Urban carbon neutralization data processing system and method

Abstract

The application discloses a kind of urban carbon neutralization data processing system and method, the processing method includes the following steps: data processing, acquisition end acquires the multiple parameters of data to establish processing coefficient, whether data needs to be processed by the comparison result of processing coefficient and preset threshold value judges, the data that does not need to be processed directly enters system database storage.The present application sequentially receives the carbon neutralization data of each city in the order of city ranking table and processes, improves the management efficiency of urban carbon neutralization data, and, the carbon neutralization benefit of each city can be shown by the reverse order of city ranking table, in the data processing process, whether data needs to be processed according to the comparison result of processing coefficient and preset threshold value judges, to improve the processing efficiency of data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental data processing, in particular to a city carbon neutralization data processing system and method. BACKGROUND

[0002] City carbon neutralization refers to the effective management of maintaining urban ecological balance by controlling and reducing urban greenhouse gas emissions, restoring vegetation and ecological environment through afforestation, gardening and other ecological measures, reducing urban carbon emissions, protecting urban environment and maintaining urban ecological balance. Through the data processing system, the city carbon emission situation can be monitored in real time, and analyzed and evaluated to put forward improvement suggestions, help the city to formulate scientific and reasonable carbon neutralization strategy and implement it. In addition, the data processing system can also be used in combination with other environmental monitoring technologies to evaluate the effect of carbon neutralization measures to ensure the effectiveness and sustainability of city carbon neutralization work.

[0003] The prior art has the following disadvantages:

[0004] 1. The existing processing system usually processes the carbon neutralization data of each city in sequence when processing city carbon neutralization data. However, this processing method can easily cause the city with poor carbon neutralization benefit to be sorted at the end, reducing the management efficiency (the city with poor carbon neutralization benefit is sorted at the beginning, and the processing system can directly send the data to the analysis system for analysis after processing the data, so that the analysis system and the processing system run synchronously).

[0005] 2. In order to improve the data processing accuracy, the existing processing system usually needs to clean a large amount of data after collecting the data. However, when the data transmission amount is small, the data will first enter the system for data cleaning, which not only reduces the data processing efficiency, but also wastes system resources. SUMMARY

[0006] The purpose of the present application is to provide a city carbon neutralization data processing system and method to solve the problems in the background art.

[0007] In order to achieve the above purpose, the present application provides the following technical scheme: a city carbon neutralization data processing method, the processing method comprising the following steps:

[0008] S1: collecting the carbon emission Tpf and the carbon absorption Txs of the city in n time periods, and obtaining the carbon neutralization deviation Tpc by subtracting the carbon emission Tpf from the carbon absorption Txs;

[0009] S2: the processing end sorts the multiple cities according to the carbon neutralization deviation Tpc from small to large to generate a city sorting table;

[0010] S3: The data processing system sequentially receives and processes the carbon neutralization data of each city in ascending order of the city ranking table;

[0011] S4: During data processing, the processing coefficient is established by collecting multiple parameters of the collected data, and the comparison result of the processing coefficient and the preset threshold value is used to judge whether the data needs to be processed. The data that does not need to be processed is directly stored in the system database;

[0012] S5: When the carbon neutralization benefits of each city need to be displayed, the carbon neutralization benefits of each city can be visualized and displayed in descending order of the city ranking table.

[0013] In a preferred embodiment, in step S1, collecting the carbon emissions Tpf and carbon absorption Txs of the city in n periods includes the following steps:

[0014] S1.1: Set n period value as 24h;

[0015] S1.2: Obtain the carbon emissions Tpf of the city in 24h by carbon emissions Tpf=energy consumption carbon emissions+transportation carbon emissions+industrial carbon emissions+building carbon emissions+public service carbon emissions+residential life carbon emissions+garbage disposal carbon emissions;

[0016] S1.3: Obtain the carbon absorption Txs of the city in 24h by satellite remote sensing technology.

[0017] In a preferred embodiment, in step S1, the carbon neutralization deviation value Tpc is obtained by subtracting the carbon emissions Tpf from the carbon absorption Txs, which includes the following steps:

[0018] S1.4: If carbon absorption Txs-carbon emissions Tpf=deviation value Tpc>0, it means that the carbon emissions of the city are lower than the carbon absorption, and the larger the deviation value Tpc, the higher the carbon neutralization benefit of the city;

[0019] S1.5: If carbon absorption Txs-carbon emissions Tpf=deviation value Tpc=0, it means that the carbon emissions of the city are equal to the carbon absorption;

[0020] S1.6: If carbon absorption Txs-carbon emissions Tpf=deviation value Tpc<0, it means that the carbon emissions of the city are higher than the carbon absorption, and the smaller the deviation value Tpc, the lower the carbon neutralization benefit of the city.

[0021] In a preferred embodiment, in step S5, the carbon neutralization benefit of the city can be visualized and displayed, which includes:

[0022] S5.1: Use a selected visualization tool to establish a visualization model and configure parameters;

[0023] S5.2: Fill the data into the visualization model and draw a graph;

[0024] S5.3: Save after graphic adjustment.

[0025] In a preferred embodiment, in step S4, the plurality of parameters of the collected data establishes the processing coefficient, including the following steps:

[0026] S4.1: Collect the data repetition rate, data reception amount, data format standard rate, and data source quantity of the collected data;

[0027] S4.2: The data repetition rate, data reception amount, data format standard rate, and data source quantity are respectively calibrated as Sjcf, Sjsl, Sjbz, and Sjyl;

[0028] S4.3: Normalize the data repetition rate, data reception amount, data format standard rate, and data source quantity, remove the unit, and establish the processing coefficient Clxs, the expression is:

[0029]

[0030] In the formula, a1, a2, a3, and a4 are the proportional coefficients of the data repetition rate, data reception amount, data format standard rate, and data source quantity, respectively, and a1+a2+a3+a4=4.226, and a3>a1>a2>a4.

[0031] In a preferred embodiment, in step S4, the comparison result of the processing coefficient and the preset threshold value is used to determine whether the data needs to be processed, including the following steps:

[0032] S4.1: Set a preset threshold value Yszi, and compare the processing coefficient Clxs with the preset threshold value Yszi;

[0033] S4.2: If the processing coefficient Clxs is less than the preset threshold value Yszi, the data is classified into the data processing partition by the processing system, the data is cleaned by the data processing partition, and then the data is transmitted to the system database for storage;

[0034] S4.3: If the processing coefficient Clxs is greater than or equal to the preset threshold value Yszi, the data directly enters the system database for storage.

[0035] In a preferred embodiment, the data repetition rate is collected by using a data analysis tool to analyze the data in the data evaluation stage, counting the number of repeated data and comparing it with the total data amount to determine the repetition rate;

[0036] The data reception amount is collected by recording the amount of data obtained from the data source in the data acquisition stage to determine the data reception amount;

[0037] Data format standard rate collection: In the data evaluation stage, use data analysis tools to evaluate the format of the data, count the amount of data that meets the standard format and compare it with the total data amount to determine the data format standard rate.

[0038] Data source quantity collection: In the data acquisition stage, record the number of data sources used to determine the number of data sources.

[0039] In a preferred embodiment, the city carbon neutral data cleaning includes the following steps:

[0040] Data acquisition: Obtain raw data from different data sources;

[0041] Data evaluation: Evaluate data quality, data content and data architecture;

[0042] Data cleaning: Delete errors, inconsistencies, duplicates or useless data;

[0043] Data conversion: Convert raw data to meet the required format and data model;

[0044] Data normalization: Convert raw data to standard format;

[0045] Data missing treatment: Fill in missing data;

[0046] Data integration: Merge multiple data sources into one data set;

[0047] Data verification: Verify whether the cleaned data meets the predetermined standards and identify any remaining errors or inconsistencies.

[0048] In a preferred embodiment, it also includes:

[0049] Collect the fire frequency and power outage frequency in the city n period, respectively marked as Hzpl, Tdpl;

[0050] The fire frequency and power outage frequency are dimensionless, and the error coefficient Wcxs is established after removing the unit, and the expression is:

[0051]

[0052] In the formula, alpha and beta are the proportional coefficients of fire frequency and power outage frequency,

[0053] Alpha + beta = 2.562, and alpha > beta;

[0054] Then recalculate the actual carbon emissions Sjtp = carbon emissions Tpf / error coefficient Wcxs;

[0055] An actual carbon neutralization deviation value Stpc is obtained by subtracting the actual carbon emission amount Sjtp from the carbon absorption amount Txs, the actual carbon neutralization deviation value Stpc is greater, the urban carbon neutralization benefit is higher, and the processing end sorts the plurality of cities according to the actual carbon neutralization deviation value Stpc from small to large, and updates the urban sorting.

[0056] The application further provides an urban carbon neutralization data processing system, comprising a first acquisition module, a calculation module, a sorting module, a processing module, a second acquisition module, a comparative analysis module, a cleaning module and a display module.

[0057] The first acquisition module acquires the carbon emission amount Tpf and the carbon absorption amount Txs of the city in n periods, the calculation module obtains a carbon neutralization deviation value Tpc by subtracting the carbon emission amount Tpf from the carbon absorption amount Txs, the sorting module sorts the plurality of cities according to the carbon neutralization deviation value Tpc from small to large to generate an urban sorting table, the processing module sequentially receives and processes the carbon neutralization data of each city in the urban sorting table in a normal order, in the data processing, the second acquisition module acquires a plurality of parameters of the data to establish a processing coefficient, the comparative analysis module judges whether the data needs to be processed according to the comparison result of the processing coefficient and a preset threshold value, the data that does not need to be processed is directly stored in a system database, the data that needs to be processed is cleaned by the cleaning module, and when the carbon neutralization benefits of the plurality of cities need to be displayed, the display module visually displays the carbon neutralization benefits of the plurality of cities in a reverse order according to the urban sorting table.

[0058] In the above technical solution, the application has the following technical effects and advantages:

[0059] 1. In the collection and processing of the carbon neutralization data of the plurality of cities, the carbon emission amount Tpf and the carbon absorption amount Txs of the city in n periods are first acquired, the carbon neutralization deviation value Tpc is obtained by subtracting the carbon emission amount Tpf from the carbon absorption amount Txs, and then the urban sorting table is generated according to the carbon neutralization deviation value Tpc, so that the carbon neutralization data of each city is sequentially received and processed in a normal order according to the urban sorting table in the processing of the data of each city, the management efficiency of the urban carbon neutralization data is improved, the carbon neutralization benefits of the plurality of cities can be displayed in a reverse order according to the urban sorting table, and the processing efficiency of the data is improved according to the comparison result of the processing coefficient and the preset threshold value in the data processing process.

[0060] 2. The application establishes the processing coefficient by normalizing the data repetition rate, the data reception amount, the data format standard rate and the data source quantity, removes the unit, and judges whether the transmitted data needs to be cleaned according to the comparison result of the processing coefficient Clxs and the preset threshold value Yszi, thereby improving the data processing efficiency of the processing system and avoiding waste of system resources.

[0061] 3、The application removes the unit to establish the error coefficient Wcxs by collecting the fire frequency and power failure frequency in the city n period and making dimensionless processing, and corrects the carbon emission Tpf through the error coefficient Wcxs to obtain the actual carbon emission Sjtp, obtains the actual carbon neutral deviation Stpc by subtracting the actual carbon emission Sjtp from the carbon absorption Txs, and finally updates the city ranking according to the actual carbon neutral deviation Stpc, thereby reducing the calculation error. BRIEF DESCRIPTION OF DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings described below are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0063] Figure 1 The method flowchart of the present application. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical scheme and advantages of the embodiments of the present application more clear, the technical scheme in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0065] Embodiment 1

[0066] Please refer to Figure 1 As shown in the drawings, the city carbon neutral data processing method described in the present embodiment comprises the following steps:

[0067] Collect the carbon emission Tpf and the carbon absorption Txs of the city in n period, obtain the carbon neutral deviation Tpc by subtracting the carbon emission Tpf from the carbon absorption Txs, the larger the carbon neutral deviation Tpc, the higher the city carbon neutral benefit, the processing end sorts a plurality of cities from small to large according to the carbon neutral deviation Tpc to generate a city ranking table, the data processing system receives and processes the carbon neutral data of each city in turn through the city ranking table in ascending order, when processing data, the collection end collects a plurality of parameters of the data to establish a processing coefficient, and judges whether the data needs to be processed through the comparison result of the processing coefficient and the preset threshold value, the data that does not need to be processed is directly stored in the system database, and when the carbon neutral benefit of each city needs to be displayed, the carbon neutral benefit of each city can be visualized and displayed through the city ranking table in descending order.

[0068] In the process of collecting and processing carbon neutralization data of various cities, the carbon emission Tpf and carbon absorption Txs of the city in n time period are collected first, then the carbon neutralization deviation Tpc is obtained by subtracting the carbon emission Tpf from the carbon absorption Txs, and the city ranking table is generated according to the carbon neutralization deviation Tpc. In this way, in the process of processing data of various cities, the carbon neutralization data of various cities is accepted in turn according to the city ranking table in ascending order and processed, so as to improve the management efficiency of the carbon neutralization data of the city. Moreover, the carbon neutralization benefits of various cities can be displayed by descending order of the city ranking table. In the process of data processing, it is judged whether the data needs to be processed according to the comparison result of the processing coefficient and the preset threshold, so as to improve the processing efficiency of the data.

[0069] In this embodiment, the carbon emission Tpf and carbon absorption Txs of the city in n time period include the following steps:

[0070] It is assumed that the value of n time period is 24h;

[0071] The carbon emission Tpf of the city includes:

[0072] Energy consumption data: the energy consumption (such as electricity, fuel, natural gas, etc.) of the city is monitored to determine the 24h energy consumption carbon emission;

[0073] Traffic data: the traffic data (such as cars, public transportation, etc.) of the city is monitored to determine the 24h traffic carbon emission;

[0074] Industrial data: the industrial production and processing process of the city is monitored to determine the 24h industrial carbon emission;

[0075] Building data: the energy consumption of the buildings in the city is monitored to determine the 24h building carbon emission;

[0076] Public service data: the energy consumption of the public service institutions (such as schools, hospitals, government agencies, etc.) in the city is monitored to determine the 24h public service carbon emission;

[0077] Resident life data: the lifestyle and energy use data of the residents in the city are monitored to determine the 24h resident life carbon emission;

[0078] Garbage disposal data: the garbage disposal of the city is monitored to determine the 24h garbage disposal carbon emission.

[0079] The 24h city carbon emission Tpf of the city can be calculated by carbon emission Tpf = energy consumption carbon emission + traffic carbon emission + industrial carbon emission + building carbon emission + public service carbon emission + resident life carbon emission + garbage disposal carbon emission.

[0080] The carbon absorption amount Txs of the city is collected in a manner including ground research, satellite remote sensing, model establishment, and monitoring station.

[0081] Ground research: data of carbon absorption amount is obtained by observing and measuring the growth and death of plants in the city.

[0082] Satellite remote sensing: data of carbon absorption amount is obtained by monitoring the ground green coverage of the city using satellite or aerial remote sensing technology.

[0083] Model establishment: data of carbon absorption amount is obtained by simulating the growth of plants according to environmental data by establishing a plant growth model.

[0084] Monitoring station: data of carbon absorption amount is obtained by measuring the concentration of carbon dioxide in the air in the city by establishing an environmental monitoring station in the city.

[0085] According to the characteristics of the city and the data collection requirements, in order to improve the data collection accuracy, satellite remote sensing is selected to collect the 24-hour carbon absorption amount Txs of the city in this embodiment.

[0086] Embodiment 2

[0087] The carbon neutralization deviation value Tpc is obtained by subtracting the carbon emission amount Tpf from the carbon absorption amount Txs, and the larger the carbon neutralization deviation value Tpc is, the higher the carbon neutralization benefit of the city is.

[0088] The carbon neutralization deviation value Tpc is obtained by subtracting the carbon emission amount Tpf from the carbon absorption amount Txs, and the larger the carbon neutralization deviation value Tpc is, the higher the carbon neutralization benefit of the city is.

[0089] If the carbon absorption amount Txs minus the carbon emission amount Tpf equals the deviation value Tpc>0, it means that the carbon emission amount of the city is lower than the carbon absorption amount, that is, the carbon neutralization benefit of the city is high, and the larger the deviation value Tpc is, the higher the carbon neutralization benefit of the city is.

[0090] If the carbon absorption amount Txs minus the carbon emission amount Tpf equals the deviation value Tpc=0, it means that the carbon emission amount of the city is equal to the carbon absorption amount, that is, the carbon neutralization of the city is in a stable state.

[0091] If the carbon absorption amount Txs minus the carbon emission amount Tpf equals the deviation value Tpc<0, it means that the carbon emission amount of the city is higher than the carbon absorption amount, that is, the carbon neutralization benefit of the city is low, and the smaller the deviation value Tpc is, the lower the carbon neutralization benefit of the city is.

[0092] According to the carbon neutralization deviation value Tpc from small to large, the multiple cities are sorted to generate a city sorting table, so that the city with low carbon neutralization benefit is sorted in front, so that the system can preferentially process the carbon neutralization data of the city with low carbon neutralization benefit in data processing, so that the analysis system can run simultaneously with the processing system, and the management efficiency is improved.

[0093] And in the visualization of the city carbon neutral benefit, the visualization can be directly performed through the reverse order of the sorting table.

[0094] The city carbon neutral benefit visualization includes:

[0095] Using the selected visualization tool, a visualization model is established, and appropriate parameters are configured;

[0096] The data is filled into the visualization model, and the graph is drawn;

[0097] If the graph is not satisfactory, the graph is adjusted to achieve the desired visualization effect;

[0098] The graph is saved for future viewing and use.

[0099] The above steps can help realize data visualization, making data more intuitive and easy to understand.

[0100] Embodiment 3

[0101] During data processing, the processing coefficient is established by collecting multiple parameters of the data collected by the collection end, and the comparison result of the processing coefficient and the preset threshold value is used to judge whether the data needs to be processed. The data that does not need to be processed is directly stored in the system database, which specifically includes the following steps:

[0102] The data repetition rate, data reception amount, data format standard rate and data source quantity of the data collected by the collection end;

[0103] The data repetition rate, data reception amount, data format standard rate and data source quantity are respectively calibrated as Sjcf, Sjsl, Sjbz and Sjyl;

[0104] The data repetition rate, data reception amount, data format standard rate and data source quantity are normalized, and the processing coefficient Clxs is established after removing the unit, and the expression is:

[0105]

[0106] In the formula, a1, a2, a3, a4 are the proportional coefficients of the data repetition rate, data reception amount, data format standard rate and data source quantity, a1+a2+a3+a4=4.226, and a3>a1>a2>a4, and the specific values of the proportional coefficients a1, a2, a3, a4 are set by the person skilled in the art according to the processing efficiency of the data processing system, which is not limited here.

[0107] Set the preset threshold Yszi, and compare the processing coefficient Clxs with the preset threshold Yszi;

[0108] If the processing coefficient Clxs is less than the preset threshold Yszi, the processing system divides the data into a data processing partition, and after the data processing partition cleans the data, the data is transmitted to the system database storage;

[0109] If the processing coefficient Clxs is greater than or equal to the preset threshold Yszi, the data directly enters the system database storage.

[0110] The present application establishes a processing coefficient by normalizing the data repetition rate, data reception amount, data format standard rate, and data source quantity, removes the unit, and according to the comparison result of the processing coefficient Clxs and the preset threshold Yszi, judges whether the transmitted data needs to be cleaned, thereby improving the data processing efficiency of the processing system and avoiding waste of system resources.

[0111] Data repetition rate: In the data evaluation stage, use data analysis tools (such as Excel, SQL, etc.) to analyze the data, count the number of repeated data and compare it with the total data volume to determine the repetition rate.

[0112] Data reception amount: In the data acquisition stage, record the amount of data acquired from the data source to determine the data reception amount.

[0113] Data format standard rate: In the data evaluation stage, use data analysis tools to evaluate the format of the data, count the amount of data that meets the standard format and compare it with the total data volume to determine the data format standard rate.

[0114] Data source quantity: In the data acquisition stage, record the number of data sources used to determine the data source quantity.

[0115] In this embodiment, the city carbon neutralization data cleaning includes the following steps:

[0116] Data acquisition: acquire raw data from different data sources;

[0117] Data evaluation: evaluate data quality, data content and data architecture;

[0118] Data cleaning: delete errors, inconsistencies, duplicates or useless data;

[0119] Data conversion: convert raw data to meet the required format and data model;

[0120] Data normalization: use standardization procedures to convert raw data into a standard format to improve data readability and analyzability;

[0121] Data missing treatment: fill in missing data;

[0122] Data integration: combine multiple data sources into a data set;

[0123] Data validation: Verify that the cleaned data meets predetermined standards and identify any remaining errors or inconsistencies.

[0124] Wherein, the data quality assessment includes the following aspects:

[0125] Completeness: Check if there are missing, missing or inconsistent values in the data;

[0126] Accuracy: Check if there are errors or errors in the data, and assess whether they affect the accuracy of the analysis;

[0127] Consistency: Check if the data follows consistent formats and conventions to ensure that the analysis can be effectively performed;

[0128] Effectiveness: Check if the data is real, valid and ensure that they meet business requirements;

[0129] Novelty: Check if the data is up-to-date, valuable and ensure that they are not reused.

[0130] Therefore, the assessment of data quality includes the following steps:

[0131] First, collect the data to be evaluated, determine the scope of the data according to the requirements, analyze the data to evaluate the quality of the data, make an evaluation report, and clearly state the score of the data quality, and give suggestions to improve the quality of the data, implement measures to improve the quality of the data, such as: modify the data format, improve the data management process, verify the data source, etc. Re-evaluate the quality of the data to ensure that the measures to improve the quality of the data have achieved the expected effect.

[0132] Filling in missing data includes the following steps:

[0133] Define missing data: Identify which data is missing and define the number of missing data;

[0134] Analyze the reasons for missing data: analyze the reasons for missing data, which may include technical errors, missing data sources, human errors, etc.

[0135] Evaluate the impact of missing data: Evaluate the impact of missing data on the results and decide whether to fill in the missing data;

[0136] Select a method to fill in the missing data: Select a method to fill in the missing data, which may include deleting missing data, filling in missing data, replacing missing data with other data sources, etc.

[0137] Apply the method to fill in the missing data: Fill in the missing data using the selected filling method;

[0138] Verification of the results of the imputed data: Verify the results of the imputed data to ensure accuracy;

[0139] Data cleaning: Clean the imputed data to ensure data quality;

[0140] Data updating: Update the imputed data and save it in the database for future use.

[0141] Converting raw data to the required format and data model includes the following steps:

[0142] Identifying the format of the raw data: Evaluate the format and content of the raw data to determine whether they meet the requirements of the predefined data model;

[0143] Data conversion: Convert the raw data to a format that meets the requirements of the predefined data model;

[0144] Data standardization: Ensure that the data has the same units and format for uniform analysis and comparison;

[0145] Data integration: Combine data from multiple sources into a central database for overall analysis;

[0146] Data validation: Check whether the data meets the predefined data model to ensure the accuracy of the data;

[0147] Data reporting: Includes generating reports to provide detailed information about data quality and format.

[0148] Example 5

[0149] In the above example 1, the carbon emissions Tpf and carbon absorption Txs of the city in the n period are collected, the carbon neutral deviation Tpc is obtained by subtracting the carbon emissions Tpf from the carbon absorption Txs, the larger the carbon neutral deviation Tpc, the higher the carbon neutral benefit of the city, the processing end sorts the multiple cities according to the carbon neutral deviation Tpc from small to large, and generates a city ranking table;

[0150] However, when there are some unexpected situations within the city, it will cause the carbon emissions of the city in the n period to increase or decrease, at this time, if the deviation value Tpc is still calculated by collecting the carbon emissions Tpf of the city, it will cause the calculation to have errors (uncertain factors do not occur all the time, and the judgment of the city carbon neutral will have judgment errors, that is, it cannot be used to judge the effort of the city carbon neutral value), therefore we make the following scheme:

[0151] Collect the fire frequency and power outage frequency of the city in the n period, respectively marked as Hzpl, Tdpl;

[0152] The fire frequency and the power outage frequency are dimensionless, and the error coefficient Wcxs is established after removing the unit, and the expression is:

[0153]

[0154] In the formula, α and β are the proportional coefficients of the fire frequency and the power outage frequency, respectively,

[0155] α+β=2.562, and α>β;

[0156] Then the actual carbon emission Sjtp=carbon emission Tpf / error coefficient Wcxs is recalculated and corrected;

[0157] The actual carbon neutral deviation Stpc is obtained by subtracting the actual carbon emission Sjtp from the carbon absorption Txs. The larger the actual carbon neutral deviation Stpc, the higher the carbon neutral benefit of the city. The processing end sorts the multiple cities from small to large according to the actual carbon neutral deviation Stpc, and updates the city ranking.

[0158] The application collects the fire frequency and the power outage frequency in the city n period, and the error coefficient Wcxs is established after removing the unit. The carbon emission Tpf is recalculated and corrected by the error coefficient Wcxs to obtain the actual carbon emission Sjtp. The actual carbon neutral deviation Stpc is obtained by subtracting the actual carbon emission Sjtp from the carbon absorption Txs. Finally, the city ranking is updated according to the actual carbon neutral deviation Stpc, thereby reducing the calculation error.

[0159] Embodiment 6

[0160] The city carbon neutral data processing system described in this embodiment includes a first acquisition module, a calculation module, a sorting module, a processing module, a second acquisition module, a comparative analysis module, a cleaning module, and a display module.

[0161] Among them,

[0162] The first acquisition module: acquires the carbon emission Tpf and the carbon absorption Txs of the city in the n period;

[0163] The calculation module: obtains the carbon neutral deviation Tpc by subtracting the carbon emission Tpf from the carbon absorption Txs;

[0164] The sorting module: sorts the multiple cities from small to large according to the carbon neutral deviation Tpc, and generates a city ranking table;

[0165] The processing module: sequentially receives and processes the carbon neutral data of each city through the city ranking table in order;

[0166] The second acquisition module: when processing data, the acquisition end collects multiple parameters of the data to establish a processing coefficient;

[0167] The comparative analysis module: by processing the comparison result of the coefficient and the preset threshold value to determine whether the data needs to be processed, the data that does not need to be processed directly enters the system database storage;

[0168] The cleaning module: for cleaning the data that needs to be processed;

[0169] The display module: when the carbon neutralization benefits of each city need to be displayed, the carbon neutralization benefits of each city can be visualized by the reverse order of the city ranking table.

[0170] The above embodiments can be realized by software, hardware, firmware or any combination thereof, in whole or in part. When realized by software, the above embodiments can be realized in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid state disk.

[0171] It should be understood that the term "and / or" herein is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone, where A and B can be singular or plural. In addition, the character " / " herein generally represents that the associated objects before and after are an "or" relationship, but can also represent an "and / or" relationship, which can be understood according to the context before and after.

[0172] In this application, "at least one" means one or more, "multiple" means two or more. "At least one of the following (one)" or the like means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can mean a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.

[0173] It should be understood that the size of the sequence of the above-mentioned processes does not mean the order of execution in various embodiments of the present application. The execution order of the processes should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0174] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art 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 application.

[0175] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0176] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed units can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0177] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, i.e. they can be located in one place or distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0178] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0179] The functions, if implemented in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in part, or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and various other media that can store program codes.

[0180] The above is merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which shall be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for processing urban carbon neutrality data, characterized in that: The processing method includes the following steps: S1: Collect the carbon emissions Tpf and carbon absorption Txs of the city within n time periods, and obtain the carbon neutrality deviation value Tpc by subtracting the carbon emissions Tpf from the carbon absorption Txs; S2: The processing end sorts multiple cities from smallest to largest based on the carbon neutrality deviation value Tpc, and generates a city ranking table; S3: The data processing system receives and processes the carbon neutrality data of each city in ascending order of the city ranking table. S4: During data processing, the acquisition end collects multiple parameters of the data to establish processing coefficients. The comparison between the processing coefficients and preset thresholds determines whether the data needs to be processed. Data that does not need to be processed is directly stored in the system database. S5: When it is necessary to display the carbon neutrality benefits of each city, the carbon neutrality benefits of each city can be visualized by using a reverse order of city ranking tables. In step S4, establishing processing coefficients for multiple parameters of the collected data includes the following steps: S4.1: Data duplication rate, data reception volume, data format standardization rate, and number of data sources collected by the acquisition terminal; S4.2: Data duplication rate, data reception volume, data format standardization rate, and number of data sources are respectively labeled as Sjcf, Sjsl, Sjbz, and Sjyl; S4.3: Normalize the data duplication rate, data reception volume, data format standardization rate, and number of data sources, remove the units, and establish a processing coefficient Clxs, with the expression: ； In the formula, These are the proportional coefficients for data duplication rate, data volume received, data format standardization rate, and number of data sources, respectively. and ; In step S4, determining whether the data needs processing based on the comparison between the processing coefficient and the preset threshold includes the following steps: S4.1: Set a preset threshold Yszi, and compare the processing coefficient Clxs with the preset threshold Yszi; S4.2: If the processing coefficient Clxs With a preset threshold Yszi, the processing system divides the data into a data processing partition. After the data processing partition cleans the data, it is then sent to the system database for storage. S4.3: If the processing coefficient Clxs The preset threshold Yszi allows data to be directly stored in the system database. Also includes: Collect the frequency of fires and power outages in the city during time n, and label them as Hzpl and Tdpl, respectively. After dimensionlessly processing the frequency of fires and power outages and removing units, an error coefficient Wcxs is established, expressed as follows: ； In the formula, These are the ratios of fire frequency and power outage frequency, respectively. and ; The actual carbon emissions after recalibration are calculated as follows: Sjtp = carbon emissions Tpf / error coefficient Wcxs; The actual carbon neutrality deviation value Stpc is obtained by subtracting the actual carbon emissions Sjtp from the carbon absorption Txs. The larger the actual carbon neutrality deviation value Stpc is, the higher the carbon neutrality benefit of the city. The processing end sorts multiple cities from small to large according to the actual carbon neutrality deviation value Stpc and updates the city ranking.

2. The urban carbon neutrality data processing method according to claim 1, characterized in that: In step S1, collecting the city's carbon emissions Tpf and carbon absorption Txs over time period n includes the following steps: S1.1: Let n be a time period of 24 hours; S1.2: Obtain the 24-hour carbon emissions Tpf of the city by calculating carbon emissions Tpf = carbon emissions from energy consumption + carbon emissions from transportation + carbon emissions from industry + carbon emissions from buildings + carbon emissions from public services + carbon emissions from residential life + carbon emissions from waste disposal. S1.3: Obtain the city's carbon absorption (Txs) 24 hours a day using satellite remote sensing technology.

3. The urban carbon neutrality data processing method according to claim 2, characterized in that: In step S1, obtaining the carbon neutrality deviation value Tpc by subtracting the carbon emission amount Tpf from the carbon absorption amount Txs includes the following steps: S1.4: If the carbon absorption amount Txs Carbon emissions Tpf = Deviation value Tpc 0 indicates that the carbon emissions of a city are lower than its carbon absorption. The larger the deviation value Tpc, the higher the carbon neutrality benefit of the city. S1.5: If the carbon absorption amount Txs Carbon emissions Tpf = Deviation value Tpc 0, the carbon emissions of a city are equal to its carbon absorption. S1.6: If the carbon absorption amount Txs Carbon emissions Tpf = Deviation value Tpc 0 indicates that the carbon emissions of a city are higher than its carbon absorption. The smaller the deviation value Tpc, the lower the carbon neutrality benefit of the city.

4. The urban carbon neutrality data processing method according to claim 3, characterized in that: In step S5, the visualization of urban carbon neutrality benefits includes: S5.1: Use the selected visualization tool to build a visualization model and configure the parameters; S5.2: Populate the data into the visualization model and draw graphs; S5.3: Save after making graphic adjustments.

5. The urban carbon neutrality data processing method according to claim 1, characterized in that: Data duplication rate collection is as follows: During the data evaluation phase, data analysis tools are used to analyze the data, count the number of duplicate data, and compare it with the total amount of data to determine the duplication rate; Data reception volume collection is as follows: During the data acquisition phase, the amount of data acquired from the data source is recorded to determine the data reception volume; The data format standard rate collection process involves: during the data evaluation phase, using data analysis tools to evaluate the data format, counting the amount of data that conforms to the standard format and comparing it with the total amount of data to determine the data format standard rate; Data source quantity collection involves recording the number of data sources used during the data acquisition phase to determine the total number of data sources.

6. The urban carbon neutrality data processing method according to claim 5, characterized in that: Cleaning urban carbon neutrality data includes the following steps: Data acquisition: Obtaining raw data from different data sources; Data evaluation: assessing data quality, data content, and data architecture; Data cleaning: Remove erroneous, inconsistent, duplicate, or useless data; Data transformation: Transform raw data to conform to the required format and data model; Data normalization: Converting raw data into a standard format; Data missing handling: filling in missing data; Data integration: merging multiple data sources into a single dataset; Data validation: Verify whether the cleaned data meets the predetermined standards and identify any remaining errors or inconsistencies.

7. A city carbon neutrality data processing system, used to implement the processing method according to any one of claims 1-6, characterized in that: It includes a first acquisition module, a calculation module, a sorting module, a processing module, a second acquisition module, a comparison and analysis module, a cleaning module, and a display module; The first acquisition module collects the carbon emissions (Tpf) and carbon absorption (Txs) of cities within n time periods. The calculation module subtracts the carbon emissions (Tpf) from the carbon absorption (Txs) to obtain the carbon neutrality deviation (Tpc). The sorting module sorts multiple cities from smallest to largest based on the carbon neutrality deviation (Tpc) to generate a city ranking table. The processing module receives the carbon neutrality data of each city in ascending order from the city ranking table and processes it. During data processing, the second acquisition module collects multiple parameters of the data to establish processing coefficients. The comparison and analysis module determines whether the data needs to be processed by comparing the processing coefficients with preset thresholds. Data that does not need to be processed is directly stored in the system database. Data that needs to be processed is cleaned by the cleaning module. When it is necessary to display the carbon neutrality benefits of each city, the display module visualizes the carbon neutrality benefits of each city in reverse order from the city ranking table.

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