A smart park dual-carbon smart management system based on big data cloud platform

Abstract

The present invention relates to the field of park management technology, and the present invention discloses a smart park dual-carbon smart management system based on a big data cloud platform; it includes obtaining comprehensive carbon emission data of energy-consuming enterprises, identifying carbon peak data and carbon neutrality data, constructing a carbon emission model, determining whether to enter the carbon neutrality management mode, calculating the abnormal management index of the smart park, and formulating a carbon neutrality management level, and selecting an emission management plan corresponding to the carbon neutrality management level; compared with the prior art, the present invention can avoid the time span of data collection being too large or too small, and construct a carbon emission model through the collected data, so as to quickly and accurately identify and analyze whether the carbon emissions in the smart park have reached their peak, and realize the multi-dimensional and unified identification effect of carbon peak, so as to ensure that the smart park can, under the guidance of the emission management plan, ultimately achieve the goal of zero net carbon dioxide emissions in the smart park.

Description

Technical Field

[0001] The present invention relates to the field of park management technology, and more specifically, to a smart park dual-carbon smart management system based on a big data cloud platform. Background Art

[0002] In the operation and management of smart parks, it is necessary to reduce the carbon dioxide emissions of energy-consuming enterprises in the smart parks as much as possible and ultimately achieve the effect of carbon neutrality. Therefore, it is necessary to carry out intelligent monitoring and management of carbon emissions in smart parks.

[0003] The patent application with reference publication number CN116822988A discloses a carbon neutrality management device and storage medium based on the Internet of Things, including a data acquisition module, a proofreading and evaluation model and a storage module; the data acquisition module is used for daily electricity consumption data of users and energy data generated by clean energy equipment; the proofreading and evaluation module is used to proofread and evaluate the energy data generated by clean energy equipment in the Internet of Things to ensure the authenticity of the user's behavior data and determine the carbon-saving energy value; the storage module is used to upload and store the user's behavior data and carbon-saving energy value;

[0004] The existing smart park management system adopts a method of collecting and analyzing carbon emission data and carbon absorption data within the smart park one by one and independently, which can provide a basis for the carbon emission treatment measures of the smart park and achieve dual-carbon smart management effects. For example, in the above-mentioned patent application, it achieves dual-carbon smart management effects by continuously expanding the amount of data, continuously updating standard data, and continuously collecting data for analysis. This method requires the collection and analysis of a large amount of dual-carbon data, and determines whether the carbon peak has been reached based on the changes in the dual-carbon data one by one, making it impossible to perform overall and rapid analysis and identification of a large amount of dual-carbon data, which in turn leads to limitations in the analysis and identification process of the carbon peak, and also reduces the accuracy of the carbon peak analysis and identification results.

[0005] In view of this, the present invention proposes a smart park dual-carbon smart management system based on a big data cloud platform to solve the above problems. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solutions: a smart park dual-carbon smart management system based on a big data cloud platform, applied to a smart management platform, comprising:

[0007] The data acquisition module is used to identify energy-consuming enterprises in the smart park, formulate the management and monitoring cycle of energy-consuming enterprises, and obtain the comprehensive carbon emission data of energy-consuming enterprises during the management and monitoring cycle. The comprehensive carbon emission data includes production energy consumption data and carbon dioxide emission data;

[0008] A data division module is used to identify effective carbon emission data from comprehensive carbon emission data, and divide the effective carbon emission data into carbon peak data and carbon neutrality data based on data differentiation criteria;

[0009] The identification and determination module is used to build a carbon emission model based on the carbon peak data, identify the model attributes of the carbon emission model, and determine whether to enter the carbon neutrality management mode;

[0010] The index calculation module is used to identify abnormal data from the carbon peak data, calculate the abnormal management index of the smart park based on the abnormal data and carbon neutrality data, and formulate the carbon neutrality management level;

[0011] The smart management module is used to select the emission management plan corresponding to the carbon neutrality management level, send instructions corresponding to the emission management plan to the smart park, and display it visually through a management report.

[0012] Further, the method for developing a management monitoring cycle includes:

[0013] Through the big data cloud platform, the energy consumption labels of all enterprises in the smart park are queried one by one, and the identification values ​​in the energy consumption labels are extracted;

[0014] Enterprises with identification values ​​of 11 or 10 are recorded as energy-consuming enterprises, and A energy-consuming enterprises are obtained;

[0015] Find out the time when the identification value of A energy-consuming enterprises first appears to be 11 or 10 one by one, obtain A energy consumption moments, and arrange the A energy consumption moments in order according to the chronological order;

[0016] The energy consumption moment at the first position is recorded as the cycle start moment, the current moment is recorded as the cycle end moment, and the period between the cycle start moment and the cycle end moment is recorded as the management monitoring cycle.

[0017] Furthermore, the identification method of effective carbon emission data includes:

[0018] The key semantics of energy consumption data and carbon dioxide emission data are identified one by one through natural language processing technology, and the key semantics are split into text and numeric parts through word segmentation technology;

[0019] The recording times of the text part and the number part are queried one by one through the timestamp, and are recorded as the first time and the second time respectively, and the duration between the first time and the second time is recorded as the recording difference duration;

[0020] The recorded difference duration that is less than the standard deviation duration is recorded as the effective duration, and the production energy consumption data or carbon dioxide emission data corresponding to the effective duration is recorded as the effective carbon emission data.

[0021] Furthermore, the criteria for data differentiation are: only the repeated data of the last recording moment are retained;

[0022] The division methods of carbon peak data and carbon neutrality data include:

[0023] The oil volume, gas volume, water volume and electricity volume are recorded as the first target words, and the emission volume, absorption volume and emission concentration ratio are recorded as the second target words;

[0024] Compare the text part in the effective carbon emission data with the first target text and the second target text one by one;

[0025] Record the valid carbon emission data containing any first target character in the text part as the first data to be verified, and record the valid carbon emission data containing any second target character in the text part as the second data to be verified;

[0026] The first data to be verified and the second data to be verified that are repeated are recorded as the first repeated data and the second repeated data, respectively, and the recording times of all the first repeated data and the second repeated data are queried one by one through the timestamps;

[0027] All recorded moments are arranged in chronological order, and the first duplicate data and the second duplicate data except the last one in the recorded moment are eliminated. The remaining first data to be verified and the first duplicate data are recorded as carbon peak data, and the remaining second data to be verified and the second duplicate data are recorded as carbon neutrality data.

[0028] Furthermore, the method for constructing the carbon emission model includes:

[0029] The basic model is selected from the model database, and two model frames with upper and lower parallel distribution are constructed in the basic model, which are respectively recorded as the emission frame and the data frame;

[0030] Input the carbon peak data into the data frame one by one, and count the consumption values ​​of oil, gas, water and electricity in the data frame, which are recorded as the first consumption value, the second consumption value, the third consumption value and the fourth consumption value respectively;

[0031] The first consumption value, the second consumption value, the third consumption value and the fourth consumption value are respectively multiplied by the corresponding emission coefficients, and all the product values ​​are accumulated to obtain the total carbon emissions;

[0032] D emission horizontal lines are drawn at equal intervals on the vertical boundary of the emission frame, and the carbon emission values ​​of the D emission horizontal lines are marked one by one;

[0033] The emission level line whose carbon emission value is consistent with the total carbon emission is recorded as the target line, and the target line is rendered and colored to construct a carbon emission model.

[0034] Furthermore, the model attributes include peaking models and non-peaking models;

[0035] The identification methods of peaking models and non-peaking models include:

[0036] The carbon emission value of the target line in the carbon emission model constructed for the first time is recorded as the initial carbon emission value;

[0037] The carbon emission model is constructed C times through the carbon peak data, C target lines are marked, and the carbon emission values ​​of the C target lines are recorded one by one to obtain C reconstructed carbon emission values;

[0038] When the C reconstructed carbon emission values ​​are all less than or equal to the initial carbon emission values, the model attribute of the carbon emission model is the peak attribute;

[0039] When one of the C reconstructed carbon emission values ​​is greater than the initial carbon emission value, the model attribute of the carbon emission model is the non-peak attribute;

[0040] The methods for determining whether to enter the carbon neutral management mode include:

[0041] When the model attribute of the carbon emission model is the peak attribute, it is determined to enter the carbon neutral management mode;

[0042] When the model attribute of the carbon emission model is the non-peak attribute, it is determined not to enter the carbon neutrality management mode.

[0043] Furthermore, the method for identifying abnormal data includes:

[0044] The carbon peak data whose text parts are oil volume, gas volume, water volume and electricity volume are respectively combined with their corresponding digital parts to generate oil volume data, gas volume data, water volume data and electricity volume data;

[0045] Respectively comparing the digital parts of the oil quantity data, the gas quantity data, the water quantity data and the electricity quantity data with the first safety value, the second safety value, the third safety value and the fourth safety value;

[0046] The oil quantity data, gas quantity data, water quantity data and electricity quantity data whose numerical parts are respectively larger than the first safety value, the second safety value, the third safety value and the fourth safety value are recorded as abnormal data.

[0047] Furthermore, the calculation method of the abnormal management index includes:

[0048] The carbon neutrality data whose text parts are emissions, absorption and emission concentration ratio are respectively combined with their corresponding numerical parts to generate emissions data, absorption data and emission concentration ratio data;

[0049] Compare the numerical part of the emission data with the total carbon emissions, and record the maximum value after comparison as the available emissions;

[0050] Multiply the numerical parts of the available emission and emission concentration ratio data, and subtract the product value from the numerical part of the absorption data to obtain the emission difference value;

[0051] The expression for the difference in emissions is:

[0052] PF cz =PF ky *PF nd -XS zl ;

[0053] In the formula, PF cz is the difference in emissions, PF ky is the available discharge, PF nd is the numerical part of the emission concentration ratio data, XS zl It is the numerical part of the absorption data;

[0054] Count the number of abnormal data, obtain the abnormal value, and calculate the abnormal management index based on the abnormal value and the difference between the emission amount;

[0055] The expression of abnormal management index is:

[0056]

[0057] In the formula, GL zs is the abnormal management index, YC z is an outlier.

[0058] Furthermore, the carbon neutrality management level includes primary management level and secondary management level;

[0059] The method for establishing the primary management level and the secondary management level includes:

[0060] The exception management index GL zs With the preset abnormal management threshold GL yz Compare;

[0061] When GL zs Smaller than GL yz When the first level of management is established;

[0062] When GL zs Greater than or equal to GL yz A two-level management hierarchy was established.

[0063] Furthermore, the emission management plan includes a plan to reduce carbon emissions and a plan to increase the proportion of green electricity;

[0064] The selection methods for reducing carbon emissions and increasing the proportion of green electricity include:

[0065] When the carbon neutrality management level is level 1, choose the carbon emission reduction plan;

[0066] When the carbon neutrality management level is the secondary management level, choose the plan to reduce carbon emissions and increase the proportion of green electricity.

[0067] The technical effects and advantages of the smart park dual-carbon smart management system based on the big data cloud platform of the present invention are as follows:

[0068] The present invention identifies energy-consuming enterprises in the smart park, formulates a management and monitoring cycle for the energy-consuming enterprises, and obtains the comprehensive carbon emission data of the energy-consuming enterprises within the management and monitoring cycle, and identifies effective carbon emission data from the comprehensive carbon emission data. By formulating the management and monitoring cycle, a reasonable and accurate collection time limit can be provided for data affecting dual-carbon smart management to avoid excessive or short time spans for collecting data, and based on data distinction criteria, effective carbon emission data is divided into carbon peak data and carbon neutrality data. A carbon emission model is constructed according to the carbon peak data, and model attributes of the carbon emission model are identified to determine whether to enter the carbon neutrality management mode. By constructing a carbon emission model through the collected data, whether the carbon emissions in the smart park have reached their peak can be quickly and accurately identified and analyzed, achieving a multi-dimensional and unified identification effect of carbon peak, thereby providing specific measures for subsequent carbon neutrality. It provides a solid and real foundation, avoids the limitations and inaccuracies brought by the independent identification of carbon peak data one by one, identifies abnormal data from the carbon peak data, calculates the abnormal management index of the smart park based on the abnormal data and carbon neutrality data, and formulates a carbon neutrality management level, selects the emission management plan corresponding to the carbon neutrality management level, sends instructions corresponding to the emission management plan to the smart park, and visualizes it through a management report. At the same time, the carbon neutrality management level can provide a basis for the smart park to formulate targeted emission management plans, so as to ensure that the smart park can ultimately achieve the goal of zero net carbon dioxide emissions in the smart park under the guidance of the emission management plan, thereby ensuring that the smart park can achieve the dual carbon goals as scheduled and efficiently, and greatly improve the management ability and level of the management department for economic and social development, and effectively promote the intelligence of the management department's dual carbon governance capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 A schematic diagram of a smart park dual-carbon smart management system based on a big data cloud platform provided in Embodiment 1 of the present invention;

[0070] Figure 2 A schematic diagram of a process flow of a smart park dual-carbon smart management method based on a big data cloud platform provided in Embodiment 2 of the present invention;

[0071] Figure 3A schematic diagram of a structural diagram of an electronic device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0072] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0073] Example 1: Please refer to Figure 1 As shown, the smart park dual-carbon smart management system based on the big data cloud platform described in this embodiment is applied to the smart management platform, including:

[0074] The data acquisition module identifies energy-consuming enterprises in the smart park, formulates the management and monitoring cycle of energy-consuming enterprises, and obtains the comprehensive carbon emission data of energy-consuming enterprises during the management and monitoring cycle;

[0075] Energy-consuming enterprises refer to enterprises that consume energy and produce carbon dioxide emissions in smart parks that can participate in the smart management of carbon peak and carbon neutrality, and serve as the objects of subsequent data collection and smart management of smart parks, thereby narrowing the scope of subsequent data collection and clarifying the objects of data collection;

[0076] When energy-consuming enterprises are identified, it is necessary to limit the corresponding duration of dual-carbon smart management of energy-consuming enterprises, so as to limit the collection of relevant data of energy-consuming enterprises and record it as the management monitoring cycle to ensure that the data collected subsequently are within the management monitoring cycle;

[0077] Methods for developing a management monitoring cycle include:

[0078] The energy consumption labels of all enterprises in the smart park are queried one by one through the big data cloud platform, and the identification value in the energy consumption label is extracted; the energy consumption label is used to indicate whether the enterprise consumes energy, and the identification value indicates whether the energy consumption label consumes energy. The identification values ​​include 11, 10 and 00; 11 means that the enterprise has been consuming energy, 10 means that the enterprise is currently consuming energy, and 00 means that the enterprise does not consume energy;

[0079] Enterprises with identification values ​​of 11 or 10 are recorded as energy-consuming enterprises, and A energy-consuming enterprises are obtained;

[0080] Find out the time when the identification value of A energy-consuming enterprises first appears to be 11 or 10 one by one, obtain A energy consumption moments, and arrange the A energy consumption moments in order according to the chronological order;

[0081] The energy consumption moment at the first position is recorded as the cycle start moment, the current moment is recorded as the cycle end moment, and the period between the cycle start moment and the cycle end moment is recorded as the management monitoring cycle.

[0082] It should be noted that the management monitoring cycle is used to meet the time requirements for collecting relevant data in all energy-consuming enterprises. Therefore, the time span of the management monitoring cycle is relatively large, thereby ensuring that each energy-consuming enterprise A can obtain the required data within the management monitoring cycle.

[0083] Comprehensive carbon emission data refers to the data that can affect the smart management of carbon peak and carbon neutrality of energy-consuming enterprises during the management and monitoring cycle. Since the data that affect the smart management of carbon peak and carbon neutrality needs to be implemented from the two aspects of energy consumption and carbon dioxide emissions, the comprehensive carbon emission data also needs to include data on energy consumption and carbon dioxide emissions;

[0084] Comprehensive carbon emission data includes production energy consumption data and carbon dioxide emission data; production energy consumption data is used to collect data on energy consumed by energy-consuming enterprises during production within the management and monitoring period, and can represent the total amount and changes in energy consumption of energy-consuming enterprises. Production energy consumption data includes but is not limited to gas consumption, water consumption, electricity consumption and oil consumption. Carbon dioxide emission data is used to collect data on carbon dioxide emitted by energy-consuming enterprises during the management and monitoring period, and can represent the total amount of carbon dioxide emissions of energy-consuming enterprises. Carbon dioxide emission data includes but is not limited to carbon dioxide concentration and carbon dioxide emissions;

[0085] Specifically, if you want to obtain production energy consumption data, you can do so through manual reporting, connecting to the big data cloud platform system and collection and metering equipment for automatic collection. If you want to obtain carbon dioxide emission data, you can collect it through sampling pipelines, analysis cabinets, infrared analyzers and other collection equipment.

[0086] The data division module identifies effective carbon emission data from the comprehensive carbon emission data, and divides the effective carbon emission data into carbon peak data and carbon neutrality data based on the data differentiation criteria;

[0087] Effective carbon emission data refers to the ability to express the meaning and type of comprehensive carbon emission data, so as to concisely and accurately express large amounts of complex comprehensive carbon emission data and facilitate the subsequent accurate distinction of comprehensive carbon emission data;

[0088] Methods for identifying effective carbon emission data include:

[0089] The key semantics of production energy consumption data and carbon dioxide emission data are identified one by one through natural language processing technology, and the key semantics are split into text part and digital part through word segmentation technology; the text part and digital part are used to represent the text and digital of each data in the production energy consumption data and carbon dioxide emission data respectively, so that each data can be split into two dimensions, which is convenient for the subsequent analysis and identification of production energy consumption data and carbon dioxide emission data;

[0090] The recording times of the text part and the number part are queried one by one through the timestamp, and are recorded as the first time and the second time respectively, and the duration between the first time and the second time is recorded as the recording difference duration;

[0091] The recording difference time that is less than the standard deviation time is recorded as the effective time, and the production energy consumption data or carbon dioxide emission data corresponding to the effective time is recorded as the effective carbon emission data. The standard deviation time refers to the maximum time between the first moment and the second moment in the effective carbon emission data, which can limit the recording time corresponding to the text part and the numerical part in the effective carbon emission data.

[0092] When valid carbon emission data is obtained, the valid carbon emission data at this time is a comprehensive representation of the carbon peak and carbon neutrality in the smart park. In order to achieve the ultimate goal of carbon neutrality in the smart park, it is necessary to identify the data that affects whether the smart park has reached its carbon peak. Therefore, it is necessary to distinguish the valid carbon emission data and obtain carbon peak data and carbon neutrality data, so that the carbon peak data can indicate whether the carbon emissions in the smart park have reached their peak, and the carbon neutrality data can indicate zero net carbon dioxide emissions in the smart park;

[0093] When distinguishing valid carbon emission data, it is necessary to divide the data under the constraints of the data differentiation criteria to ensure that the valid carbon emission data can maintain order and accuracy when divided;

[0094] The data distinction criteria are: only retain the duplicate data of the last recording moment; this ensures that there will not be two or more identical and duplicate data in the divided carbon peak data and carbon neutrality data, thereby ensuring the uniqueness of the carbon peak data and carbon neutrality data, and ensuring that the retained data is the latest data of energy-consuming enterprises;

[0095] The division methods of carbon peak data and carbon neutrality data include:

[0096] The oil volume, gas volume, water volume and electricity volume are recorded as the first target words, and the emission volume, absorption volume and emission concentration ratio are recorded as the second target words;

[0097] Compare the text part in the effective carbon emission data with the first target text and the second target text one by one;

[0098] Record the valid carbon emission data containing any first target character in the text part as the first data to be verified, and record the valid carbon emission data containing any second target character in the text part as the second data to be verified;

[0099] The first data to be verified and the second data to be verified that are repeated are recorded as the first repeated data and the second repeated data, respectively, and the recording times of all the first repeated data and the second repeated data are queried one by one through the timestamps;

[0100] All recorded moments are arranged in chronological order, and the first duplicate data and the second duplicate data except the last one in the recorded moment are eliminated. The remaining first data to be verified and the first duplicate data are recorded as carbon peak data, and the remaining second data to be verified and the second duplicate data are recorded as carbon neutrality data.

[0101] By identifying duplicates and eliminating redundant data, we can ensure that each carbon peak data and carbon neutrality data is unique, which can provide a solid data foundation for the subsequent accurate analysis and precise management of carbon peak and carbon neutrality in smart parks.

[0102] The identification and determination module builds a carbon emission model based on the carbon peak data, identifies the model attributes of the carbon emission model, and determines whether to enter the carbon neutrality management mode;

[0103] The carbon emission model is a virtual simulation model used to comprehensively and accurately display the carbon peak data of the smart park, and provide an intuitive and accurate basis for subsequent judgment on whether the smart park has reached its carbon peak.

[0104] The methods for building the carbon emission model include:

[0105] The basic model is selected from the model database, and two model frames with upper and lower parallel distribution are constructed in the basic model, which are respectively recorded as emission frame and data frame; the basic model is the basic framework for constructing the carbon emission model, and the model frame is the smallest unit that constitutes the basic model and provides the input location for subsequent data;

[0106] Input the carbon peak data into the data frame one by one, and count the consumption values ​​of oil, gas, water and electricity in the data frame, which are recorded as the first consumption value, the second consumption value, the third consumption value and the fourth consumption value respectively;

[0107] The first consumption value, the second consumption value, the third consumption value and the fourth consumption value are respectively multiplied by the corresponding emission coefficients, and all the product values ​​are accumulated to obtain the total carbon emissions; the emission coefficient refers to the ratio of the amount of oil, gas, water and electricity converted into carbon dioxide for emission when consumed, so that the amount of carbon dioxide emitted when the amount of oil, gas, water and electricity is consumed can be accurately and comprehensively obtained;

[0108] D emission horizontal lines are drawn at equal intervals on the vertical boundary of the emission frame, and the carbon emission values ​​of the D emission horizontal lines are marked one by one; the emission horizontal lines are used to indicate the horizontal height positions of carbon emission values ​​of different sizes, so that the numerical value of the total carbon emissions can be intuitively and clearly understood;

[0109] The emission level line whose carbon emission value is consistent with the total carbon emission is recorded as the target line, and the target line is rendered and colored to construct a carbon emission model.

[0110] After the carbon emission model is constructed, it is necessary to identify and divide the model attributes of the carbon emission model, so as to indicate whether the carbon peak data has reached the carbon emission peak and serve as the basis for identifying the model attributes;

[0111] Model attributes include peak model and non-peak model; peak model means that the total carbon emissions in the carbon emission model have reached the peak, and non-peak model means that the total carbon emissions in the carbon emission model have not reached the peak;

[0112] The identification methods of peaking models and non-peaking models include:

[0113] The carbon emission value of the target line in the carbon emission model constructed for the first time is recorded as the initial carbon emission value;

[0114] The carbon emission model is constructed C times through the carbon peak data, C target lines are marked, and the carbon emission values ​​of the C target lines are recorded one by one to obtain C reconstructed carbon emission values;

[0115] When the C reconstructed carbon emission values ​​are all less than or equal to the initial carbon emission values, it means that the total carbon emission in the carbon emission model has reached its peak state, and the model attribute of the carbon emission model is the peak attribute;

[0116] When one of the C reconstructed carbon emission values ​​is greater than the initial carbon emission value, it means that the total carbon emissions in the carbon emission model have not reached the peak state. At this time, the model attribute of the carbon emission model is the non-peak attribute.

[0117] According to the different attributes of the model, it can provide a basis for whether the smart park will enter the carbon neutrality management mode in the future. Since the smart park needs to reach the carbon peak state when conducting carbon neutrality management, it can only enter the carbon neutrality management mode when it reaches the carbon peak state, ensuring that the smart park can achieve dual-carbon smart management effects;

[0118] The methods for determining whether to enter the carbon neutral management mode include:

[0119] When the model attribute of the carbon emission model is the peak attribute, the total carbon emissions of the smart park have reached the peak state, and it is determined to enter the carbon neutral management mode;

[0120] When the model attribute of the carbon emission model is the non-peak attribute, the total carbon emissions of the smart park have not reached the peak state, and it is determined not to enter the carbon neutrality management mode.

[0121] The index calculation module identifies abnormal data from the carbon peak data, calculates the abnormal management index of the smart park based on the abnormal data and carbon neutrality data, and formulates the carbon neutrality management level;

[0122] Since the carbon emission model is constructed based on carbon peak data, the basis for constructing the carbon emission model is a large amount of carbon peak data, and the corresponding data for identifying and judging the carbon emission model is also carbon peak data. When an abnormal phenomenon occurs in the carbon emission model, the carbon peak data that causes the abnormal phenomenon in the carbon emission model will become abnormal data, and the abnormal data also needs to be identified;

[0123] Methods for identifying abnormal data include:

[0124] The carbon peak data whose text parts are oil volume, gas volume, water volume and electricity volume are respectively combined with their corresponding digital parts to generate oil volume data, gas volume data, water volume data and electricity volume data;

[0125] The digital parts of the oil quantity data, gas quantity data, water quantity data and electricity quantity data are respectively compared with the first safety value, the second safety value, the third safety value and the fourth safety value; the first safety value, the second safety value, the third safety value and the fourth safety value respectively refer to the maximum values ​​of the corresponding digital parts of the oil quantity data, the gas quantity data, the water quantity data and the electricity quantity data when they are not recorded as abnormal data, which can provide a basis for the subsequent identification of abnormal data;

[0126] The oil quantity data, gas quantity data, water quantity data and electricity quantity data whose numerical parts are respectively larger than the first safety value, the second safety value, the third safety value and the fourth safety value are recorded as abnormal data.

[0127] The abnormal management index is used to indicate the difficulty of carbon neutrality management in smart parks, and can provide a basis for the specific methods and measures of subsequent carbon neutrality, thereby ensuring that smart parks can achieve rapid and accurate dual-carbon management effects;

[0128] The calculation method of the abnormal management index includes:

[0129] The carbon neutrality data whose text parts are emissions, absorption and emission concentration ratio are respectively combined with their corresponding numerical parts to generate emissions data, absorption data and emission concentration ratio data;

[0130] Compare the numerical part of the emission data with the total carbon emissions, and record the maximum value after comparison as the available emissions;

[0131] Multiply the numerical parts of the available emission and emission concentration ratio data, and subtract the product value from the numerical part of the absorption data to obtain the emission difference value;

[0132] The expression for the difference in emissions is:

[0133] PF cz =PF ky *PF nd -XS zl ;

[0134] In the formula, PF cz is the difference in emissions, PF ky is the available discharge, PF nd is the numerical part of the emission concentration ratio data, XS zl It is the numerical part of the absorption data;

[0135] Count the number of abnormal data, obtain the abnormal value, and calculate the abnormal management index based on the abnormal value and the difference between the emission amount;

[0136] The expression of abnormal management index is:

[0137]

[0138] In the formula, GL zs is the abnormal management index, YC z is an outlier.

[0139] After calculating the abnormal management index, it is necessary to indicate the difficulty level of carbon neutrality management for the smart park according to the size of the abnormal management index, so as to achieve the effect of distinguishing carbon neutrality management of different difficulty levels and realize efficient and accurate dual carbon management effect of the smart park;

[0140] The carbon neutrality management level includes the first-level management level and the second-level management level; the difficulty of carbon neutrality management corresponding to the first-level management level is lower than that corresponding to the second-level management level;

[0141] The method for establishing the primary management level and the secondary management level includes:

[0142] The exception management index GL zs With the preset abnormal management threshold GL yzComparison; The preset abnormal management threshold refers to the abnormal management index when the carbon neutrality management level is at the critical point between the first-level management level and the second-level management level, so as to accurately identify the carbon neutrality management level; The preset abnormal management threshold is obtained by collecting the maximum values ​​of the abnormal management indexes corresponding to a large number of first-level management levels in history and calculating their average value;

[0143] When GL zs Smaller than GL yz When , it means that the abnormal management index is less than the preset abnormal management threshold. At this time, the difficulty of carbon neutrality management in the smart park is relatively low, and a first-level management level is formulated;

[0144] When GL zs Greater than or equal to GL yz When , it means that the abnormal management index is greater than or equal to the preset abnormal management threshold. At this time, it is difficult for the smart park to carry out carbon neutrality management, and a secondary management level is formulated.

[0145] It should be noted that when the carbon neutrality management measures corresponding to the first-level management level and the second-level management level are within an overall scope, they can both play a role in carbon neutrality management for smart parks. The difference lies in that the number or depth of specific carbon neutrality management measures corresponding to the first-level management level will be slightly inferior to the number or depth of specific carbon neutrality management measures corresponding to the second-level management level.

[0146] The smart management module selects the emission management plan corresponding to the carbon neutrality management level, sends instructions corresponding to the emission management plan to the smart park, and displays them visually through the management report;

[0147] The emission management plan refers to the specific measures for carbon neutrality management of smart parks under the primary and secondary management levels, thereby reducing the difference between the total carbon emissions and absorption in the smart park until the emissions and absorption are equal, and finally achieving the carbon neutrality effect of the smart park and achieving the goal of zero net carbon dioxide emissions;

[0148] The emission management plan includes a plan to reduce carbon emissions and a plan to increase the proportion of green electricity. The plan to reduce carbon emissions refers to a plan to reduce the consumption of non-renewable energy in the smart park, thereby reducing carbon dioxide emissions. The plan to increase the proportion of green electricity refers to a plan to increase the proportion of green renewable energy in the smart park, thereby reducing carbon dioxide emissions.

[0149] The selection methods for reducing carbon emissions and increasing the proportion of green electricity include:

[0150] When the carbon neutrality management level is level one, it is relatively easy for the smart park to achieve carbon neutrality, so the carbon emission reduction solution is selected;

[0151] When the carbon neutrality management level is the second-level management level, it is more difficult for the smart park to achieve carbon neutrality, so the plan to reduce carbon emissions and increase the proportion of green electricity will be chosen.

[0152] When a plan to reduce carbon emissions or a plan to increase the proportion of green electricity is selected, it is necessary to send corresponding specific instructions to the smart park according to the difference between the plan to reduce carbon emissions and the plan to increase the proportion of green electricity, so that the smart park can implement specific carbon neutrality management measures for energy-consuming enterprises in the smart park under the instructions;

[0153] Specifically, when choosing a plan to reduce carbon emissions, specific carbon neutrality measures include but are not limited to reducing fuel usage, reducing gas usage, reducing industrial water usage, and reducing fuel electricity usage, thereby reducing carbon emissions from the direction of reducing the consumption of non-renewable energy to achieve the effect of carbon neutrality. When choosing a plan to increase the proportion of green electricity, specific carbon neutrality measures include but are not limited to increasing the use of wind power, increasing the use of solar power, increasing the use of tidal power, and increasing the area of ​​green plant cultivation to achieve the effect of carbon neutrality.

[0154] After selecting the corresponding emission management plan, the specific data and measures of the smart park in the dual carbon monitoring management need to be visualized through the management report, so that the energy-consuming enterprises in the smart park can timely, comprehensively and quickly understand the corresponding information, which is convenient for the energy-consuming enterprises in the smart park to achieve the purpose of dual carbon smart management;

[0155] The management report is a report used to comprehensively present the carbon peak data, carbon neutrality data, abnormal data, abnormal management index, carbon neutrality management level and emission management plan of the smart park. It can represent the comprehensive data information of the smart park's dual-carbon smart management. At the same time, the management report is visualized through the visual terminal devices in the smart park, so that energy-consuming enterprises in the smart park can understand the management report and ultimately achieve the dual-carbon smart management effect of the smart park.

[0156] In this embodiment, energy-consuming enterprises in the smart park are identified, a management and monitoring cycle for the energy-consuming enterprises is formulated, and the comprehensive carbon emission data of the energy-consuming enterprises during the management and monitoring cycle is obtained. Effective carbon emission data is identified from the comprehensive carbon emission data, and based on data differentiation criteria, the effective carbon emission data is divided into carbon peak data and carbon neutrality data. A carbon emission model is constructed according to the carbon peak data, and the model attributes of the carbon emission model are identified to determine whether to enter the carbon neutrality management mode. Abnormal data is identified from the carbon peak data, and based on the abnormal data and carbon neutrality data, the abnormal management index of the smart park is calculated, and a carbon neutrality management level is formulated. The emission management plan corresponding to the carbon neutrality management level is selected, and instructions corresponding to the emission management plan are sent to the smart park, and the results are visualized through a management report. Compared with the prior art, by formulating the management monitoring cycle, factors affecting the dual-carbon smart management can be controlled. The data provides a reasonable and accurate collection time limit to avoid the time span of collecting data being too large or too small, and a carbon emission model is constructed through the collected data. It can quickly and accurately identify and analyze whether the carbon emissions in the smart park have reached its peak, and achieve a multi-dimensional and unified identification effect of carbon peak, thereby providing a solid and real foundation for subsequent specific carbon neutrality measures, avoiding the limitations and inaccuracies brought about by the independent identification of carbon peak data one by one. At the same time, the carbon neutrality management level can be used to provide a basis for the smart park to formulate targeted emission management plans, so as to ensure that the smart park can ultimately achieve the goal of zero net carbon dioxide emissions in the smart park under the guidance of the emission management plan, thereby ensuring that the smart park can achieve the dual carbon goals as scheduled and efficiently, and greatly improve the management ability and level of the management department for economic and social development, and effectively promote the intelligence of the management department's dual carbon governance capabilities.

[0157] Example 2: Please refer to Figure 2 As shown, the part not described in detail in this embodiment is described in the first embodiment, which provides a smart park dual-carbon smart management method based on a big data cloud platform, which is applied to a smart management platform and is implemented based on a smart park dual-carbon smart management system based on a big data cloud platform, including:

[0158] S1: Identify energy-consuming enterprises in the smart park, formulate a management and monitoring cycle for energy-consuming enterprises, and obtain comprehensive carbon emission data of energy-consuming enterprises during the management and monitoring cycle. The comprehensive carbon emission data includes production energy consumption data and carbon dioxide emission data;

[0159] S2: Identify effective carbon emission data from comprehensive carbon emission data, and divide effective carbon emission data into carbon peak data and carbon neutrality data based on data differentiation criteria;

[0160] S3: Build a carbon emission model based on carbon peak data, identify the model attributes of the carbon emission model, and determine whether to enter the carbon neutrality management mode;

[0161] S4: If the carbon neutrality management mode is entered, abnormal data is identified from the carbon peak data, and based on the abnormal data and carbon neutrality data, the abnormal management index of the smart park is calculated, and the carbon neutrality management level is formulated;

[0162] S5: Select the emission management plan corresponding to the carbon neutrality management level, send instructions corresponding to the emission management plan to the smart park, and visualize it through a management report.

[0163] Example 3: Please refer to Figure 3 As shown, this embodiment discloses an electronic device, including a processor and a memory;

[0164] Wherein, the memory stores a computer program that can be called by the processor;

[0165] The processor executes the smart park dual-carbon smart management method based on the big data cloud platform by calling the computer program stored in the memory.

[0166] Since the electronic device introduced in this embodiment is an electronic device used to implement a smart park dual-carbon intelligent management method based on a big data cloud platform in Example 2 of this application, based on the smart park dual-carbon intelligent management method based on a big data cloud platform introduced in the embodiment of this application, the technical personnel of this field can understand the specific implementation method of the electronic device of this embodiment and its various variations, so how the electronic device implements the method in the embodiment of this application is not introduced in detail here. As long as the technical personnel of this field implement the electronic device adopted by the smart park dual-carbon intelligent management method based on a big data cloud platform in the embodiment of this application, it belongs to the scope of protection of this application.

[0167] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A smart park dual-carbon smart management system based on a big data cloud platform, applied to a smart management platform, characterized in that: include: The data acquisition module is used to identify energy-consuming enterprises in the smart park, formulate the management and monitoring cycle of energy-consuming enterprises, and obtain the comprehensive carbon emission data of energy-consuming enterprises during the management and monitoring cycle. The comprehensive carbon emission data includes production energy consumption data and carbon dioxide emission data; A data division module is used to identify effective carbon emission data from comprehensive carbon emission data, and divide the effective carbon emission data into carbon peak data and carbon neutrality data based on data differentiation criteria; The identification and determination module is used to build a carbon emission model based on the carbon peak data, identify the model attributes of the carbon emission model, and determine whether to enter the carbon neutrality management mode; The index calculation module is used to identify abnormal data from the carbon peak data, calculate the abnormal management index of the smart park based on the abnormal data and carbon neutrality data, and formulate the carbon neutrality management level; The smart management module is used to select the emission management plan corresponding to the carbon neutrality management level, send instructions corresponding to the emission management plan to the smart park, and display it visually through a management report.

2. According to the smart park dual-carbon smart management system based on big data cloud platform according to claim 1, it is characterized in that: Methods for developing a management monitoring cycle include: Through the big data cloud platform, the energy consumption labels of all enterprises in the smart park are queried one by one, and the identification values ​​in the energy consumption labels are extracted; Enterprises with identification values ​​of 11 or 10 are recorded as energy-consuming enterprises, and A energy-consuming enterprises are obtained; Find out the time when the identification value of A energy-consuming enterprises first appears to be 11 or 10 one by one, obtain A energy consumption moments, and arrange the A energy consumption moments in sequence according to the chronological order; The energy consumption moment at the first position is recorded as the cycle start moment, the current moment is recorded as the cycle end moment, and the period between the cycle start moment and the cycle end moment is recorded as the management monitoring cycle.

3. According to claim 2, a smart park dual-carbon smart management system based on a big data cloud platform is characterized in that: Methods for identifying effective carbon emission data include: The key semantics of energy consumption data and carbon dioxide emission data are identified one by one through natural language processing technology, and the key semantics are split into text and numeric parts through word segmentation technology; The recording times of the text part and the number part are queried one by one through the timestamp, and are recorded as the first time and the second time respectively, and the duration between the first time and the second time is recorded as the recording difference duration; The recorded difference duration that is less than the standard deviation duration is recorded as the effective duration, and the production energy consumption data or carbon dioxide emission data corresponding to the effective duration is recorded as the effective carbon emission data.

4. According to claim 3, a smart park dual-carbon smart management system based on a big data cloud platform is characterized in that: The criteria for data distinction are: only keep the duplicate data of the last recording moment; The division methods of carbon peak data and carbon neutrality data include: The oil volume, gas volume, water volume and electricity volume are recorded as the first target words, and the emission volume, absorption volume and emission concentration ratio are recorded as the second target words; Compare the text part in the effective carbon emission data with the first target text and the second target text one by one; Record the valid carbon emission data containing any first target character in the text part as the first data to be verified, and record the valid carbon emission data containing any second target character in the text part as the second data to be verified; The first data to be verified and the second data to be verified that are repeated are recorded as the first repeated data and the second repeated data, respectively, and the recording times of all the first repeated data and the second repeated data are queried one by one through the timestamps; All recorded moments are arranged in chronological order, and the first duplicate data and the second duplicate data except the last one in the recorded moment are eliminated. The remaining first data to be verified and the first duplicate data are recorded as carbon peak data, and the remaining second data to be verified and the second duplicate data are recorded as carbon neutrality data.

5. According to claim 4, a smart park dual-carbon smart management system based on a big data cloud platform is characterized in that: The methods for building the carbon emission model include: The basic model is selected from the model database, and two model frames with upper and lower parallel distribution are constructed in the basic model, which are respectively recorded as the emission frame and the data frame; Input the carbon peak data into the data frame one by one, and count the consumption values ​​of oil, gas, water and electricity in the data frame, which are recorded as the first consumption value, the second consumption value, the third consumption value and the fourth consumption value respectively; The first consumption value, the second consumption value, the third consumption value and the fourth consumption value are respectively multiplied by the corresponding emission coefficients, and all the product values ​​are accumulated to obtain the total carbon emissions; D emission horizontal lines are drawn at equal intervals on the vertical boundary of the emission frame, and the carbon emission values ​​of the D emission horizontal lines are marked one by one; The emission level line whose carbon emission value is consistent with the total carbon emission is recorded as the target line, and the target line is rendered and colored to construct a carbon emission model.

6. According to claim 5, a smart park dual-carbon smart management system based on a big data cloud platform is characterized in that: Model attributes include peaking model and non-peaking model; The identification methods of peaking models and non-peaking models include: The carbon emission value of the target line in the carbon emission model constructed for the first time is recorded as the initial carbon emission value; The carbon emission model is constructed C times through the carbon peak data, C target lines are marked, and the carbon emission values ​​of the C target lines are recorded one by one to obtain C reconstructed carbon emission values; When the C reconstructed carbon emission values ​​are all less than or equal to the initial carbon emission values, the model attribute of the carbon emission model is the peak attribute; When one of the C reconstructed carbon emission values ​​is greater than the initial carbon emission value, the model attribute of the carbon emission model is the non-peak attribute; The methods for determining whether to enter the carbon neutral management mode include: When the model attribute of the carbon emission model is the peak attribute, it is determined to enter the carbon neutral management mode; When the model attribute of the carbon emission model is the non-peak attribute, it is determined not to enter the carbon neutrality management mode.

7. According to claim 6, a smart park dual-carbon smart management system based on a big data cloud platform is characterized in that: Methods for identifying abnormal data include: The carbon peak data whose text parts are oil volume, gas volume, water volume and electricity volume are respectively combined with their corresponding digital parts to generate oil volume data, gas volume data, water volume data and electricity volume data; Respectively comparing the digital parts of the oil quantity data, the gas quantity data, the water quantity data and the electricity quantity data with the first safety value, the second safety value, the third safety value and the fourth safety value; The oil quantity data, gas quantity data, water quantity data and electricity quantity data whose numerical parts are respectively larger than the first safety value, the second safety value, the third safety value and the fourth safety value are recorded as abnormal data.

8. According to claim 7, a smart park dual-carbon smart management system based on a big data cloud platform is characterized in that: The calculation method of the abnormal management index includes: The carbon neutrality data whose text parts are emissions, absorption and emission concentration ratio are respectively combined with their corresponding numerical parts to generate emissions data, absorption data and emission concentration ratio data; Compare the numerical part of the emission data with the total carbon emissions, and record the maximum value after comparison as the available emissions; Multiply the numerical parts of the available emission and emission concentration ratio data, and subtract the product value from the numerical part of the absorption data to obtain the emission difference value; The expression for the difference in emissions is: PF cz =PF ky *PF nd -XS zl ; In the formula, PF cz is the difference in emissions, PF ky is the available discharge, PF nd is the numerical part of the emission concentration ratio data, XS zl It is the numerical part of the absorption data; Count the number of abnormal data, obtain the abnormal value, and calculate the abnormal management index based on the abnormal value and the difference between the emission amount; The expression of abnormal management index is: In the formula, GL zs is the abnormal management index, YC z is an outlier.

9. According to claim 8, a smart park dual-carbon smart management system based on a big data cloud platform is characterized in that: The carbon neutrality management level includes primary management level and secondary management level; The method for establishing the primary management level and the secondary management level includes: The abnormal management index GL zs Compared with the preset abnormal management threshold GL yz Compare; When GL zs Smaller than GL yz When the first level of management is established; When GL zs Greater than or equal to GL yz A two-level management hierarchy was established.

10. The smart park dual-carbon smart management system based on big data cloud platform according to claim 9 is characterized in that: Emission management solutions include solutions to reduce carbon emissions and solutions to increase the proportion of green electricity; The selection methods for reducing carbon emissions and increasing the proportion of green electricity include: When the carbon neutrality management level is level 1, choose the carbon emission reduction plan; When the carbon neutrality management level is the secondary management level, choose the plan to reduce carbon emissions and increase the proportion of green electricity.

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