CN116720667A - Intelligent enterprise carbon data management and control method and system based on big data analysis - Google Patents

Intelligent enterprise carbon data management and control method and system based on big data analysis Download PDF

Info

Publication number
CN116720667A
CN116720667A CN202311005014.8A CN202311005014A CN116720667A CN 116720667 A CN116720667 A CN 116720667A CN 202311005014 A CN202311005014 A CN 202311005014A CN 116720667 A CN116720667 A CN 116720667A
Authority
CN
China
Prior art keywords
carbon emission
disturbance
monitoring
period
carbon
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202311005014.8A
Other languages
Chinese (zh)
Other versions
CN116720667B (en
Inventor
晏路辉
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Carbonstop Beijing Technology Co ltd
Original Assignee
Carbonstop Beijing Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Carbonstop Beijing Technology Co ltd filed Critical Carbonstop Beijing Technology Co ltd
Priority to CN202311005014.8A priority Critical patent/CN116720667B/en
Publication of CN116720667A publication Critical patent/CN116720667A/en
Application granted granted Critical
Publication of CN116720667B publication Critical patent/CN116720667B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/0004—Gaseous mixtures, e.g. polluted air
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/80—Management or planning
    • Y02P90/84—Greenhouse gas [GHG] management systems

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Human Resources & Organizations (AREA)
  • Theoretical Computer Science (AREA)
  • Strategic Management (AREA)
  • Chemical & Material Sciences (AREA)
  • Economics (AREA)
  • Health & Medical Sciences (AREA)
  • Tourism & Hospitality (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Development Economics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Business, Economics & Management (AREA)
  • Data Mining & Analysis (AREA)
  • Educational Administration (AREA)
  • Marketing (AREA)
  • General Health & Medical Sciences (AREA)
  • Mathematical Physics (AREA)
  • Operations Research (AREA)
  • Computational Mathematics (AREA)
  • Algebra (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Primary Health Care (AREA)
  • Pure & Applied Mathematics (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Quality & Reliability (AREA)
  • Combustion & Propulsion (AREA)
  • Game Theory and Decision Science (AREA)
  • Food Science & Technology (AREA)
  • Medicinal Chemistry (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention provides an enterprise carbon data intelligent management and control method and system based on big data analysis. The enterprise carbon data intelligent control method comprises the following steps: setting a group of carbon emission disturbance monitoring time periods before each preset carbon emission data monitoring unit time period, wherein the group of carbon emission disturbance monitoring time periods comprises three carbon emission disturbance monitoring time periods with different time lengths; monitoring the corresponding carbon emission of enterprises in real time in each group of carbon emission monitoring time periods, and setting a carbon emission disturbance factor according to the carbon emission of each carbon emission monitoring time period; after entering a carbon emission data monitoring unit time period corresponding to each group of carbon emission monitoring time periods, acquiring carbon emission index parameters of enterprises in the carbon emission data monitoring unit time period by utilizing carbon emission disturbance factors; and when the carbon emission index parameter exceeds a preset index threshold value, carrying out carbon emission exceeding early warning. The system comprises modules corresponding to the method steps.

Description

Intelligent enterprise carbon data management and control method and system based on big data analysis
Technical Field
The invention provides an enterprise carbon data intelligent control method and system based on big data analysis, and belongs to the technical field of carbon emission control.
Background
As global concerns over climate change have increased, management and monitoring of enterprise carbon emission data has become increasingly important. The development of big data analysis technology provides an overall, deep and real-time carbon data management means for enterprises, can help the enterprises to better understand self carbon emission conditions, formulate effective emission reduction measures, and improve the operation efficiency and sustainability of the enterprises while monitoring emission data.
In the prior art, the carbon emission monitoring is only carried out for the total carbon emission amount of each period, the fluctuation amount of the carbon emission is not considered, the carbon emission of enterprises is repeatedly controlled, the problem of repeated early warning occurs, the carbon emission monitoring efficiency is further reduced, the problems of increasing the carbon emission monitoring operation load and the energy consumption occur sometimes, and the carbon emission amount is increased due to the increase of the energy consumption of the system instead.
Disclosure of Invention
The invention provides an enterprise carbon data intelligent management and control method and system based on big data analysis, which are used for solving the problems that in the prior art, carbon emission monitoring is only carried out for the total carbon emission amount of each period, fluctuation of carbon emission is not considered, the management and control of the carbon emission of an enterprise are repeated, and early warning is repeated, so that the carbon emission monitoring efficiency is reduced, and the carbon emission monitoring operation load and energy consumption are increased:
An enterprise carbon data intelligent management and control method based on big data analysis, the enterprise carbon data intelligent management and control method comprising:
s1: setting a group of carbon emission disturbance monitoring time periods before each preset carbon emission data monitoring unit time period, wherein the group of carbon emission disturbance monitoring time periods comprises three carbon emission disturbance monitoring time periods with different time lengths;
s2: monitoring the corresponding carbon emission of enterprises in real time in each group of carbon emission monitoring time periods, and setting a carbon emission disturbance factor according to the carbon emission of each carbon emission monitoring time period;
s3: after entering a carbon emission data monitoring unit time period corresponding to each group of carbon emission monitoring time periods, acquiring carbon emission index parameters of enterprises in the carbon emission data monitoring unit time period by utilizing carbon emission disturbance factors;
s4: and when the carbon emission index parameter exceeds a preset index threshold value, carrying out carbon emission exceeding early warning.
Further, a set of carbon emission disturbance monitoring periods is set before each preset carbon emission data monitoring unit period, including:
s11: extracting the enterprise carbon emission parameters in the previous carbon emission data monitoring unit time period;
s12: setting a first carbon emission disturbance monitoring period, a second carbon emission disturbance monitoring period and a third carbon emission disturbance monitoring period by using the enterprise carbon emission parameters;
S13: and sequencing and combining the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period according to a monitoring period arrangement principle to form a group of carbon emission disturbance monitoring periods.
Wherein the first carbon emission perturbation monitoring period, the second carbon emission perturbation monitoring period, and the third carbon emission perturbation monitoring period are obtained by the following formula:
wherein ,T 1 、T 2 andT 3 respectively representing a first carbon emission disturbance monitoring period, a second carbon emission disturbance monitoring period and a third carbon emission disturbance monitoring period;nrepresenting the number of unit times included in the previous carbon emission data monitoring unit time period, and one unit time is 24 hours (one day);T 0 the value range of the preset standard duration is 8-10 days;P ci represent the firstiCarbon emissions on a daily basis;C i represent the firstiDaily product throughput;E i represent the firstiTotal daily energy consumption.
Further, the monitoring period arrangement principle is as follows:
when the difference value among the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period is not more than 10.3% of the total duration of the carbon emission disturbance monitoring period with longer time between every two, randomly sequencing and combining the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period;
When the carbon emission disturbance monitoring period longer than the two carbon emission disturbance monitoring periods is 10.3 percent but not more than 11.7 percent of the total duration of the carbon emission disturbance monitoring period in any group of comparison of the difference values between the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period, the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period are combined in a sequence from small to large according to the time length;
when the total duration of the carbon emission disturbance monitoring periods longer than one between every two of the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period is 11.7% in any one set of comparison of the differences between every two of the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period, the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period are combined in a sequence from large to small according to the time length.
Further, the carbon emission amount corresponding to the enterprise is monitored in real time in each group of carbon emission monitoring periods, and a carbon emission disturbance factor is set according to the carbon emission amount of each carbon emission monitoring period, including:
s21: sequentially extracting enterprise carbon emission data in a first carbon emission disturbance monitoring period, a second carbon emission disturbance monitoring period and a third carbon emission disturbance monitoring period in the group of carbon emission disturbance monitoring periods according to a sequencing and combining sequence respectively;
S22: and acquiring a carbon emission disturbance factor corresponding to the next carbon emission data monitoring unit time period of the enterprise by using the carbon emission data.
Wherein the carbon emission disturbance factor is obtained by the following formula:
wherein ,Q i=1.2.3 separate tableThe first disturbance factor, the second disturbance factor and the third disturbance factor corresponding to the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period are shown;mrepresenting the number of unit times included in the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period, and the third carbon emission disturbance monitoring period, and one unit time is 24 hours (one day);P cj represent the firstjCarbon emissions on a daily basis;C j represent the firstjDaily product throughput;E j represent the firstjTotal daily energy consumption;Qrepresenting a carbon emission disturbance factor;Q p representing the mean value of the factors.
Further, after entering a carbon emission data monitoring unit time period corresponding to each group of carbon emission monitoring time periods, obtaining carbon emission index parameters of an enterprise in the carbon emission data monitoring unit time period by using a carbon emission disturbance factor, including:
s31: after entering the next carbon emission data monitoring unit time period corresponding to each group of carbon emission monitoring time periods, collecting the carbon emission data of enterprises in the next carbon emission data monitoring unit time period in real time;
S32: extracting a carbon emission disturbance factor corresponding to the next carbon emission data monitoring unit time period at the end time of the next carbon emission data monitoring unit time period;
s33: and acquiring the carbon emission index parameters of the enterprises in the carbon emission data monitoring unit time period by utilizing the carbon emission disturbance factor and the carbon emission data of the enterprises in the next carbon emission data monitoring unit time period.
Wherein, the carbon emission index parameter is obtained by the following formula:
wherein ,Zrepresents the index parameter of the carbon emission,P c representing the total amount of carbon emissions of the business for the current carbon emission data monitoring unit time period,Cindicating whenMonitoring total production of the enterprise per unit time period with the front carbon emission data;P 0 representing a preset unit carbon emission threshold value corresponding to each product.
An enterprise carbon data intelligent management and control system based on big data analysis, the enterprise carbon data intelligent management and control system comprising:
the system comprises a time period setting module, a carbon emission disturbance monitoring module and a control module, wherein the time period setting module is used for setting a group of carbon emission disturbance monitoring time periods before each preset carbon emission data monitoring unit time period, and the group of carbon emission disturbance monitoring time periods comprises three carbon emission disturbance monitoring time periods with different time lengths;
The factor setting module is used for monitoring the carbon emission amount corresponding to the enterprise in real time in each group of carbon emission monitoring time periods, and setting a carbon emission disturbance factor according to the carbon emission amount in each carbon emission monitoring time period;
the index acquisition module is used for acquiring carbon emission index parameters of enterprises in the carbon emission data monitoring unit time period by utilizing the carbon emission disturbance factors after entering the carbon emission data monitoring unit time period corresponding to each group of carbon emission monitoring time periods;
and the early warning module is used for carrying out early warning of exceeding carbon emission when the carbon emission index parameter exceeds a preset index threshold.
Further, the period setting module includes:
the parameter extraction module is used for extracting the enterprise carbon emission parameters in the previous carbon emission data monitoring unit time period;
the monitoring period setting module is used for setting a first carbon emission disturbance monitoring period, a second carbon emission disturbance monitoring period and a third carbon emission disturbance monitoring period by utilizing the carbon emission parameters of the enterprise;
and the combination module is used for sequencing and combining the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period according to a monitoring period arrangement principle to form a group of carbon emission disturbance monitoring periods.
Wherein the first carbon emission perturbation monitoring period, the second carbon emission perturbation monitoring period, and the third carbon emission perturbation monitoring period are obtained by the following formula:
wherein ,T 1 、T 2 andT 3 respectively representing a first carbon emission disturbance monitoring period, a second carbon emission disturbance monitoring period and a third carbon emission disturbance monitoring period;nrepresenting the number of unit times included in the previous carbon emission data monitoring unit time period, and one unit time is 24 hours (one day);T 0 the value range of the preset standard duration is 8-10 days;P ci represent the firstiCarbon emissions on a daily basis;C i represent the firstiDaily product throughput;E i represent the firstiTotal daily energy consumption.
Further, the monitoring period arrangement principle is as follows:
when the difference value among the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period is not more than 10.3% of the total duration of the carbon emission disturbance monitoring period with longer time between every two, randomly sequencing and combining the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period;
when the carbon emission disturbance monitoring period longer than the two carbon emission disturbance monitoring periods is 10.3 percent but not more than 11.7 percent of the total duration of the carbon emission disturbance monitoring period in any group of comparison of the difference values between the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period, the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period are combined in a sequence from small to large according to the time length;
When the total duration of the carbon emission disturbance monitoring periods longer than one between every two of the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period is 11.7% in any one set of comparison of the differences between every two of the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period, the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period are combined in a sequence from large to small according to the time length.
Further, the factor setting module includes:
the data extraction module is used for sequentially extracting enterprise carbon emission data in a first carbon emission disturbance monitoring period, a second carbon emission disturbance monitoring period and a third carbon emission disturbance monitoring period in the group of carbon emission disturbance monitoring periods according to the sequencing and combining sequence respectively;
and the factor acquisition module is used for acquiring the carbon emission disturbance factor corresponding to the next carbon emission data monitoring unit time period of the enterprise by utilizing the carbon emission data.
Wherein the carbon emission disturbance factor is obtained by the following formula:
wherein ,Q i=1.2.3 the method comprises the steps of respectively representing a first disturbance factor, a second disturbance factor and a third disturbance factor corresponding to a first carbon emission disturbance monitoring period, a second carbon emission disturbance monitoring period and a third carbon emission disturbance monitoring period; mRepresenting the number of unit times included in the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period, and the third carbon emission disturbance monitoring period, and one unit time is 24 hours (one day);P cj represent the firstjCarbon emissions on a daily basis;C j represent the firstjDaily product throughput;E j represent the firstjTotal daily energy consumption;Qrepresenting a carbon emission disturbance factor;Q p representing the mean value of the factors.
Further, the index obtaining module includes:
the data acquisition module is used for acquiring the carbon emission data of enterprises in the next carbon emission data monitoring unit time period in real time after entering the next carbon emission data monitoring unit time period corresponding to each group of carbon emission monitoring time periods;
the factor extraction module is used for extracting a carbon emission disturbance factor corresponding to the next carbon emission data monitoring unit time period at the end time of the next carbon emission data monitoring unit time period;
and the parameter acquisition module is used for acquiring the carbon emission index parameter of the enterprise in the carbon emission data monitoring unit time period by utilizing the carbon emission disturbance factor and the carbon emission data of the enterprise in the next carbon emission data monitoring unit time period.
Wherein, the carbon emission index parameter is obtained by the following formula:
wherein ,Zrepresents the index parameter of the carbon emission,P c representing the total amount of carbon emissions of the business for the current carbon emission data monitoring unit time period,Crepresenting the total production of the enterprise for the current carbon emission data monitoring unit time period;P 0 representing a preset unit carbon emission threshold value corresponding to each product.
The invention has the beneficial effects that: through big data analysis, a large amount of carbon emission data can be collected and processed, personal errors and data uncertainty are reduced, and the accuracy and the reliability of the data are improved; the management and control method based on big data analysis can realize the functions of monitoring and early warning the carbon data of enterprises in real time. By collecting and analyzing the carbon emission data in real time, abnormal conditions and trend changes can be found in time, enterprises are helped to take countermeasures rapidly, and carbon emission risks are reduced; through a big data analysis technology, the implicit association and rule in the carbon emission data can be deeply mined and analyzed; the method is beneficial to enterprises to identify main influencing factors and potential problems of carbon emission, and provides scientific basis and insight for formulating carbon emission reduction strategies; the management and control method based on big data analysis can help enterprises optimize carbon management strategies. By comprehensively analyzing and evaluating the carbon data, enterprises can determine key fields and potentials of carbon emission reduction, formulate more targeted and efficient carbon emission reduction measures and improve the effect and efficiency of carbon management; the big data analysis can identify the energy waste and the carbon emission peak period existing in the production process of enterprises, and helps the enterprises to optimize the resource utilization, save energy and reduce emission. Through intelligent prediction and scheduling, reasonable allocation and utilization of energy sources can be realized, carbon emission is reduced, and environmental impact is reduced.
Drawings
FIG. 1 is a step diagram of an enterprise carbon data intelligent management and control method based on big data analysis.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
According to one embodiment of the invention, an enterprise carbon data intelligent control method based on big data analysis comprises the following steps:
an enterprise carbon data intelligent management and control method based on big data analysis, the enterprise carbon data intelligent management and control method comprising:
s1: setting a group of carbon emission disturbance monitoring time periods before each preset carbon emission data monitoring unit time period, wherein the group of carbon emission disturbance monitoring time periods comprises three carbon emission disturbance monitoring time periods with different time lengths;
s2: monitoring the corresponding carbon emission of enterprises in real time in each group of carbon emission monitoring time periods, and setting a carbon emission disturbance factor according to the carbon emission of each carbon emission monitoring time period;
s3: after entering a carbon emission data monitoring unit time period corresponding to each group of carbon emission monitoring time periods, acquiring carbon emission index parameters of enterprises in the carbon emission data monitoring unit time period by utilizing carbon emission disturbance factors;
S4: and when the carbon emission index parameter exceeds a preset index threshold value, carrying out carbon emission exceeding early warning.
The working principle of the technical scheme is as follows: a set of carbon emission disturbance monitoring periods is set before each preset carbon emission data monitoring unit period. The lengths of these time periods may be different for monitoring the carbon emissions of the enterprise over different time periods; and monitoring the carbon emission amount corresponding to the enterprise in real time in each group of carbon emission monitoring time periods, and setting a carbon emission disturbance factor according to the carbon emission amount of each carbon emission monitoring time period. Calculating a disturbance factor by monitoring deviation between actual emission and a preset value; and after entering a carbon emission data monitoring unit time period corresponding to each group of carbon emission monitoring time period, acquiring carbon emission index parameters of enterprises in the time period by utilizing the carbon emission disturbance factors. These parameters may be used to assess the carbon emission status of the enterprise; when the carbon emission index parameter exceeds a preset index threshold, the system triggers a carbon emission exceeding early warning mechanism. This can be achieved by setting a threshold and comparing with the actual data.
The technical scheme has the effects that: by setting carbon emission disturbance monitoring time periods of a plurality of time periods, the carbon emission condition of an enterprise can be monitored more accurately, and monitoring of different time periods can reflect the carbon emission change trend of the enterprise and provide more comprehensive data; the disturbance factor is set according to the carbon emission of each monitoring period, so that the carbon emission condition of enterprises can be reflected more accurately, deviation of single factor on carbon emission evaluation can be avoided, and the evaluation result is more objective and accurate; the method has the advantages that the latest data can be timely obtained by monitoring the carbon emission of enterprises in real time, and after entering a carbon emission data monitoring unit time period, the carbon emission index parameter is calculated by using a disturbance factor, and when the index parameter exceeds a preset index threshold, the system triggers carbon emission out-of-standard early warning and timely reminds the enterprises to take corresponding measures; through the scheme, enterprises can better know the carbon emission condition of the enterprises and timely find out the problem of exceeding the standard. The method is beneficial to enterprises to take active measures to reduce the carbon emission, improve the environmental conditions and achieve the aims of environmental protection and emission reduction. Through implementation of the technical scheme, enterprises can monitor and manage carbon emission better, help the enterprises to know other emission conditions of greenhouses generated by the enterprises, and accordingly realize influence of the emission conditions of the enterprises on climate and environment, so that environmental protection consciousness is enhanced, environmental management level is improved, and support is provided for environmental management and realization of carbon emission reduction targets. Meanwhile, enterprises can be promoted to realize the importance of carbon emission, the enterprises are stimulated to actively take emission reduction measures, and the low-carbon economic development is promoted.
In one embodiment of the present invention, a set of carbon emission disturbance monitoring periods is set before each preset carbon emission data monitoring unit period, including:
s11: extracting the enterprise carbon emission parameters in the previous carbon emission data monitoring unit time period;
s12: setting a first carbon emission disturbance monitoring period, a second carbon emission disturbance monitoring period and a third carbon emission disturbance monitoring period by using the enterprise carbon emission parameters;
s13: and sequencing and combining the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period according to a monitoring period arrangement principle to form a group of carbon emission disturbance monitoring periods.
Wherein the first carbon emission perturbation monitoring period, the second carbon emission perturbation monitoring period, and the third carbon emission perturbation monitoring period are obtained by the following formula:
wherein ,T 1 、T 2 andT 3 respectively representing a first carbon emission disturbance monitoring period, a second carbon emission disturbance monitoring period and a third carbon emission disturbance monitoring period;nrepresenting the number of unit times included in the previous carbon emission data monitoring unit time period, and one unit time is 24 hours (one day);T 0 the value range of the preset standard duration is 8-10 days; P ci Represent the firstiCarbon emissions on a daily basis;C i represent the firstiDaily product throughput;E i represent the firstiTotal daily energy consumption.
The working principle of the technical scheme is as follows: at the end of each carbon emission data monitoring unit time period, extracting corresponding parameters, such as total carbon emission or average daily carbon emission, from the enterprise carbon emission data; and setting first, second and third carbon emission disturbance monitoring periods by using the extracted enterprise carbon emission parameters. These time periods may be set according to actual needs, for example, the monitoring time period is divided into morning, afternoon and evening, or divided by week, month or year; the first, second and third carbon emission disturbance monitoring periods are ordered and combined according to a certain ordering principle (e.g. chronological order). Ensuring that there is no overlap between the monitoring periods for independent carbon emission monitoring and assessment; by the above-described sequencing and combining, a set of carbon emission disturbance monitoring periods are formed. In these periods, real-time monitoring and evaluation can be performed according to the previously extracted carbon emission parameters of the enterprise, and carbon emission index parameters of each disturbance monitoring period are calculated.
The technical scheme has the effects that: the carbon emission condition of the enterprise can be accurately obtained by extracting the enterprise emission parameters in the previous carbon emission data monitoring unit time period, and the carbon emission condition of the enterprise can be comprehensively known and evaluated; by setting the carbon emission disturbance monitoring period, carbon emission fluctuation and abnormal conditions in a specific period can be captured, factors causing abnormal carbon emission can be rapidly identified and found, and corresponding measures can be timely taken for adjustment and improvement; the carbon emission disturbance monitoring time periods are sequenced and combined according to a monitoring time period arrangement principle, so that no overlap exists among the monitoring time periods, continuous monitoring and evaluation of carbon emission conditions are realized, a systematic carbon emission monitoring system can be established, and comprehensive data support and reference are provided for enterprises; through monitoring carbon emission disturbance period in real time and orderly, enterprises can better know the trend and rule of carbon emission change, can optimize carbon emission management strategies, formulate targeted emission reduction plans, improve energy utilization efficiency and reduce carbon emission level. Through implementation of the technical scheme, the emission condition of an enterprise in the current carbon emission data monitoring unit time period can be predicted more accurately, the prediction accuracy and reliability can be improved, meanwhile, the carbon emission condition of the enterprise can be monitored more comprehensively and carefully by using the set carbon emission disturbance monitoring time period, the monitoring accuracy and reliability are improved, the first carbon emission disturbance monitoring time period, the second carbon emission disturbance monitoring time period and the third carbon emission disturbance monitoring time period are combined in a sequencing mode according to the monitoring time period arrangement principle, the carbon emission condition of the enterprise can be analyzed and predicted more finely, and the enterprise is helped to find potential emission reduction opportunities. The system can also provide data support and technical support for government departments and third party institutions, promote the emission reduction action of the whole society, can help to relieve the problem of climate change, reduce the emission of greenhouse gases, reduce the rising speed of global temperature, thereby reducing the occurrence of extreme climate events, protecting the stability and biodiversity of an ecosystem, improving the life and health of human beings, promoting the energy transformation and the energy structure optimization, promoting the development and the utilization of renewable energy sources, reducing the consumption and the emission of fossil energy sources and improving the energy utilization efficiency and the energy economy. Meanwhile, the emission reduction movement can promote technical innovation and industrial upgrading, promote economic development and social progress, and improve the life quality and happiness of human beings. The method can realize the fine monitoring and disturbance monitoring of the carbon emission of enterprises, help the enterprises find and cope with the abnormal carbon emission, guide the optimization of management strategies, thereby achieving the aim of emission reduction and promoting sustainable development. Meanwhile, by setting the standard time length, the data in the previous carbon emission data monitoring unit time period can be divided into a plurality of time periods, so that continuous monitoring and evaluation are realized, enterprises can better know the change trend and rule of carbon emission, and accordingly, the development of emission reduction plans and management strategies is guided; by setting the number of unit time, the number of unit time contained in the previous carbon emission data monitoring unit time period can be extracted; the method is beneficial to acquiring more comprehensive data and increases the reliability and accuracy of the data; obtaining an intuitive digital representation of the emission level of the enterprise by calculating the daily carbon emission; the method is beneficial to monitoring the change and trend of the carbon emission of enterprises, and is convenient for evaluating and comparing the carbon emission of the enterprises; providing basis for enterprises to make emission reduction measures; by calculating the daily production capacity of the product, the carbon emission can be related to actual production, and the carbon emission intensity of enterprises can be estimated, so that the production efficiency and resource utilization condition of the enterprises can be known; and adjusting the emission reduction strategy according to the change of the production quantity; the energy utilization condition and the energy efficiency level of an enterprise can be known by calculating the total energy consumption of each day; the problem of excessively high or excessively low energy consumption is solved, and corresponding measures are taken for adjustment; thereby optimizing energy consumption and reducing carbon emission. On the other hand, the setting of the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period according to the enterprise carbon emission parameters in the previous carbon emission data monitoring unit time period can effectively improve the matching property of the setting of the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period with the actual carbon emission law of the enterprise, further improve the rationality and the accuracy of the time length setting of the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period, effectively prevent the problem that the carbon emission monitoring efficiency is reduced due to overlong setting of the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period, and prevent the problem that the data acquisition accuracy is reduced due to insufficient setting of the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period.
In one embodiment of the present invention, the monitoring period arrangement principle is as follows:
when the difference value among the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period is not more than 10.3% of the total duration of the carbon emission disturbance monitoring period with longer time between every two, randomly sequencing and combining the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period;
when the carbon emission disturbance monitoring period longer than the two carbon emission disturbance monitoring periods is 10.3 percent but not more than 11.7 percent of the total duration of the carbon emission disturbance monitoring period in any group of comparison of the difference values between the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period, the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period are combined in a sequence from small to large according to the time length;
when the total duration of the carbon emission disturbance monitoring periods longer than one between every two of the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period is 11.7% in any one set of comparison of the differences between every two of the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period, the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period are combined in a sequence from large to small according to the time length.
The working principle of the technical scheme is as follows: when the difference value among the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period is not more than 10.3% of the total duration of the carbon emission disturbance monitoring period with longer time between every two, randomly sequencing and combining the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period; when the carbon emission disturbance monitoring period longer than the two carbon emission disturbance monitoring periods is 10.3 percent but not more than 11.7 percent of the total duration of the carbon emission disturbance monitoring period in any group of comparison of the difference values between the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period, the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period are combined in a sequence from small to large according to the time length; when the total duration of the carbon emission disturbance monitoring periods longer than one between every two of the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period is 11.7% in any one set of comparison of the differences between every two of the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period, the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period are combined in a sequence from large to small according to the time length.
The technical scheme has the effects that: when none of the differences between the first, second and third carbon emission disturbance monitoring periods exceeds a certain threshold, the three periods are randomly ordered and combined. The flexible and random ordering mode can reduce the dependence on a certain specific time period, and avoid the overlarge influence of the fixed order on the results; when a certain threshold value is exceeded but another higher threshold value is not exceeded in any one set of difference value comparisons, the three time periods are combined in a sequence from small to large in time length. The sorting mode can provide an incremental visual angle, and helps to observe and analyze the change of carbon emission in different time periods; when any one of the difference comparisons exceeds another higher threshold, the three time periods are combined in order of time length from large to small. This ordering provides a decreasing viewing angle, which helps to observe and analyze the trend of carbon emissions over different time periods. Through implementation of the technical scheme, the carbon emission condition can be comprehensively and carefully analyzed according to the difference between monitoring periods, the carbon emission condition of an enterprise in the current carbon emission data monitoring unit time period can be accurately predicted through comprehensive and careful analysis, and the accuracy and reliability of prediction are improved. Meanwhile, through monitoring and analyzing the differences, the carbon emission condition of enterprises can be monitored more comprehensively and carefully, and the monitoring accuracy and reliability are improved. Different combinations of sequencing can provide different viewing angles, help researchers better understand and explain patterns and trends of carbon emission disturbances, and provide beneficial reference for relevant policy formulation and environmental protection.
In one embodiment of the present invention, the method for monitoring the carbon emission of the enterprises in real time during each group of carbon emission monitoring periods, and setting the carbon emission disturbance factor according to the carbon emission of each carbon emission monitoring period comprises:
s21: sequentially extracting enterprise carbon emission data in a first carbon emission disturbance monitoring period, a second carbon emission disturbance monitoring period and a third carbon emission disturbance monitoring period in the group of carbon emission disturbance monitoring periods according to a sequencing and combining sequence respectively;
s22: and acquiring a carbon emission disturbance factor corresponding to the next carbon emission data monitoring unit time period of the enterprise by using the carbon emission data.
Wherein the carbon emission disturbance factor is obtained by the following formula:
wherein ,Q i=1.2.3 the method comprises the steps of respectively representing a first disturbance factor, a second disturbance factor and a third disturbance factor corresponding to a first carbon emission disturbance monitoring period, a second carbon emission disturbance monitoring period and a third carbon emission disturbance monitoring period;mrepresenting the number of unit times included in the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period, and the third carbon emission disturbance monitoring period, and one unit time is 24 hours (one day);P cj represent the firstjCarbon emissions on a daily basis; C j Represent the firstjDaily product throughput;E j represent the firstjTotal daily energy consumption;Qrepresenting a carbon emission disturbance factor;Q p representing the mean value of the factors.
The working principle of the technical scheme is as follows: sequentially extracting the enterprise carbon emission data of the first, second and third carbon emission disturbance monitoring periods in the group of carbon emission disturbance monitoring periods according to the ordered combination sequence. The carbon emission data for the business may be obtained from a related monitoring record or database, as the case may be. For each carbon emission disturbance monitoring period, the carbon emission data of the enterprise is consolidated into one data set. Ensuring that each dataset contains the same business and is aligned for the same period of time (e.g., daily, weekly, etc.); and calculating a carbon emission disturbance factor corresponding to the next carbon emission data monitoring unit time period of the enterprise by using the extracted carbon emission data.
The technical scheme has the effects that: the data of the specific time period in the carbon emission disturbance monitoring time period are extracted according to the sequencing combination sequence, so that the required data can be ensured to be obtained, and errors and omission are avoided; the carbon emission data of enterprises are arranged and aligned, each data set is ensured to contain the same enterprises, and the alignment is carried out according to the same time period, so that the data in different time periods can be reliably compared and analyzed; the extracted carbon emission data is utilized to calculate a carbon emission disturbance factor corresponding to the next carbon emission data monitoring unit time period of an enterprise; the method is beneficial to judging whether the carbon emission of enterprises deviates from the normal range and carrying out early warning, and helps the enterprises monitor and manage the carbon emission risk; by calculating the carbon emission disturbance factor, abnormal carbon emission conditions of enterprises can be found and treated in time, and the method is beneficial to improving the environmental protection and the resource utilization efficiency. At the same time, the carbon emissions of the enterprise can be more effectively monitored and controlled by government and other regulatory authorities. According to the technical scheme, the carbon emission data of enterprises are extracted according to a specific time period, so that the monitoring result is more accurate and reliable; by comparing the carbon emission data of different time periods, the carbon emission conditions of enterprises in different time periods can be analyzed, and the problems and the improvement spaces can be identified; the carbon emission disturbance factor of the enterprise in the next time period is obtained, so that the future carbon emission trend can be predicted, and a basis is provided for the emission reduction plan and target setting of the enterprise; based on the analysis results of the carbon emission data and the disturbance factors, enterprises can pertinently formulate emission reduction measures and management strategies to promote the reduction and sustainable development of carbon emission. Meanwhile, the carbon emission disturbance factor is calculated by the formula, so that the carbon emission condition of enterprises can be deeply evaluated, the abnormal emission condition can be found and solved, and the improvement of the resource utilization efficiency is promoted; the method is beneficial to realizing sustainable development and promoting transformation of low-carbon economy for enterprises. By calculating the carbon emission disturbance factor, the periodic disturbance analysis and trend prediction can be carried out on the carbon emission; the method is beneficial to finding out the periodic variation and the aperiodic disturbance of the carbon emission and predicting and planning the future carbon emission; by monitoring and calculating the carbon emission disturbance factor, the abnormal carbon emission condition of enterprises can be found in time, and corresponding management and control measures are adopted for the abnormal carbon emission condition; the method is beneficial to improving the carbon emission management effect, reducing the environmental risk of enterprises and promoting the development of low-carbon economy; by paying attention to indexes such as product production, total energy consumption and the like and considering the indexes in the calculation of the carbon emission disturbance factor, enterprises can be helped to pay more attention to and optimize the resource utilization efficiency; is beneficial to reducing energy consumption and carbon emission and improving the environmental performance and competitiveness of enterprises. On the other hand, the first disturbance factor, the second disturbance factor and the third disturbance factor which are obtained through the formula and the corresponding carbon emission disturbance factors can effectively improve the response intensity of the enterprise carbon emission fluctuation, and further effectively improve the characterizability of the carbon emission disturbance factors on the enterprise carbon emission fluctuation response.
According to one embodiment of the invention, after entering a carbon emission data monitoring unit time period corresponding to each group of carbon emission monitoring time periods, a carbon emission disturbance factor is utilized to obtain carbon emission index parameters of an enterprise in the carbon emission data monitoring unit time period, and the method comprises the following steps:
s31: after entering the next carbon emission data monitoring unit time period corresponding to each group of carbon emission monitoring time periods, collecting the carbon emission data of enterprises in the next carbon emission data monitoring unit time period in real time;
s32: extracting a carbon emission disturbance factor corresponding to the next carbon emission data monitoring unit time period at the end time of the next carbon emission data monitoring unit time period;
s33: and acquiring the carbon emission index parameters of the enterprises in the carbon emission data monitoring unit time period by utilizing the carbon emission disturbance factor and the carbon emission data of the enterprises in the next carbon emission data monitoring unit time period.
Wherein, the carbon emission index parameter is obtained by the following formula:
wherein ,Zrepresents the index parameter of the carbon emission,P c representing the total amount of carbon emissions of the business for the current carbon emission data monitoring unit time period,Crepresenting the total production of the enterprise for the current carbon emission data monitoring unit time period; P 0 Representing a preset unit carbon emission threshold value corresponding to each product.
The working principle of the technical scheme is as follows: after entering the next carbon emission data monitoring unit time period corresponding to each group of carbon emission monitoring time period, collecting the carbon emission data of enterprises in the time period in real time; and at the end time of the next carbon emission data monitoring unit time period, extracting a carbon emission disturbance factor corresponding to the time period. The carbon emission disturbance factor can be an index for measuring the fluctuation degree of the carbon emission of enterprises; and monitoring the carbon emission data of the enterprise in the unit time period by using the extracted carbon emission disturbance factor and the next carbon emission data, and calculating the carbon emission index parameter of the enterprise in the time period. The index parameters may include average carbon emissions, carbon emission intensity, etc. of the business.
The technical scheme has the effects that: the carbon emission data of the enterprises in the next carbon emission data monitoring unit time period can be acquired in real time, so that the real-time monitoring can be realized. The method is beneficial to enterprises to know the self carbon emission level and take corresponding adjustment and measures in time; and accurately calculating the carbon emission index parameter of the enterprise in the carbon emission data monitoring unit time period by using the extracted carbon emission disturbance factor and the carbon emission data of the enterprise. Thus, errors and uncertainties possibly existing in the traditional method can be avoided, and the accuracy of the evaluation result is improved; by obtaining the carbon emission index parameters of the enterprise, a reference basis can be provided for environmental management of the enterprise. Enterprises can evaluate the carbon emission reduction measure effect of the enterprises according to the parameters, formulate a carbon emission reduction strategy with more pertinence and feasibility, and promote sustainable development and environmental protection; according to the technical scheme, through collecting the data in real time, the carbon emission condition of an enterprise can be known in time, and the enterprise is helped to make a timely management decision; by combining the extracted carbon emission disturbance factor with the carbon emission data of the enterprise, the carbon emission index parameter of the enterprise can be calculated and estimated more accurately; the data of each time period can be recorded and retrieved, so that enterprises can conveniently review and compare and analyze the historical data; and the data is automatically collected and processed, so that the time and cost of manual operation are reduced, and the data processing efficiency is improved. The formula can comprehensively consider the carbon emission and output conditions of enterprises, provide the effects of quantitatively measuring and guiding the carbon emission reduction of the enterprises, and is beneficial to the enterprises to realize the aims of environmental protection and sustainable development. Meanwhile, the formula divides the total carbon emission amount by the total product yield, so that comprehensive consideration of carbon emission and yield is realized. The carbon emission condition of the enterprise can be more comprehensively evaluated through calculation of the carbon emission index parameters, the method is not limited to a single index of the total emission amount or the output level, the fluctuation degree of the carbon emission of the enterprise can be reflected at maximum intensity, index parameter characterization is comprehensively carried out through the total carbon emission amount and the fluctuation degree of the carbon emission, and the accuracy and the comprehensiveness of monitoring the carbon emission of the enterprise are effectively improved; meanwhile, a unit carbon emission threshold value is adopted as a reference value, so that the carbon emission index parameter can be quantitatively measured; the control and evaluation of the self carbon emission level of enterprises are facilitated, the comparability is provided, and the comparison and analysis with other enterprises or industry standards are facilitated; the carbon emission index parameter calculated by the formula can reflect the carbon emission effect of enterprises in the carbon emission data monitoring unit time period. When the carbon emission index parameter is smaller than 1, the carbon emission level of the enterprise is lower than a preset unit carbon emission threshold, and the enterprise is shown to obtain a certain effect in the aspect of carbon emission reduction; according to the calculation result, the enterprise can evaluate the effectiveness of the carbon emission reduction measures of the enterprise, and accordingly, a carbon emission reduction strategy with more pertinence and feasibility is formulated. By continuously monitoring the change of the carbon emission index parameter, enterprises can timely adjust and improve carbon emission reduction measures, and the sustainable development goal is realized.
According to one embodiment of the invention, an enterprise carbon data intelligent management and control system based on big data analysis comprises:
the system comprises a time period setting module, a carbon emission disturbance monitoring module and a control module, wherein the time period setting module is used for setting a group of carbon emission disturbance monitoring time periods before each preset carbon emission data monitoring unit time period, and the group of carbon emission disturbance monitoring time periods comprises three carbon emission disturbance monitoring time periods with different time lengths;
the factor setting module is used for monitoring the carbon emission amount corresponding to the enterprise in real time in each group of carbon emission monitoring time periods, and setting a carbon emission disturbance factor according to the carbon emission amount in each carbon emission monitoring time period;
the index acquisition module is used for acquiring carbon emission index parameters of enterprises in the carbon emission data monitoring unit time period by utilizing the carbon emission disturbance factors after entering the carbon emission data monitoring unit time period corresponding to each group of carbon emission monitoring time periods;
and the early warning module is used for carrying out early warning of exceeding carbon emission when the carbon emission index parameter exceeds a preset index threshold.
The working principle of the technical scheme is as follows: a set of carbon emission disturbance monitoring periods is set before each preset carbon emission data monitoring unit period. The lengths of these time periods may be different for monitoring the carbon emissions of the enterprise over different time periods; and monitoring the carbon emission amount corresponding to the enterprise in real time in each group of carbon emission monitoring time periods, and setting a carbon emission disturbance factor according to the carbon emission amount of each carbon emission monitoring time period. Calculating a disturbance factor by monitoring deviation between actual emission and a preset value; and after entering a carbon emission data monitoring unit time period corresponding to each group of carbon emission monitoring time period, acquiring carbon emission index parameters of enterprises in the time period by utilizing the carbon emission disturbance factors. These parameters may be used to assess the carbon emission status of the enterprise; when the carbon emission index parameter exceeds a preset index threshold, the system triggers a carbon emission exceeding early warning mechanism. This can be achieved by setting a threshold and comparing with the actual data.
The technical scheme has the effects that: by setting carbon emission disturbance monitoring time periods of a plurality of time periods, the carbon emission condition of an enterprise can be monitored more accurately, and monitoring of different time periods can reflect the carbon emission change trend of the enterprise and provide more comprehensive data; the disturbance factor is set according to the carbon emission of each monitoring period, so that the carbon emission condition of enterprises can be reflected more accurately, deviation of single factor on carbon emission evaluation can be avoided, and the evaluation result is more objective and accurate; the method has the advantages that the latest data can be timely obtained by monitoring the carbon emission of enterprises in real time, and after entering a carbon emission data monitoring unit time period, the carbon emission index parameter is calculated by using a disturbance factor, and when the index parameter exceeds a preset index threshold, the system triggers carbon emission out-of-standard early warning and timely reminds the enterprises to take corresponding measures; through the scheme, enterprises can better know the carbon emission condition of the enterprises and timely find out the problem of exceeding the standard. The method is beneficial to enterprises to take active measures to reduce the carbon emission, improve the environmental conditions and achieve the aims of environmental protection and emission reduction. Through implementation of the technical scheme, enterprises can monitor and manage carbon emission better, help the enterprises to know other emission conditions of greenhouses generated by the enterprises, and accordingly realize influence of the emission conditions of the enterprises on climate and environment, so that environmental protection consciousness is enhanced, environmental management level is improved, and support is provided for environmental management and realization of carbon emission reduction targets. Meanwhile, enterprises can be promoted to realize the importance of carbon emission, the enterprises are stimulated to actively take emission reduction measures, and the low-carbon economic development is promoted.
In one embodiment of the present invention, the period setting module includes:
the parameter extraction module is used for extracting the enterprise carbon emission parameters in the previous carbon emission data monitoring unit time period;
the monitoring period setting module is used for setting a first carbon emission disturbance monitoring period, a second carbon emission disturbance monitoring period and a third carbon emission disturbance monitoring period by utilizing the carbon emission parameters of the enterprise;
and the combination module is used for sequencing and combining the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period according to a monitoring period arrangement principle to form a group of carbon emission disturbance monitoring periods.
Wherein the first carbon emission perturbation monitoring period, the second carbon emission perturbation monitoring period, and the third carbon emission perturbation monitoring period are obtained by the following formula:
wherein ,T 1 、T 2 andT 3 respectively representing a first carbon emission disturbance monitoring period, a second carbon emission disturbance monitoring period and a third carbon emission disturbance monitoring period;nrepresenting the previous carbon emission data monitoring unit time periodThe number of the contained unit time, and one unit time is 24 hours (one day);T 0 the value range of the preset standard duration is 8-10 days; P ci Represent the firstiCarbon emissions on a daily basis;C i represent the firstiDaily product throughput;E i represent the firstiTotal daily energy consumption.
The working principle of the technical scheme is as follows: at the end of each carbon emission data monitoring unit time period, extracting corresponding parameters, such as total carbon emission or average daily carbon emission, from the enterprise carbon emission data; and setting first, second and third carbon emission disturbance monitoring periods by using the extracted enterprise carbon emission parameters. These time periods may be set according to actual needs, for example, the monitoring time period is divided into morning, afternoon and evening, or divided by week, month or year; the first, second and third carbon emission disturbance monitoring periods are ordered and combined according to a certain ordering principle (e.g. chronological order). Ensuring that there is no overlap between the monitoring periods for independent carbon emission monitoring and assessment; by the above-described sequencing and combining, a set of carbon emission disturbance monitoring periods are formed. In these periods, real-time monitoring and evaluation can be performed according to the previously extracted carbon emission parameters of the enterprise, and carbon emission index parameters of each disturbance monitoring period are calculated.
The technical scheme has the effects that: the carbon emission condition of the enterprise can be accurately obtained by extracting the enterprise emission parameters in the previous carbon emission data monitoring unit time period, and the carbon emission condition of the enterprise can be comprehensively known and evaluated; by setting the carbon emission disturbance monitoring period, carbon emission fluctuation and abnormal conditions in a specific period can be captured, factors causing abnormal carbon emission can be rapidly identified and found, and corresponding measures can be timely taken for adjustment and improvement; the carbon emission disturbance monitoring time periods are sequenced and combined according to a monitoring time period arrangement principle, so that no overlap exists among the monitoring time periods, continuous monitoring and evaluation of carbon emission conditions are realized, a systematic carbon emission monitoring system can be established, and comprehensive data support and reference are provided for enterprises; through monitoring carbon emission disturbance period in real time and orderly, enterprises can better know the trend and rule of carbon emission change, can optimize carbon emission management strategies, formulate targeted emission reduction plans, improve energy utilization efficiency and reduce carbon emission level. Through implementation of the technical scheme, the emission condition of an enterprise in the current carbon emission data monitoring unit time period can be predicted more accurately, the prediction accuracy and reliability can be improved, meanwhile, the carbon emission condition of the enterprise can be monitored more comprehensively and carefully by using the set carbon emission disturbance monitoring time period, the monitoring accuracy and reliability are improved, the first carbon emission disturbance monitoring time period, the second carbon emission disturbance monitoring time period and the third carbon emission disturbance monitoring time period are combined in a sequencing mode according to the monitoring time period arrangement principle, the carbon emission condition of the enterprise can be analyzed and predicted more finely, and the enterprise is helped to find potential emission reduction opportunities. The system can also provide data support and technical support for government departments and third party institutions, promote the emission reduction action of the whole society, can help to relieve the problem of climate change, reduce the emission of greenhouse gases, reduce the rising speed of global temperature, thereby reducing the occurrence of extreme climate events, protecting the stability and biodiversity of an ecosystem, improving the life and health of human beings, promoting the energy transformation and the energy structure optimization, promoting the development and the utilization of renewable energy sources, reducing the consumption and the emission of fossil energy sources and improving the energy utilization efficiency and the energy economy. Meanwhile, the emission reduction movement can promote technical innovation and industrial upgrading, promote economic development and social progress, and improve the life quality and happiness of human beings. The method can realize the fine monitoring and disturbance monitoring of the carbon emission of enterprises, help the enterprises find and cope with the abnormal carbon emission, guide the optimization of management strategies, thereby achieving the aim of emission reduction and promoting sustainable development. Meanwhile, by setting the standard time length, the data in the previous carbon emission data monitoring unit time period can be divided into a plurality of time periods, so that continuous monitoring and evaluation are realized, enterprises can better know the change trend and rule of carbon emission, and accordingly, the development of emission reduction plans and management strategies is guided; by setting the number of unit time, the number of unit time contained in the previous carbon emission data monitoring unit time period can be extracted; the method is beneficial to acquiring more comprehensive data and increases the reliability and accuracy of the data; obtaining an intuitive digital representation of the emission level of the enterprise by calculating the daily carbon emission; the method is beneficial to monitoring the change and trend of the carbon emission of enterprises, and is convenient for evaluating and comparing the carbon emission of the enterprises; providing basis for enterprises to make emission reduction measures; by calculating the daily production capacity of the product, the carbon emission can be related to actual production, and the carbon emission intensity of enterprises can be estimated, so that the production efficiency and resource utilization condition of the enterprises can be known; and adjusting the emission reduction strategy according to the change of the production quantity; the energy utilization condition and the energy efficiency level of an enterprise can be known by calculating the total energy consumption of each day; the problem of excessively high or excessively low energy consumption is solved, and corresponding measures are taken for adjustment; thereby optimizing energy consumption and reducing carbon emission.
In one embodiment of the present invention, the monitoring period arrangement principle is as follows:
when the difference value among the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period is not more than 10.3% of the total duration of the carbon emission disturbance monitoring period with longer time between every two, randomly sequencing and combining the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period;
when the carbon emission disturbance monitoring period longer than the two carbon emission disturbance monitoring periods is 10.3 percent but not more than 11.7 percent of the total duration of the carbon emission disturbance monitoring period in any group of comparison of the difference values between the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period, the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period are combined in a sequence from small to large according to the time length;
when the total duration of the carbon emission disturbance monitoring periods longer than one between every two of the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period is 11.7% in any one set of comparison of the differences between every two of the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period, the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period are combined in a sequence from large to small according to the time length.
The working principle of the technical scheme is as follows: when the difference value among the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period is not more than 10.3% of the total duration of the carbon emission disturbance monitoring period with longer time between every two, randomly sequencing and combining the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period; when the carbon emission disturbance monitoring period longer than the two carbon emission disturbance monitoring periods is 10.3 percent but not more than 11.7 percent of the total duration of the carbon emission disturbance monitoring period in any group of comparison of the difference values between the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period, the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period are combined in a sequence from small to large according to the time length; when the total duration of the carbon emission disturbance monitoring periods longer than one between every two of the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period is 11.7% in any one set of comparison of the differences between every two of the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period, the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period are combined in a sequence from large to small according to the time length.
The technical scheme has the effects that: when none of the differences between the first, second and third carbon emission disturbance monitoring periods exceeds a certain threshold, the three periods are randomly ordered and combined. The flexible and random ordering mode can reduce the dependence on a certain specific time period, and avoid the overlarge influence of the fixed order on the results; when a certain threshold value is exceeded but another higher threshold value is not exceeded in any one set of difference value comparisons, the three time periods are combined in a sequence from small to large in time length. The sorting mode can provide an incremental visual angle, and helps to observe and analyze the change of carbon emission in different time periods; when any one of the difference comparisons exceeds another higher threshold, the three time periods are combined in order of time length from large to small. This ordering provides a decreasing viewing angle, which helps to observe and analyze the trend of carbon emissions over different time periods. Through implementation of the technical scheme, the carbon emission condition can be comprehensively and carefully analyzed according to the difference between monitoring periods, the carbon emission condition of an enterprise in the current carbon emission data monitoring unit time period can be accurately predicted through comprehensive and careful analysis, and the accuracy and reliability of prediction are improved. Meanwhile, through monitoring and analyzing the differences, the carbon emission condition of enterprises can be monitored more comprehensively and carefully, and the monitoring accuracy and reliability are improved. Different combinations of sequencing can provide different viewing angles, help researchers better understand and explain patterns and trends of carbon emission disturbances, and provide beneficial reference for relevant policy formulation and environmental protection.
In one embodiment of the present invention, the factor setting module includes:
the data extraction module is used for sequentially extracting enterprise carbon emission data in a first carbon emission disturbance monitoring period, a second carbon emission disturbance monitoring period and a third carbon emission disturbance monitoring period in the group of carbon emission disturbance monitoring periods according to the sequencing and combining sequence respectively;
and the factor acquisition module is used for acquiring the carbon emission disturbance factor corresponding to the next carbon emission data monitoring unit time period of the enterprise by utilizing the carbon emission data.
Wherein the carbon emission disturbance factor is obtained by the following formula:
wherein ,Q i=1.2.3 respectively representing a first carbon emission disturbance monitoring period, a second carbon emission disturbance monitoring period, and a third carbon emission disturbance monitoring periodThe first disturbance factor, the second disturbance factor and the third disturbance factor correspond to each other;mrepresenting the number of unit times included in the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period, and the third carbon emission disturbance monitoring period, and one unit time is 24 hours (one day);P cj represent the firstjCarbon emissions on a daily basis;C j represent the firstjDaily product throughput;E j represent the firstjTotal daily energy consumption; QRepresenting a carbon emission disturbance factor;Q p representing the mean value of the factors.
The working principle of the technical scheme is as follows: sequentially extracting the enterprise carbon emission data of the first, second and third carbon emission disturbance monitoring periods in the group of carbon emission disturbance monitoring periods according to the ordered combination sequence. The carbon emission data for the business may be obtained from a related monitoring record or database, as the case may be. For each carbon emission disturbance monitoring period, the carbon emission data of the enterprise is consolidated into one data set. Ensuring that each dataset contains the same business and is aligned for the same period of time (e.g., daily, weekly, etc.); and calculating a carbon emission disturbance factor corresponding to the next carbon emission data monitoring unit time period of the enterprise by using the extracted carbon emission data.
The technical scheme has the effects that: the data of the specific time period in the carbon emission disturbance monitoring time period are extracted according to the sequencing combination sequence, so that the required data can be ensured to be obtained, and errors and omission are avoided; the carbon emission data of enterprises are arranged and aligned, each data set is ensured to contain the same enterprises, and the alignment is carried out according to the same time period, so that the data in different time periods can be reliably compared and analyzed; the extracted carbon emission data is utilized to calculate a carbon emission disturbance factor corresponding to the next carbon emission data monitoring unit time period of an enterprise; the method is beneficial to judging whether the carbon emission of enterprises deviates from the normal range and carrying out early warning, and helps the enterprises monitor and manage the carbon emission risk; by calculating the carbon emission disturbance factor, abnormal carbon emission conditions of enterprises can be found and treated in time, and the method is beneficial to improving the environmental protection and the resource utilization efficiency. At the same time, the carbon emissions of the enterprise can be more effectively monitored and controlled by government and other regulatory authorities. According to the technical scheme, the carbon emission data of enterprises are extracted according to a specific time period, so that the monitoring result is more accurate and reliable; by comparing the carbon emission data of different time periods, the carbon emission conditions of enterprises in different time periods can be analyzed, and the problems and the improvement spaces can be identified; the carbon emission disturbance factor of the enterprise in the next time period is obtained, so that the future carbon emission trend can be predicted, and a basis is provided for the emission reduction plan and target setting of the enterprise; based on the analysis results of the carbon emission data and the disturbance factors, enterprises can pertinently formulate emission reduction measures and management strategies to promote the reduction and sustainable development of carbon emission. Meanwhile, the carbon emission disturbance factor is calculated by the formula, so that the carbon emission condition of enterprises can be deeply evaluated, the abnormal emission condition can be found and solved, and the improvement of the resource utilization efficiency is promoted; the method is beneficial to realizing sustainable development and promoting transformation of low-carbon economy for enterprises. By calculating the carbon emission disturbance factor, the periodic disturbance analysis and trend prediction can be carried out on the carbon emission; the method is beneficial to finding out the periodic variation and the aperiodic disturbance of the carbon emission and predicting and planning the future carbon emission; by monitoring and calculating the carbon emission disturbance factor, the abnormal carbon emission condition of enterprises can be found in time, and corresponding management and control measures are adopted for the abnormal carbon emission condition; the method is beneficial to improving the carbon emission management effect, reducing the environmental risk of enterprises and promoting the development of low-carbon economy; by paying attention to indexes such as product production, total energy consumption and the like and considering the indexes in the calculation of the carbon emission disturbance factor, enterprises can be helped to pay more attention to and optimize the resource utilization efficiency; is beneficial to reducing energy consumption and carbon emission and improving the environmental performance and competitiveness of enterprises.
In one embodiment of the present invention, the index obtaining module includes:
the data acquisition module is used for acquiring the carbon emission data of enterprises in the next carbon emission data monitoring unit time period in real time after entering the next carbon emission data monitoring unit time period corresponding to each group of carbon emission monitoring time periods;
the factor extraction module is used for extracting a carbon emission disturbance factor corresponding to the next carbon emission data monitoring unit time period at the end time of the next carbon emission data monitoring unit time period;
and the parameter acquisition module is used for acquiring the carbon emission index parameter of the enterprise in the carbon emission data monitoring unit time period by utilizing the carbon emission disturbance factor and the carbon emission data of the enterprise in the next carbon emission data monitoring unit time period.
Wherein, the carbon emission index parameter is obtained by the following formula:
wherein ,Zrepresents the index parameter of the carbon emission,P c representing the total amount of carbon emissions of the business for the current carbon emission data monitoring unit time period,Crepresenting the total production of the enterprise for the current carbon emission data monitoring unit time period;P 0 representing a preset unit carbon emission threshold value corresponding to each product.
The working principle of the technical scheme is as follows: after entering the next carbon emission data monitoring unit time period corresponding to each group of carbon emission monitoring time period, collecting the carbon emission data of enterprises in the time period in real time; and at the end time of the next carbon emission data monitoring unit time period, extracting a carbon emission disturbance factor corresponding to the time period. The carbon emission disturbance factor can be an index for measuring the fluctuation degree of the carbon emission of enterprises; and monitoring the carbon emission data of the enterprise in the unit time period by using the extracted carbon emission disturbance factor and the next carbon emission data, and calculating the carbon emission index parameter of the enterprise in the time period. The index parameters may include average carbon emissions, carbon emission intensity, etc. of the business.
The technical scheme has the effects that: the carbon emission data of the enterprises in the next carbon emission data monitoring unit time period can be acquired in real time, so that the real-time monitoring can be realized. The method is beneficial to enterprises to know the self carbon emission level and take corresponding adjustment and measures in time; and accurately calculating the carbon emission index parameter of the enterprise in the carbon emission data monitoring unit time period by using the extracted carbon emission disturbance factor and the carbon emission data of the enterprise. Thus, errors and uncertainties possibly existing in the traditional method can be avoided, and the accuracy of the evaluation result is improved; by obtaining the carbon emission index parameters of the enterprise, a reference basis can be provided for environmental management of the enterprise. Enterprises can evaluate the carbon emission reduction measure effect of the enterprises according to the parameters, formulate a carbon emission reduction strategy with more pertinence and feasibility, and promote sustainable development and environmental protection; according to the technical scheme, through collecting the data in real time, the carbon emission condition of an enterprise can be known in time, and the enterprise is helped to make a timely management decision; by combining the extracted carbon emission disturbance factor with the carbon emission data of the enterprise, the carbon emission index parameter of the enterprise can be calculated and estimated more accurately; the data of each time period can be recorded and retrieved, so that enterprises can conveniently review and compare and analyze the historical data; and the data is automatically collected and processed, so that the time and cost of manual operation are reduced, and the data processing efficiency is improved. The formula can comprehensively consider the carbon emission and output conditions of enterprises, provide the effects of quantitatively measuring and guiding the carbon emission reduction of the enterprises, and is beneficial to the enterprises to realize the aims of environmental protection and sustainable development. Meanwhile, the formula divides the total carbon emission amount by the total product yield, so that comprehensive consideration of carbon emission and yield is realized. Through calculation of the carbon emission index parameters, the carbon emission condition of enterprises can be more comprehensively estimated, and the method is not limited to a single index of the total emission or the output level; the unit carbon emission threshold is used as a reference value, so that the carbon emission index parameter can be quantitatively measured; the control and evaluation of the self carbon emission level of enterprises are facilitated, the comparability is provided, and the comparison and analysis with other enterprises or industry standards are facilitated; the carbon emission index parameter calculated by the formula can reflect the carbon emission effect of enterprises in the carbon emission data monitoring unit time period. When the carbon emission index parameter is smaller than 1, the carbon emission level of the enterprise is lower than a preset unit carbon emission threshold, and the enterprise is shown to obtain a certain effect in the aspect of carbon emission reduction; according to the calculation result, the enterprise can evaluate the effectiveness of the carbon emission reduction measures of the enterprise, and accordingly, a carbon emission reduction strategy with more pertinence and feasibility is formulated. By continuously monitoring the change of the carbon emission index parameter, enterprises can timely adjust and improve carbon emission reduction measures, and the sustainable development goal is realized.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims (10)

1. The enterprise carbon data intelligent control method based on big data analysis is characterized by comprising the following steps of:
setting a group of carbon emission disturbance monitoring time periods before each preset carbon emission data monitoring unit time period, wherein the group of carbon emission disturbance monitoring time periods comprises three carbon emission disturbance monitoring time periods with different time lengths;
monitoring the corresponding carbon emission of enterprises in real time in each group of carbon emission monitoring time periods, and setting a carbon emission disturbance factor according to the carbon emission of each carbon emission monitoring time period;
after entering a carbon emission data monitoring unit time period corresponding to each group of carbon emission monitoring time periods, acquiring carbon emission index parameters of enterprises in the carbon emission data monitoring unit time period by utilizing carbon emission disturbance factors;
and when the carbon emission index parameter exceeds a preset index threshold value, carrying out carbon emission exceeding early warning.
2. The method for intelligent management and control of carbon data in an enterprise according to claim 1, wherein a set of carbon emission disturbance monitoring periods is set before each preset carbon emission data monitoring unit period, comprising:
extracting the enterprise carbon emission parameters in the previous carbon emission data monitoring unit time period;
setting a first carbon emission disturbance monitoring period, a second carbon emission disturbance monitoring period and a third carbon emission disturbance monitoring period by using the enterprise carbon emission parameters;
and sequencing and combining the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period according to a monitoring period arrangement principle to form a group of carbon emission disturbance monitoring periods.
3. The method for intelligently managing and controlling the carbon data of the enterprise according to claim 2, wherein the monitoring period arrangement principle is as follows:
when the difference value among the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period is not more than 10.3% of the total duration of the carbon emission disturbance monitoring period with longer time between every two, randomly sequencing and combining the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period;
When the carbon emission disturbance monitoring period longer than the two carbon emission disturbance monitoring periods is 10.3 percent but not more than 11.7 percent of the total duration of the carbon emission disturbance monitoring period in any group of comparison of the difference values between the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period, the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period are combined in a sequence from small to large according to the time length;
when the total duration of the carbon emission disturbance monitoring periods longer than one between every two of the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period is 11.7% in any one set of comparison of the differences between every two of the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period, the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period are combined in a sequence from large to small according to the time length.
4. The method for intelligently controlling carbon data in an enterprise according to claim 1, wherein the monitoring of the corresponding carbon emission of the enterprise in real time in each group of carbon emission monitoring periods, and the setting of the carbon emission disturbance factor according to the carbon emission of each carbon emission monitoring period, comprises:
Sequentially extracting enterprise carbon emission data in a first carbon emission disturbance monitoring period, a second carbon emission disturbance monitoring period and a third carbon emission disturbance monitoring period in the group of carbon emission disturbance monitoring periods according to a sequencing and combining sequence respectively;
and acquiring a carbon emission disturbance factor corresponding to the next carbon emission data monitoring unit time period of the enterprise by using the carbon emission data.
5. The method for intelligently managing and controlling carbon data of an enterprise according to claim 1, wherein after entering a carbon emission data monitoring unit time period corresponding to each group of carbon emission monitoring time periods, obtaining carbon emission index parameters of the enterprise in the carbon emission data monitoring unit time period by using a carbon emission disturbance factor comprises the following steps:
after entering the next carbon emission data monitoring unit time period corresponding to each group of carbon emission monitoring time periods, collecting the carbon emission data of enterprises in the next carbon emission data monitoring unit time period in real time;
extracting a carbon emission disturbance factor corresponding to the next carbon emission data monitoring unit time period at the end time of the next carbon emission data monitoring unit time period;
and acquiring the carbon emission index parameters of the enterprises in the carbon emission data monitoring unit time period by utilizing the carbon emission disturbance factor and the carbon emission data of the enterprises in the next carbon emission data monitoring unit time period.
6. An enterprise carbon data intelligent management and control system based on big data analysis, which is characterized in that the enterprise carbon data intelligent management and control system comprises:
the system comprises a time period setting module, a carbon emission disturbance monitoring module and a control module, wherein the time period setting module is used for setting a group of carbon emission disturbance monitoring time periods before each preset carbon emission data monitoring unit time period, and the group of carbon emission disturbance monitoring time periods comprises three carbon emission disturbance monitoring time periods with different time lengths;
the factor setting module is used for monitoring the carbon emission amount corresponding to the enterprise in real time in each group of carbon emission monitoring time periods, and setting a carbon emission disturbance factor according to the carbon emission amount in each carbon emission monitoring time period;
the index acquisition module is used for acquiring carbon emission index parameters of enterprises in the carbon emission data monitoring unit time period by utilizing the carbon emission disturbance factors after entering the carbon emission data monitoring unit time period corresponding to each group of carbon emission monitoring time periods;
and the early warning module is used for carrying out early warning of exceeding carbon emission when the carbon emission index parameter exceeds a preset index threshold.
7. The enterprise carbon data intelligent management and control system of claim 6, wherein the period setting module comprises:
the parameter extraction module is used for extracting the enterprise carbon emission parameters in the previous carbon emission data monitoring unit time period;
The monitoring period setting module is used for setting a first carbon emission disturbance monitoring period, a second carbon emission disturbance monitoring period and a third carbon emission disturbance monitoring period by utilizing the carbon emission parameters of the enterprise;
and the combination module is used for sequencing and combining the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period according to a monitoring period arrangement principle to form a group of carbon emission disturbance monitoring periods.
8. The intelligent management and control system for enterprise carbon data of claim 7, wherein the monitoring period arrangement principle is as follows:
when the difference value among the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period is not more than 10.3% of the total duration of the carbon emission disturbance monitoring period with longer time between every two, randomly sequencing and combining the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period;
when the carbon emission disturbance monitoring period longer than the two carbon emission disturbance monitoring periods is 10.3 percent but not more than 11.7 percent of the total duration of the carbon emission disturbance monitoring period in any group of comparison of the difference values between the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period, the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period are combined in a sequence from small to large according to the time length;
When the total duration of the carbon emission disturbance monitoring periods longer than one between every two of the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period is 11.7% in any one set of comparison of the differences between every two of the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period, the first carbon emission disturbance monitoring period, the second carbon emission disturbance monitoring period and the third carbon emission disturbance monitoring period are combined in a sequence from large to small according to the time length.
9. The enterprise carbon data intelligent management system of claim 6, wherein the factor setting module comprises:
the data extraction module is used for sequentially extracting enterprise carbon emission data in a first carbon emission disturbance monitoring period, a second carbon emission disturbance monitoring period and a third carbon emission disturbance monitoring period in the group of carbon emission disturbance monitoring periods according to the sequencing and combining sequence respectively;
and the factor acquisition module is used for acquiring the carbon emission disturbance factor corresponding to the next carbon emission data monitoring unit time period of the enterprise by utilizing the carbon emission data.
10. The enterprise carbon data intelligent management and control system of claim 6, wherein the indicator acquisition module comprises:
The data acquisition module is used for acquiring the carbon emission data of enterprises in the next carbon emission data monitoring unit time period in real time after entering the next carbon emission data monitoring unit time period corresponding to each group of carbon emission monitoring time periods;
the factor extraction module is used for extracting a carbon emission disturbance factor corresponding to the next carbon emission data monitoring unit time period at the end time of the next carbon emission data monitoring unit time period;
and the parameter acquisition module is used for acquiring the carbon emission index parameter of the enterprise in the carbon emission data monitoring unit time period by utilizing the carbon emission disturbance factor and the carbon emission data of the enterprise in the next carbon emission data monitoring unit time period.
CN202311005014.8A 2023-08-10 2023-08-10 Intelligent enterprise carbon data management and control method and system based on big data analysis Active CN116720667B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202311005014.8A CN116720667B (en) 2023-08-10 2023-08-10 Intelligent enterprise carbon data management and control method and system based on big data analysis

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202311005014.8A CN116720667B (en) 2023-08-10 2023-08-10 Intelligent enterprise carbon data management and control method and system based on big data analysis

Publications (2)

Publication Number Publication Date
CN116720667A true CN116720667A (en) 2023-09-08
CN116720667B CN116720667B (en) 2023-10-31

Family

ID=87870217

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202311005014.8A Active CN116720667B (en) 2023-08-10 2023-08-10 Intelligent enterprise carbon data management and control method and system based on big data analysis

Country Status (1)

Country Link
CN (1) CN116720667B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117312888A (en) * 2023-11-28 2023-12-29 天津市扬天环保科技有限公司 Data integration processing method and system for fixed pollution source

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106383501A (en) * 2014-07-29 2017-02-08 马慧 Carbon emission process monitoring and control method
CN113869660A (en) * 2021-09-06 2021-12-31 江苏荣辉信息科技有限公司 Enterprise carbon data comprehensive intelligent management and control system based on big data analysis
CN114707949A (en) * 2022-03-11 2022-07-05 北京航天智造科技发展有限公司 Carbon emission management system and method based on edge gateway
CN114994264A (en) * 2022-07-28 2022-09-02 江苏荣泽信息科技股份有限公司 Park digital monitoring platform based on block chain enterprise-level ledger

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106383501A (en) * 2014-07-29 2017-02-08 马慧 Carbon emission process monitoring and control method
CN113869660A (en) * 2021-09-06 2021-12-31 江苏荣辉信息科技有限公司 Enterprise carbon data comprehensive intelligent management and control system based on big data analysis
CN114707949A (en) * 2022-03-11 2022-07-05 北京航天智造科技发展有限公司 Carbon emission management system and method based on edge gateway
CN114994264A (en) * 2022-07-28 2022-09-02 江苏荣泽信息科技股份有限公司 Park digital monitoring platform based on block chain enterprise-level ledger

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117312888A (en) * 2023-11-28 2023-12-29 天津市扬天环保科技有限公司 Data integration processing method and system for fixed pollution source
CN117312888B (en) * 2023-11-28 2024-02-06 天津市扬天环保科技有限公司 Data integration processing method and system for fixed pollution source

Also Published As

Publication number Publication date
CN116720667B (en) 2023-10-31

Similar Documents

Publication Publication Date Title
CN111898873A (en) Group company carbon emission early warning information system and early warning method thereof
JP5027053B2 (en) Work analysis apparatus, production management method, and production management system
CN116720667B (en) Intelligent enterprise carbon data management and control method and system based on big data analysis
US20090216602A1 (en) Schedule Analyzer
EP2204712A1 (en) Method and system for monitoring and analysing energy consumption in operated chemical plants
CN113962468A (en) Energy consumption monitoring and statistics-based energy consumption carbon emission management method and system
CN102034148A (en) Method for realizing event alert and storm-proof policy of monitoring system
CN115114346B (en) Fine carbon emission informatization acquisition and accounting method, system and terminal
CN112308414A (en) Income abnormity detection method and device, electronic equipment and storage medium
CN111310803A (en) Environment data processing method and device
CN116345700B (en) Energy consumption monitoring method and monitoring system for energy storage power station
CN108510200A (en) The computational methods and device of data assets operation benefits based on Life cycle
CN115755614A (en) Energy consumption optimization regulation and control method and device based on carbon emission monitoring
CN112418548B (en) Intelligent workshop management method and system based on artificial intelligence technology
CN110264056B (en) Method and system for analyzing electric quantity consumption
CN112001551B (en) Ground and commercial power grid sales electricity quantity prediction method based on large-user electricity quantity information
CN114168409A (en) Service system running state monitoring and early warning method and system
CN113887831A (en) Novel power load prediction influence factor correlation analysis method
CN114049103A (en) Power grid infrastructure project management and control method, system, equipment and medium
CN112669093A (en) Ocean economy prediction method, system, electronic device and storage medium
Ocneanu et al. Improving managerial accounting and calculation of labor costs in the context of using standard cost
Vaslavskaya et al. The Concept of Assessing the Level of the Enterprise Economic Security
CN117524374B (en) Environmental impact evaluation system and device for product ecological process
Kahraman A data-driven multi-regime approach for predicting real-time energy consumption of industrial machines.
CN116797388A (en) Enterprise operation and financial health index generation system

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant