Carbon emission metering method and device, terminal equipment and storage medium

Abstract

The invention discloses a carbon emission metering method and device, terminal equipment and a storage medium, and the method comprises the steps: calculating the theoretical power generation amount and theoretical pollutant emission amount which should be generated in each power generation period of a coal-fired power plant, and combining the actual power generation amount and actual pollutant emission amount which are actually detected; and the second fire coal component content and the second fire coal carbon oxidation rate of each power generation period are speculated. Correcting the first fire coal component content and the first fire coal carbon oxidation rate according to a first average value of a first deviation value between the second fire coal component content and the first fire coal component content in a plurality of power generation periods and a second average value of a second deviation value between the second fire coal carbon oxidation rate and the first fire coal carbon oxidation rate; and finally, according to the corrected component content of the fire coal and the carbon oxidation rate of the fire coal, calculating the carbon emission by adopting a material balance method. Therefore, the actual fire coal component content and the fire coal carbon oxidation rate of the coal can be directly calculated, and the metering efficiency of the carbon emission is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of electrical automation technology, and in particular to a carbon emission measurement method, device, terminal equipment and storage medium. Background Art

[0002] At present, in order to ensure power generation and the stable development of the carbon trading market, the power grid must strengthen the promotion of accurate quantification of carbon measurement, form a complete carbon emission measurement system as soon as possible, and promote the coordinated development of electricity and carbon.

[0003] The carbon emission measurement methods commonly used in coal-fired power plants are emission factor method, material balance method and actual measurement method. At present, my country has not formed an accurate standard for the selection of emission factors. The emission factors of different institutions lead to a maximum deviation of 40% in the calculation results; and the actual measurement method requires the installation of a professional continuous monitoring system, and the cost of CO2 continuous monitoring system is relatively high. Therefore, most coal-fired power plants currently choose the material balance method to calculate carbon emissions. The material balance method mainly calculates carbon emissions based on the coal component content and coal carbon oxidation rate of coal. Different types of coal have different coal component contents, and the coal component content and coal carbon oxidation rate of coal are easily affected by combustion temperature and combustion time, resulting in the actual coal component content and coal carbon oxidation rate of coal being different from the theoretical coal component content and coal carbon oxidation rate. Therefore, the material balance method is used to calculate carbon emissions. It is necessary to detect the actual coal component content and coal carbon oxidation rate of coal, but the time required to detect the actual coal component content and coal carbon oxidation rate of coal is too long, resulting in low efficiency in carbon emission measurement. Summary of the invention

[0004] The embodiments of the present invention provide a carbon emission measurement method, an apparatus, a terminal device and a storage medium, which can effectively improve the measurement efficiency of carbon emissions.

[0005] An embodiment of the present invention provides a carbon emission measurement method, including:

[0006] Obtaining actual power generation, actual pollutant emissions, power generation coal consumption, waste carbon content of generated waste, theoretical first coal component content of the coal used, and first coal carbon oxidation rate of the coal-fired power plant in several power generation periods; wherein the type and combustion method of the coal used in the several power generation periods are the same;

[0007] Calculate the theoretical power generation and theoretical pollutant emissions that the coal-fired power plant should generate in each power generation period according to the power generation coal consumption, the first coal component content, the first coal carbon oxidation rate, and the carbon content of the waste;

[0008] Estimate the possible second coal component content and the second coal carbon oxidation rate in each power generation period according to the theoretical power generation, the theoretical pollutant emission, the actual power generation and the actual pollutant emission;

[0009] Calculate a first deviation between the second coal component content and the first coal component content, and a second deviation between the second coal carbon oxidation rate and the first coal carbon oxidation rate in each power generation period, and then calculate a first average value of a plurality of the first deviation values ​​and a second average value of the second deviation values;

[0010] Correcting the first coal component content and the first coal carbon oxidation rate according to the first average value and the second average value to generate a corrected third coal component content and a third coal carbon oxidation rate;

[0011] The carbon emission measurement of the coal-fired power plant in each power generation period is calculated using a material balance method according to the third coal component content and the third coal carbon oxidation rate.

[0012] Furthermore, the theoretical power generation and theoretical pollutant emissions that should be generated by the coal-fired power plant in each power generation period are calculated based on the power generation coal consumption, the first coal component content, the first coal carbon oxidation rate, and the waste carbon content, including:

[0013] Inputting the power generation coal consumption, the first coal component content, the first coal carbon oxidation rate, and the waste carbon content into a preset calculation model, so that the calculation model outputs theoretical power generation and theoretical pollutant emissions;

[0014] The calculation model is a neural network model trained with inputs of historical coal consumption for power generation, historical coal component content, historical coal carbon oxidation rate, and historical waste carbon content, and with outputs of corresponding theoretical power generation and theoretical pollutant emissions.

[0015] Furthermore, the construction of the calculation model includes:

[0016] Obtaining several historical coal consumption for power generation, historical waste carbon content, historical coal component content, historical coal carbon oxidation rate, theoretical power generation, and theoretical pollutant emissions of the coal-fired power plant;

[0017] Constructing a training data set according to the historical coal consumption for power generation, the historical coal component content, the historical coal carbon oxidation rate, the historical waste carbon content, the theoretical power generation and the theoretical pollutant emissions;

[0018] Construct the initial measurement model of BP neural network structure;

[0019] The initial estimation model is trained by taking the historical coal consumption for power generation, the historical coal component content, the historical coal carbon oxidation rate, and the historical waste carbon content in the training data set as inputs, and taking the corresponding theoretical power generation and the theoretical pollutant emissions as outputs;

[0020] During the training process, the initial calculation model is evaluated using a preset loss function according to the power generation and pollutant emissions output by the initial calculation model, and an evaluation result is generated;

[0021] When the evaluation result converges and is minimized, it is determined that the training of the initial estimation model is completed, and the trained initial estimation model is used as the estimation model.

[0022] Furthermore, the obtaining of several historical coal consumption for power generation, historical coal component content, historical coal carbon oxidation rate, historical waste carbon content, theoretical power generation, and theoretical pollutant emissions of the coal-fired power plant includes:

[0023] Acquire several groups of historical data of the coal-fired power plant, wherein each group of historical data includes: historical coal consumption for power generation, historical coal component content, historical coal carbon oxidation rate, historical waste carbon content, historical power generation, and historical pollutant emissions;

[0024] According to the historical coal consumption for power generation, the historical coal component content, the historical coal carbon oxidation rate, and the historical waste carbon content, the historical data are divided into several data sets; wherein the historical coal consumption for power generation, the historical coal component content, the historical coal carbon oxidation rate, and the historical waste carbon content of several historical data in one data set are in the same numerical range;

[0025] According to the historical power generation and historical pollutant emissions of several historical data in the same numerical value set, the theoretical power generation and theoretical pollutant emissions corresponding to each data set are calculated.

[0026] Furthermore, the inferring possible second coal component content and second coal carbon oxidation rate in each power generation period according to the theoretical power generation, the theoretical pollutant emission, the actual power generation and the actual pollutant emission includes:

[0027] According to the theoretical power generation and theoretical pollutant emission of each power generation period, several groups of initial data in each power generation period are randomly generated; wherein the initial data include: initial coal component content and several initial coal carbon oxidation rates;

[0028] According to a plurality of groups of initial data in each power generation period, an initial population in each power generation period is constructed, and the plurality of groups of initial data in each power generation period are used as a plurality of initial individuals in the corresponding initial population;

[0029] Performing genetic optimization operations on the initial population of each power generation period in turn until the number of executions of the genetic optimization operation reaches a preset maximum number of executions threshold, thereby obtaining the possible second coal component content and the second coal carbon oxidation rate in each power generation period;

[0030] Wherein, the genetic optimization operation includes:

[0031] Acquire a population to be optimized, wherein initially, the population to be optimized is an initial population;

[0032] Input each individual to be optimized in the population to be optimized, the coal consumption for power generation, and the carbon content of the waste into the calculation model to obtain the individual power generation and individual pollutant emissions of each individual to be optimized; wherein, initially, the individual to be optimized is an initial individual;

[0033] Calculating the fitness value of each individual to be optimized according to a first difference between the individual power generation and the actual power generation, and a second difference between the individual pollutant emission and the actual pollutant emission;

[0034] When it is determined that the number of executions of the genetic optimization operation is less than the maximum number of executions threshold, a selection operation, a crossover operation, and a mutation operation are performed on each individual to be optimized in the population to be optimized according to the fitness value to obtain an optimized population;

[0035] Using the optimized population as the population to be optimized required for the next round of genetic operation;

[0036] When it is determined that the execution times of the genetic optimization operation is not less than the maximum execution times threshold, the individual to be optimized corresponding to the minimum fitness value is used as the second coal component content and the second coal carbon oxidation rate in the corresponding power generation period.

[0037] Another embodiment of the present invention provides a carbon emission metering device, comprising:

[0038] A data acquisition module, used to acquire actual power generation, actual pollutant emissions, power generation coal consumption, waste carbon content of generated waste, theoretical first coal component content of the coal used, and first coal carbon oxidation rate of the coal-fired power plant in several power generation periods; wherein the type and combustion method of the coal used in the several power generation periods are the same;

[0039] A data calculation module, for calculating the theoretical power generation and theoretical pollutant emissions that the coal-fired power plant should generate in each power generation period according to the power generation coal consumption, the first coal component content, the first coal carbon oxidation rate, and the carbon content of the waste;

[0040] A data inference module, used for inferring the possible second coal component content and the second coal carbon oxidation rate in each power generation period according to the theoretical power generation, the theoretical pollutant emission, the actual power generation and the actual pollutant emission;

[0041] a difference calculation module, used to calculate a first deviation value between the second coal component content and the first coal component content, and a second deviation value between the second coal carbon oxidation rate and the first coal carbon oxidation rate in each power generation period, and then calculate a first average value of a plurality of the first deviation values ​​and a second average value of the second deviation values;

[0042] A data correction module, used for correcting the first coal component content and the first coal carbon oxidation rate according to the first average value and the second average value, and generating a corrected third coal component content and a third coal carbon oxidation rate;

[0043] The carbon emission metering module is used to calculate the carbon emission metering of the coal-fired power plant in each power generation period by using the material balance method according to the third coal component content and the third coal carbon oxidation rate.

[0044] Furthermore, the theoretical power generation and theoretical pollutant emissions that should be generated by the coal-fired power plant in each power generation period are calculated based on the power generation coal consumption, the first coal component content, the first coal carbon oxidation rate, and the waste carbon content, including:

[0045] Inputting the power generation coal consumption, the first coal component content, the first coal carbon oxidation rate, and the waste carbon content into a preset calculation model, so that the calculation model outputs theoretical power generation and theoretical pollutant emissions;

[0046] The calculation model is a neural network model trained with inputs of historical coal consumption for power generation, historical coal component content, historical coal carbon oxidation rate, and historical waste carbon content, and with outputs of corresponding theoretical power generation and theoretical pollutant emissions.

[0047] Furthermore, the inferring possible second coal component content and second coal carbon oxidation rate in each power generation period according to the theoretical power generation, the theoretical pollutant emission, the actual power generation and the actual pollutant emission includes:

[0048] According to the theoretical power generation and theoretical pollutant emission of each power generation period, several groups of initial data in each power generation period are randomly generated; wherein the initial data include: initial coal component content and several initial coal carbon oxidation rates;

[0049] According to a plurality of groups of initial data in each power generation period, an initial population in each power generation period is constructed, and the plurality of groups of initial data in each power generation period are used as a plurality of initial individuals in the corresponding initial population;

[0050] Performing genetic optimization operations on the initial population of each power generation period in turn until the number of executions of the genetic optimization operation reaches a preset maximum number of executions threshold, thereby obtaining the possible second coal component content and the second coal carbon oxidation rate in each power generation period;

[0051] Wherein, the genetic optimization operation includes:

[0052] Acquire a population to be optimized, wherein initially, the population to be optimized is an initial population;

[0053] Input each individual to be optimized in the population to be optimized, the coal consumption for power generation, and the carbon content of the waste into the calculation model to obtain the individual power generation and individual pollutant emissions of each individual to be optimized; wherein, initially, the individual to be optimized is an initial individual;

[0054] Calculating the fitness value of each individual to be optimized according to a first difference between the individual power generation and the actual power generation, and a second difference between the individual pollutant emission and the actual pollutant emission;

[0055] When it is determined that the number of executions of the genetic optimization operation is less than the maximum number of executions threshold, a selection operation, a crossover operation, and a mutation operation are performed on each individual to be optimized in the population to be optimized according to the fitness value to obtain an optimized population;

[0056] Using the optimized population as the population to be optimized required for the next round of genetic operation;

[0057] When it is determined that the execution times of the genetic optimization operation is not less than the maximum execution times threshold, the individual to be optimized corresponding to the minimum fitness value is used as the second coal component content and the second coal carbon oxidation rate in the corresponding power generation period.

[0058] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, a carbon emission measurement method as described in any one of the embodiments is implemented.

[0059] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a carbon emission measurement method as described in any one of the above embodiments.

[0060] The following beneficial effects are achieved by implementing the present invention:

[0061] The present invention discloses a carbon emission measurement method, device, terminal equipment and storage medium. The method calculates the theoretical power generation and theoretical pollutant emission that a coal-fired power plant should generate in each power generation period, and combines the actual power generation and actual pollutant emission detected to infer the possible second coal component content and the second coal carbon oxidation rate in each power generation period. Since the type of coal used and the combustion method adopted in each power generation period are the same, the actual coal component content and the coal carbon oxidation rate of the coal should be the same. Therefore, the present invention corrects the first coal component content according to the first average value of the first deviation value of the second coal component content and the first coal component content in several power generation periods, and corrects the first coal carbon oxidation rate according to the second average value of the second coal carbon oxidation rate and the second deviation value of the first coal carbon oxidation rate in several power generation periods. Finally, according to the corrected third coal component content and the third coal carbon oxidation rate, the carbon emission measurement of the coal-fired power plant in each power generation period is calculated. Therefore, the present invention can directly calculate the actual coal component content and the coal carbon oxidation rate of the coal, effectively improving the measurement efficiency of carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a flow chart of a carbon emission measurement method provided by one embodiment of the present invention.

[0063] Figure 2 It is a structural schematic diagram of a carbon emission metering device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0065] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0066] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0067] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0068] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0069] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0070] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0071] See also Figure 1 , is a flow chart of a carbon emission measurement method provided by an embodiment of the present invention, comprising:

[0072] S1. Obtaining the actual power generation, actual pollutant emissions, power generation coal consumption, waste carbon content of generated waste, theoretical first coal component content of the coal used, and first coal carbon oxidation rate of the coal-fired power plant in several power generation periods; wherein the type and combustion method of the coal used in the several power generation periods are the same;

[0073] In a preferred embodiment of the present invention, the pollutant emissions include: SO2 concentration and flow rate and emission of flue gas at the inlet and outlet of the desulfurization device, NO x Concentration, flow rate and emission, waste carbon content, including: slag carbon content and fly ash carbon content. Furthermore, the power generation period can be set to one hour, two hours, etc. according to actual needs, which is not limited in this embodiment.

[0074] S2. Calculate the theoretical power generation and theoretical pollutant emissions that the coal-fired power plant should generate in each power generation period according to the power generation coal consumption, the first coal component content, the first coal carbon oxidation rate, and the carbon content of the waste;

[0075] Preferably, the calculation of the theoretical power generation and theoretical pollutant emissions that the coal-fired power plant should generate in each power generation period according to the power generation coal consumption, the first coal component content, the first coal carbon oxidation rate, and the waste carbon content includes:

[0076] S21, inputting the power generation coal consumption, the first coal component content, the first coal carbon oxidation rate, and the waste carbon content into a preset calculation model, so that the calculation model outputs theoretical power generation and theoretical pollutant emissions;

[0077] The calculation model is a neural network model trained with inputs of historical coal consumption for power generation, historical coal component content, historical coal carbon oxidation rate, and historical waste carbon content, and with outputs of corresponding theoretical power generation and theoretical pollutant emissions.

[0078] Preferably, the construction of the calculation model includes:

[0079] S01. Obtaining several historical coal consumption for power generation, historical waste carbon content, historical coal component content, historical coal carbon oxidation rate, theoretical power generation, and theoretical pollutant emissions of the coal-fired power plant;

[0080] Preferably, the obtaining of several historical coal consumption for power generation, historical coal component content, historical coal carbon oxidation rate, historical waste carbon content, theoretical power generation, and theoretical pollutant emissions of the coal-fired power plant includes:

[0081] S011. Acquire several groups of historical data of the coal-fired power plant, wherein each group of historical data includes: historical coal consumption for power generation, historical coal component content, historical coal carbon oxidation rate, historical waste carbon content, historical power generation, and historical pollutant emissions;

[0082] S012. Divide the historical data into several data sets according to the historical coal consumption for power generation, the historical coal component content, the historical coal carbon oxidation rate, and the historical waste carbon content; wherein the historical coal consumption for power generation, the historical coal component content, the historical coal carbon oxidation rate, and the historical waste carbon content of several historical data in one data set are in the same numerical range;

[0083] S013. Calculate theoretical power generation and theoretical pollutant emissions corresponding to each data set based on historical power generation and historical pollutant emissions of several historical data in the same numerical value set.

[0084] S02. Constructing a training data set according to the historical coal consumption for power generation, the historical coal component content, the historical coal carbon oxidation rate, the historical waste carbon content, the theoretical power generation and the theoretical pollutant emissions;

[0085] S03, constructing an initial measurement model of the BP neural network structure;

[0086] S04, using the historical coal consumption for power generation, the historical coal component content, the historical coal carbon oxidation rate, and the historical waste carbon content in the training data set as input, and using the corresponding theoretical power generation and the theoretical pollutant emissions as output to train the initial measurement model;

[0087] S05. During the training process, the initial calculation model is evaluated using a preset loss function according to the power generation and pollutant emissions output by the initial calculation model, and an evaluation result is generated;

[0088] S06. When the evaluation result converges and is minimized, it is determined that the training of the initial estimation model is completed, and the trained initial estimation model is used as the estimation model.

[0089] In a preferred embodiment of the present invention, the training data set has 70% of training data and 30% of test data. Furthermore, the initial measurement model is a BP neural network including an input layer, multiple hidden layers and an output layer. During the training process, the weight adjustment adopts the gradient descent method of the deviation value.

[0090] It should be further explained that the Tanh function is used as the activation function, as shown below:

[0091]

[0092] Among them, x is the output of each layer;

[0093] The mean square deviation is used as the loss function as shown below:

[0094]

[0095] Among them, Y i is the power generation and pollutant emissions output by the initial estimation model based on the i-th group of training data, is the theoretical power generation and theoretical pollutant emissions corresponding to the i-th group of training data.

[0096] S3, inferring the possible second coal component content and the second coal carbon oxidation rate in each power generation period according to the theoretical power generation, the theoretical pollutant emission, the actual power generation and the actual pollutant emission;

[0097] Preferably, the inferring the possible second coal component content and the second coal carbon oxidation rate in each power generation period according to the theoretical power generation, the theoretical pollutant emission, the actual power generation and the actual pollutant emission includes:

[0098] S31, randomly generating several groups of initial data in each power generation period according to the theoretical power generation and theoretical pollutant emission in each power generation period; wherein the initial data includes: initial coal component content and several initial coal carbon oxidation rates;

[0099] S32, constructing an initial population for each power generation period according to a plurality of groups of initial data in each power generation period, and using the plurality of groups of initial data in each power generation period as a plurality of initial individuals in the corresponding initial population;

[0100] S33, sequentially performing a genetic optimization operation on the initial population of each power generation period until the number of executions of the genetic optimization operation reaches a preset maximum number of executions threshold, thereby obtaining a possible second coal component content and a second coal carbon oxidation rate in each power generation period;

[0101] Wherein, the genetic optimization operation includes:

[0102] Step a, obtaining a population to be optimized, wherein, initially, the population to be optimized is an initial population;

[0103] Step b, inputting each individual to be optimized in the population to be optimized, the coal consumption for power generation, and the carbon content of the waste into the calculation model to obtain the individual power generation and individual pollutant emissions of each individual to be optimized; wherein, initially, the individual to be optimized is an initial individual;

[0104] Step c, calculating the fitness value of each individual to be optimized according to a first difference between the individual power generation and the actual power generation, and a second difference between the individual pollutant emission and the actual pollutant emission;

[0105] Step d, when it is determined that the number of executions of the genetic optimization operation is less than the maximum execution number threshold, performing a selection operation, a crossover operation, and a mutation operation on each individual to be optimized in the population to be optimized according to the fitness value, to obtain an optimized population;

[0106] Step e, using the optimized population as the population to be optimized required for the next round of genetic operation;

[0107] Step f: when it is determined that the number of executions of the genetic optimization operation is not less than the maximum execution number threshold, the individual to be optimized corresponding to the minimum fitness value is used as the second coal component content and the second coal carbon oxidation rate in the corresponding power generation period.

[0108] In a preferred embodiment of the present invention, the maximum execution number threshold is set to 100. This embodiment first generates several groups of initial coal component contents and several initial coal carbon oxidation rates in each power generation period according to the theoretical power generation and theoretical pollutant emissions in each power generation period; it can be understood that although the actual coal component content and coal carbon oxidation rate of coal will be affected by the combustion temperature and combustion time, they also fluctuate around its theoretical coal component content and coal carbon oxidation rate. Therefore, this embodiment first generates several groups of initial coal component contents and several initial coal carbon oxidation rates in each power generation period according to the theoretical power generation and theoretical pollutant emissions in each power generation period, which can enable the particle swarm optimization algorithm to find the possible second coal component content and second coal carbon oxidation rate in each power generation period faster and more accurately.

[0109] S4, calculating a first deviation value between the second coal component content and the first coal component content, and a second deviation value between the second coal carbon oxidation rate and the first coal carbon oxidation rate in each power generation period, and then calculating a first average value of a plurality of the first deviation values ​​and a second average value of the second deviation values;

[0110] S5. Correct the first coal component content and the first coal carbon oxidation rate according to the first average value and the second average value to generate a corrected third coal component content and a corrected third coal carbon oxidation rate;

[0111] In a preferred embodiment of the present invention, the first average value is added to the first coal component content to obtain the third coal component content, and the second average value is added to the first coal carbon oxidation rate to obtain the third coal carbon oxidation rate.

[0112] S6. Calculate the carbon emission measurement of the coal-fired power plant in each power generation period using a material balance method according to the third coal component content and the third coal carbon oxidation rate.

[0113] This embodiment provides a carbon emission measurement method, which calculates the theoretical power generation and theoretical pollutant emissions that a coal-fired power plant should generate in each power generation period, and combines the actual power generation and actual pollutant emissions that are actually detected to infer the possible second coal component content and the second coal carbon oxidation rate in each power generation period. Since the type of coal used and the combustion method adopted in each power generation period are the same, the actual coal component content and the coal carbon oxidation rate of the coal should be the same. Therefore, the present invention corrects the first coal component content according to the first average value of the first deviation value of the second coal component content and the first coal component content in several power generation periods, and corrects the first coal carbon oxidation rate according to the second average value of the second deviation value of the second coal carbon oxidation rate and the first coal carbon oxidation rate in several power generation periods. Finally, according to the corrected third coal component content and the third coal carbon oxidation rate, the carbon emission measurement of the coal-fired power plant in each power generation period is calculated. Therefore, the present invention can directly calculate the actual coal component content and coal carbon oxidation rate of the coal, effectively improving the measurement efficiency of carbon emissions.

[0114] See also Figure 2 , is a structural schematic diagram of a carbon emission metering device provided by an embodiment of the present invention, comprising:

[0115] A data acquisition module, used to acquire actual power generation, actual pollutant emissions, power generation coal consumption, waste carbon content of generated waste, theoretical first coal component content of the coal used, and first coal carbon oxidation rate of the coal-fired power plant in several power generation periods; wherein the type and combustion method of the coal used in the several power generation periods are the same;

[0116] A data calculation module, for calculating the theoretical power generation and theoretical pollutant emissions that the coal-fired power plant should generate in each power generation period according to the power generation coal consumption, the first coal component content, the first coal carbon oxidation rate, and the carbon content of the waste;

[0117] A data inference module, used for inferring the possible second coal component content and the second coal carbon oxidation rate in each power generation period according to the theoretical power generation, the theoretical pollutant emission, the actual power generation and the actual pollutant emission;

[0118] a difference calculation module, used to calculate a first deviation value between the second coal component content and the first coal component content, and a second deviation value between the second coal carbon oxidation rate and the first coal carbon oxidation rate in each power generation period, and then calculate a first average value of a plurality of the first deviation values ​​and a second average value of the second deviation values;

[0119] A data correction module, used for correcting the first coal component content and the first coal carbon oxidation rate according to the first average value and the second average value, and generating a corrected third coal component content and a third coal carbon oxidation rate;

[0120] The carbon emission metering module is used to calculate the carbon emission metering of the coal-fired power plant in each power generation period by using the material balance method according to the third coal component content and the third coal carbon oxidation rate.

[0121] Preferably, the calculation of the theoretical power generation and theoretical pollutant emissions that the coal-fired power plant should generate in each power generation period according to the power generation coal consumption, the first coal component content, the first coal carbon oxidation rate, and the waste carbon content includes:

[0122] Inputting the power generation coal consumption, the first coal component content, the first coal carbon oxidation rate, and the waste carbon content into a preset calculation model, so that the calculation model outputs theoretical power generation and theoretical pollutant emissions;

[0123] The calculation model is a neural network model trained with inputs of historical coal consumption for power generation, historical coal component content, historical coal carbon oxidation rate, and historical waste carbon content, and with outputs of corresponding theoretical power generation and theoretical pollutant emissions.

[0124] Preferably, the inferring the possible second coal component content and the second coal carbon oxidation rate in each power generation period according to the theoretical power generation, the theoretical pollutant emission, the actual power generation and the actual pollutant emission includes:

[0125] According to the theoretical power generation and theoretical pollutant emission of each power generation period, several groups of initial data in each power generation period are randomly generated; wherein the initial data include: initial coal component content and several initial coal carbon oxidation rates;

[0126] According to a plurality of groups of initial data in each power generation period, an initial population in each power generation period is constructed, and the plurality of groups of initial data in each power generation period are used as a plurality of initial individuals in the corresponding initial population;

[0127] Performing genetic optimization operations on the initial population of each power generation period in turn until the number of executions of the genetic optimization operation reaches a preset maximum number of executions threshold, thereby obtaining the possible second coal component content and the second coal carbon oxidation rate in each power generation period;

[0128] Wherein, the genetic optimization operation includes:

[0129] Acquire a population to be optimized, wherein initially, the population to be optimized is an initial population;

[0130] Input each individual to be optimized in the population to be optimized, the coal consumption for power generation, and the carbon content of the waste into the calculation model to obtain the individual power generation and individual pollutant emissions of each individual to be optimized; wherein, initially, the individual to be optimized is an initial individual;

[0131] Calculating the fitness value of each individual to be optimized according to a first difference between the individual power generation and the actual power generation, and a second difference between the individual pollutant emission and the actual pollutant emission;

[0132] When it is determined that the number of executions of the genetic optimization operation is less than the maximum number of executions threshold, a selection operation, a crossover operation, and a mutation operation are performed on each individual to be optimized in the population to be optimized according to the fitness value to obtain an optimized population;

[0133] Using the optimized population as the population to be optimized required for the next round of genetic operation;

[0134] When it is determined that the execution times of the genetic optimization operation is not less than the maximum execution times threshold, the individual to be optimized corresponding to the minimum fitness value is used as the second coal component content and the second coal carbon oxidation rate in the corresponding power generation period.

[0135] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0136] Those skilled in the art can clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0137] Another preferred embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a carbon emission measurement method as described in any one of the above embodiments is implemented.

[0138] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0139] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0140] The memory can be used to store the computer program, and the processor realizes various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Med i aCard, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0141] Another preferred embodiment of the present invention provides a storage medium, the storage medium is a computer-readable storage medium, the computer program is stored in the computer-readable storage medium, and the computer program, when executed by the processor, can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0142] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A carbon emission measurement method, characterized in that: include: Obtaining actual power generation, actual pollutant emissions, power generation coal consumption, waste carbon content of generated waste, theoretical first coal component content of the coal used, and first coal carbon oxidation rate of the coal-fired power plant in several power generation periods; wherein the type and combustion method of the coal used in the several power generation periods are the same; Calculate the theoretical power generation and theoretical pollutant emissions that the coal-fired power plant should generate in each power generation period according to the power generation coal consumption, the first coal component content, the first coal carbon oxidation rate, and the carbon content of the waste; Estimate the possible second coal component content and the second coal carbon oxidation rate in each power generation period according to the theoretical power generation, the theoretical pollutant emission, the actual power generation and the actual pollutant emission; Calculate a first deviation between the second coal component content and the first coal component content, and a second deviation between the second coal carbon oxidation rate and the first coal carbon oxidation rate in each power generation period, and then calculate a first average value of a plurality of the first deviation values ​​and a second average value of the second deviation values; Correcting the first coal component content and the first coal carbon oxidation rate according to the first average value and the second average value to generate a corrected third coal component content and a third coal carbon oxidation rate; The carbon emission measurement of the coal-fired power plant in each power generation period is calculated using a material balance method according to the third coal component content and the third coal carbon oxidation rate.

2. A carbon emission measurement method according to claim 1, characterized in that: The theoretical power generation and theoretical pollutant emissions that should be generated by the coal-fired power plant in each power generation period are calculated based on the power generation coal consumption, the first coal component content, the first coal carbon oxidation rate, and the waste carbon content, including: Inputting the power generation coal consumption, the first coal component content, the first coal carbon oxidation rate, and the waste carbon content into a preset calculation model, so that the calculation model outputs theoretical power generation and theoretical pollutant emissions; The calculation model is a neural network model trained with inputs of historical coal consumption for power generation, historical coal component content, historical coal carbon oxidation rate, and historical waste carbon content, and with outputs of corresponding theoretical power generation and theoretical pollutant emissions.

3. A carbon emission measurement method as claimed in claim 2, characterized in that: The construction of the calculation model includes: Obtaining several historical coal consumption for power generation, historical waste carbon content, historical coal component content, historical coal carbon oxidation rate, theoretical power generation, and theoretical pollutant emissions of the coal-fired power plant; Constructing a training data set according to the historical coal consumption for power generation, the historical coal component content, the historical coal carbon oxidation rate, the historical waste carbon content, the theoretical power generation and the theoretical pollutant emissions; Construct the initial measurement model of BP neural network structure; The initial estimation model is trained by taking the historical coal consumption for power generation, the historical coal component content, the historical coal carbon oxidation rate, and the historical waste carbon content in the training data set as inputs, and taking the corresponding theoretical power generation and the theoretical pollutant emissions as outputs; During the training process, the initial calculation model is evaluated using a preset loss function according to the power generation and pollutant emissions output by the initial calculation model, and an evaluation result is generated; When the evaluation result converges and is minimized, it is determined that the training of the initial estimation model is completed, and the trained initial estimation model is used as the estimation model.

4. A carbon emission measurement method as claimed in claim 3, characterized in that: The obtaining of several historical coal consumption for power generation, historical coal component content, historical coal carbon oxidation rate, historical waste carbon content, theoretical power generation, and theoretical pollutant emissions of the coal-fired power plant includes: Acquire several groups of historical data of the coal-fired power plant, wherein each group of historical data includes: historical coal consumption for power generation, historical coal component content, historical coal carbon oxidation rate, historical waste carbon content, historical power generation, and historical pollutant emissions; According to the historical coal consumption for power generation, the historical coal component content, the historical coal carbon oxidation rate, and the historical waste carbon content, the historical data are divided into several data sets; wherein the historical coal consumption for power generation, the historical coal component content, the historical coal carbon oxidation rate, and the historical waste carbon content of several historical data in one data set are in the same numerical range; According to the historical power generation and historical pollutant emissions of several historical data in the same numerical value set, the theoretical power generation and theoretical pollutant emissions corresponding to each data set are calculated.

5. A carbon emission measurement method as claimed in claim 2, characterized in that: The inferring the possible second coal component content and the second coal carbon oxidation rate in each power generation period according to the theoretical power generation, the theoretical pollutant emission, the actual power generation and the actual pollutant emission includes: According to the theoretical power generation and theoretical pollutant emission of each power generation period, several groups of initial data in each power generation period are randomly generated; wherein the initial data include: initial coal component content and several initial coal carbon oxidation rates; According to a plurality of groups of initial data in each power generation period, an initial population in each power generation period is constructed, and the plurality of groups of initial data in each power generation period are used as a plurality of initial individuals in the corresponding initial population; Performing genetic optimization operations on the initial population of each power generation period in turn until the number of executions of the genetic optimization operation reaches a preset maximum number of executions threshold, thereby obtaining the possible second coal component content and the second coal carbon oxidation rate in each power generation period; Wherein, the genetic optimization operation includes: Acquire a population to be optimized, wherein initially, the population to be optimized is an initial population; Input each individual to be optimized in the population to be optimized, the coal consumption for power generation, and the carbon content of the waste into the calculation model to obtain the individual power generation and individual pollutant emissions of each individual to be optimized; wherein, initially, the individual to be optimized is an initial individual; Calculating the fitness value of each individual to be optimized according to a first difference between the individual power generation and the actual power generation, and a second difference between the individual pollutant emission and the actual pollutant emission; When it is determined that the number of executions of the genetic optimization operation is less than the maximum number of executions threshold, a selection operation, a crossover operation, and a mutation operation are performed on each individual to be optimized in the population to be optimized according to the fitness value to obtain an optimized population; Using the optimized population as the population to be optimized required for the next round of genetic operation; When it is determined that the execution times of the genetic optimization operation is not less than the maximum execution times threshold, the individual to be optimized corresponding to the minimum fitness value is used as the second coal component content and the second coal carbon oxidation rate in the corresponding power generation period.

6. A carbon emission metering device, characterized in that: include: A data acquisition module, used to acquire actual power generation, actual pollutant emissions, power generation coal consumption, waste carbon content of generated waste, theoretical first coal component content of the coal used, and first coal carbon oxidation rate of the coal-fired power plant in several power generation periods; wherein the type and combustion method of the coal used in the several power generation periods are the same; A data calculation module, for calculating the theoretical power generation and theoretical pollutant emissions that the coal-fired power plant should generate in each power generation period according to the power generation coal consumption, the first coal component content, the first coal carbon oxidation rate, and the carbon content of the waste; A data inference module, used for inferring the possible second coal component content and the second coal carbon oxidation rate in each power generation period according to the theoretical power generation, the theoretical pollutant emission, the actual power generation and the actual pollutant emission; a difference calculation module, used to calculate a first deviation value between the second coal component content and the first coal component content, and a second deviation value between the second coal carbon oxidation rate and the first coal carbon oxidation rate in each power generation period, and then calculate a first average value of a plurality of the first deviation values ​​and a second average value of the second deviation values; A data correction module, used for correcting the first coal component content and the first coal carbon oxidation rate according to the first average value and the second average value, and generating a corrected third coal component content and a third coal carbon oxidation rate; The carbon emission metering module is used to calculate the carbon emission metering of the coal-fired power plant in each power generation period by using the material balance method according to the third coal component content and the third coal carbon oxidation rate.

7. A carbon emission metering device as claimed in claim 6, characterized in that: The theoretical power generation and theoretical pollutant emissions that should be generated by the coal-fired power plant in each power generation period are calculated based on the power generation coal consumption, the first coal component content, the first coal carbon oxidation rate, and the waste carbon content, including: Inputting the power generation coal consumption, the first coal component content, the first coal carbon oxidation rate, and the waste carbon content into a preset calculation model, so that the calculation model outputs theoretical power generation and theoretical pollutant emissions; The calculation model is a neural network model trained with inputs of historical coal consumption for power generation, historical coal component content, historical coal carbon oxidation rate, and historical waste carbon content, and with outputs of corresponding theoretical power generation and theoretical pollutant emissions.

8. A carbon emission metering device as claimed in claim 7, characterized in that: The inferring the possible second coal component content and the second coal carbon oxidation rate in each power generation period according to the theoretical power generation, the theoretical pollutant emission, the actual power generation and the actual pollutant emission includes: According to the theoretical power generation and theoretical pollutant emission of each power generation period, several groups of initial data in each power generation period are randomly generated; wherein the initial data include: initial coal component content and several initial coal carbon oxidation rates; According to a plurality of groups of initial data in each power generation period, an initial population in each power generation period is constructed, and the plurality of groups of initial data in each power generation period are used as a plurality of initial individuals in the corresponding initial population; Performing genetic optimization operations on the initial population of each power generation period in turn until the number of executions of the genetic optimization operation reaches a preset maximum number of executions threshold, thereby obtaining the possible second coal component content and the second coal carbon oxidation rate in each power generation period; Wherein, the genetic optimization operation includes: Acquire a population to be optimized, wherein initially, the population to be optimized is an initial population; Input each individual to be optimized in the population to be optimized, the coal consumption for power generation, and the carbon content of the waste into the calculation model to obtain the individual power generation and individual pollutant emissions of each individual to be optimized; wherein, initially, the individual to be optimized is an initial individual; Calculating the fitness value of each individual to be optimized according to a first difference between the individual power generation and the actual power generation, and a second difference between the individual pollutant emission and the actual pollutant emission; When it is determined that the number of executions of the genetic optimization operation is less than the maximum number of executions threshold, a selection operation, a crossover operation, and a mutation operation are performed on each individual to be optimized in the population to be optimized according to the fitness value to obtain an optimized population; Using the optimized population as the population to be optimized required for the next round of genetic operation; When it is determined that the execution times of the genetic optimization operation is not less than the maximum execution times threshold, the individual to be optimized corresponding to the minimum fitness value is used as the second coal component content and the second coal carbon oxidation rate in the corresponding power generation period.

9. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a carbon emission measurement method as claimed in any one of claims 1 to 5 is implemented.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is executed, the device where the storage medium is located is controlled to execute a carbon emission measurement method as described in any one of claims 1 to 5.

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