CN116561519A - Electric carbon emission process monitoring method and system based on big data of power grid - Google Patents
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Abstract
The invention discloses an electric carbon emission process monitoring method and system based on power grid big data, and belongs to the technical field of carbon emission monitoring. In order to solve the problems that sample data are huge and trend prediction is difficult to carry out, data verification is carried out on the electric power data samples based on constraint conditions, edge data are removed based on verification results, electric power data are output, so that the data base number during subsequent data processing is reduced, meanwhile, the accuracy of processing results can be further improved, meanwhile, the number of the electric power data samples which need to be processed is reduced, the calculation processing workload of a system is reduced, an unknown electric carbon emission variable trend simulation image is generated, the system can calculate according to the existing electric carbon emission increasing trend, and therefore future carbon emission change conditions under the current increasing and decreasing trend are simulated, a certain simulation data support can be provided for staff, more effective prevention and control are facilitated, and reasonable control over electric carbon emission is effectively improved.
Description
Technical Field
The invention relates to the technical field of carbon emission monitoring, in particular to an electric carbon emission process monitoring method and system based on power grid big data.
Background
Carbon emissions are a generic term for greenhouse gas emissions, which can produce significant amounts of greenhouse gases both in life and in production; the enterprises can generate a large amount of waste gas in the production process, and the waste gas is discharged after being treated, namely the carbon emission process; since carbon emissions can have a great impact on the environment, accurate monitoring and management of carbon emissions is very important.
Related patents such as publication number CN114240463A disclose a carbon emission monitoring management system based on big data, which relates to the technical field of carbon emission monitoring and solves the technical problem that the prior art cannot realize accurate monitoring of enterprise carbon emission data, so that a supervision department cannot early warn and control in time; the central processing unit and the edge processing units collect monitoring data, and the power data and the experience data are assisted to analyze and early warn carbon emission data of the jurisdiction area or the monitoring area; the method can not only monitor the heavy-point area timely and accurately, but also perform macroscopic analysis regulation and control on the whole area, and is convenient for a supervision unit to manage and control carbon emission data timely.
The above patent has the following problems in actual operation:
1. in the process of processing the electric power data samples of the electric carbon emission, a large number of electric power data samples are often collected for inspection and analysis, and abnormal data in the data samples can influence the processing speed and accuracy, so that the monitoring effect on the electric carbon emission is influenced, and the rationality of management and control decisions of technicians is influenced.
2. When monitoring the electric carbon emission, the electric carbon emission is often analyzed according to detection data, the current electric carbon emission is calculated and processed, the electric carbon emission is processed immediately exceeding or already exceeding, the electric carbon emission is difficult to be automatically predicted in an effective trend of increasing and decreasing through a system, and more effective and reasonable management and control cannot be achieved.
Disclosure of Invention
The invention aims to provide an electric carbon emission process monitoring method and system based on grid big data, so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: the electric carbon emission process monitoring method based on the big data of the power grid comprises the following steps:
acquiring a sample, and acquiring a power data sample of a power grid, load power information, power grid voltage information and power grid current information of the power grid;
checking data, reading a power data sample, generating constraint conditions for data checking, performing data checking on the power data sample based on the constraint conditions, removing edge data based on a checking result, and outputting power data;
data processing, namely receiving power data, determining a data classification identifier, determining a classification expression of the data classification identifier, constructing a power data classification model based on the classification expression, classifying and transmitting the power data;
analyzing and processing electric power data, calculating the carbon emission and outputting an analysis result;
outputting a result, namely generating an emission variation trend in the electric carbon emission process based on the electric power data and the time data, generating an electric carbon emission variation curve, predicting the emission trend of the future electric carbon emission according to the curve trend, and outputting an image of the electric carbon emission variation curve;
and (3) risk early warning, namely comparing the electric carbon emission with a safe emission threshold, determining whether to generate an early warning alarm according to a comparison result, and sending out the early warning alarm when the comparison result shows that the electric carbon emission is close to or higher than the highest safe emission threshold.
Further, the power grid comprises a public power grid, a local power grid and a household power grid, and the power data samples comprise standard fuel consumption of unit power generation amount, external power transmission amount, carbon emission factors at the power transmission side, power consumption of production activities, carbon emission data and industry flow production value data of the power grids of different local areas.
Further, the result output comprises generating an unknown electric carbon emission variation trend analog image, carrying out numerical ranking on data of the electric carbon emission according to the electric carbon emission calculated by data analysis, ranking the data of the electric carbon emission according to the electric data detection time corresponding to the electric carbon emission during the numerical ranking, generating an electric carbon emission curve image on the data of the ranked electric carbon emission, carrying out trend calculation on an electric carbon emission curve image, and carrying out curve prediction on the increasing and decreasing trend of the electric carbon emission curve image.
Further, generating an electric carbon emission curve image of the sequenced data of the electric carbon emission, calculating a trend of the electric carbon emission curve image, and predicting a trend of increasing and decreasing the electric carbon emission curve image, wherein the method comprises the following steps:
marking data points of the electric carbon emission quantity on a coordinate graph according to the detection time sequence, and generating an electric carbon emission curve image through curve fitting according to the data points;
carrying out smoothing treatment on the electric carbon emission curve image, and generating an electric carbon emission change function according to the electric carbon emission curve image after the smoothing treatment;
obtaining a change trend function of the electric carbon emission quantity by deriving the change function of the electric carbon emission, and carrying out trend calculation on an electric carbon emission curve image by adopting the change trend function to obtain the current trend change rate;
respectively calculating the power data change amount and the power data change rate of the power data of different classifications, and respectively calculating the ratio of the current trend change rate to the power data change amount of each classification to obtain classification influence factors; performing weight assignment on the power data of different classifications according to the mutual proportion of each classification influence factor to obtain classification weights; adopting the sum of the power data change rate of each category and the product of the category weights as the trend change rate of the next stage;
and taking the product of the current electric carbon emission and the trend change rate of the next stage as an electric carbon emission change predicted value, and judging the increasing and decreasing trend of the electric carbon emission curve image according to the electric carbon emission change predicted value.
Further, the electric carbon emission process monitoring system based on the big data of the power grid is applied to the electric carbon emission process monitoring method based on the big data of the power grid, and comprises the following steps:
a data acquisition unit configured to:
collecting and checking a power data sample of the power grid, and generating power data after checking;
a data processing unit for:
extracting power data characteristics of the power data, determining data classification identifiers, determining classification expressions of the data classification identifiers, constructing a power data classification model based on the classification expressions, and inputting the power data into the power data classification model for classification and transmission;
a data analysis unit for:
analyzing and processing the electric power data, calculating the carbon emission and outputting an analysis result;
an image output unit configured to:
generating an electric carbon emission amount change trend in the carbon emission process based on an analysis result of the data analysis unit, and outputting an image of the analysis result, wherein the image comprises a curve trend graph;
risk early warning unit for:
and carrying out risk early warning on the electric carbon emission process based on the analysis result of the data analysis unit.
Further, the data acquisition unit includes:
the data acquisition module is used for:
collecting a power data sample of a power grid;
a constraint confirmation module for:
reading a power data sample, determining a data fluctuation range and a data type of power data sample data, and generating constraint conditions of data verification based on the data fluctuation range and the data type of the power data sample data;
the data verification module is used for:
and carrying out data verification on the power data samples based on the constraint conditions, marking the power data samples which do not meet the constraint conditions based on the verification results, taking the power data samples which do not meet the constraint conditions as edge data, removing the edge data from the power data samples, and outputting data except the edge data as power data.
Further, the data processing unit includes:
the data classification module is used for:
receiving power data, acquiring power data characteristics, determining data classification identifiers based on the power data characteristics, inputting the data classification identifiers into a preset neural network for learning, and determining classification expressions of the data classification identifiers;
the model building module is used for:
constructing a power data classification model based on the classification expression, and simultaneously inputting power data into the power data classification model for classification to obtain classified power sub-data;
the data transmission module is used for:
and transmitting the classified power sub-data to a data analysis unit for analysis and processing.
Further, the image output unit includes:
a time marking module for:
acquiring the acquisition time of the power data sample, generating time data, and marking the power data and the time data in a matching way;
a curve generating module for:
generating an emission amount variation trend in the electric carbon emission process based on a matching result of the electric power data and the time data, generating an electric carbon emission amount variation curve, and carrying out curve prediction on the emission trend of the future electric carbon emission amount according to the curve trend;
an image output module for:
the image is output based on the electric carbon emission amount variation curve generated by the curve generation module.
Further, the risk early warning unit includes:
a threshold setting module, configured to:
setting a safe emission threshold of the electric carbon emission, and referencing the safe emission threshold in the threshold setting module when the result comparison module works;
the result comparison module is used for:
comparing the electric carbon emission amount analyzed by the data analysis unit with a safe emission threshold value to generate a comparison result, and generating an electric carbon emission control instruction when the electric carbon emission amount is close to or higher than the highest safe emission threshold value;
the early warning alarm module is used for:
and determining whether to generate an early warning alarm according to the comparison result of the result comparison module, and sending out the early warning alarm when the comparison result shows that the electric carbon emission is close to or higher than the highest safe emission threshold.
Further, the data verification module includes:
a condition matching sub-module for extracting data type A of the power data sample, and for the power data sampleThe data type A carries out morpheme analysis to obtain a plurality of morphemes, and the morphemes are expressed as A i Traversing constraint condition B, performing correlation calculation on data type A of the power data sample and each constraint condition, and calculating data type A and j constraint condition B of the power data sample by adopting the following formula j Is a correlation index of (2):
in the above-mentioned method, the step of,data type A and jth constraint B representing a power data sample j Is a correlation index of (2); n represents the total number of morphemes of data type a of the power data sample; omega i The weight of the ith morpheme is represented by the ith morpheme A i The logarithm of the frequency of occurrence in the constraint condition, the logarithm taking 2 of the computer binary number as the base; s (A) i ，B j ) Representing the ith morpheme A i With the j-th constraint B j Is a correlation score of (2);
determining the constraint condition with the maximum data type correlation index as a matched constraint condition, and using the constraint condition for verifying the corresponding type of power data sample;
and the verification sub-module is used for judging whether the corresponding electric power data sample accords with the constraint condition by adopting the matched constraint condition, if not, adding a mark which does not accord with the constraint condition to the electric power data sample, and taking the electric power data sample which does not accord with the constraint condition as edge data.
Compared with the prior art, the invention has the beneficial effects that:
1. in the prior art, in the process of processing electric power data samples of electric carbon emission, a large number of electric power data samples are often collected for inspection and analysis, and abnormal data existing in the data samples can influence the processing speed and accuracy, so that the monitoring effect on the electric carbon emission is influenced, and the rationality of management and control decisions of technicians is influenced.
2. In the prior art, when the electric carbon emission is monitored, the current electric carbon emission is often calculated and processed according to detection data, the current electric carbon emission is processed when exceeding or exceeding, effective increase and decrease trend prediction on the electric carbon emission is difficult to automatically and effectively control through a system, a non-electric carbon emission change trend simulation image cannot be generated, the system can calculate according to the current electric carbon emission increase trend, so that future carbon emission change conditions under the current increase and decrease trend can be simulated, a certain simulation data support can be provided for staff, the staff is helped to prepare the electric carbon emission conditions in advance, more effective prevention and control are facilitated, and reasonable control on the electric carbon emission is effectively improved.
Drawings
FIG. 1 is a schematic flow chart of the method for monitoring the electric carbon emission process based on big data of a power grid;
FIG. 2 is a schematic block diagram of the system for monitoring the electrical carbon emission process based on grid big data according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the method for monitoring the electric carbon emission process based on the big data of the power grid comprises the following steps:
acquiring a sample, and acquiring a power data sample of a power grid, load power information, power grid voltage information and power grid current information of the power grid, wherein the power grid comprises a public power grid, a local power grid and a household power grid, and the power data sample comprises standard fuel consumption of unit power generation amount, external power transmission amount, carbon emission factors at the power transmission side, power consumption of production activities, carbon emission amount data and industry mobile production value data of the power grid in different local areas;
checking data, reading a power data sample, generating constraint conditions for data checking, performing data checking on the power data sample based on the constraint conditions, removing edge data based on a checking result, and outputting power data;
data processing, namely receiving power data, determining a data classification identifier, determining a classification expression of the data classification identifier, constructing a power data classification model based on the classification expression, classifying and transmitting the power data;
analyzing and processing electric power data, calculating the carbon emission and outputting an analysis result;
outputting results, namely generating emission variation trend in the electric carbon emission process based on electric data and time data, generating an electric carbon emission variation curve, carrying out curve prediction on the emission trend of future electric carbon emission according to the curve trend, outputting an image according to the electric carbon emission variation curve, generating an electric carbon emission variation trend analog image which does not come, carrying out numerical ranking on the data of the electric carbon emission according to the electric carbon emission calculated by data analysis, ranking the data of the electric carbon emission according to the electric carbon emission detection time corresponding to the electric carbon emission during numerical ranking, carrying out electric carbon emission curve image generation on the data of the ranked electric carbon emission, carrying out trend calculation on the electric carbon emission curve image, and carrying out curve prediction on the increasing trend of the electric carbon emission curve image;
and (3) risk early warning, namely comparing the electric carbon emission with a safe emission threshold, determining whether to generate an early warning alarm according to a comparison result, and sending out the early warning alarm when the comparison result shows that the electric carbon emission is close to or higher than the highest safe emission threshold.
Specifically, before analyzing the electric power data samples of the electric carbon emission, checking and screening the electric power data samples, removing abnormal data through preset constraint conditions, so that the data base in the subsequent processing of the data is reduced, meanwhile, the accuracy of the processing result can be further improved, meanwhile, the number of the electric power data samples to be processed can be reduced, the calculation processing workload of the system is reduced, the response speed and the processing efficiency of the system are improved, meanwhile, the system can calculate according to the increasing trend of the existing electric carbon emission, and therefore future carbon emission change conditions under the current increasing and decreasing trend are simulated, a certain simulation data support can be provided for staff, the staff is helped to prepare the situation of the electric carbon emission in advance, more effective prevention and control are facilitated, and reasonable control of the electric carbon emission is effectively improved.
The electric carbon emission process monitoring method based on the big data of the electric network carries out electric carbon emission curve image generation on the data of the sequenced electric carbon emission quantity, carries out trend calculation on the electric carbon emission curve image, carries out curve prediction on the increasing and decreasing trend of the electric carbon emission curve image, and comprises the following steps:
marking data points of the electric carbon emission quantity on a coordinate graph according to the detection time sequence, and generating an electric carbon emission curve image through curve fitting according to the data points;
carrying out smoothing treatment on the electric carbon emission curve image, and generating an electric carbon emission change function according to the electric carbon emission curve image after the smoothing treatment;
obtaining a change trend function of the electric carbon emission quantity by deriving the change function of the electric carbon emission, and carrying out trend calculation on an electric carbon emission curve image by adopting the change trend function to obtain the current trend change rate;
respectively calculating the power data change amount and the power data change rate of the power data of different classifications, and respectively calculating the ratio of the current trend change rate to the power data change amount of each classification to obtain classification influence factors; performing weight assignment on the power data of different classifications according to the mutual proportion of each classification influence factor to obtain classification weights; adopting the sum of the power data change rate of each category and the product of the category weights as the trend change rate of the next stage;
and taking the product of the current electric carbon emission and the trend change rate of the next stage as an electric carbon emission change predicted value, and judging the increasing and decreasing trend of the electric carbon emission curve image according to the electric carbon emission change predicted value.
According to the scheme, through function conversion of an electric carbon emission curve, a mathematical calculus theory is adopted, function derivation is carried out to obtain a change trend function of electric carbon emission, and trend calculation is carried out to obtain the current trend change rate; according to the historically stored power data with different classifications, calculating the power data variable quantity and the power data variable rate of the power data variable quantity respectively, wherein the power data variable quantity is the difference between the current power data of the same type and the initial power data in a calculation period, and the power data variable rate is the ratio of the power data variable quantity to the data collection duration in the calculation period; the ratio of the current trend change rate to the change amount of the power data of each classification is used as a corresponding classification influence factor, then the power data of different classifications is subjected to weight assignment according to the classification influence factors to obtain classification weights, and the data obtained by summing the product of the change rate of the power data of each classification and the corresponding classification weights is used as the trend change rate of the next stage; finally, taking the product of the current electric carbon emission and the trend change rate of the next stage as an electric carbon emission change predicted value, and judging the increasing and decreasing trend of the electric carbon emission curve image according to the electric carbon emission change predicted value; according to the scheme, a mathematical quantitative analysis means is cited, so that doping subjective judgment in analysis is avoided, the accuracy of analysis is improved, the objectivity and reliability of an analysis result are ensured, and an effective means is provided for prediction and control of electric carbon emission.
Referring to fig. 2, an electric carbon emission process monitoring system based on grid big data includes:
a data acquisition unit configured to:
collecting and checking a power data sample of the power grid, and generating power data after checking;
a data processing unit for:
extracting power data characteristics of the power data, determining data classification identifiers, determining classification expressions of the data classification identifiers, constructing a power data classification model based on the classification expressions, and inputting the power data into the power data classification model for classification and transmission;
a data analysis unit for:
analyzing and processing the electric power data, calculating the carbon emission and outputting an analysis result;
an image output unit configured to:
generating an electric carbon emission amount change trend in the carbon emission process based on an analysis result of the data analysis unit, and outputting an image of the analysis result, wherein the image comprises a curve trend graph;
risk early warning unit for:
and carrying out risk early warning on the electric carbon emission process based on the analysis result of the data analysis unit.
Specifically, when the system works, firstly, a power data sample of a power grid is obtained through a data obtaining unit, then, based on the power data sample, data verification is carried out on the power data sample, edge data is removed, and power data is output, after the power data is obtained, a power data classification model is built by a data processing unit, classification transmission is carried out on the power data, then, analysis processing is carried out on the power data by a data analysis unit, calculation is carried out on carbon emission amount, an analysis result is output, after the analysis result is obtained, an image output unit generates an emission amount change trend in the electric carbon emission process based on the power data and time data, an electric carbon emission amount change curve is generated, curve prediction is carried out on the emission trend of future electric carbon emission amount according to the curve trend, an image is output on the electric carbon emission amount change curve, a risk early warning unit compares the electric carbon emission amount with a safety emission threshold, and an early warning alarm is sent when the comparison result shows that the electric carbon emission amount is close to or higher than the highest safety emission threshold.
In order to solve the technical problems that a large amount of electric power data samples are often collected for inspection and analysis in the process of processing electric power data samples of electric carbon emission, and abnormal data in the data samples can influence the processing speed and accuracy, thereby influencing the monitoring effect of the electric carbon emission and influencing the rationality of management and control decisions of technicians, the invention provides the following technical scheme:
the data acquisition unit includes:
the data acquisition module is used for:
collecting a power data sample of a power grid;
a constraint confirmation module for:
reading a power data sample, determining a data fluctuation range and a data type of power data sample data, and generating constraint conditions of data verification based on the data fluctuation range and the data type of the power data sample data;
the data verification module is used for:
and carrying out data verification on the power data samples based on the constraint conditions, marking the power data samples which do not meet the constraint conditions based on the verification results, taking the power data samples which do not meet the constraint conditions as edge data, removing the edge data from the power data samples, and outputting data except the edge data as power data.
Specifically, when the system works, before the electric power data samples of electric carbon emission are analyzed, the electric power data samples are checked and screened, abnormal data are removed through preset constraint conditions, so that the data base number during subsequent data processing is reduced, the accuracy of processing results can be further improved, the number of the electric power data samples to be processed can be reduced, the calculation processing workload of the system is reduced, and the response speed and the processing efficiency of the system are improved.
The data processing unit includes:
the data classification module is used for:
receiving power data, acquiring power data characteristics, determining data classification identifiers based on the power data characteristics, inputting the data classification identifiers into a preset neural network for learning, and determining classification expressions of the data classification identifiers;
the model building module is used for:
constructing a power data classification model based on the classification expression, and simultaneously inputting power data into the power data classification model for classification to obtain classified power sub-data;
the data transmission module is used for:
and transmitting the classified power sub-data to a data analysis unit for analysis and processing.
In order to solve the technical problems that when the electric carbon emission is monitored, the electric carbon emission is often analyzed according to detection data, the current electric carbon emission is calculated and processed when exceeding the standard or exceeding the standard, and the electric carbon emission is difficult to be automatically and effectively predicted in a trend manner and the electric carbon emission cannot be more effectively and reasonably managed and controlled through a system, the invention provides the following technical scheme:
the image output unit includes:
a time marking module for:
acquiring the acquisition time of the power data sample, generating time data, and marking the power data and the time data in a matching way;
a curve generating module for:
generating an emission amount variation trend in the electric carbon emission process based on a matching result of the electric power data and the time data, generating an electric carbon emission amount variation curve, and carrying out curve prediction on the emission trend of the future electric carbon emission amount according to the curve trend;
an image output module for:
the image is output based on the electric carbon emission amount variation curve generated by the curve generation module.
Specifically, when the system works, the curve generation module carries out numerical ranking on data of the electric carbon emission according to the electric carbon emission calculated by data analysis, and the numerical ranking carries out ranking through the electric data detection time corresponding to the electric carbon emission, carries out electric carbon emission curve image generation on the data of the electric carbon emission after ranking, carries out trend calculation on the electric carbon emission curve image and carries out curve prediction on the increasing and decreasing trend of the electric carbon emission curve image, so that the system can calculate according to the increasing trend of the existing electric carbon emission, simulate the future carbon emission change condition under the current increasing and decreasing trend, provide certain analog data support for staff, help the staff prepare the situation of the electric carbon emission in advance, be convenient for carrying out more effective prevention and control, and effectively improve the reasonable control on the electric carbon emission.
The risk early warning unit includes:
a threshold setting module, configured to:
setting a safe emission threshold of the electric carbon emission, and referencing the safe emission threshold in the threshold setting module when the result comparison module works;
the result comparison module is used for:
comparing the electric carbon emission amount analyzed by the data analysis unit with a safe emission threshold value to generate a comparison result, and generating an electric carbon emission control instruction when the electric carbon emission amount is close to or higher than the highest safe emission threshold value;
the early warning alarm module is used for:
determining whether to generate an early warning alarm according to the comparison result of the result comparison module, and sending out the early warning alarm when the comparison result shows that the electric carbon emission is close to or higher than the highest safe emission threshold
Specifically, when the system works, the safe emission threshold of the electric carbon emission can be set through the threshold setting module, the threshold can be obtained through manual input or system calculation, then the result comparison module compares the threshold of the carbon emission, and an electric carbon emission control instruction can be sent to a worker through the system under the condition that the carbon emission is approaching to exceed the standard, so that the worker can conveniently control the electric carbon emission, and meanwhile, an early warning alarm can be sent to remind when the electric carbon emission exceeds the standard, so that the worker can conveniently make corresponding preparation measures in advance.
The data verification module comprises:
a condition matching sub-module for extracting electric powerThe data type A of the data sample is subjected to morpheme analysis to obtain a plurality of morphemes, and the morphemes are expressed as A i Traversing constraint condition B, performing correlation calculation on data type A of the power data sample and each constraint condition, and calculating data type A and j constraint condition B of the power data sample by adopting the following formula j Is a correlation index of (2):
in the above-mentioned method, the step of,data type A and jth constraint B representing a power data sample j Is a correlation index of (2); n represents the total number of morphemes of data type a of the power data sample; omega i The weight of the ith morpheme is represented by the ith morpheme A i The logarithm of the frequency of occurrence in the constraint condition, the logarithm taking 2 of the computer binary number as the base; s (A) i ，B j ) Representing the ith morpheme A i With the j-th constraint B j The correlation score of (2) is scored by adopting a preset rule;
determining the constraint condition with the maximum data type correlation index as a matched constraint condition, and using the constraint condition for verifying the corresponding type of power data sample;
and the verification sub-module is used for judging whether the corresponding electric power data sample accords with the constraint condition by adopting the matched constraint condition, if not, adding a mark which does not accord with the constraint condition to the electric power data sample, and taking the electric power data sample which does not accord with the constraint condition as edge data.
According to the scheme, the condition matching submodule is adopted to extract the data types of the power data samples, the morpheme analysis is carried out to obtain a plurality of morphemes, the constraint conditions are traversed, the correlation analysis is carried out on the data types of the power data samples and the constraint conditions, the correlation analysis introduces the correlation index calculation formula, the calculation speed of the adopted calculation formula is high, the occupied resources are small, and the reduction of the electric carbon emission can be further facilitated; through calculation, adopting the constraint condition with the maximum data type correlation index as the constraint condition for power data sample verification; then a verification sub-module is adopted, whether the corresponding electric power data sample accords with the constraint condition is judged according to the matched constraint condition, and if not, the electric power data sample is marked as edge data; by adopting the scheme, the accuracy of the power data sample matching constraint condition can be improved, the situation that the power data sample cannot be checked or the error is prompted due to the matching error is avoided, the checking error rate is reduced, the operation reliability of the system is improved, and the effectiveness and the practicability of electric carbon emission monitoring and management are ensured.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should be covered by the protection scope of the present invention by making equivalents and modifications to the technical solution and the inventive concept thereof.
Claims (10)
1. The electric carbon emission process monitoring method based on the big data of the power grid is characterized by comprising the following steps of:
acquiring a sample, and acquiring a power data sample of a power grid, load power information, power grid voltage information and power grid current information of the power grid;
checking data, reading a power data sample, generating constraint conditions for data checking, performing data checking on the power data sample based on the constraint conditions, removing edge data based on a checking result, and outputting power data;
data processing, namely receiving power data, determining a data classification identifier, determining a classification expression of the data classification identifier, constructing a power data classification model based on the classification expression, classifying and transmitting the power data;
analyzing and processing electric power data, calculating the carbon emission and outputting an analysis result;
outputting a result, namely generating an emission variation trend in the electric carbon emission process based on the electric power data and the time data, generating an electric carbon emission variation curve, predicting the emission trend of the future electric carbon emission according to the curve trend, and outputting an image of the electric carbon emission variation curve;
and (3) risk early warning, namely comparing the electric carbon emission with a safe emission threshold, determining whether to generate an early warning alarm according to a comparison result, and sending out the early warning alarm when the comparison result shows that the electric carbon emission is close to or higher than the highest safe emission threshold.
2. The electrical carbon emission process monitoring method based on grid big data according to claim 1, wherein: the power grid comprises a public power grid, a local power grid and a household power grid, wherein the power data samples comprise standard fuel consumption of unit power generation amount, external power transmission amount, carbon emission factors at the power transmission side, power consumption of production activities, carbon emission data and industry flow production value data of the power grids at different local areas.
3. The electrical carbon emission process monitoring method based on grid big data according to claim 1, wherein: the result output comprises the steps of generating a simulated image of variation trend of the future electric carbon emission, carrying out numerical ranking on data of the electric carbon emission according to the electric carbon emission calculated by data analysis, ranking the data of the electric carbon emission through the electric data detection time corresponding to the electric carbon emission during the numerical ranking, generating an electric carbon emission curve image on the data of the electric carbon emission after ranking, carrying out trend calculation on the electric carbon emission curve image, and carrying out curve prediction on the increasing and decreasing trend of the electric carbon emission curve image.
4. The electrical carbon emission process monitoring method based on grid big data as set forth in claim 3, wherein: generating an electric carbon emission curve image of the ordered data of the electric carbon emission quantity, calculating a trend of the electric carbon emission curve image, and predicting the increasing and decreasing trend of the electric carbon emission curve image, wherein the method comprises the following steps:
marking data points of the electric carbon emission quantity on a coordinate graph according to the detection time sequence, and generating an electric carbon emission curve image through curve fitting according to the data points;
carrying out smoothing treatment on the electric carbon emission curve image, and generating an electric carbon emission change function according to the electric carbon emission curve image after the smoothing treatment;
obtaining a change trend function of the electric carbon emission quantity by deriving the change function of the electric carbon emission, and carrying out trend calculation on an electric carbon emission curve image by adopting the change trend function to obtain the current trend change rate;
respectively calculating the power data change amount and the power data change rate of the power data of different classifications, and respectively calculating the ratio of the current trend change rate to the power data change amount of each classification to obtain classification influence factors; performing weight assignment on the power data of different classifications according to the mutual proportion of each classification influence factor to obtain classification weights; adopting the sum of the power data change rate of each category and the product of the category weights as the trend change rate of the next stage;
and taking the product of the current electric carbon emission and the trend change rate of the next stage as an electric carbon emission change predicted value, and judging the increasing and decreasing trend of the electric carbon emission curve image according to the electric carbon emission change predicted value.
5. An electric carbon emission process monitoring system based on grid big data, applied to the electric carbon emission process monitoring method based on grid big data as set forth in any one of claims 1 to 4, comprising:
a data acquisition unit configured to:
collecting and checking a power data sample of the power grid, and generating power data after checking;
a data processing unit for:
extracting power data characteristics of the power data, determining data classification identifiers, determining classification expressions of the data classification identifiers, constructing a power data classification model based on the classification expressions, and inputting the power data into the power data classification model for classification and transmission;
a data analysis unit for:
analyzing and processing the electric power data, calculating the carbon emission and outputting an analysis result;
an image output unit configured to:
generating an electric carbon emission amount change trend in the carbon emission process based on an analysis result of the data analysis unit, and outputting an image of the analysis result, wherein the image comprises a curve trend graph;
risk early warning unit for:
and carrying out risk early warning on the electric carbon emission process based on the analysis result of the data analysis unit.
6. The grid big data based electrical carbon emission process monitoring system of claim 5, wherein: the data acquisition unit includes:
the data acquisition module is used for:
collecting a power data sample of a power grid;
a constraint confirmation module for:
reading a power data sample, determining a data fluctuation range and a data type of power data sample data, and generating constraint conditions of data verification based on the data fluctuation range and the data type of the power data sample data;
the data verification module is used for:
and carrying out data verification on the power data samples based on the constraint conditions, marking the power data samples which do not meet the constraint conditions based on the verification results, taking the power data samples which do not meet the constraint conditions as edge data, removing the edge data from the power data samples, and outputting data except the edge data as power data.
7. The grid big data based electrical carbon emission process monitoring system of claim 5, wherein: the data processing unit includes:
the data classification module is used for:
receiving power data, acquiring power data characteristics, determining data classification identifiers based on the power data characteristics, inputting the data classification identifiers into a preset neural network for learning, and determining classification expressions of the data classification identifiers;
the model building module is used for:
constructing a power data classification model based on the classification expression, and simultaneously inputting power data into the power data classification model for classification to obtain classified power sub-data;
the data transmission module is used for:
and transmitting the classified power sub-data to a data analysis unit for analysis and processing.
8. The grid big data based electrical carbon emission process monitoring system of claim 5, wherein: the image output unit includes:
a time marking module for:
acquiring the acquisition time of the power data sample, generating time data, and marking the power data and the time data in a matching way;
a curve generating module for:
generating an emission amount variation trend in the electric carbon emission process based on a matching result of the electric power data and the time data, generating an electric carbon emission amount variation curve, and carrying out curve prediction on the emission trend of the future electric carbon emission amount according to the curve trend;
an image output module for:
the image is output based on the electric carbon emission amount variation curve generated by the curve generation module.
9. The grid big data based electrical carbon emission process monitoring system of claim 5, wherein: the risk early warning unit includes:
a threshold setting module, configured to:
setting a safe emission threshold of the electric carbon emission, and referencing the safe emission threshold in the threshold setting module when the result comparison module works;
the result comparison module is used for:
comparing the electric carbon emission amount analyzed by the data analysis unit with a safe emission threshold value to generate a comparison result, and generating an electric carbon emission control instruction when the electric carbon emission amount is close to or higher than the highest safe emission threshold value;
the early warning alarm module is used for:
and determining whether to generate an early warning alarm according to the comparison result of the result comparison module, and sending out the early warning alarm when the comparison result shows that the electric carbon emission is close to or higher than the highest safe emission threshold.
10. The grid big data based electrical carbon emission process monitoring system of claim 6, wherein: the data verification module comprises:
the condition matching sub-module is used for extracting the data type A of the electric power data sample, carrying out morpheme analysis on the data type A of the electric power data sample to obtain a plurality of morphemes, and representing the morphemes as A i Traversing constraint condition B, performing correlation calculation on data type A of the power data sample and each constraint condition, and calculating data type A and j constraint condition B of the power data sample by adopting the following formula j Is a correlation index of (2):
in the above-mentioned method, the step of,data type A and jth constraint B representing a power data sample j Is a correlation index of (2); n represents the total number of morphemes of data type a of the power data sample; omega i The weight of the ith morpheme is represented by the ith morpheme A i The logarithm of the frequency of occurrence in the constraint condition, the logarithm taking 2 of the computer binary number as the base; s (A) i ，B j ) Representing the ith morpheme A i With the j-th constraint B j Is a correlation score of (2);
determining the constraint condition with the maximum data type correlation index as a matched constraint condition, and using the constraint condition for verifying the corresponding type of power data sample;
and the verification sub-module is used for judging whether the corresponding electric power data sample accords with the constraint condition by adopting the matched constraint condition, if not, adding a mark which does not accord with the constraint condition to the electric power data sample, and taking the electric power data sample which does not accord with the constraint condition as edge data.
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| CN116933951A (en) * | 2023-09-19 | 2023-10-24 | 北京中电飞华通信有限公司 | Low-carbon park carbon emission monitoring system and method based on big data |
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| CN116933951A (en) * | 2023-09-19 | 2023-10-24 | 北京中电飞华通信有限公司 | Low-carbon park carbon emission monitoring system and method based on big data |
| CN116933951B (en) * | 2023-09-19 | 2023-12-08 | 北京中电飞华通信有限公司 | Low-carbon park carbon emission monitoring system and method based on big data |
| CN117764416A (en) * | 2023-12-22 | 2024-03-26 | 国网宁夏电力有限公司电力科学研究院 | Urban carbon discharge monitoring system and method based on electric power big data |
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