtain nitrogen income and expenditure data, the nitrogen income and expenditure data is input into the nitrogen income and expenditure quantification module for nitrogen income and expenditure quantification to obtain nitrogen income and expenditure quantification data, the nitrogen income and expenditure quantification data serve as the urban nitrogen income and expenditure quantification result of the target area, and the nitrogen income and expenditure of the city can be quantified only through the nitrogen related data. According to the method, urban nitrogen income and expenditure quantification is realized, the method is used for training a nitrogen income and expenditure quantification prediction model, the accuracy and efficiency of nitrogen income and expenditure quantification prediction are improved, and scientific and reasonable guidance is provided for managing cyclic utilization of nitrogen.

Description

Technical Field

[0001] The present invention relates to the field of data analysis, and in particular to a method, device, computer equipment and storage medium for predicting urban nitrogen budget. Background Art

[0002] With the growth of the global population and rapid economic development, the demand for and emissions of nitrogen from agricultural production, industrial manufacturing, and urbanization have increased significantly. As one of the essential elements for life, nitrogen plays a vital role in agricultural production, industrial manufacturing, and urban ecosystems. However, excessive use and improper management of nitrogen have led to serious environmental problems, such as eutrophication of water bodies, soil acidification, and increased greenhouse gas emissions. Therefore, how to scientifically and rationally manage the recycling of nitrogen and reduce its negative impact on the environment has become an important research topic in the field of environmental science.

[0003] Managing nitrogen recycling and emissions across agricultural production, industrial manufacturing, urban ecosystems, and environmental subsystems is a complex systems project. While existing nitrogen management technologies and approaches have achieved some success in reducing its negative environmental impacts, they still face numerous shortcomings. Therefore, developing a comprehensive and systematic approach to managing nitrogen recycling and emissions is of great practical significance and practical value. Summary of the Invention

[0004] Based on this, the purpose of the present invention is to provide a method, device, computer equipment and storage medium for predicting urban nitrogen budget, which can quantify the nitrogen budget of a city only by nitrogen-related data without complex calculations, thereby realizing the quantification of urban nitrogen budget and expenditure, and training a nitrogen budget quantification prediction model, thereby improving the accuracy and efficiency of nitrogen budget quantification prediction, and providing scientific and reasonable guidance for managing the recycling of nitrogen.

[0005] In a first aspect, an embodiment of the present application provides a method for predicting urban nitrogen budget, comprising the following steps:

[0006] Obtaining nitrogen-related data for a target area at several time points within a sample period and a preset urban nitrogen budget quantification model, wherein the urban nitrogen budget quantification model includes a nitrogen budget calculation module and a nitrogen budget quantification module;

[0007] Inputting the nitrogen-related data at the plurality of time points into the nitrogen budget calculation module, performing nitrogen budget calculation according to the nitrogen-related data and a corresponding nitrogen budget calculation method, and obtaining the nitrogen budget data at the plurality of time points;

[0008] Inputting the nitrogen budget data at a plurality of time points into the nitrogen budget quantification module to quantify the nitrogen budget, thereby obtaining the nitrogen budget quantification data at a plurality of time points;

[0009] Constructing nitrogen budget triplet data at several time points, the nitrogen budget triplet data including nitrogen-related data, a corresponding nitrogen budget calculation method, and nitrogen budget data; inputting the nitrogen budget triplet data at several time points and the nitrogen budget quantification data into a preset nitrogen budget quantification prediction model for training to obtain a target nitrogen budget quantification prediction model;

[0010] According to the target nitrogen budget quantitative prediction model, the nitrogen budget quantitative data of the target area at several time points in the prediction time period are obtained.

[0011] In a second aspect, an embodiment of the present application provides a device for predicting urban nitrogen budget, comprising:

[0012] A data acquisition module, configured to obtain nitrogen-related data of a target area at several time points within a sample period and a preset urban nitrogen budget quantification model, wherein the urban nitrogen budget quantification model includes a nitrogen budget calculation module and a nitrogen budget quantification module;

[0013] a nitrogen budget processing module, configured to input the nitrogen-related data at the plurality of time points into the nitrogen budget calculation module, perform nitrogen budget calculations based on the nitrogen-related data and a corresponding nitrogen budget calculation method, and obtain nitrogen budget data at the plurality of time points;

[0014] A nitrogen budget quantification module, configured to input the nitrogen budget data at a plurality of time points into the nitrogen budget quantification module for nitrogen budget quantification, thereby obtaining the nitrogen budget quantification data at a plurality of time points;

[0015] A model training module is used to construct nitrogen budget triplet data at several time points, wherein the nitrogen budget triplet data includes nitrogen-related data, a corresponding nitrogen budget calculation method, and nitrogen budget data; input the nitrogen budget triplet data at several time points and the nitrogen budget quantification data into a preset nitrogen budget quantification prediction model for training to obtain a target nitrogen budget quantification prediction model;

[0016] The nitrogen budget prediction module is used to obtain nitrogen budget quantitative data of the target area at several time points in the prediction time period according to the target nitrogen budget quantitative prediction model.

[0017] In a third aspect, an embodiment of the present application provides a computer device comprising: a processor, a memory, and a computer program stored on the memory and executable on the processor; when the computer program is executed by the processor, the steps of the urban nitrogen budget prediction method as described in the first aspect are implemented.

[0018] In a fourth aspect, an embodiment of the present application provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the urban nitrogen budget prediction method as described in the first aspect.

[0019] In an embodiment of the present application, a method, apparatus, computer equipment, and storage medium for predicting urban nitrogen budget are provided. This method, which does not require complex calculations and can quantify a city's nitrogen budget solely based on nitrogen-related data, achieves quantification of urban nitrogen budget, and is used to train a nitrogen budget quantification prediction model. This improves the accuracy and efficiency of nitrogen budget quantification predictions and provides scientific and reasonable guidance for managing nitrogen recycling.

[0020] For better understanding and implementation, the present invention is described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flow chart of a method for predicting urban nitrogen budget provided in one embodiment of the present application;

[0022] Figure 2 A schematic diagram of the process of step S3 in the urban nitrogen budget prediction method provided in one embodiment of the present application;

[0023] Figure 3 A schematic diagram of the process of step S4 in the urban nitrogen budget prediction method provided in one embodiment of the present application;

[0024] Figure 4 Schematic diagram of the process of S43 in the urban nitrogen budget prediction method provided in one embodiment of the present application

[0025] Figure 5 This is a flow chart of S44 in the urban nitrogen budget prediction method provided in one embodiment of the present application;

[0026] Figure 6 A schematic diagram of the process of step S5 in the urban nitrogen budget prediction method provided in one embodiment of the present application;

[0027] Figure 7 A schematic diagram of the structure of an urban nitrogen budget prediction device provided in one embodiment of the present application;

[0028] Figure 8 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0029] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0030] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0031] It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words "if" / "if" as used herein may be interpreted as "at the time of" or "when" or "in response to a determination."

[0032] See also Figure 1 , Figure 1 This is a flow chart of a method for predicting urban nitrogen budget provided in one embodiment of the present application, the method comprising the following steps:

[0033] S1: Obtain nitrogen-related data for the target area at several time points within the sample period and a preset urban nitrogen budget quantification model.

[0034] The executor of the urban nitrogen budget prediction method is a prediction device of the urban nitrogen budget prediction method (hereinafter referred to as the prediction device). In an optional embodiment, the prediction device can be a computer device, a server, or a server cluster composed of multiple computer devices.

[0035] The prediction equipment obtains nitrogen-related data of the target area at several time points within the sample time period, wherein the time points are in years, and the nitrogen-related data are obtained from statistical yearbooks, including the use of nitrogen fertilizer in the cities in the target area (including the conversion of pure nitrogen fertilizer and compound fertilizer into pure nitrogen fertilizer), soybean and peanut production, cultivated land area, paddy field area, dry land area, irrigation water consumption, bamboo forest area, grassland area, aquaculture types and areas, the number of food product varieties, the number of livestock and poultry varieties, the number of major livestock and poultry raised, the amount of energy (standard coal) consumed in industrial production, the types and output of industrial nitrogen products, nitrogen oxide emissions, urban park green space area, major consumer food types, per capita industrial nitrogen consumption, permanent population, per capita daily domestic water consumption, wet garbage production, pipeline leakage rate, nitrogen content in effluent after sewage treatment, river and lake area, regional area, etc.

[0036] A prediction device and a preset urban nitrogen budget quantification model, wherein the urban nitrogen budget quantification model includes a nitrogen budget calculation module and a nitrogen budget quantification module.

[0037] S2: Inputting the nitrogen-related data at the plurality of time points into the nitrogen budget calculation module, performing nitrogen budget calculation according to the nitrogen-related data and a corresponding nitrogen budget calculation method, and obtaining the nitrogen budget data at the plurality of time points.

[0038] In this embodiment, the prediction device inputs the nitrogen-related data at the plurality of time points into the nitrogen budget calculation module, performs nitrogen budget calculations based on the nitrogen-related data and a corresponding nitrogen budget calculation method, and obtains nitrogen budget data at the plurality of time points, wherein the nitrogen income data and nitrogen expenditure data are included. The nitrogen budget data includes production nitrogen budget data, human consumption nitrogen budget data, waste disposal nitrogen budget data, and environmental nitrogen budget data.

[0039] Specifically, the nitrogen production income data include the amount of nitrogen input from agricultural production nitrogen fertilizer, the amount of biological nitrogen fixation, the amount of manure returned to the field, the amount of atmospheric nitrogen deposition on cultivated land, the amount of nitrogen input from agricultural production irrigation water, the amount of straw returned to the field, the amount of nitrogen fixation by forest and grass, the amount of atmospheric nitrogen deposition on grassland, the amount of nitrogen input from livestock feed, the amount of nitrogen fixation by aquatic products, the amount of atmospheric nitrogen deposition by aquatic products, the amount of nitrogen input from industrial production raw materials, the amount of nitrogen input from industrial production energy consumption, the amount of maintenance and fertilization of urban park green spaces, the amount of biological nitrogen fixation in urban park green spaces, the amount of fallen objects in urban park green spaces, and the amount of atmospheric nitrogen deposition in urban park green spaces.

[0040] The nitrogen input of agricultural production nitrogen fertilizer represents the amount of nitrogen fertilizer used in the application of chemical fertilizers during the crop production process, including the amount of nitrogen fertilizer converted into pure nitrogen fertilizer and the amount of nitrogen fertilizer converted into pure compound fertilizer.

[0041] The amount of biological nitrogen fixation includes symbiotic nitrogen fixation and non-symbiotic nitrogen fixation. Symbiotic nitrogen fixation refers to the formation of rhizobia in the roots of legumes, which rely on legumes to fix nitrogen in the atmosphere into Nr for use by other organisms. Non-symbiotic nitrogen fixation refers to nitrogen-fixing bacteria scattered in the soil, which can directly fix nitrogen without symbiosis with plants. The symbiotic nitrogen fixation amount = soybean and peanut yield × 5.99% + straw yield × 1.83%; the non-symbiotic nitrogen fixation amount = 15 × dryland area + 37.5 × paddy field area, wherein the paddy field area is based on the paddy field area in the statistical yearbook, and the dry field area is calculated as the total cultivated land area minus the paddy field area.

[0042] The amount of manure returned to the fields is calculated based on the number of livestock and poultry on hand at the end of the year, the livestock and poultry pollution discharge coefficient and the river discharge coefficient. The amount of manure returned to the fields = the number of livestock and poultry on hand at the end of the year × the pollution discharge coefficient × the river discharge coefficient.

[0043] The atmospheric nitrogen deposition amount on cultivated land is used to indicate the atmospheric nitrogen deposition above the cultivated land. The atmospheric nitrogen deposition amount on cultivated land=nitrogen deposition rate×cultivated land area.

[0044] The nitrogen input of agricultural production irrigation water refers to the amount of nitrogen entering farmland through crop production irrigation water, which is estimated based on the annual agricultural irrigation water volume and the average nitrogen content in the irrigation water. The nitrogen input of agricultural production irrigation water = 1.5 mgN·L-1×irrigation water volume.

[0045] The forest and grass nitrogen fixation capacity is obtained by adding the forest nitrogen fixation capacity and grassland nitrogen fixation capacity. In the forest nitrogen fixation capacity part, 5kg Nhm-2a-1 is used as the average symbiotic nitrogen fixation rate of the forest. In the grassland nitrogen fixation part, the symbiotic nitrogen fixation capacity is provided by the legumes in the grassland. The proportion of legumes in the grassland biomass is estimated to be 200-300kg N·hm-1. -2 ·a -1 , take 200-300kg N·hm -2 ·a -1 The amount of nitrogen fixed by legumes in grassland. The non-symbiotic nitrogen fixation in grassland refers to the nitrogen fixed by non-symbiotic bacteria in grassland soil, which is about 5 kg N·hm -2 ·a -1 Nitrogen fixation by forest and grass = evergreen broad-leaved forest area × (5.0 + 12.9) + bamboo forest area × (5.0 + 4.9) + grassland area × proportion of legumes × 200 + grassland area × (1 - proportion of legumes) × 5.

[0046] The atmospheric nitrogen deposition amount of grassland is used to indicate the atmospheric nitrogen deposition above the grassland. The atmospheric nitrogen deposition amount of grassland=nitrogen deposition rate×grassland area.

[0047] The amount of nitrogen put into the breeding feed represents the nitrogen content of the feed put into the breeding process. The livestock and poultry breeding part is calculated by the proportion of raw materials in the livestock and poultry breeding feed, the raw material consumption per unit of livestock and poultry products (the ratio of the amount of grain consumed by unit livestock and poultry to the output of unit livestock and poultry main products in the data) and the nitrogen content of the raw materials in the feed (as a percentage of dry matter). The amount of aquatic bait put in during the aquaculture part and the nitrogen fertilizer matched with it. The amount of aquatic bait put in is calculated by the output of nitrogen products of aquaculture animals and the recommended feed ratio. The generally recommended feed ratio is 1.8. In the calculation of the amount of nitrogen fertilizer matched with the bait, studies have shown that the input of nitrogen fertilizer is generally about 20% of the nitrogen input of the bait. The average nitrogen content of freshwater products is about 2.83%, and BRIN is calculated. crop Feed and nitrogen fertilizer values ​​in aquaculture.

[0048] The amount of nitrogen fixed in aquaculture refers to the biological nitrogen fixation in the aquaculture process. Aquaculture nitrogen fixation = aquaculture area × 0.05475 t·hm -2 ·a -1 .

[0049] The aquatic atmospheric nitrogen deposition amount is used to indicate the atmospheric nitrogen deposition above aquaculture. The aquatic atmospheric nitrogen deposition amount=nitrogen deposition rate×aquaculture area.

[0050] The nitrogen input from industrial production raw materials includes the nitrogen input from nitrogen-containing products that are input as raw materials from product outputs in other modules into the industrial production module for further manufacturing and processing without considering the input of external industrial raw materials, as well as the nitrogen income from the combustion of fossil fuels to certain sectors.

[0051] The amount of fertilizer applied to urban park green land maintenance refers to the amount of fertilizer applied during the maintenance process of urban park green land. According to existing research, the average fertilizer application rate is 300 kg N·hm -2 ·a -1 The area of ​​urban park green space is obtained from the statistical yearbook. The amount of fertilizer applied for urban park green space maintenance = area of ​​urban park green space × amount of fertilizer applied per unit area.

[0052] The biological nitrogen fixation rate of urban park green space represents the biological nitrogen fixation rate of urban park green space. The same nitrogen fixation rate as the dry land nitrogen fixation rate in the crop production module is used in the calculation, which is 15 kg N·hm -2 ·a -1 To calculate and estimate, the amount of biological nitrogen fixation in urban park green spaces = area of ​​urban park green spaces × 15.

[0053] The amount of fallen objects in urban park green spaces represents the fallen objects in urban park green spaces, such as tree branches and leaves, lawn weeds, etc., including both natural fallen objects in urban park green spaces and fallen objects due to pruning in urban park green spaces. At the same time, due to the removal of green waste, not all fallen objects will be returned to urban park green spaces. For the convenience of calculation, it is assumed that 50% of the green waste generated by fallen and pruning in urban park green spaces is returned to this module, and the remaining 50% is disposed of separately and centrally.

[0054] The atmospheric nitrogen deposition amount of the urban park green space is used to indicate the atmospheric nitrogen deposition above the urban park green space. The atmospheric nitrogen deposition amount of the urban park green space=nitrogen deposition rate×area of ​​the urban park green space.

[0055] The production nitrogen expenditure data include nitrogen expenditure for human food consumption, nitrogen consumption for livestock feed, nitrogen loss from agricultural production, nitrogen loss from forest land, denitrification of grassland, nitrogen output from livestock products, nitrogen excretion from livestock products, nitrogen loss from livestock products, nitrogen output from industrial products, industrial nitrogen loss, nitrogen expenditure in urban parks and green spaces, and nitrogen loss in urban parks and green spaces; the nitrogen output from livestock products includes the nitrogen output from products of several types of food; the nitrogen output from industrial products includes the nitrogen output from industrial nitrogen products produced from synthetic ammonia and the nitrogen output from industrial nitrogen products produced from biological raw materials.

[0056] The nitrogen expenditure of human food consumption represents the nitrogen income of human dietary consumption, including two categories: food products and livestock products (red meat and white meat).

[0057] The nitrogen consumption of the breeding feed is obtained from the livestock and poultry breeding part of the nitrogen amount of the breeding feed.

[0058] The agricultural production nitrogen loss refers to the nitrogen loss from this module during crop production, primarily nitrogen loss from fertilizers. This loss occurs through surface runoff, leaching into groundwater, denitrification, and NH3 volatilization. According to statistics and estimates, runoff accounts for 5% of nitrogen loss from fertilizers in agricultural soils, 2% from leaching into groundwater, 34% from denitrification (including N2O emissions, but its value cannot be calculated), 11% from NH3 volatilization, 13% from unknown sources, and only 35% from crop absorption. Based on the calculations of nitrogen inputs from fertilizers in agricultural production in recent years, the amount of nitrogen loss from agricultural production and the values ​​of its components can be calculated.

[0059] Since forest land is not used for grazing, that is, it does not produce forage grass, and the grassland is almost naturally grown, the discharge of livestock and poultry manure and nitrogen loss from surface runoff are not considered. The nitrogen loss from forest land mainly includes surface runoff, denitrification, and N2O emissions from the forest land.

[0060] The grassland denitrification amount is the nitrogen loss caused by N2O emissions from grassland denitrification. It requires understanding the N2O emission flux of grasslands in similar areas. The N2O emission flux can be set to 0.083 kg N ha-1 based on the N2O flux in the "Estimation of Nitrous Oxide Emissions from Forests and Grasslands in China".

[0061] The nitrogen yield of aquaculture products is obtained by accumulating the nitrogen yields of products of several food types, including red meat, white meat, eggs and milk, etc., wherein the nitrogen yield of aquaculture products is:

[0062]

[0063] Where, BROUT Meat is the nitrogen yield of aquaculture products, L i is the nitrogen output of the product of the i-th food type, m is the number of food types, p i is the calculation coefficient of the nitrogen content of the product of the i-th food type.

[0064] The aquaculture nitrogen excretion is used to indicate the nitrogen excretion of livestock and poultry manure during livestock and poultry farming. The aquaculture nitrogen excretion is obtained by accumulating the nitrogen production of products of several types of livestock and poultry. The aquaculture nitrogen excretion is:

[0065]

[0066] Where, BROUT Exc is the nitrogen excretion of aquaculture, E i is the nitrogen content of excreted feces of the i-th livestock and poultry type, k is the number of livestock and poultry types, e i is the excretion coefficient of the i-th livestock and poultry type.

[0067] Aquaculture nitrogen loss represents nitrogen lost during aquaculture, primarily through denitrification, NH3 volatilization, horizontal runoff, N2O release, and sediment deposition closely associated with aquaculture. Aquaculture nitrogen loss = the aquaculture portion of nitrogen input from feed + nitrogen fixation from aquaculture + atmospheric nitrogen deposition from aquaculture - the aquaculture portion of nitrogen output from aquaculture products.

[0068] The nitrogen output of industrial products is obtained by accumulating the nitrogen output of industrial nitrogen products produced by synthetic ammonia and the nitrogen output of industrial nitrogen products produced by biological raw materials, wherein the nitrogen output of industrial nitrogen products produced by synthetic ammonia is obtained by accumulating the nitrogen content of industrial nitrogen products of several industrial product varieties, and the industrial product varieties include chemical fibers, artificial pharmaceuticals, rubber, synthetic detergents, plastics and other products; the nitrogen output of industrial nitrogen products produced by biological raw materials is obtained by accumulating the nitrogen content of industrial nitrogen products of several biological raw material varieties, and the biological raw material varieties mainly include wool, cowhide and other products, wherein the nitrogen output of industrial nitrogen products produced by synthetic ammonia is:

[0069]

[0070] Where, IPOUT NA The nitrogen output of industrial nitrogen products produced from synthetic ammonia, I i is the nitrogen content of industrial nitrogen products of the i-th industrial product variety, k is the number of industrial product varieties, c i is the nitrogen content coefficient of the i-th industrial product variety.

[0071] The industrial nitrogen loss represents the nitrogen loss in the industrial production process, which is obtained by adding the nitrogen emissions in industrial wastewater and the nitrogen emissions in industrial waste gas. Among them, the nitrogen emissions in industrial waste gas are mainly nitrogen oxide emissions from the combustion of fossil fuels, and the data are obtained from the statistical yearbook. The nitrogen emissions in industrial wastewater are estimated using the data from the second national pollution source census due to the large differences in pollution discharge characteristics among various industries.

[0072] The nitrogen expenditure of urban park green spaces represents the nitrogen expenditure during the process of falling or maintaining urban park green spaces. The amount of nitrogen lost in urban park green spaces is twice the amount of nitrogen lost in urban park green spaces. The nitrogen loss of urban park green spaces represents the nitrogen loss of urban park green spaces.

[0073] The human consumption nitrogen income data includes the human food consumption nitrogen income, human industrial consumption nitrogen income and human life fossil fuel combustion nitrogen emissions.

[0074] Specifically, the nitrogen income from human food consumption represents the nitrogen income from human dietary consumption. The main dietary products of humans are food products and livestock products (red meat and white meat). The nitrogen income from human food consumption is:

[0075]

[0076] Where HCIN Food is the nitrogen income from human food consumption, D i is the total nitrogen content of the per capita consumption of food of the i-th type of consumption food, d iis the conversion coefficient of nitrogen content in the i-th type of consumer food, u is the number of consumer food types, and o is the population.

[0077] The industrial nitrogen consumption income of humans is the sum of the content of industrial nitrogen products consumed by humans and the nitrogen oxide emissions caused by the combustion of fossil fuels in people's daily lives. The content of industrial nitrogen products consumed by humans = per capita industrial nitrogen consumption × population; the nitrogen oxide emissions caused by the combustion of fossil fuels in people's daily lives can be obtained from the statistical yearbook.

[0078] The human consumption nitrogen expenditure data include the nitrogen discharge from human consumption sewage, the nitrogen content of human domestic garbage and waste, and the nitrogen oxide emissions from human consumption energy.

[0079] Specifically, nitrogen emissions from domestic sewage after human consumption represent the nitrogen content in domestic sewage discharged after human consumption. It is calculated based on the average daily domestic water consumption per capita and the pollution reduction coefficient. Nitrogen emissions from domestic sewage after human consumption = average daily domestic water consumption per capita × population × emission coefficient.

[0080] The nitrogen oxide emissions from energy consumption in human life represent the nitrogen oxide emissions from energy consumption in the human life process. The fossil fuel nitrogen input into the human consumption module is released into the atmosphere in the form of nitrogen oxides through combustion. The value of nitrogen oxide emissions from energy consumption in human life is equal to the value of nitrogen oxide emissions caused by the combustion of fossil fuels in people's daily lives.

[0081] The waste nitrogen income data includes the amount of human domestic sewage treatment, industrial sewage treatment, urban park green space landfill and human domestic waste treatment.

[0082] Specifically, the human domestic sewage treatment volume represents the nitrogen content of sewage discharged from the human consumption module entering the sewage treatment facility, and the value of the human domestic sewage treatment volume is equal to the value of nitrogen discharge of human consumption domestic sewage.

[0083] The industrial wastewater treatment capacity represents the nitrogen content of wastewater discharged from industrial production modules entering the wastewater treatment facility. The value of the industrial wastewater treatment capacity is equal to the value of the total nitrogen production of industrial wastewater in the industrial nitrogen loss.

[0084] The amount of landfill waste in urban parks and green spaces represents the amount of waste landfilled by the fallen objects in urban park and green space modules that enters the unified waste disposal system. The value of the landfill waste amount in urban parks and green spaces is equal to the value of the amount of fallen objects in urban parks and green spaces.

[0085] The human domestic waste treatment volume represents the treatment volume of human domestic waste entering the waste treatment system. The value of the human domestic waste treatment volume is equal to the value of the nitrogen content of the human domestic waste.

[0086] The waste nitrogen expenditure data includes the amount of wastewater treatment transportation loss, the nitrogen content of wastewater treatment discharge, and the amount of leachate generated by garbage decomposition.

[0087] Specifically, the sewage treatment transportation loss represents the leakage that occurs during the transportation of sewage through pipelines to sewage treatment facilities after it is generated. This part of the leakage value enters the soil and groundwater, rather than the surface water system. The sewage treatment transportation loss = (total total nitrogen discharge in wastewater + total nitrogen removal in sewage treatment facilities) / (1-pipeline network leakage rate) × pipeline network leakage rate.

[0088] The nitrogen content of sewage treatment discharge represents the nitrogen content in the effluent after sewage treatment. Water treated by the sewage treatment plant that meets the discharge standards will be directly discharged into surface water. The nitrogen content of sewage treatment discharge is obtained from the statistical yearbook.

[0089] The amount of garbage decomposition leachate produced represents the leachate produced by garbage decomposition in a landfill after a period of time, which contains a large amount of Nr and mainly goes to the groundwater system.

[0090] The environmental nitrogen input data includes nitrogen loss through surface runoff, atmospheric nitrogen deposition in surface water, and atmospheric nitrogen emissions. Specifically, the nitrogen loss through surface runoff represents the amount of nitrogen input into surface water through surface runoff, including nitrogen loss from agricultural production, nitrogen loss from forest land, nitrogen loss from grassland denitrification, nitrogen loss from aquaculture, nitrogen loss from urban parks and green spaces, and total nitrogen discharge from wastewater into surface water.

[0091] The atmospheric nitrogen deposition on surface water is used to indicate the atmospheric nitrogen deposition above the surface water. The atmospheric nitrogen emission is used to indicate the Nr emitted in the atmospheric environment, mainly including the volatilization of NH3 and the emission of various nitrogen oxides.

[0092] The environmental nitrogen expenditure data include surface water denitrification, nitrogen content in surface water used for farmland irrigation, and regional atmospheric nitrogen deposition.

[0093] Specifically, the surface water denitrification rate represents the denitrification process of nitrogen in surface water. The quantitative data for surface water nitrogen budget is calculated as follows: river and lake area × river denitrification rate. The nitrogen content of surface water used for farmland irrigation represents the nitrogen content of surface water used for farmland irrigation. The nitrogen content of surface water used for farmland irrigation is equal to the nitrogen input of agricultural irrigation water. The regional atmospheric nitrogen deposition rate indicates the atmospheric nitrogen deposition over the entire region.

[0094] S3: Inputting the nitrogen budget data at a plurality of time points into the nitrogen budget quantification module to quantify the nitrogen budget, thereby obtaining the nitrogen budget quantification data at a plurality of time points.

[0095] In this embodiment, the prediction device inputs the nitrogen budget data at several time points into the nitrogen budget quantification module to quantify the nitrogen budget, thereby obtaining the nitrogen budget quantification data at several time points, wherein the nitrogen budget quantification data includes production nitrogen budget quantification data, human consumption nitrogen budget quantification data, waste treatment nitrogen budget quantification data, and environmental nitrogen budget quantification data. The human consumption nitrogen budget data includes human consumption nitrogen income quantification data and human consumption nitrogen expenditure quantification data; the waste nitrogen budget quantification data includes waste nitrogen income quantification data and waste nitrogen expenditure quantification data; and the environmental nitrogen budget quantification data includes environmental nitrogen income quantification data and environmental nitrogen expenditure quantification data. Without complex calculations, the nitrogen-related data alone can be used to quantify the nitrogen budget of a city, thereby achieving the quantification of the city's nitrogen budget.

[0096] See also Figure 2 , Figure 2 The flow diagram of S3 in the urban nitrogen budget prediction method provided in one embodiment of the present application includes steps S31 to S34, which are specifically as follows:

[0097] S31: According to the agricultural nitrogen revenue and expenditure data, forest and grassland nitrogen revenue and expenditure data, aquaculture nitrogen revenue and expenditure data, industrial nitrogen revenue and expenditure data and urban park and green space nitrogen revenue and expenditure data in the production nitrogen revenue and expenditure data, nitrogen revenue and expenditure quantification data of agriculture, forest and grassland nitrogen revenue and expenditure data, aquaculture nitrogen revenue and expenditure data, industrial nitrogen revenue and expenditure data and urban park and green space nitrogen revenue and expenditure data are obtained respectively, and the production nitrogen revenue and expenditure quantification data is constructed.

[0098] In this embodiment, the prediction device quantifies the nitrogen budget according to the agricultural nitrogen budget data, forest and grassland nitrogen budget data, aquaculture nitrogen budget data, industrial nitrogen budget data and urban park and green space nitrogen budget data in the production nitrogen budget data, obtains the agricultural nitrogen budget quantification data, forest and grassland nitrogen budget quantification data, aquaculture nitrogen budget quantification data, industrial nitrogen budget quantification data and urban park and green space nitrogen budget quantification data, and constructs the production nitrogen budget quantification data.

[0099] Specifically, the prediction device accumulates the nitrogen input from agricultural production nitrogen fertilizers, the amount of biological nitrogen fixation, the amount of manure returned to the field, the amount of atmospheric nitrogen deposition on cultivated land, the nitrogen input from agricultural production irrigation water, and the amount of straw returned to the field to obtain quantitative data on agricultural production nitrogen income, wherein the quantitative data on agricultural production nitrogen income is:

[0100] CP IN =CPIN Fer +CPIN BNF +CPIN exc +CPIN Dep +CPIN Irr +CPIN Str

[0101] Where, CP IN Quantifying data on nitrogen income for agricultural production, CPIN Fer CPIN is the nitrogen input of agricultural production nitrogen fertilizer BNF is the amount of biological nitrogen fixation, CPIN exc is the amount of manure returned to the field, and CPIN Dep Atmospheric nitrogen deposition on cultivated land, CPIN Irr CPIN is the amount of nitrogen input into agricultural irrigation water. Str The amount of straw returned to the field.

[0102] The prediction device accumulates the nitrogen expenditure of human food consumption, the nitrogen consumption of livestock feed, and the nitrogen loss of agricultural production to obtain the quantitative data of nitrogen expenditure of agricultural production, wherein the quantitative data of nitrogen income of agricultural production is:

[0103] CP OUT =CPOUT Crop +CPOUT Str +CPOUT Loss

[0104] Where, CP OUr Quantifying agricultural production nitrogen expenditure, CPOUT Crop Nitrogen expenditure for human food consumption, CPOUT Str Nitrogen consumption for livestock feed, CPOUT Loss Nitrogen losses to agricultural production.

[0105] The prediction device combines the agricultural production nitrogen income quantification data and the agricultural production nitrogen expenditure quantification data to obtain the agricultural production nitrogen expenditure quantification data.

[0106] The prediction device accumulates the forest and grass nitrogen fixation amount and the grassland atmospheric nitrogen deposition amount to obtain the forest and grass nitrogen income quantitative data, wherein the forest and grass nitrogen income quantitative data is:

[0107] FG IN =FGIN BNF +FGIN Dep

[0108] Where FG IN Quantifying data on forest and grassland nitrogen income, FGIN BNF FGIN is the amount of nitrogen fixed by forest and grass, Dep is the atmospheric nitrogen deposition on grassland.

[0109] The prediction device accumulates the forest nitrogen loss and grassland denitrification amount to obtain the quantitative data of forest and grassland nitrogen expenditure, wherein the quantitative data of forest and grassland nitrogen expenditure is:

[0110] FG OUT =FGOUT Loss1 +FGOUT Loss2

[0111] Where FG OUT Quantifying nitrogen expenditure in forests and grasslands, FGOUT Loss1 FGOUT is the nitrogen loss from forest land, Loss2 is the amount of grassland denitrification.

[0112] The prediction device combines the forest and grassland nitrogen income quantification data and the forest and grassland nitrogen expenditure quantification data to obtain the forest and grassland nitrogen expenditure quantification data.

[0113] The prediction device accumulates the amount of nitrogen input into the aquaculture feed, the amount of nitrogen fixed by aquatic products, and the amount of atmospheric nitrogen deposition by aquatic products to obtain quantitative data on aquaculture nitrogen income, wherein the quantitative data on aquaculture nitrogen income is:

[0114] BR IN =BRIN crop +BRIN BNF +BRIN Dep

[0115] Where BR IN Quantifying nitrogen income from aquaculture, BRIN crop The amount of nitrogen added to livestock feed, BRIN BNF is the amount of nitrogen fixed by aquaculture, BRIN Dep is the atmospheric nitrogen deposition into aquatic products.

[0116] The prediction device accumulates the nitrogen production of the aquaculture products, the nitrogen excretion of the aquaculture products, and the nitrogen loss of the aquaculture products to obtain quantitative data of aquaculture nitrogen expenditure, wherein the quantitative data of aquaculture nitrogen expenditure is:

[0117] BR OUT =BROUT Meat +BROUT Exc +BROUT Loss

[0118] Where BR OUT Quantifying nitrogen expenditure in aquaculture, BROUT Meat Nitrogen production for aquaculture products, BROUT Exc is the nitrogen excretion of aquaculture, BROUT Loss The amount of nitrogen lost from aquaculture.

[0119] The prediction device combines the aquaculture nitrogen income quantified data and the aquaculture nitrogen expenditure quantified data to obtain the aquaculture nitrogen expenditure quantified data.

[0120] The prediction device accumulates the nitrogen input of industrial production raw materials and the nitrogen input of industrial production energy consumption to obtain quantitative data of industrial nitrogen income, wherein the quantitative data of industrial nitrogen income is:

[0121] IP IN =IPIN Raw +IPIN Fuel

[0122] Where, IP IN Quantifying data for industrial nitrogen income, IPIN Raw IPIN is the nitrogen input for industrial production raw materials. Fuel The amount of nitrogen consumed in industrial production energy.

[0123] The prediction device accumulates the industrial product nitrogen output and the industrial nitrogen loss to obtain quantitative data of industrial nitrogen expenditure, wherein the quantitative data of industrial nitrogen income and expenditure is:

[0124] IP OUT =IPOUT NA +IPOUT NB +IPOUT Loss

[0125] Where, IP OUT Quantifying data for industrial nitrogen expenditure, IPOUT NA IPOUT is the nitrogen output of industrial nitrogen products produced by synthetic ammonia in the industrial nitrogen output. NB IPOUT is the nitrogen output of industrial nitrogen products produced from biological raw materials in the industrial nitrogen output. Loss Industrial nitrogen loss.

[0126] The forecasting device combines the industrial nitrogen income quantitative data and the industrial nitrogen expenditure quantitative data to obtain the industrial nitrogen expenditure quantitative data.

[0127] The prediction device accumulates the amount of fertilization for urban park green space maintenance, the amount of biological nitrogen fixation in urban park green space, the amount of fallen materials in urban park green space, and the amount of atmospheric nitrogen deposition in urban park green space to obtain quantitative data on nitrogen income of urban park green space, wherein the quantitative data on nitrogen income of urban park green space is:

[0128] UGG IN =UGGIN Fer +UGGIN BNF +UGGIN Dep +UGGIN Liter

[0129] In the formula, UGG IN Quantifying nitrogen income of urban park green spaces, UGGIN FerFertilizer application for urban park green space maintenance, UGGIN BNF For the amount of biological nitrogen fixation in urban park green spaces, UGGIN Dep is the atmospheric nitrogen deposition in urban park green spaces, UGGIN Liter The amount of debris falling from urban parks and green spaces.

[0130] The prediction device accumulates the nitrogen expenditure of the urban park green land and the loss of the urban park green land to obtain the quantitative data of nitrogen expenditure of the urban park green land, wherein the quantitative data of nitrogen expenditure of the urban park green land is:

[0131] UGG OUT =UGGOUT Clip +UGGOUT Loss

[0132] In the formula, UGG OUT Quantifying nitrogen expenditure in urban parks and green spaces, UGGOUT Clip Nitrogen expenditure for urban park green space, UGGOUT Loss The amount of green space lost in urban parks.

[0133] The prediction device combines the urban park green land nitrogen income quantification data and the urban park green land nitrogen expenditure quantification data to obtain the urban park green land nitrogen expenditure quantification data.

[0134] S32: Based on the nitrogen income from human food consumption, the nitrogen income from human industrial consumption and the nitrogen emissions from fossil fuel combustion in human life in the human consumption nitrogen income and expenditure data, obtain the quantitative data on human consumption nitrogen income; based on the nitrogen emissions from human domestic sewage, the nitrogen content of human domestic garbage and waste and the nitrogen oxide emissions from human domestic energy consumption, obtain the quantitative data on human consumption nitrogen expenditure.

[0135] In this embodiment, the prediction device obtains the quantitative data of human consumption nitrogen income based on the human food consumption nitrogen income, the human industrial consumption nitrogen income, and the human life fossil fuel combustion nitrogen emissions in the human consumption nitrogen income and expenditure data. Specifically, the prediction device accumulates the human food consumption nitrogen income, the human industrial consumption nitrogen income, and the human life fossil fuel combustion nitrogen emissions to obtain the quantitative data of human consumption nitrogen income, wherein the quantitative data of human consumption nitrogen income is:

[0136] HC IN =HCIN Food +HCIN Indu +HCIN Fuel

[0137] Where HC IN Quantifying data on nitrogen income for human consumption, HCIN InduHCIN is the nitrogen income for human industrial consumption Fuel Nitrogen emissions from fossil fuel combustion for human life.

[0138] The prediction device obtains quantitative data on human consumption nitrogen expenditure based on the nitrogen discharge of human consumption domestic sewage, the nitrogen content of human domestic garbage and waste, and the nitrogen oxide emissions of human life energy consumption.

[0139] In this embodiment, the prediction device obtains the quantitative data of human consumption nitrogen expenditure based on the nitrogen emission amount of human consumption domestic sewage, the nitrogen content of human domestic waste, and the nitrogen oxide emission amount of human life energy consumption. Specifically, the prediction device accumulates the nitrogen emission amount of human consumption domestic sewage, the nitrogen content of human domestic waste, and the nitrogen oxide emission amount of human life energy consumption to obtain the quantitative data of human consumption nitrogen expenditure, wherein the quantitative data of human consumption nitrogen expenditure is:

[0140] HC OUT =HCOUT WW +HCOUT GA +HCOUT NOx

[0141] Where HC OUT Quantifying nitrogen expenditure for human consumption, HCOUT WW Nitrogen emissions from domestic sewage for human consumption, HCOUT GA HCOUT is the nitrogen content of human domestic waste. NOx Nitrogen oxide emissions from human energy consumption.

[0142] The prediction device combines the quantitative data of human consumption income and the quantitative data of human consumption expenditure to obtain quantitative data of human consumption nitrogen expenditure.

[0143] S33: According to the human domestic sewage treatment volume, industrial sewage treatment volume, urban park green space garbage landfill volume and human domestic garbage treatment volume in the waste nitrogen income and expenditure data, the waste nitrogen income quantitative data is obtained; according to the sewage treatment transportation loss volume, sewage treatment discharge nitrogen content and garbage decomposition leachate generation volume in the waste nitrogen income and expenditure data, the waste nitrogen expenditure quantitative data is obtained.

[0144] In this embodiment, the prediction device obtains the waste nitrogen income quantitative data based on the human domestic sewage treatment volume, industrial sewage treatment volume, urban park green landfill volume and human domestic waste treatment volume in the waste nitrogen income and expenditure data.

[0145] Specifically, the prediction device accumulates the human domestic sewage treatment volume and the industrial sewage treatment volume to obtain the sewage treatment nitrogen income quantitative data, wherein the sewage treatment nitrogen income quantitative data is:

[0146] WWT IN =WWTIN HC +WWTIN IP

[0147] Where, WWT IN Quantifying data for wastewater treatment nitrogen income, WWTIN HC The amount of human sewage treated, WWTIN IP The amount of industrial wastewater treated.

[0148] The prediction device accumulates the amount of landfilled urban park green land and the amount of human domestic waste processed to obtain quantitative data on nitrogen income from waste treatment, wherein the quantitative data on nitrogen income from waste treatment is:

[0149] GT IN =GTIN UGG +GTIN HC

[0150] Where, GT IN Quantifying data on nitrogen income from waste treatment, GTIN UGG The amount of landfill waste in urban parks and green spaces, GTIN HC The amount of human domestic waste processed.

[0151] The prediction device combines the sewage treatment nitrogen income quantification data and the garbage treatment nitrogen income quantification data to obtain the waste nitrogen income quantification data.

[0152] The prediction device obtains the waste nitrogen expenditure quantitative data based on the sewage treatment transportation loss, the sewage treatment discharge nitrogen content and the garbage decomposition leachate generation in the waste nitrogen income and expenditure data.

[0153] Specifically, the prediction device accumulates the sewage treatment transportation loss and the sewage treatment discharge nitrogen content to obtain the sewage treatment nitrogen expenditure quantitative data, wherein the sewage treatment nitrogen expenditure quantitative data is:

[0154] WWT OUT =WWTOUT Lea +WWTOUT River

[0155] Where, WWT OUT Quantifying data for wastewater treatment nitrogen expenditure, WWTOUT Lea is the wastewater treatment transportation loss, WWTOUT RiverNitrogen content of wastewater discharge.

[0156] The prediction device uses the amount of leachate generated by the garbage decomposition as the garbage treatment nitrogen expenditure quantitative data, combines the sewage treatment nitrogen expenditure quantitative data and the garbage treatment nitrogen expenditure quantitative data, and obtains the waste nitrogen expenditure quantitative data.

[0157] S34: Based on the amount of nitrogen lost with surface runoff, the amount of atmospheric nitrogen deposition in surface water, and the amount of atmospheric nitrogen emissions in the environmental nitrogen budget data, quantitative data on environmental nitrogen income is obtained; based on the amount of surface water denitrification, the nitrogen content of surface water used for farmland irrigation, and the amount of regional atmospheric nitrogen deposition in the environmental nitrogen budget data, quantitative data on environmental nitrogen expenditure is obtained.

[0158] In this embodiment, the prediction device obtains quantitative data on environmental nitrogen income based on the amount of nitrogen lost with surface runoff, the amount of atmospheric nitrogen deposition in surface water, and the amount of atmospheric nitrogen emissions in the environmental nitrogen budget data.

[0159] Specifically, the prediction device accumulates the amount of nitrogen lost with surface runoff and the amount of atmospheric nitrogen deposition in surface water to obtain quantitative data of nitrogen income in surface water, wherein the quantitative data of nitrogen income in surface water is:

[0160] SW IN =SWIN Loss +SWIN Dep

[0161] Where SW IN Quantifying nitrogen income in surface water, SWIN Loss is the amount lost with surface runoff, SWIN Dep is the atmospheric nitrogen deposition to surface water.

[0162] The prediction device uses the atmospheric environment nitrogen emissions as the atmospheric nitrogen income quantification data, and uses the surface water nitrogen income quantification data and the atmospheric nitrogen income quantification data to obtain the environmental nitrogen income quantification data.

[0163] The prediction device obtains quantitative data on environmental nitrogen expenditure based on the amount of surface water denitrification, the nitrogen content of surface water used for farmland irrigation, and the amount of regional atmospheric nitrogen deposition in the environmental nitrogen budget data.

[0164] Specifically, the prediction device accumulates the denitrification amount of the surface water and the nitrogen content of the surface water used for farmland irrigation to obtain quantitative data of nitrogen expenditure of the surface water, wherein the quantitative data of nitrogen expenditure of the surface water is:

[0165] SW OUT =SWOUT Den +SWOUt Irr

[0166] Where SW OUT Quantifying nitrogen expenditure in surface water, SWOUT Den is the amount of surface water denitrification, SWOUT Irr The nitrogen content of surface water used for agricultural irrigation.

[0167] The prediction device uses the regional atmospheric nitrogen deposition as the atmospheric nitrogen expenditure quantitative data, combines the surface water nitrogen expenditure quantitative data and the atmospheric nitrogen expenditure quantitative data, and obtains the environmental nitrogen expenditure quantitative data.

[0168] S4: Construct nitrogen budget ternary data at several time points, wherein the nitrogen budget ternary data includes nitrogen-related data, a corresponding nitrogen budget calculation method, and nitrogen budget data; input the nitrogen budget ternary data at several time points and the nitrogen budget quantification data into a preset nitrogen budget quantification prediction model for training to obtain a target nitrogen budget quantification prediction model.

[0169] In this embodiment, the prediction device constructs nitrogen budget triplet data at several time points, wherein the nitrogen budget triplet data includes nitrogen-related data, a corresponding nitrogen budget calculation method, and nitrogen budget data.

[0170] The prediction device inputs the nitrogen budget triplet data and the nitrogen budget quantification data at several time points into a preset nitrogen budget quantification prediction model for training to obtain a target nitrogen budget quantification prediction model.

[0171] See also Figure 3 , Figure 3 The flow diagram of S4 in the urban nitrogen budget prediction method provided in one embodiment of the present application includes steps S41 to S44, which are specifically as follows:

[0172] S41: Using the Lagrange interpolation method, the nitrogen-related data in the nitrogen budget triplet data at several time points are processed for missing values ​​and outliers to obtain the processed nitrogen budget triplet data at several time points.

[0173] In this embodiment, the prediction device uses the Lagrange interpolation method to process the nitrogen-related data in the nitrogen budget triplet data at several time points for missing values ​​and outliers, and obtains the nitrogen budget triplet data at several time points after processing.

[0174] Specifically, to reduce errors, the prediction device traverses the nitrogen-related data in the nitrogen budget triplet data at several time points, and uses the nitrogen-related data of the two years before and after the missing values ​​(a total of four years) to construct a Lagrange polynomial. The interpolation nodes and their corresponding values ​​are substituted into the Lagrange polynomial, and then the points to be interpolated are substituted into the Lagrange polynomial for calculation to obtain the interpolation results. Finally, the calculated interpolation results are used to replace the missing values ​​of the original data to complete the interpolation process.

[0175] The outlier processing is that the prediction device divides the nitrogen-related data in the nitrogen budget triplet data at several time points after missing value processing into nitrogen-related data at several time points corresponding to several sliding windows according to a preset sliding window, obtains outlier test values ​​of several sliding windows according to a preset outlier test algorithm, judges the sliding window as a normal window or an abnormal window according to the outlier test value and a preset test threshold, identifies the time point corresponding to the abnormal window as a mutation point, and corrects the nitrogen-related data at the mutation point, wherein the outlier test algorithm is:

[0176]

[0177] Where Z is the outlier test value, μ1 is the mean of the nitrogen-related data at several time points of the previous sliding window of the current sliding window, μ2 is the mean of the nitrogen-related data at several time points of the next sliding window of the current sliding window, s1 is the variance of the nitrogen-related data at several time points of the previous sliding window of the current sliding window, s2 is the variance of the nitrogen-related data at several time points of the next sliding window of the current sliding window, n1 is the number of time points of the previous sliding window of the current sliding window, and n2 is the number of time points of the next sliding window of the current sliding window.

[0178] S42: constructing a feature matrix and converting the covariance matrix of the processed nitrogen budget triplet data at several time points to obtain the covariance matrix of several time points; confirming several target feature vectors in the covariance matrix of several time points, combining several target feature vectors in the covariance matrix of the same time point, and obtaining feature data of several time points.

[0179] In this embodiment, the prediction device constructs a feature matrix and converts the covariance matrix of the processed nitrogen budget triplet data at several time points to obtain the covariance matrices at the several time points.

[0180] Specifically, the prediction device takes the nitrogen-related data and the nitrogen budget data in the nitrogen budget triplet data as entities, the corresponding nitrogen budget calculation method as a relationship type, and the nitrogen-related data, the corresponding nitrogen budget calculation method, and the nitrogen budget data as each assigned a unique integer or string ID, with the row corresponding to the entity and the column corresponding to the relationship type. According to the relationship type, a value is filled in the corresponding row and column position as the initial value of the feature matrix, and the feature matrix is ​​constructed to obtain the feature matrix of several time points. The prediction device performs standardization on the feature matrix of several time points to eliminate the influence of the data scale. The minimum-maximum standardization can be used to calculate the covariance matrix of the standardized feature matrix to obtain the covariance matrix of several time points, wherein the covariance matrix includes several eigenvectors.

[0181] The prediction device adopts the principal component analysis method to select the largest several eigenvectors as target eigenvectors based on the values ​​of several eigenvectors in the covariance matrix at the same time point and the preset number of target eigenvectors, confirm several target eigenvectors in the covariance matrix at several time points, combine several target eigenvectors in the covariance matrix at the same time point, and obtain feature data of several time points.

[0182] S43: According to a preset time window, the characteristic data of the plurality of time points are divided into a plurality of time windows to construct characteristic data corresponding to the plurality of time windows; the nitrogen budget quantification data of the plurality of time points and the characteristic data corresponding to the plurality of time windows are input into the long short-term memory network, and prediction is performed based on the characteristic data corresponding to the plurality of time windows to obtain prediction data corresponding to the plurality of time windows;

[0183] In this embodiment, the prediction device divides the feature data of several time points into several time windows according to a preset time window, and constructs feature data corresponding to the several time windows.

[0184] The prediction device inputs the nitrogen budget quantification data at several time points and the characteristic data corresponding to several time windows into the long short-term memory network, performs prediction based on the characteristic data corresponding to the several time windows, and obtains the prediction data corresponding to the several time windows.

[0185] The forecast data includes the nitrogen budget forecast data for the next time point corresponding to several time points corresponding to the current time window; please refer to Figure 4 , Figure 4 The flowchart of S43 in the urban nitrogen budget prediction method provided in one embodiment of the present application includes steps S431 to S432, which are specifically as follows:

[0186] S431: Obtain the predicted data and target cell state corresponding to the previous time window of the current time window.

[0187] In this embodiment, the prediction device obtains the prediction data and the target cell state corresponding to the previous time window of the current time window.

[0188] S432: Calculate the candidate cell state based on the feature data corresponding to the current time window and the predicted data corresponding to the previous time window to obtain the candidate cell state corresponding to the current time window; calculate the input gating signal and the forgetting gating signal based on the feature data corresponding to the current time window and the target cell state corresponding to the previous time window to obtain the candidate cell state input gating signal and the forgetting gating signal corresponding to the current time window; calculate the target cell state based on the candidate cell state, input gating signal, forgetting gating signal corresponding to the current time window and the target cell state corresponding to the previous time window to obtain the target cell state corresponding to the current time window; calculate the output gating signal based on the feature data, target cell state corresponding to the current time window and the predicted data corresponding to the previous time window to obtain the output gating signal corresponding to the current time window; calculate the predicted data based on the target cell state and the output gating signal corresponding to the current time window to obtain the predicted data corresponding to the current time window.

[0189] In this embodiment, the prediction device calculates the candidate cell state according to the preset nitrogen budget quantitative prediction algorithm, based on the feature data corresponding to the current time window and the prediction data corresponding to the previous time window, to obtain the candidate cell state corresponding to the current time window; calculates the input gating signal and the forgetting gating signal according to the feature data corresponding to the current time window and the target cell state corresponding to the previous time window, respectively, to obtain the candidate cell state input gating signal and the forgetting gating signal corresponding to the current time window; calculates the target cell state according to the candidate cell state, input gating signal, forgetting gating signal corresponding to the current time window and the target cell state corresponding to the previous time window, to obtain the target cell state corresponding to the current time window; calculates the output gating signal according to the feature data, target cell state corresponding to the current time window and the prediction data corresponding to the previous time window, to obtain the output gating signal corresponding to the current time window; calculates the prediction data according to the target cell state and the output gating signal corresponding to the current time window, to obtain the prediction data corresponding to the current time window, wherein the nitrogen budget quantitative prediction algorithm is:

[0190]

[0191] Where z t is the candidate cell state corresponding to the t-th time window, x tis the feature data corresponding to the t-th time window, is the predicted data corresponding to the t-1th time window, i t is the input gating signal corresponding to the t-th time window, c t -1 is the target cell state corresponding to the t-1th time window, f t is the forget gating signal corresponding to the t-th time window, o t is the output gating signal corresponding to the t-th time window, W z 、W i 、W f 、W o are the first, second, third and fourth input weights, R z 、R i 、R f 、R o are the first, second, third and fourth cycle weights, p i 、p f 、p o are the first, second and third peephole weights, b z 、b i 、b f 、b o are the first, second, third, and fourth bias weights, σ(·) is the gate activation function, g(·) is the input activation function, h(·) is the output activation function, and ⊙ represents point-by-point multiplication.

[0192] S44: Constructing real data corresponding to several time windows, wherein the real data includes nitrogen budget quantification data at several time points, and training the nitrogen budget quantification prediction model based on the real data and prediction data corresponding to the several time windows to obtain a target nitrogen budget quantification prediction model.

[0193] In this embodiment, the prediction device constructs real data corresponding to several time windows, and the real data includes nitrogen budget quantitative data at several time points. Based on the real data and predicted data corresponding to the several time windows, the nitrogen budget quantitative prediction model is trained to obtain the target nitrogen budget quantitative prediction model.

[0194] See also Figure 5 , Figure 5 The flowchart of S44 in the urban nitrogen budget prediction method provided in one embodiment of the present application includes step S441, which is specifically as follows:

[0195] S441: Obtain loss values ​​based on real data, predicted data, and a preset loss function corresponding to a plurality of time windows, and train the nitrogen budget quantitative prediction model based on the loss values ​​to obtain a target nitrogen budget quantitative prediction model.

[0196] In this embodiment, the prediction device obtains a loss value based on the real data, predicted data, and a preset loss function corresponding to several time windows, and trains the nitrogen budget quantitative prediction model based on the loss value to obtain a target nitrogen budget quantitative prediction model, wherein the loss function is:

[0197]

[0198] Where, is the loss value, T is the number of time windows, is the nitrogen budget quantitative data at the i-th time point in the t-th time window, is the nitrogen budget forecast data at the i-th time point in the t-th time window.

[0199] S5: According to the target nitrogen budget quantitative prediction model, obtain nitrogen budget quantitative data of the target area at several time points in the prediction time period.

[0200] In this embodiment, the prediction device obtains the nitrogen budget quantitative data of the target area at several time points in the prediction time period according to the target nitrogen budget quantitative prediction model.

[0201] See also Figure 6 , Figure 6 The flow diagram of S5 in the urban nitrogen budget prediction method provided in one embodiment of the present application includes step S51, which is specifically as follows:

[0202] S51: Input the characteristic data corresponding to the last time window into the target nitrogen budget quantitative prediction model to perform nitrogen budget quantitative prediction, obtain the prediction data corresponding to the last time window, and use the nitrogen budget prediction data of several time points in the prediction data corresponding to the last time window as the nitrogen budget quantitative data of several time points in the prediction time period of the target area.

[0203] In this embodiment, the prediction device inputs the characteristic data corresponding to the last time window into the target nitrogen budget quantitative prediction model to perform nitrogen budget quantitative prediction, obtains the prediction data corresponding to the last time window, and uses the nitrogen budget prediction data of several time points in the prediction data corresponding to the last time window as the nitrogen budget quantitative data of several time points in the prediction time period of the target area.

[0204] Please refer to Figure 7 , Figure 7 This is a schematic diagram of the structure of an urban nitrogen budget prediction device provided by one embodiment of the present application. The device can implement all or part of the urban nitrogen budget quantification device through software, hardware, or a combination of both. The device 7 includes:

[0205] A data acquisition module 71 is used to obtain nitrogen-related data of a target area at several time points within a sample period and a preset urban nitrogen budget quantification model, wherein the urban nitrogen budget quantification model includes a nitrogen budget calculation module and a nitrogen budget quantification module;

[0206] a nitrogen budget processing module 72 for inputting the nitrogen-related data at the plurality of time points into the nitrogen budget calculation module, performing nitrogen budget calculations based on the nitrogen-related data and a corresponding nitrogen budget calculation method, and obtaining nitrogen budget data at the plurality of time points;

[0207] A nitrogen budget quantification module 73 is configured to input the nitrogen budget data at a plurality of time points into the nitrogen budget quantification module for nitrogen budget quantification, thereby obtaining nitrogen budget quantification data at a plurality of time points;

[0208] A model training module 74 is configured to construct nitrogen budget triplet data at a plurality of time points, wherein the nitrogen budget triplet data includes nitrogen-related data, a corresponding nitrogen budget calculation method, and nitrogen budget data; input the nitrogen budget triplet data at a plurality of time points and the nitrogen budget quantification data into a preset nitrogen budget quantification prediction model for training to obtain a target nitrogen budget quantification prediction model;

[0209] The nitrogen budget prediction module 75 is configured to obtain nitrogen budget quantitative data of the target area at a plurality of time points in a prediction time period according to the target nitrogen budget quantitative prediction model.

[0210] In an embodiment of the present application, nitrogen-related data of a target area at several time points within a sample time period and a preset urban nitrogen budget quantification model are obtained through a data acquisition module, wherein the urban nitrogen budget quantification model includes a nitrogen budget calculation module and a nitrogen budget quantification module; the nitrogen-related data of the several time points are input into the nitrogen budget calculation module through a nitrogen budget processing module, and nitrogen budget calculation is performed according to the nitrogen-related data and the corresponding nitrogen budget calculation method to obtain nitrogen budget data of the several time points; the nitrogen budget quantification module inputs the nitrogen budget data of the several time points into the nitrogen budget quantification module. The nitrogen budget is quantified in the module to obtain nitrogen budget data at several time points; the nitrogen budget triplet data at several time points are constructed through the model training module, and the nitrogen budget triplet data includes nitrogen-related data, the corresponding nitrogen budget calculation method and nitrogen budget data; the nitrogen budget triplet data and the nitrogen budget quantification data at several time points are input into a preset nitrogen budget quantification prediction model for training to obtain a target nitrogen budget quantification prediction model; through the nitrogen budget prediction module, according to the target nitrogen budget quantification prediction model, the nitrogen budget quantification data of the target area at several time points in the prediction time period are obtained. Without complex calculations, the nitrogen budget of the city can be quantified only by nitrogen-related data, realizing the quantification of the nitrogen budget of the city, which is used to train the nitrogen budget quantification prediction model, improving the accuracy and efficiency of the nitrogen budget quantification prediction, and providing scientific and reasonable guidance for the management of nitrogen recycling.

[0211] Please refer to Figure 8 , Figure 8 This is a schematic diagram of the structure of a computer device provided in one embodiment of the present application. The computer device 8 includes: a processor 81, a memory 82, and a computer program 83 stored in the memory 82 and executable on the processor 81; the computer device may store multiple instructions, which are suitable for being loaded and executed by the processor 81. Figures 1 to 6 The method steps of the embodiment shown, the specific execution process can be found in Figures 1 to 6 The detailed description of the illustrated embodiment will not be repeated here.

[0212] The processor 81 may include one or more processing cores. The processor 81 utilizes various interfaces and lines to connect various parts within the server, and executes various functions and processes data of the urban nitrogen budget prediction device 7 by running or executing instructions, programs, code sets, or instruction sets stored in the memory 82, as well as calling data in the memory 82. Optionally, the processor 81 may be implemented in the form of at least one hardware of a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 81 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the touch screen; and the modem is used to handle wireless communications. It is understood that the modem may not be integrated into the processor 81 and may be implemented separately via a single chip.

[0213] Among them, the memory 82 may include a random access memory 82 (Random Access Memory, RAM), and may also include a read-only memory 82 (Read-Only Memory). Optionally, the memory 82 includes a non-transitory computer-readable storage medium. The memory 82 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 82 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch instructions, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 82 may also be optionally at least one storage device located away from the aforementioned processor 81.

[0214] The embodiment of the present application also provides a storage medium, which can store multiple instructions, which are suitable for the processor to load and execute the above Figures 1 to 6 The method steps of the embodiment shown, the specific execution process can be found in Figures 1 to 6 The detailed description of the illustrated embodiment will not be repeated here.

[0215] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0216] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0217] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0218] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components 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 units, which can be electrical, mechanical or other forms.

[0219] The units described as separate components may or may not be physically separate, and the components shown as units 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0220] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0221] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form.

[0222] The present invention is not limited to the above-mentioned embodiments. If various changes or modifications of the present invention do not depart from the spirit and scope of the present invention, and if these changes and modifications fall within the scope of the claims of the present invention and equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A method for predicting urban nitrogen budget, characterized in that: The following steps are involved: Obtaining nitrogen-related data for a target area at several time points within a sample period and a preset urban nitrogen budget quantification model, wherein the urban nitrogen budget quantification model includes a nitrogen budget calculation module and a nitrogen budget quantification module; Inputting the nitrogen-related data at the plurality of time points into the nitrogen budget calculation module, performing nitrogen budget calculation according to the nitrogen-related data and a corresponding nitrogen budget calculation method, and obtaining the nitrogen budget data at the plurality of time points; Inputting the nitrogen budget data at a plurality of time points into the nitrogen budget quantification module to quantify the nitrogen budget, thereby obtaining the nitrogen budget quantification data at a plurality of time points; Constructing nitrogen budget triplet data at several time points, the nitrogen budget triplet data including nitrogen-related data, a corresponding nitrogen budget calculation method, and nitrogen budget data; inputting the nitrogen budget triplet data at several time points and the nitrogen budget quantification data into a preset nitrogen budget quantification prediction model for training to obtain a target nitrogen budget quantification prediction model; According to the target nitrogen budget quantitative prediction model, the nitrogen budget quantitative data of the target area at several time points in the prediction time period are obtained.

2. The urban nitrogen budget prediction method according to claim 1, wherein: The nitrogen budget quantitative prediction model adopts a long short-term memory network; The nitrogen budget triplet data and the nitrogen budget quantification data at a plurality of time points are input into a preset nitrogen budget quantification prediction model for training to obtain a target nitrogen budget quantification prediction model, comprising the steps of: Using the Lagrange interpolation method, processing missing values ​​and outliers on nitrogen-related data in the nitrogen budget triplet data at several time points to obtain processed nitrogen budget triplet data at several time points; Performing feature matrix construction and covariance matrix conversion on the processed nitrogen budget triplet data at several time points to obtain covariance matrices at several time points; confirming several target feature vectors in the covariance matrices at several time points, and combining several target feature vectors in the covariance matrices at the same time point to obtain feature data at several time points; According to a preset time window, characteristic data of several time points are divided into several time windows to construct characteristic data corresponding to the several time windows; the nitrogen budget quantification data of the several time points and the characteristic data corresponding to the several time windows are input into the long short-term memory network, and prediction is performed based on the characteristic data corresponding to the several time windows to obtain prediction data corresponding to the several time windows; Real data corresponding to several time windows are obtained, wherein the real data include nitrogen budget quantification data at several time points; and the nitrogen budget quantification prediction model is trained based on the real data corresponding to the several time windows and the predicted data to obtain a target nitrogen budget quantification prediction model.

3. The urban nitrogen budget prediction method according to claim 2, characterized in that: The prediction data includes nitrogen budget prediction data for the next time point corresponding to a plurality of time points corresponding to the current time window; The method of performing prediction based on the feature data corresponding to the plurality of time windows to obtain prediction data corresponding to the plurality of time windows comprises the steps of: Obtain the predicted data and target cell state corresponding to the previous time window of the current time window; The candidate cell state is calculated based on the feature data corresponding to the current time window and the predicted data corresponding to the previous time window to obtain the candidate cell state corresponding to the current time window; the input gating signal and the forgetting gating signal are calculated based on the feature data corresponding to the current time window and the target cell state corresponding to the previous time window to obtain the candidate cell state input gating signal and the forgetting gating signal corresponding to the current time window; the target cell state is calculated based on the candidate cell state, input gating signal, forgetting gating signal corresponding to the current time window and the target cell state corresponding to the previous time window to obtain the target cell state corresponding to the current time window; the output gating signal is calculated based on the feature data, target cell state corresponding to the current time window and the predicted data corresponding to the previous time window to obtain the output gating signal corresponding to the current time window; the predicted data is calculated based on the target cell state and the output gating signal corresponding to the current time window to obtain the predicted data corresponding to the current time window.

4. The urban nitrogen budget prediction method according to claim 3, characterized in that: The method of training the nitrogen budget quantitative prediction model based on the real data and the predicted data corresponding to the plurality of time windows to obtain the target nitrogen budget quantitative prediction model comprises the following steps: According to the real data, predicted data and a preset loss function corresponding to a plurality of time windows, a loss value is obtained, and the nitrogen budget quantitative prediction model is trained according to the loss value to obtain a target nitrogen budget quantitative prediction model, wherein the loss function is: Where, is the loss value, T is the number of time windows, is the nitrogen budget quantitative data at the i-th time point in the t-th time window, is the nitrogen budget forecast data at the i-th time point in the t-th time window.

5. The urban nitrogen budget prediction method according to claim 4, characterized in that: The step of obtaining nitrogen budget quantitative data of the target area at a plurality of time points in the prediction time period according to the target nitrogen budget quantitative prediction model comprises the following steps: The characteristic data corresponding to the last time window is input into the target nitrogen budget quantitative prediction model to perform nitrogen budget quantitative prediction, and the prediction data corresponding to the last time window is obtained. The nitrogen budget prediction data of several time points in the prediction data corresponding to the last time window are used as the nitrogen budget quantitative data of the target area at several time points in the prediction time period.

6. The urban nitrogen budget prediction method according to claim 1, characterized in that: The nitrogen budget data include production nitrogen budget data, human consumption nitrogen budget data, nitrogen budget data and environmental nitrogen budget data; the human consumption nitrogen budget data include quantitative human consumption nitrogen income data and quantitative human consumption nitrogen expenditure data; the nitrogen budget data also include waste nitrogen budget data; the waste nitrogen budget data include quantitative waste nitrogen income data and quantitative waste nitrogen expenditure data.

7. The urban nitrogen budget prediction method according to claim 6, characterized in that: The step of inputting the nitrogen-related data at the plurality of time points into the nitrogen budget calculation module, performing nitrogen budget calculation according to the nitrogen-related data and a corresponding nitrogen budget calculation method, and obtaining the nitrogen budget data at the plurality of time points comprises the following steps: quantifying the nitrogen budget according to the agricultural nitrogen budget, forest and grassland nitrogen budget, aquaculture nitrogen budget, industrial nitrogen budget, and urban park and green space nitrogen budget in the production nitrogen budget, obtaining the agricultural nitrogen budget quantification data, forest and grassland nitrogen budget quantification data, aquaculture nitrogen budget quantification data, industrial nitrogen budget quantification data, and urban park and green space nitrogen budget quantification data, and constructing the production nitrogen budget quantification data; Quantitative data on human consumption nitrogen income are obtained based on the human food consumption nitrogen income, human industrial consumption nitrogen income, and human life fossil fuel combustion nitrogen emissions in the human consumption nitrogen budget data; quantitative data on human consumption nitrogen expenditure are obtained based on the human consumption domestic sewage nitrogen emissions, the human life garbage waste nitrogen content, and human life energy consumption nitrogen oxide emissions; Obtaining quantitative data on waste nitrogen income based on the human domestic sewage treatment volume, industrial sewage treatment volume, urban park green space landfill volume, and human domestic waste treatment volume in the waste nitrogen budget data; obtaining quantitative data on waste nitrogen expenditure based on the sewage treatment transportation loss volume, sewage treatment discharge nitrogen content, and garbage decomposition leachate production volume in the waste nitrogen budget data; Quantitative data on environmental nitrogen income are obtained based on the amount of loss with surface runoff, atmospheric nitrogen deposition in surface water, and atmospheric nitrogen emissions in the environmental nitrogen budget data; quantitative data on environmental nitrogen expenditure are obtained based on the amount of surface water denitrification, the nitrogen content of surface water used for farmland irrigation, and regional atmospheric nitrogen deposition in the environmental nitrogen budget data.

8. A device for predicting urban nitrogen budget, characterized in that: include: A data acquisition module, configured to obtain nitrogen-related data of a target area at several time points within a sample period and a preset urban nitrogen budget quantification model, wherein the urban nitrogen budget quantification model includes a nitrogen budget calculation module and a nitrogen budget quantification module; a nitrogen budget processing module, configured to input the nitrogen-related data at the plurality of time points into the nitrogen budget calculation module, perform nitrogen budget calculations based on the nitrogen-related data and a corresponding nitrogen budget calculation method, and obtain nitrogen budget data at the plurality of time points; A nitrogen budget quantification module, configured to input the nitrogen budget data at a plurality of time points into the nitrogen budget quantification module for nitrogen budget quantification, thereby obtaining the nitrogen budget quantification data at a plurality of time points; A model training module is used to construct nitrogen budget triplet data at several time points, wherein the nitrogen budget triplet data includes nitrogen-related data, a corresponding nitrogen budget calculation method, and nitrogen budget data; input the nitrogen budget triplet data at several time points and the nitrogen budget quantification data into a preset nitrogen budget quantification prediction model for training to obtain a target nitrogen budget quantification prediction model; The nitrogen budget prediction module is used to obtain nitrogen budget quantitative data of the target area at several time points in the prediction time period according to the target nitrogen budget quantitative prediction model.

9. A computer device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor; when the computer program is executed by the processor, the steps of the urban nitrogen budget prediction method according to any one of claims 1 to 7 are implemented.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the urban nitrogen budget prediction method according to any one of claims 1 to 7 are implemented.

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