NL2030102A - Computer system and method based on evaluation of a regional air pollution regulation plan - Google Patents
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Abstract
The present disclosure discloses a computer system and method based on evaluation of a regional air pollution regulation plan. The evaluation system includes: a simulation system structure module, a causal relationship module, a system dynamics model module, a calculation module and a simulation analysis module. The present disclosure also discloses an evaluation method of a regional air pollution regulation plan. The present disclosure uses a Vensim software to establish a system dynamics model, analyze an internal structure of the system and its dynamic behavior by simulating the interrelationship among economy, population, energy and atmospheric environment, and compare with historical data to verify the authenticity and effectiveness of the model, thereby solving the problem that the economy and society are difficult to test directly and providing reference for government decision.
Description
REGIONAL AIR POLLUTION REGULATION PLAN 5S TECHNICAL FIELD
[01] The present disclosure belongs to a field of air pollution prevention and control, and specifically relates to a computer system and method based on evaluation of a regional air pollution regulation plan.
[02] In order to promote the smooth and effective progress of air pollution prevention and control, experts and scholars have carried out research from multiple aspects such as collaborative governance, legal regulation, energy conservation and emission reduction, and performance evaluation, and have put forward corresponding suggestions on this basis. But in general, research perspectives are mostly focused on unilateral influencing factors, unable to simulate the feedback influence and dynamic interaction between factors, unable to conduct systematic research on the integration of the system as a whole and part, internal and external, and it is difficult to evaluate the overall impact of the policy on the economy, society, and environment after the implementation of the policy.
[03] The implementation of comprehensive control policies for air pollution will have complex effects on the economy, society, energy, and the environment, forming an extremely complex large system. It is difficult to ensure the effect of implementation by relying on intuition and subjective judgment to formulate policies, and the actual implementation of tests 1s too costly. To this end, it is necessary to use computer simulation to simulate and analyze the effects of different policy implementations, try different policy combinations, and make corresponding recommendations based on this.
[04] The purpose of the present disclosure is to overcome the shortcomings of the prior art and provide a computer system and method based on evaluation of a regional air pollution regulation plan. The evaluation system and method use a Vensim software to establish a system dynamics model, analyze an internal structure of the system and its dynamic behavior by simulating the mutual relationship among economy, population, energy and atmospheric environment, and compare with historical data to verify the authenticity and effectiveness of the model, thereby solving the problem that the economy and society are difficult to test directly and providing reference for government decision.
[05] The present disclosure is realized through the following technical solutions:
[06] A computer system based on evaluation of a regional air pollution regulation plan includes a processor, wherein the processor includes:
[07] a simulation system structure module: establishing the simulation system structure module according to an atmospheric environment theory and ecological economics theory, wherein the simulation system structure module comprises an economic subsystem, a population subsystem, an energy subsystem, an environmental subsystem and a government subsystem;
[08] a causal relationship module: manually selecting plan variables, determining a causal relationship between the variables, and drawing a causal loop diagram according to structure and function of the evaluation system;
[09] asystem dynamics model module: using a Vensim software to establish a system dynamics model according to the causal relationship obtained by the causal relationship module, and determining simulation variables in the system dynamics model;
[10] a calculation module: determining mathematical equations and coefficients of the simulation variables according to the causal relationship obtained by the causal relationship module and the model obtained by the system dynamics model;
[11] a simulation analysis module: adding the equations and coefficients obtained by the calculation module to the model obtained by the system dynamics model, and setting a reference scenario and a simulation scenario for simulation analysis.
[12] An evaluation method of a regional air pollution regulation plan includes:
[13] step 1, creating a simulation system structure for a regional air pollution regulation plan;
[14] according to an atmospheric environment theory and ecological economics theory, clarifying that a simulation system of air pollution regulation plan comprises five components: economy, population, energy, environment, and government, and each component influences and restricts each other, and each part constitutes a subsystem , thereby creating the simulation system structure of the regional air pollution regulation plan;
[15] the structure comprises an economic subsystem, a population subsystem, an energy subsystem, an environment subsystem and a government subsystem;
[16] the economic subsystem is for analyzing economic aggregates, economic structure, and economic costs in a process of policy implementation;
[17] the population subsystem is for analyzing total population and changes in population structure;
[18] the energy subsystem is for calculating energy consumption and analyzing changes in energy structure;
[19] the environmental subsystem is for analyzing the generation, treatment and emission of air pollutants;
[20] the government subsystem is for analyzing implementation effects of various policies according to selected historical policy measures;
[21] step 2: determining plan variables and a causal relationship;
[22] according to structure and function of the system in step 1, manually selecting plan variables, determining a causal relationship between the variables, and drawing a causal loop diagram; determining a start and end time of historical data, and obtaining the historical data of the variables from existing statistical data;
[23] the plan variables include: economic data, population data, energy types, energy structure, types of air pollutants, sources of air pollutants, economic policies, and production capacity policies , energy policies, technological progress;
[24] qualitatively describing the causal relationship between plan variables through a causal relationship loop diagram to, and forming a feedback loop by combination of two or more causal relationships, reflecting dynamic changes of the system;
[25] step 3: establishing a system dynamics model using a Vensim software;
[26] according to the plan variables and causal relationship diagram determined in step 2, manually selecting simulation variables in the system dynamics model, and establishing the system dynamics model using the Vensim software;
[27] the simulation variables comprise state variables, rate variables, and auxiliary variables, and the state variables reflect a cumulative effect and reflect a state of the system, the rate variables reflect state changes, and the auxiliary variables help form a complete feedback loop;
[28] step 4: determining mathematical equations of the simulation variables and coefficients in the equations;
[29] step 401: determining the mathematical equations of the simulation variables according to the causal relationship obtained in step 2 and the model obtained in step 3;
[30] step 402: determine the coefficients in some equations by means of expert evaluation and reference from existing data,
[31] step 403: adding the equations and partial coefficients obtained above to the model obtained in step 3;
[32] step 404: for the coefficients in undetermined equations, a set of initial values are randomly determined and added to the model obtained in step 3, and performed simulation;
[33] step 405: if a relative error between a simulated value and a real value is greater than 20%, returning to step 404 to modify a coefficient value, and continuing to simulate until the relative error between the simulated value and the real value is not greater than 20%;
[34] step 5: simulation analysis and prediction
[35] adding the equations and coefficients obtained in step 4 to the model obtained in step 3, and setting a reference scenario and a simulation scenario for simulation analysis;
[36] wherein the reference scenario and simulation scenario are used to simulate and analyze effects of different strategies, and to choose corresponding strategies to prevent and/or control air pollutants. 5S [37] Further, the types of air pollutants include SO,, NOx, VOCs and soot.
[38] Compared with the prior art, the advantages and positive effects of the present disclosure are:
[39] The evaluation system and method of a regional air pollution regulation plan proposed by the present disclosure truly simulate the relationship between economy, population, energy and atmospheric environment, analyze the internal structure of the system and its dynamic behavior, and compare with historical data to verify the authenticity and validity of the model;
[40] The established system dynamics model includes a variety of air pollution control policies. It is possible to control whether each policy is implemented and the intensity of implementation by changing the policy parameters, observe the changing trend of each variable in the system, analyze the implementation effect and implementation cost of the policies, comprehensively consider the economic and environmental benefits brought about by the policy; at the same time, a variety of policy combinations can be set, so that multiple policies can complement each other's strengths and seek the best policy combination to maximize economic and environmental benefits as much as possible, solve problems that are difficult for economic and social experiments to be directly tested, and provide reference for government decision.
[41] FIG. 1 is a flow chart of an evaluation method of a regional air pollution regulation plan according to the present disclosure;
[42] FIG. 2 is a structural diagram of a regional air pollution regulation plan created in embodiment 1 of the present disclosure;
[43] FIG. 3 is a causal relationship loop diagram of embodiment 1 of the present disclosure;
[44] FIG. 41s a comparison diagram between a real value and a simulated value of a determined coefficient in embodiment 1 of the present disclosure; wherein FIG. 4a shows comparison between the real value and the simulated value of a GDP value, and FIG. 4b shows the comparison between the real value and the simulated value of SO, FIG. 4c shows the comparison between the real value and the simulated value of NOx value, and FIG. 4d shows the comparison between the real value and the simulated value of soot emission;
[45] FIG. 5 is a result comparison diagram of a reference scenario and a simulation scenario of embodiment 1 of the present disclosure; wherein, FIG. 5a shows the result comparison diagram of the reference scenario and the simulation scenario of a GDP value; FIG. 5b shows the result comparison diagram of the reference scenario and the simulation scenario of total energy consumption; FIG. Sc shows the result comparison diagram of the reference scenario and the simulation scenario of SOz; FIG. 5d shows the result comparison diagram of the reference scenario and the simulation scenario of NOx emission; FIG. Se shows the result comparison diagram of the reference scenario and the simulation scenario of soot emission; FIG. 5f shows the result comparison diagram of the reference scenario and the simulation scenario of VOCs emission;
[46] FIG. 6 1s a simulation flow diagram of an economic subsystem in the establishment of a system dynamics model using a Vensim software in embodiment 1 of the present disclosure.
[47] In order to make the purpose, technical solutions, beneficial effects and significant progress of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be described clearly and completely in conjunction with the accompanying drawings provided in the embodiments of the present disclosure. Obviously, all the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments; based on the embodiments of the present disclosure, those of ordinary skill in the art can obtain all other embodiments without creative work. The embodiments all belong to the protection scope of the present disclosure.
[48] Embodiment 1
[49] As shown in FIG. 1, an evaluation method based on a regional air pollution regulation plan includes:
[50] Step 1, creating a simulation system structure for a regional air pollution regulation plan
[51] The types of air pollutants consider the four pollutants of SO», NOx, VOCs and soot. From the analysis of the sources of air pollutants, air pollutants are mainly produced by fossil energy consumption, and energy consumption mainly occurs in industrial production and lives of residents, while air pollution regulation plan usually includes economic policies (such as environmental taxes, etc.), capacity policies (such as eliminating backward production capacity, etc.), energy policies (such as coal to gas, coal to electricity, etc.), technological progress (such as pollutant end treatment, etc.). The implementation process will have many complex impacts on the economy, society, energy, and the environment. In order to ensure that the relationship between economy, population, energy and the atmospheric environment is truly simulated, the environmental and economic benefits of the air pollution regulation plan are comprehensively evaluated to determine that the simulation system includes five subsystems: economy, population, energy, environment, and government.
[52] As shown in FIG. 2, the structure includes five subsystems: economy, population, energy, environment, and government. Wherein, the environment subsystem mainly analyzes the generation, treatment and emission of air pollutants; the energy subsystem considers five types of coal, oil, natural gas, electricity, and renewable energy, calculates the consumption of various energy sources and analyzes changes in energy structure; the population subsystem analyze the total population and the changes in the population structure. The population structure affects economic development; the economic subsystem mainly analyzes the total economic volume, economic structure and economic costs in the implementation of policies, in addition to the output value of the energy industry, metallurgy and building materials industry, equipment manufacturing, light industry and high-tech industry, the output value of the primary and tertiary industries and the construction industry is also included; the government subsystem mainly includes various policies and measures implemented by the government, and analyzes the implementation effects of various policies.
[53] Step 2: determining plan variables and a causal relationship
[54] According to structure and function of the system in step 1, manually selecting plan variables, determining a causal relationship between the variables, and drawing a causal loop diagram; determining a start and end time of historical data, and obtaining the historical data of the variables from existing statistical data.
[55] The selected plan variables are as follows:
[56] Economic subsystem: output value of various industries, fixed assets of various industries, fixed asset investment of various industries, GDP, GDP per capita;
[57] Population subsystem: total population, birth population, death population, mechanical population, labor force in various industries; [S8] Energy subsystem: coal, oil, natural gas, electricity consumption in various industries, domestic oil, coal, gas, electricity, clean energy consumption, local power generation, external power purchase, thermal power generation, clean energy power generation, thermal power installed capacity, clean energy installed capacity, clean energy power generation ratio;
[59] Environment subsystem: the amount of pollutants produced by each industry, the amount of pollutants discharged by each industry, the removal rate of pollutants, the amount of domestic pollutants discharged, and the degree of environmental pollution;
[60] Policy subsystem: pollution control cost, environmental tax, coal-to-gas, coal- to-electricity, variable cost of energy in various industries, clean energy power generation ratio target, clean energy policy cost, production capacity change rate of each industry, unit GDP energy consumption target, energy consumption control policy factors, increase in energy equipment investment, and policy implementation costs.
[61] The causal relationship between plan variables is qualitatively described through a causal relationship loop diagram to, and a feedback loop is formed by combination of two or more causal relationships to reflect dynamic changes of the system. As shown in FIG. 3, the most important causal loop is the growth in industry output value, the increase in energy consumption, the increase in pollutant emissions, the decline in environmental quality, and the increase in environmental costs, which adversely affect the growth in output value; at the same time, the total population increases, the total labor force increases, the output value increases, the consumption of domestic energy increases, and the discharge of domestic pollutants increases, the quality of the environment is declined, the number of deaths is increased, and the total population is decreased; air pollution control policies reduce the amount of pollutants generated by reducing the total energy consumption, or directly reduce pollutant emissions.
[62] The start and end time of data is determined to be 2006-2017. The statistical data of the variables can be obtained from the existing statistical data. Part of the data is shown in Table 1-1 and Table 1-2.
[63] Table 1-1 Main data in the simulation system of regional air pollution regulation plan
[64] Primary Second Tertiary industry | Total industry output | industry output | output value | population Years value (100 | value (100 | (100 million | (10,000 million yuan) | million yuan) | yuan) people)
[65] Table 1-2 Main data in the simulation system of regional air pollution regulation plan
[66] Natural gas | Electricity Total coal | Total oil (100 million | (100 million (10,000 tons) | (10,000 tons) cubic meters) | kWh)
[67] Step 3: establishing a system dynamics model using a Vensim software
[68] According to the causal relationship diagram drawn in step 2, the simulation variables in the system dynamics model are determined, and the system dynamics model is established using the Vensim software for quantitative calculation. Wherein, the simulation variables comprise state variables, rate variables, and auxiliary variables, and the state variables reflect a cumulative effect and reflect a state of the system, the rate variables reflect state changes, and the auxiliary variables help form a complete feedback loop. FIG. 6 illustrates a simulation flow diagram of the economic subsystem in the establishment of the system dynamics model using Vensim software.
[69] In the model, the total population, fixed assets, output value, energy consumption per unit output value, and installed capacity are set as state variables. Variables related to changes in state variables such as output value growth, fixed asset investment, birth population, death population, installed capacity growth are set as rate variables, and the total energy consumption, pollutant emissions, policy parameters, etc. are set as auxiliary variables.
[70] Step 4: according to the causal relationship obtained in step 2 and the model obtained in step 3, determining the mathematical equations of the simulation variables of each subsystem; determine the coefficients in some equations by means of expert evaluation and reference from existing data, such as pollutant treatment cost, variable energy cost, pollutant emission factor, environmental pollution degree and so on; adding the equations and partial coefficients obtained above to the model obtained in step 3; for the coefficients in undetermined equations, a set of initial values are randomly determined and added to the model obtained in step 3, and performed simulation; if a relative error between a simulated value and a real value is greater than 20%, returning to step 404 to modify a coefficient value, and continuing to simulate until the relative error between the simulated value and the real value is not greater than 20%.
[71] The data required for the model are collected and sorted out, taking 2006-2017 statistical data of Tianjin as the standard, and the main equations and parameters are established based on the above causal relationship and system flow diagrams, using data fitting, expert evaluation, data reference and other means, as follows:
[72] (1) Economic subsystem
[73] The industries in the economic subsystem are divided into primary industry, tertiary industry, construction industry, energy industry, metallurgical building materials industry, equipment manufacturing industry, light industry and high-tech industry. The calculation formulas for each industry are the same, but some coefficients are different.
[74] Fixed assets of the tertiary industry = INTEG (investment in tertiary industry assets - depreciation of tertiary industry assets, 909.098)
[75] Tertiary industry output value growth rate = 0.427 * tertiary industry fixed asset growth rate + 0.304 + tertiary industry labor force growth rate
[76] Tertiary industry output value growth = Tertiary industry output value growth rate * total output value of the tertiary industry / (1+0.05* environment pollution degree)
[77] Total output value of the tertiary industry = INTEG (increase in the output value of the tertiary industry - decrease in the output value of the tertiary industry, 1917.67)
[78] (2) Population subsystem
[79] Total population = INTEG (born population + mechanical population - death population, 1075)
[80] Total labor = total population * proportion of labor
[81] Death population = total population * death rate * (1+environment pollution degree * 0.01)
[82] (3) Energy subsystem
[83] The energy subsystem includes five types of energy: coal, oil, natural gas, electricity, and renewable energy. The consumption of various energy sources in the production process of each industry in the economic subsystem can be calculated. Renewable energy is mainly consumed by power plants,
[84] Energy consumption of each industry = output value of each industry * output value consumption of each energy unit of each industry
[85] Energy consumption per person = energy consumption per capita * total population Renewable energy installed capacity Remwalle serpy peneation="Loml povt gans ———— ~~
[86] Therma! power tesfalled capacity + Renewable energy inslalled capacity
[87] Local power generation = total power demand - external power purchase
[88] (4) Environmental subsystem
[89] The environmental subsystem includes five pollutants: SO2, NOx, VOCs and soot, and the calculation method for the emission of each pollutant is the same. 190) Industrial $0; sss") 2 Energy consumption by indastry ® 30: enision factor of each energy ® (1 - SO» removal rate)
Thermal power SUh = Energy consamption of thermal power generation * 80; amission eraiszints TF factor of each energy > {1 - SO removal rate}
[91] Domestic SO: wisi Y Domestic energy consumption * 302 emissie factor of domestic energy + Other SO emissions
[92] |
[93] SO: emissions = industrial SO: emissions + thermal power SO: emissions + domestic SO: emissions
[94] (5) Government subsystem
[95] The government subsystem includes air pollution prevention and control measures and economic costs in the implementation process. The policies that the system can simulate are shown in Table 2, and some of the equations are shown below.
[96] Table 2 Policies that can be simulated
[97] Co Policy type Policy plan Implementation Executive Increase the order proportion of tertiary | Increase investment in fixed policy industry output value | assets in the tertiary industry | in total output value Industrial | a Reduce the output value of restructuring | Eliminate backward | __ | | | | high-pollution and high- production capacity | | energy-consuming enterprises Develop advanced | Increase investment in fixed production capacity | assets in advanced industries Increase the | Keep the energy consumption proportion of natural | per unit of output unchanged gas in primary energy | and convert coal consumption Energy consumption to natural gas consumption structure Vigorously develop | Increase the proportion of adjustment clean energy clean energy power generation Increase the | Keep the total energy proportion of coal | consumption unchanged, and used for electricity in | convert part of the domestic total coal | coal into electricity consumption consumption Improve energy | Improve the efficiency of unit efficiency energy consumption Improve — / Desulfurization, technical level EO Change the removal rate of Denitrification, dust each pollutant removal Market Environmental | Taxes are levied on the amount of air pollutants economy | tax emitted by different industries for one equivalent policy Emission reduction and | The cost required to achieve unit removal rate or governance remediation measures costs
[98] Coal reduction per unit output value of the tertiary industry = coal consumption per unit output value of the tertiary industry * (energy consumption control policy factor + coal to gas)
[99] Natural gas growth per unit output value of the tertiary industry = initial natural gas growth per unit output value of the tertiary industry + coal to gas * coal consumption per unit output value of the tertiary industry * 0.7143/13.3 Energy consumption _ { 3 Energy consumption per unit of GDP < Energy consumption per uut GDP target roptrod policy factor | {Energy consompion per wait of GDP - Energy consumption per wait GDP target +: Energy consunpting per unit GDP target, other
[100]
[101] Increase in energy equipment investment = Initial energy equipment investment * energy consumption control policy factor v 2.72% 30: removal rate, 500 removal rate < 0.53 vs | ! . SOZ governance culi 25 Kh removal rate + 0.14, 0.35 <0 SO: removal rate < 0.75 | 33 SC remo rate - 0.24. SC removal rate > OTS
[102]
[103] In the modeling process, in order to determine some of the parameters in the relational formula, the existing historical data was used for fitting and simulation analysis. Some parameters are shown in Table 3
[104] Table 3 Some parameters in the simulation system of regional air pollution regulation plan
[105] SO: emission | NOx emission | VOCs Soot emission ee en wm OOP
9.6 4 2.16 1.89 burning em On
2.09 0.12 burning me en fe
8.19 2.16 5.25 power
[106] The above equations and parameters are added to the system flow diagram, the year is set as the unit, a simulation step is 1, the start year is 2006, and the end year is
2017. Vensim software is used to simulate a constructed “Simulation system of a regional air pollution regulation plan”, wherein real values and simulation results of simulation values of GDP, SO2, NOx and soot emission are shown in FIG. 4.
[107] It can be seen from FIG. 4 that the simulation values are in good agreement with the real values, and the change trend is basically the same. The total GDP has gradually increased. Since 2014, the pollutant emissions have shown a sharp downward trend. By 2017, the discharge of various pollutants was only about one-third of that of 2014, which is basically consistent with the actual situation. In general, the model can describe the basic status of the research system more accurately, the system parameter setting is reasonable, with good forecasting effect, and the model forecasting result is credible.
[108] Step 5: Simulation analysis and prediction
[109] The method and parameters obtained in step 4 are added to the model obtained in step 3, and the start year is set as 2006 and the end year is set as 2030. Initial data for 2018 and 2019 are obtained from existing statistics. For variables determined by the table function, such as a labor force ratio and a fixed asset investment ratio, are calculated based on historical statistical data and expert forecast results to obtain forecast data for 2020-2030. The above data are input into the model, and the values of policy variables are kept unchanged to run the model to get a reference scenario.
[110] In the model, by modifying the start and end years, the initial value of the stock and the value of some auxiliary variables can be changed accordingly to realize the forecast of the future economic, social and energy environment development; at the same time, by adjusting the values of policy parameters, it is possible to simulate the implementation effects of different policies and policy combinations, and get the economic cost of policy implementation at the same time. A simulation scenario is set up here, the policy parameters from 2018 to 2030 are modified, the parameter settings are shown in Table 4, and the results are shown in FIG. 5.
[111] Table 4 Policy parameter setting table of reference scenario and simulation scenario
[112] / Policy control | Reference Simulation Policy tools / } factors scenario scenario Optimize industrial | Production capacity structure (tertiary | change rate of 0.03 industry) tertiary industry Eliminate backward | Production capacity production capacity | change rate of -0.03 (energy industry) energy industry en Production capacity Eliminate backward | ‚ [change rate of production capacity | | oo metallurgical -0.04 (metallurgical building / oo building materials materials industry) | industry Develop advanced | Production capacity manufacturing industry | change rate of (equipment equipment 0.1 manufacturing manufacturing industry) industry
Develop advanced | Production capacity manufacturing industry | change rate of high- 0.1 (high-tech industry) tech industry Increase the proportion of natural gas (coal to | Coal to Gas 0.1 Gas) Develop clean energy | Clean energy power (Clean energy power | generation ratio 03 generation) target Increase the proportion of coal used for Co Coal to Electricity 0.1 electricity (Coal to Electricity) Energy Improve energy / / consumption per | 0.95 0.2 efficiency | unit GDP target Pollutant end treatment | SO; removal rate (desulfurization, NOx removal rate 07 08 | denitrification, dust | VOCs removal rate 07 08 | removal, VOCs oo / Soot removal rate 0.7 emission reduction) Environmental | protection tax rates Environmental tax ‚10 for pollutants in various industries
[113] It can be seen from the above results that when air pollution prevention and control measures are implemented, pollutant emissions are reduced and air quality is improved. At the same time, it will also have a negative impact on GDP. Therefore, models can be used to analyze the emission reduction effects and economic impacts of different policies, continuously modify policy parameters and simulate, and screen out the optimal policy combination method and policy implementation intensity to reduce economic losses while ensuring that air quality meets standards, and achieve a win-win situation for both the economy and the environment.
[114] The above embodiments and specific cases are only used to illustrate the technical solutions of the present disclosure, not to limit them. Although the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that the technical solutions described in the foregoing embodiments may be modified, or some or all of the technical features may be equivalently replaced, and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present disclosure. Any non-essential improvements, adjustments or replacements made by those skilled in the art based on the contents of this specification fall within the scope of protection claimed by the present disclosure.
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