CN115759450B - Carbon emission accounting early warning analysis system based on big data - Google Patents
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- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 title claims abstract description 108
- 229910052799 carbon Inorganic materials 0.000 title claims abstract description 108
- 238000004458 analytical method Methods 0.000 title claims abstract description 14
- 238000012544 monitoring process Methods 0.000 claims abstract description 88
- 238000011156 evaluation Methods 0.000 claims abstract description 28
- 230000002159 abnormal effect Effects 0.000 claims abstract description 10
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims description 100
- 229910002092 carbon dioxide Inorganic materials 0.000 claims description 50
- 239000001569 carbon dioxide Substances 0.000 claims description 50
- 241000854291 Dianthus carthusianorum Species 0.000 claims description 20
- 238000012545 processing Methods 0.000 claims description 13
- 238000004891 communication Methods 0.000 claims description 12
- 238000012546 transfer Methods 0.000 claims description 9
- 238000007781 pre-processing Methods 0.000 claims description 6
- 239000000779 smoke Substances 0.000 claims description 3
- 230000007704 transition Effects 0.000 claims description 2
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 6
- 239000003546 flue gas Substances 0.000 description 6
- 238000000034 method Methods 0.000 description 5
- 238000001514 detection method Methods 0.000 description 4
- 238000012549 training Methods 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 3
- 230000001419 dependent effect Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000002035 prolonged effect Effects 0.000 description 3
- 230000005856 abnormality Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000000446 fuel Substances 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006386 neutralization reaction Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
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Abstract
The invention discloses a carbon emission accounting early warning analysis system based on big data, which comprises a monitoring module, a big data prediction module, an accounting module and an early warning module; the monitoring module is used for acquiring monitoring data in a set time interval; the big data prediction module is used for predicting the carbon emission in a set time interval to obtain a predicted value of the carbon emission; the accounting module is used for calculating an accounting value of the carbon emission in the set time interval based on the monitoring data; the early warning module is used for evaluating the correctness of the accounting value of the carbon emission based on the predicted value of the carbon emission, obtaining an evaluation result, and carrying out early warning and monitoring on the carbon emission based on the evaluation result. The invention can timely find the abnormal condition of the equipment for acquiring the monitoring data, thereby effectively guaranteeing the accuracy of the accounting result and the monitoring result.
Description
Technical Field
The invention relates to the field of early warning analysis, in particular to a carbon emission accounting early warning analysis system based on big data.
Background
Carbon emissions accounting is a measure of the direct and indirect emissions of carbon dioxide and its equivalent gases from industrial activities to the earth's biosphere, and is a generic term for activities that instruct the emissions enterprise to collect, count and record data related to carbon emissions according to a monitoring program, and calculate all emissions-related data. The carbon emission accounting can directly quantify the carbon emission data, and can also find out potential emission reduction links and methods by analyzing the carbon emission data of each link, which is important for realizing the carbon neutralization target and enterprise operation.
The thermal power enterprise needs to carry out accounting monitoring to carbon emission in the production process, the existing carbon emission accounting early warning system generally carries out carbon emission accounting by acquiring relevant data in production activities, but the accuracy of a sensor for acquiring corresponding data is reduced along with the time, and the prior art generally adopts a fixed detection period to carry out accuracy detection and replacement on the corresponding sensor, so that data abnormality in the carbon emission accounting process cannot be found in time, and the final carbon emission accounting result is inaccurate.
Disclosure of Invention
The invention aims to disclose a carbon emission accounting early warning analysis system based on big data, which solves the problems that the existing carbon emission accounting monitoring system adopts a fixed detection period to detect and replace the accuracy of a corresponding sensor, and the data abnormality in the carbon emission accounting process can not be found in time, so that the final carbon emission accounting result is inaccurate.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a carbon emission accounting early warning analysis system based on big data comprises a monitoring module, a big data prediction module, an accounting module and an early warning module;
the monitoring module is used for acquiring monitoring data in a set time interval;
the big data prediction module is used for predicting the carbon emission in a set time interval to obtain a predicted value of the carbon emission;
the accounting module is used for calculating an accounting value of the carbon emission in the set time interval based on the monitoring data;
the early warning module is used for evaluating the correctness of the accounting value of the carbon emission based on the predicted value of the carbon emission, obtaining an evaluation result, and carrying out early warning and monitoring on the carbon emission based on the evaluation result.
Preferably, the monitoring data includes first monitoring data and second monitoring data.
Preferably, the monitoring module comprises a first acquisition unit and a second acquisition unit;
the first acquisition unit is used for acquiring first monitoring data of the smoke discharge port in a set time interval;
the second acquisition unit is used for acquiring second monitoring data of the designated monitoring area around the thermal power enterprise in a set time interval.
Preferably, the first monitoring data includes carbon dioxide emissions.
Preferably, the second monitoring data comprises an average carbon dioxide concentration.
Preferably, the calculating an accounting value of the carbon emission amount in the set time interval based on the monitoring data includes:
the accounting value of the carbon emission amount was calculated using the following formula:
wherein, ficbdxes represents an accounting value of carbon emission, α, β represent proportionality coefficients, onecbdxes represents carbon dioxide emission, avecbdxe represents an average carbon dioxide concentration, stcbdxe represents a preset reference carbon dioxide concentration, and stcbdxes represents a preset carbon dioxide emission reference value.
Preferably, the early warning module comprises an evaluation unit and an early warning unit;
the evaluation unit is used for evaluating the correctness of the accounting value of the carbon emission based on the predicted value of the carbon emission to obtain an evaluation result;
the early warning unit is used for carrying out early warning and monitoring on the carbon emission based on the evaluation result.
Preferably, the estimating the correctness of the accounting value of the carbon emission based on the predicted value of the carbon emission includes:
and calculating the absolute value of the difference between the predicted value of the carbon emission and the calculated value of the carbon emission, wherein if the absolute value is smaller than the set error threshold, the calculated value of the carbon emission is normal, and if the absolute value is greater than or equal to the set error threshold, the calculated value of the carbon emission is abnormal.
Preferably, the early warning monitoring of the carbon emission based on the evaluation result includes:
and when the evaluation result is that the calculated value of the carbon emission is normal, judging whether the calculated value of the carbon emission is larger than a set monitoring threshold value, and if so, carrying out early warning prompt according to a set early warning mode.
When the invention calculates and monitors the carbon emission in the set time interval, firstly, the carbon emission in the time interval is predicted by a big data technology to obtain the predicted value of the carbon emission, and then the predicted value is compared with the calculated value to judge whether the calculated value is abnormal or not, if the calculated value is abnormal, the accuracy of equipment in the monitoring module is changed, and the equipment needs to be subjected to processing work such as calibration or replacement. The scheme of the invention can timely find the abnormal condition of the equipment for acquiring the monitoring data, thereby effectively guaranteeing the accuracy of the accounting result and the monitoring result.
Drawings
The invention will be further described with reference to the accompanying drawings, in which embodiments do not constitute any limitation of the invention, and other drawings can be obtained by one of ordinary skill in the art without inventive effort from the following drawings.
FIG. 1 is a diagram of an embodiment of a carbon emission accounting early warning analysis system based on big data according to the present invention.
FIG. 2 is a graph showing an embodiment of the present invention for predicting carbon emissions in a set time interval.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
The invention provides a carbon emission accounting early warning analysis system based on big data, which is shown in an embodiment in FIG. 1 and comprises a monitoring module, a big data prediction module, an accounting module and an early warning module;
the monitoring module is used for acquiring monitoring data in a set time interval;
the big data prediction module is used for predicting the carbon emission in a set time interval to obtain a predicted value of the carbon emission;
the accounting module is used for calculating an accounting value of the carbon emission in the set time interval based on the monitoring data;
the early warning module is used for evaluating the correctness of the accounting value of the carbon emission based on the predicted value of the carbon emission, obtaining an evaluation result, and carrying out early warning and monitoring on the carbon emission based on the evaluation result.
Preferably, the monitoring data includes first monitoring data and second monitoring data.
Preferably, the monitoring module comprises a first acquisition unit and a second acquisition unit;
the first acquisition unit is used for acquiring first monitoring data of the smoke discharge port in a set time interval;
the second acquisition unit is used for acquiring second monitoring data of the designated monitoring area around the thermal power enterprise in a set time interval.
In one embodiment, the designated monitoring area may be an area with a radius R centered on a chimney of the thermal power plant.
Preferably, the first monitoring data includes carbon dioxide emissions.
In one embodiment, the total carbon dioxide emissions are obtained by calculating the carbon dioxide emissions for each stack.
For a single flue gas discharge port, the carbon dioxide discharge amount of the flue gas discharge port can be calculated by acquiring the data of the cross-sectional area of the discharge port, the flue gas flow rate, the temperature, the discharge time, the carbon dioxide concentration in the flue gas and the like.
Preferably, the second monitoring data comprises an average carbon dioxide concentration.
In one embodiment, the second acquisition unit acquires the carbon dioxide concentration of each monitoring point in the monitoring area through a fixed time period, and the total acquisition number is recorded as N, so that the average carbon dioxide concentration of the monitoring point is as follows for the single monitoring pointdata i Represents the carbon dioxide concentration obtained at the i-th time. And for the whole monitoring area, the number of the monitoring points is recorded as M, so that the average carbon dioxide concentration of the whole monitoring area, namely the second monitoring data, is +.>data j,i Represents the carbon dioxide concentration obtained at the jth detection point at the ith time.
Preferably, the second acquisition unit comprises a wireless sensor node and a data processing device;
the wireless sensor node is used for acquiring the carbon dioxide concentration of the monitoring point in the monitoring area and sending the acquired carbon dioxide concentration to the data processing device;
the data processing device is used for preprocessing the carbon dioxide concentration from each wireless sensor node and sending the data obtained after preprocessing to the accounting module.
Preferably, all wireless sensor nodes are divided into member nodes and cluster head nodes in a clustering mode;
the member nodes are used for acquiring the carbon dioxide concentration of monitoring points in the monitoring area and transmitting the acquired carbon dioxide concentration to cluster head nodes of the cluster to which the member nodes belong;
the cluster head node is used for sending the carbon dioxide concentration sent by the member node to the data processing device.
After clustering, the communication efficiency can be improved, and the continuous working time of the wireless sensor node after single full charge can be prolonged.
Preferably, the cluster head node updates the routing table in the following manner:
after the j-th timing interval is finished, calculating a routing table updating coefficient, if the routing table updating coefficient is larger than a set coefficient threshold value xsthr, updating the routing table, and calculating the value of the j+1th timing interval;
wherein the routing table update coefficients are calculated by the following formula:
in rutupd j Indicating the update coefficient of the routing table, trns, calculated after the j-th timing interval is finished j Representing the data transfer amount of the cluster head node in the j-th timing interval, and sdtrns represents the preset standard value of the data transfer amount, c 1 Weights representing aspects of data transitions, ergcse j Representing the total amount of power consumed by the cluster head node in the jth timing interval, sderg representing a preset power consumption standard value, c 2 Weight indicating power consumption, dly com j In the j-th timing interval, the communication delay of the cluster head node for transmitting the data with unit length to the data processing device is shown, sddly shows the preset communication delay standard value, c 3 Weight, numend, representing communication delay aspects j Representing the total number of disabled member nodes within the maximum communication radius of the cluster head in the jth timing interval, sdnum represents the total number of member nodes within the maximum communication radius of the cluster head node.
In the prior art, a routing table is generally updated by presetting a fixed timing interval, however, in a period of less data transfer, the topology structure formed after the wireless sensor nodes are clustered changes slowly, and at this time, if the routing table is still updated frequently, the waste of electric quantity of the cluster head nodes is obviously caused, the continuous working time is influenced, and thus, the acquisition of the carbon dioxide concentration of the monitoring point cannot be continuously carried out. In the invention, after each timing interval is finished, whether the routing table needs to be updated is judged by calculating the routing table updating coefficient, and the routing table updating coefficient is larger as the transfer amount in the data is larger, the electric quantity consumption is larger, the communication delay of the data in a unit length is larger, and the number of member nodes losing the working capacity is larger, so that the probability of updating the routing table is also larger, and the busyness of the data transfer in the last timing interval can be comprehensively reflected by the routing table updating coefficient from multiple aspects. Meanwhile, the frequency of updating the routing table can be adaptively changed along with the busyness of data transfer, and the continuous working time of the wireless sensor node is effectively prolonged.
In one embodiment, the manner in which the routing table is updated includes both active routing and passive routing.
Active routing, also known as Table D-ven routing, has a route discovery policy similar to that of conventional routing protocols, and nodes actively discover routes by periodically broadcasting routing information packets, exchanging routing information.
Preferably, the calculating the value of the j+1th timing interval includes:
if rutupd j Less than xsthr × Ω, the value of the j+1st timing interval is calculated using the following formula:
rutupd j+1 ＝min(rutupd j +psti,matupd)
if rutupd j And (3) if the value is greater than or equal to xsthr multiplied by omega, calculating the value of the j+1th timing interval by adopting the following formula:
rutupd j+1 ＝max(rutupd j -psti,mitupd)
wherein, omega represents a comparison coefficient, omega is more than or equal to 1.2, and rutupd j+1 The value of j+1th timing interval is represented, psti represents a preset time length, matupd and mitupd represent a maximum value and a minimum value of the set timing interval respectively, min represents a smaller value of the two values in brackets, and max represents a larger value of the two values in brackets.
When the next time interval is calculated, the invention shortens the value of the next timing interval when the update coefficient of the routing table is smaller, otherwise, the value of the next timing interval is prolonged, and the change is carried out in a gradual change mode along with the busyness degree of data transfer, so that the routing table can be prevented from being updated too frequently, and the electric quantity is better saved.
Preferably, the preprocessing of the carbon dioxide concentration from each wireless sensor node includes:
carbon dioxide concentration cbdxee obtained at time g for wireless sensor node d d,g The cbdxee is determined as follows d,g Whether or not it is incorrect data:
calculating cbdxee d,g Is a comparison value of cmpcbdxee d,g ：
Wherein RU represents a set of other wireless sensor nodes d having a distance to the wireless sensor node d smaller than RU, cbdxee h,g Represents the carbon dioxide concentration acquired by the wireless sensor node h at the moment g, weihgt h A weight coefficient representing the wireless sensor node h;
if the comparison value is greater than the set comparison value threshold value, the comparison value represents cbdxee d,g For incorrect data, cbdxee will d,g Deleting; if the comparison value is smaller than or equal to the set comparison value threshold value, the cbdxee will d,g And (5) reserving.
By preprocessing the data in the data acquisition link, invalid data can be prevented from being transmitted, so that the pressure of data transmission is reduced, and meanwhile, the accuracy of the data transmitted to the accounting module is improved.
In the prior art, whether the data are correct is judged by directly comparing the data with the set comparison value, however, the setting mode of the comparison value is very dependent on experience of an implementation personnel, so that the absolute size of the comparison value is very easy to be too low or too high, and the judgment result is inaccurate. For example, when the range of variation of the data is (0,100000), the setting of the comparison value is significant, and incorrect setting of the comparison value may cause excessive erroneous judgment due to the large range of variation.
In the present invention, however, the method is carried out by combining cbdxee d,g And comparing the comparison value with the weighted value of the carbon dioxide concentration of other monitoring points at the same moment in the appointed range, and comparing the comparison value with a comparison value threshold value to judge whether the data are incorrect, thereby effectively improving the accuracy of the judgment result. The comparison value is expressed in a ratio mode, the absolute size of the value has no influence on the judgment result, and the problem that the set comparison value is too low or too high can not occur, so that the application range of the invention can be remarkably improved. The practitioner can freely set the comparison value threshold value according to the accuracy requirement.
Preferably, the cluster head node is further configured to send the acquired carbon dioxide concentration to the data processing device.
Preferably, as shown in fig. 2, the predicting the carbon emission in the set time interval to obtain the predicted value of the carbon emission includes:
acquiring historical data in the time interval, wherein the historical data comprises dependent variables and independent variables, and the independent variables comprise a power generation target, a fuel input amount, an outdoor temperature, a fuel type and the like by taking thermal power generation as an example; and the dependent variable includes carbon emissions;
dividing the historical data into a training set and a testing set;
establishing a neural network-based prediction model, such as a fitting interpolation prediction model, a hidden Markov prediction model and the like;
training parameters of the neural network prediction model by using a training set, verifying the accuracy of the model by using a testing set in the training process, and continuously adjusting the parameters until the prediction accuracy of the neural network prediction model meets the set requirement, thereby obtaining a final prediction model;
assigning the independent variable according to the current actual condition;
and taking the assigned independent variable as input data of a prediction model, and acquiring a predicted value of the carbon emission in a set time interval through a prediction module.
Preferably, the calculating an accounting value of the carbon emission amount in the set time interval based on the monitoring data includes:
the accounting value of the carbon emission amount was calculated using the following formula:
wherein, ficbdxes represents an accounting value of carbon emission, α, β represent proportionality coefficients, onecbdxes represents carbon dioxide emission, avecbdxe represents an average carbon dioxide concentration, stcbdxe represents a preset reference carbon dioxide concentration, and stcbdxes represents a preset carbon dioxide emission reference value.
In the present invention, in order to further increase the calculated value of the too-discharged amount, the present invention obtains the carbon dioxide concentration in the specified region in addition to the carbon dioxide discharge amount of the flue gas port, because a positive correlation is presented between the carbon dioxide concentration in the vicinity of the flue gas port and the discharged amount of carbon dioxide, it is possible to increase the accuracy of the result of the calculated value by comprehensively calculating the calculated value of the carbon discharge amount by obtaining data from two places.
Preferably, the early warning module comprises an evaluation unit and an early warning unit;
the evaluation unit is used for evaluating the correctness of the accounting value of the carbon emission based on the predicted value of the carbon emission to obtain an evaluation result;
the early warning unit is used for carrying out early warning and monitoring on the carbon emission based on the evaluation result.
Preferably, the estimating the correctness of the accounting value of the carbon emission based on the predicted value of the carbon emission includes:
and calculating the absolute value of the difference between the predicted value of the carbon emission and the calculated value of the carbon emission, wherein if the absolute value is smaller than the set error threshold, the calculated value of the carbon emission is normal, and if the absolute value is greater than or equal to the set error threshold, the calculated value of the carbon emission is abnormal.
Preferably, the early warning monitoring of the carbon emission based on the evaluation result includes:
and when the evaluation result is that the calculated value of the carbon emission is normal, judging whether the calculated value of the carbon emission is larger than a set monitoring threshold value, and if so, carrying out early warning prompt according to a set early warning mode.
When the invention calculates and monitors the carbon emission in the set time interval, firstly, the carbon emission in the time interval is predicted by a big data technology to obtain the predicted value of the carbon emission, and then the predicted value is compared with the calculated value to judge whether the calculated value is abnormal or not, if the calculated value is abnormal, the accuracy of equipment in the monitoring module is changed, and the equipment needs to be subjected to processing work such as calibration or replacement. The scheme of the invention can timely find the abnormal condition of the equipment for acquiring the monitoring data, thereby effectively guaranteeing the accuracy of the accounting result and the monitoring result.
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 make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (5)
1. The carbon emission accounting early warning analysis system based on the big data is characterized by comprising a monitoring module, a big data prediction module, an accounting module and an early warning module;
the monitoring module is used for acquiring monitoring data in a set time interval;
the big data prediction module is used for predicting the carbon emission in a set time interval to obtain a predicted value of the carbon emission;
the accounting module is used for calculating an accounting value of the carbon emission in the set time interval based on the monitoring data;
the early warning module is used for evaluating the correctness of the accounting value of the carbon emission based on the predicted value of the carbon emission to obtain an evaluation result, and carrying out early warning monitoring on the carbon emission based on the evaluation result;
the monitoring data comprises first monitoring data and second monitoring data;
the monitoring module comprises a first acquisition unit and a second acquisition unit;
the first acquisition unit is used for acquiring first monitoring data of the smoke discharge port in a set time interval;
the second acquisition unit is used for acquiring second monitoring data of the designated monitoring area around the thermal power enterprise in a set time interval;
the first monitoring data includes carbon dioxide emissions;
the second monitoring data includes an average carbon dioxide concentration;
the second acquisition unit comprises a wireless sensor node and a data processing device;
the wireless sensor node is used for acquiring the carbon dioxide concentration of the monitoring point in the monitoring area and sending the acquired carbon dioxide concentration to the data processing device;
the data processing device is used for preprocessing the carbon dioxide concentration from each wireless sensor node and sending the data obtained after preprocessing to the accounting module;
all the wireless sensor nodes are divided into member nodes and cluster head nodes in a clustering mode;
the member nodes are used for acquiring the carbon dioxide concentration of monitoring points in the monitoring area and transmitting the acquired carbon dioxide concentration to cluster head nodes of the cluster to which the member nodes belong;
the cluster head node is used for sending the carbon dioxide concentration sent by the member node to the data processing device;
the cluster head node updates the routing table in the following manner:
after the j-th timing interval is finished, calculating a routing table updating coefficient, if the routing table updating coefficient is larger than a set coefficient threshold value xsthr, updating the routing table, and calculating the value of the j+1th timing interval;
wherein the routing table update coefficients are calculated by the following formula:
in rutupd j Indicating the update coefficient of the routing table, trns, calculated after the j-th timing interval is finished j Representing the data transfer amount of the cluster head node in the j-th timing interval, and sdtrns represents the preset standard value of the data transfer amount, c 1 Weights representing aspects of data transitions, ergcse j Representing the total amount of power consumed by the cluster head node in the jth timing interval, sderg representing a preset power consumption standard value, c 2 Weight indicating power consumption, dly com j In the j-th timing interval, the communication delay of the cluster head node for transmitting the data with unit length to the data processing device is shown, sddly shows the preset communication delay standard value, c 3 Weight, numend, representing communication delay aspects j Representing the total number of disabled member nodes within the maximum communication radius of the cluster head in the jth timing interval, sdnum represents the total number of member nodes within the maximum communication radius of the cluster head node.
2. The big data based carbon emission accounting early warning analysis system according to claim 1, wherein the calculating an accounting value of the amount of carbon emission in the set time interval based on the monitoring data includes:
the accounting value of the carbon emission amount was calculated using the following formula:
wherein, ficbdxes represents an accounting value of carbon emission, α, β represent proportionality coefficients, onecbdxes represents carbon dioxide emission, avecbdxe represents an average carbon dioxide concentration, stcbdxe represents a preset reference carbon dioxide concentration, and stcbdxes represents a preset carbon dioxide emission reference value.
3. The carbon emission accounting early warning analysis system based on big data according to claim 1, wherein the early warning module comprises an evaluation unit and an early warning unit;
the evaluation unit is used for evaluating the correctness of the accounting value of the carbon emission based on the predicted value of the carbon emission to obtain an evaluation result;
the early warning unit is used for carrying out early warning and monitoring on the carbon emission based on the evaluation result.
4. The carbon emission accounting early warning analysis system based on big data according to claim 3, wherein the evaluation of the correctness of the accounting value of the carbon emission based on the predicted value of the carbon emission to obtain the evaluation result includes:
and calculating the absolute value of the difference between the predicted value of the carbon emission and the calculated value of the carbon emission, wherein if the absolute value is smaller than the set error threshold, the calculated value of the carbon emission is normal, and if the absolute value is greater than or equal to the set error threshold, the calculated value of the carbon emission is abnormal.
5. The big data based carbon emission accounting early warning analysis system of claim 4, wherein the early warning monitoring of the carbon emission based on the evaluation result comprises:
and when the evaluation result is that the calculated value of the carbon emission is normal, judging whether the calculated value of the carbon emission is larger than a set monitoring threshold value, and if so, carrying out early warning prompt according to a set early warning mode.
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