Intelligent management system for carbon emission coal samples of thermal power plant

Abstract

The invention relates to the technical field of thermal power plant carbon emission management, and discloses a thermal power plant carbon emission coal sample intelligent management system. Comprising a central control unit used for establishing a carbon emission coal sample coal quality prediction model according to historical data; the report generation unit is used for generating a carbon emission coal sample statistical report; the sample cabinet management unit is used for storing and taking the carbon emission coal samples in the sample cabinet; the data acquisition unit is used for weighing the carbon emission coal sample and acquiring data; the central control unit comprises a first processing module used for generating a carbon emission coal sample inspection result deviation degree f according to a carbon emission coal sample prediction result generated by the carbon emission coal sample coal quality prediction model and a carbon emission coal sample inspection result; the second processing module is used for changing the basic configuration item of the carbon emission coal sample and generating a change degree value r of the basic configuration of the carbon emission coal sample; the report generation unit comprises a report verification module used for verifying the carbon emission coal sample statistical report. The method has a report verification function, and the accuracy and reliability of data are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission management in thermal power plants, and in particular to an intelligent management system for carbon emission coal samples in thermal power plants. Background Art

[0002] At present, the carbon emissions statistics of my country's thermal power industry all adopt the accounting method, which calculates carbon emissions by measuring coal consumption and coal element carbon content. The existing accounting method is extremely dependent on the collection and management of carbon emission coal samples, but the existing carbon emission coal sample management has the following defects: human intervention is simple and easy to be tampered with, the sample preparation and extraction process is completely dependent on manual work, and there is a lack of effective supervision measures; the storage period of carbon emission coal samples is a fixed time, and there is no consideration of dynamically adjusting the storage time based on the importance of carbon emission coal samples, which will affect the accuracy and traceability of carbon emission data of thermal power plants, and is not conducive to supervision and environmental protection work. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides an intelligent management system for carbon emission coal samples in thermal power plants, which aims to realize full-cycle monitoring of the carbon emission sample processing process of the entire thermal power plant and improve the accuracy, reliability and traceability of carbon emission data management in thermal power plants.

[0004] In some embodiments of the present application, an abnormal value of the carbon emission coal sample is generated based on the change degree value of the basic configuration of the carbon emission coal sample and the first deviation of the carbon emission coal sample inspection result, and it is determined whether to re-test the carbon emission coal sample based on the abnormal value of the carbon emission coal sample, and abnormal behavior marks are added to the carbon emission coal samples that fail the verification to ensure the accuracy of the carbon emission coal sample inspection results.

[0005] In some embodiments of the present application, the storage period of carbon emission coal samples is dynamically adjusted according to the change degree value of the basic configuration of the carbon emission coal samples, the first deviation of the carbon emission coal sample inspection results, and whether the carbon emission coal samples have abnormal behavior marks, thereby enhancing the flexibility of the system and improving the reliability and traceability of the carbon emission coal sample management system.

[0006] In some embodiments of the present application, the storage period is greater than T according to the comprehensive evaluation degree. 1 The carbon emission coal samples stored in the sample cabinet are arranged in descending order, and the carbon emission coal samples at the end of the order are cleaned up to effectively utilize the storage space, reduce resource waste, and optimize resource utilization.

[0007] In some embodiments of the present application, a coal sample intelligent management system for carbon emission from a thermal power plant is provided, comprising: The central control unit is used to establish a carbon emission coal sample and coal quality prediction model based on historical data; A report generation unit, used to generate a statistical report on carbon emission coal samples; Sample cabinet management unit, used for storing and retrieving carbon emission coal samples in the sample cabinet; Data acquisition unit, used for weighing and data collection of carbon emission coal samples; The central control unit comprises: A first processing module is used to generate a first deviation f of the carbon emission coal sample inspection result according to the carbon emission coal sample prediction value generated by the carbon emission coal sample coal quality prediction model and the carbon emission coal sample inspection result; The second processing module is used to set the basic configuration of the carbon emission coal sample and generate a change degree value r of the basic configuration of the carbon emission coal sample; The report generating unit comprises: The report verification module is used to verify the carbon emission coal sample statistical report.

[0008] In some embodiments of the present application, the generating of the first deviation f of the carbon emission coal sample test result includes: f=[ e i *(b i -∆b i ) 2 ]; Among them, m is the number of carbon emission coal sample inspection indicators, e i is the preset weight coefficient of the i-th carbon emission coal sample inspection index, b i is the reference value of the i-th inspection index generated according to the inspection results of the carbon emission coal sample, ∆b i is the expected value of the i-th test indicator generated based on the prediction model.

[0009] In some embodiments of the present application, the generation of the change degree value r of the basic configuration of the carbon emission coal sample includes: r= e i '*h(i); Among them, q is the number of items in the basic configuration of carbon emission coal samples, e i ' is the preset weight coefficient of the basic configuration item of the i-th carbon emission coal sample, h 1 (i) is the first selection coefficient. When the basic configuration item of the carbon emission coal sample changes, h(i)=1, otherwise h(i)=0.

[0010] In some embodiments of the present application, the report verification module is also used to: Generate an abnormal value a of the carbon emission coal sample according to the change degree value r of the basic configuration of the carbon emission coal sample and the first deviation f of the carbon emission coal sample inspection result; a=Q 1 *e 1 *r+Q 2 *e 2 *f; Among them, Q 1 is the preset first fixed coefficient, Q 2 is the preset second fixed coefficient, e 1 is the preset first weight coefficient, e 2 is the preset second weight coefficient, r is the change degree value of the basic configuration of the carbon emission coal sample, and f is the first deviation of the carbon emission coal sample inspection result; Preset the first abnormal value threshold a 1 ; Determine whether to generate an abnormal verification instruction according to the abnormal value a of the carbon emission coal sample; when a 1 When , no exception verification instruction is generated; When a≥a 1 When an exception verification instruction is generated, the verification is performed according to the instruction.

[0011] In some embodiments of the present application, the abnormality verification instruction includes: The number of detection times n of the carbon emission coal sample is set, the carbon emission coal sample is detected n times, and an average detection data packet is generated according to the n detection data; Calculate the second deviation f' of the coal sample test results; f'=[ e i *(b i -b i ') 2 ]; b i ' is the average value of the i-th inspection index in the average detection data packet, b i is the reference value of the i-th test index in the test result of the original carbon emission coal sample; Preset the second deviation standard threshold f 0 ; If f' <f 0 , the original carbon emission coal sample test results are verified to be qualified, and a carbon emission coal sample statistical report is generated based on the original carbon emission coal sample test results; If f' f 0 , the original carbon emission coal sample test results are verified to be unqualified, an abnormal behavior mark is added to the carbon emission coal sample in the original carbon emission coal sample test results, and a carbon emission statistical report is generated based on the average test results of the carbon emission coal samples in the average test data package.

[0012] In some embodiments of the present application, the central control unit further includes: ​The third processing module is used to generate a carbon emission coal sample evaluation data packet for the carbon emission coal sample detection process, wherein the carbon emission coal sample evaluation data packet includes the change degree value of the carbon emission coal sample basic configuration, the first deviation of the carbon emission coal sample inspection result, and whether the carbon emission coal sample has an abnormal behavior mark.

[0013] In some embodiments of the present application, the sample cabinet management unit includes: The deadline adjustment module is used to generate the carbon emission coal sample evaluation coefficient according to the carbon emission coal sample evaluation data package ; and based on the carbon emission coal sample evaluation coefficient Set the storage period T of the current carbon emission coal sample 1 '; Carbon emission coal sample evaluation coefficient =Y{Q 3 *e 3 *r+Q 4 *e 4 *f+Q 5 *e 5 *h 2 (x)}; Among them, 1< <2, Q 3 The third fixed coefficient, Q 4 The fourth fixed coefficient, Q 5 is the preset fifth fixed coefficient, e 3 is the preset third weight coefficient, e 4 is the preset fourth weight coefficient, e 5 is the preset fifth weight coefficient, r is the change degree value of the basic configuration of the carbon emission coal sample, f is the first deviation of the carbon emission coal sample test result, h 2 (x) is the second selection coefficient. When the carbon emission coal sample has abnormal behavior marks, h 2 (x) = 1, otherwise h 2 (x)=0, Y{x} is used to map any real value to the interval (1,2), ; According to the carbon emission coal sample evaluation coefficient Set the storage period T of the current carbon emission coal sample 1 '; T 1 '= *T 1 , is the carbon emission coal sample evaluation coefficient, T 1 To preset the minimum carbon emission coal sample storage period.

[0014] In some embodiments of the present application, the sample cabinet management unit includes: Sample cabinet cleaning module, preset cleaning cycle T 2 , every time T 2 Time, clean up the carbon emission coal samples in the sample cabinet; Scan the sample cabinets to determine the number of free sample cabinets c 1 ; When c 1 c 2 When the inventory cleanup instruction is not generated, c 2 To preset the minimum number of free cabinets; When c 1 <c 2 When the inventory cleaning instruction is generated, the carbon emission coal samples in the sample cabinet are cleaned up, and the number of sample cabinets to be cleaned up is determined. 3 , c 3 =c 2 -c 1 .

[0015] In some embodiments of the present application, the inventory clearing instruction includes: Scan the sample cabinet to confirm that the storage period is greater than T 1 The number of carbon emissions from coal samples c 4 ; If c 4 <c 3 , stop executing inventory cleanup instructions If c 4 ≥c 3 , execute the inventory cleanup instruction, generate a storage period greater than T 1 The comprehensive evaluation degree p of the carbon emission coal sample is used to evaluate the storage period greater than T 1 The carbon emission coal samples are arranged in descending order, and the ones ranked last are 3 The carbon emission coal samples were cleaned up.

[0016] In some embodiments of the present application, the generated storage period is greater than T 1 The comprehensive evaluation degree p of the carbon emission coal sample includes: p=Q 6 *e 6 *a+Q 7 *e 7 *y; Where Q 6 The sixth fixed coefficient, Q 7 is the preset seventh fixed coefficient, e 6 is the preset sixth weight coefficient, e 7 is the preset seventh weight coefficient, a is the abnormal value of the carbon emission coal sample, and y is the importance of the carbon emission coal sample; y= *u i; Among them, k is the number of importance evaluation indicators, is the preset weight coefficient of the i-th evaluation index, u i is the reference value of the ith evaluation index.

[0017] Compared with the prior art, the intelligent management system for carbon emission coal samples in a thermal power plant according to the embodiment of the present invention has the following beneficial effects: The abnormal value of the carbon emission coal sample is generated according to the change degree value of the basic configuration of the carbon emission coal sample and the first deviation of the carbon emission coal sample inspection result, and the whether to re-test the carbon emission coal sample is determined according to the abnormal value of the carbon emission coal sample. Abnormal behavior marks are added to the carbon emission coal samples that fail the verification to ensure the accuracy of the carbon emission coal sample inspection results.

[0018] According to the change degree of the basic configuration of the carbon emission coal sample, the first deviation of the carbon emission coal sample inspection result and whether the carbon emission coal sample has abnormal behavior mark, the storage period of the carbon emission coal sample is dynamically adjusted to enhance the flexibility of the system and improve the reliability and traceability of the carbon emission coal sample management system.

[0019] According to the comprehensive evaluation degree, the storage period is greater than T 1 The carbon emission coal samples stored in the sample cabinet are arranged in descending order, and the carbon emission coal samples at the end of the order are cleaned up to effectively utilize the storage space, reduce resource waste, and optimize resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a structural diagram of an intelligent management system for carbon emission coal samples in a thermal power plant in a preferred embodiment of the present application. DETAILED DESCRIPTION

[0021] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0022] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0023] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0024] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" 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 it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0025] like Figure 1 As shown, an intelligent management system for carbon emission coal samples of a thermal power plant according to a preferred embodiment of the present invention includes: The central control unit is used to establish a carbon emission coal sample and coal quality prediction model based on historical data; A report generation unit, used to generate a statistical report on carbon emission coal samples; Sample cabinet management unit, used for storing and retrieving carbon emission coal samples in the sample cabinet; Data acquisition unit, used for weighing and data collection of carbon emission coal samples; The central control unit comprises: A first processing module is used to generate a first deviation f of the carbon emission coal sample inspection result according to the carbon emission coal sample prediction value generated by the carbon emission coal sample coal quality prediction model and the carbon emission coal sample inspection result; The second processing module is used to set the basic configuration of the carbon emission coal sample and generate a change degree value r of the basic configuration of the carbon emission coal sample; The report generating unit comprises: The report verification module is used to verify the carbon emission coal sample statistical report.

[0026] Specifically, the carbon emission coal sample statistical report should cover the sample statistical month, monthly carbon emission reference sample code, monthly carbon emission reference sample weight, coal sample extraction ratio, sample particle size, external inspection coal sample code, external inspection coal sample weight, testing agency, external delivery time, sample sender, sample receiver, and the corresponding coal quality inspection results after the external inspection is returned.

[0027] Specifically, a coal sample and coal quality prediction model is established based on historical data, and the characteristic values ​​of the coal samples in this batch are extracted and input into the coal sample and coal quality prediction model to obtain the coal sample prediction results; the characteristic values ​​of the coal samples include origin, quality, type, etc.

[0028] Specifically, with the help of technologies such as the Internet of Things and touch screens, online management of the storage, disposal and adjustment of electronic sample cabinets for carbon emission coal samples is achieved. The online management page of electronic sample cabinets for carbon emission coal samples supports a graphical display interface, which can intuitively distinguish between stored sample cabinets and free cabinets.

[0029] Specifically, the automatic weighing data collection unit is directly connected to the electronic balance equipment to realize online weighing of coal samples. The system uses a scanner to scan the QR code of the coal sample, automatically retrieves the corresponding coal feeder consumption information, and uses the uniformly configured extraction ratio to automatically calculate the planned weight of the coal sample to be extracted. The operator extracts the actual coal sample according to the planned weight of the carbon emission coal sample for weighing. The weighing weight information is automatically collected, the effective weight accuracy is 2 digits (0.01g), and the corresponding operator, operation time and other information of the process are recorded.

[0030] Specifically, the generation of the first deviation f of the carbon emission coal sample test result includes: f=[ e i *(b i -∆b i ) 2 ]; Among them, m is the number of carbon emission coal sample inspection indicators, e i is the preset weight coefficient of the i-th carbon emission coal sample inspection index, b i is the reference value of the i-th inspection index generated according to the inspection results of the carbon emission coal sample, ∆b i is the expected value of the i-th test indicator generated based on the prediction model.

[0031] Specifically, the coal sample inspection indicators should include the elemental carbon content, low calorific value, hydrogen content, total sulfur, moisture and other inspection indicators of the coal sample.

[0032] Specifically, the greater the first deviation f of the carbon emission coal sample detection result, the greater the deviation between the carbon emission coal sample detection result and the carbon emission coal sample prediction value generated by the carbon emission coal sample coal quality prediction model.

[0033] Specifically, the generation of the change degree value r of the basic configuration of the carbon emission coal sample includes: r= e i '*h(i); Among them, q is the number of items in the basic configuration of carbon emission coal samples, e i ' is the preset weight coefficient of the basic configuration item of the i-th carbon emission coal sample, h 1 (i) is the first selection coefficient. When the basic configuration item of the carbon emission coal sample changes, h(i)=1, otherwise h(i)=0.

[0034] Specifically, the greater the change degree value r of the basic configuration of the generated carbon emission coal sample, the greater the change degree of the basic configuration of the carbon emission coal sample.

[0035] Specifically, the basic configuration items of coal samples include but are not limited to the coal sample extraction ratio, carbon emission electronic sample cabinet maintenance information, testing agency, sample submitter, sample recipient information, carbon emission coal sample submission time, and carbon emission coal sample storage time. The coal sample extraction ratio should be the same within the same month.

[0036] Specifically, the report verification module is also used to: Generate an abnormal value a of the carbon emission coal sample according to the change degree value r of the basic configuration of the carbon emission coal sample and the first deviation f of the carbon emission coal sample inspection result; a=Q 1 *e 1 *r+Q 2 *e 2 *f; Among them, Q 1 is the preset first fixed coefficient, Q 2 is the preset second fixed coefficient, e 1 is the preset first weight coefficient, e 2 is the preset second weight coefficient, r is the change degree value of the basic configuration of the carbon emission coal sample, and f is the first deviation of the carbon emission coal sample inspection result; Preset the first abnormal value threshold a 1 ; Determine whether to generate an abnormal verification instruction according to the abnormal value a of the carbon emission coal sample; when a 1 When , no exception verification instruction is generated; When a≥a 1 When an exception verification instruction is generated, the verification is performed according to the instruction.

[0037] Specifically, the larger the abnormal value a of the carbon emission coal sample is, the more likely the carbon emission coal sample is Specifically, by presetting the first fixed coefficient and the second fixed coefficient, all parameters in the model are normalized so that each parameter is within the same value range.

[0038] Specifically, the carbon emission coal sample statistical report should include the change degree value of the basic configuration of the coal samples covered by this statistical report, the first deviation of the coal sample inspection results, and the abnormal value a of this carbon emission coal sample statistical report.

[0039] Specifically, the first outlier threshold a 1 Can be set based on historical data.

[0040] Specifically, the abnormality verification instruction includes: ​The number of detection times n of the carbon emission coal sample is set, the carbon emission coal sample is detected n times, and an average detection data packet is generated according to the n detection data; Calculate the second deviation f' of the coal sample test results; f'=[ e i *(b i -b i ') 2 ]; b i ' is the average value of the i-th inspection index in the average detection data packet, b i is the reference value of the i-th test index in the test result of the original carbon emission coal sample; Preset the second deviation standard threshold f 0 ; If f' <f 0 , the original carbon emission coal sample test results are verified to be qualified, and a carbon emission coal sample statistical report is generated based on the original carbon emission coal sample test results; If f' f 0 , the original carbon emission coal sample test results are verified to be unqualified, an abnormal behavior mark is added to the carbon emission coal sample in the original carbon emission coal sample test results, and a carbon emission statistical report is generated based on the average test results of the carbon emission coal samples in the average test data package.

[0041] Specifically, the greater the second deviation f' of the coal sample detection result is, the greater the deviation between the coal sample detection result and the average detection data in the average detection data packet generated according to n detection data is; Specifically, the second deviation standard threshold f 0 Can be set based on historical parameters.

[0042] Specifically, the average detection data packet is the average value of the carbon emission coal sample detection results for n times, and the average value of the i-th inspection index in the average detection data packet is b i '= ( …+ ) / n, for is the reference value of the i-th test index in the first test among n tests, is the reference value of the i-th test index in the second test among n tests, It is the reference value of the i-th test indicator in the n-th test among n tests.

[0043] It can be understood that in the above embodiment, the abnormal value of the carbon emission coal sample is generated according to the change degree value of the basic configuration of the carbon emission coal sample and the first deviation of the carbon emission coal sample inspection result, and it is determined whether to re-test the carbon emission coal sample based on the abnormal value of the carbon emission coal sample, and abnormal behavior marks are added to the carbon emission coal samples that fail the verification, so as to ensure the accuracy of the carbon emission coal sample inspection results.

[0044] In a preferred embodiment of the present application, the central control unit further includes: The third processing module is used to generate a carbon emission coal sample evaluation data packet for the carbon emission coal sample detection process, wherein the carbon emission coal sample evaluation data packet includes the change degree value of the carbon emission coal sample basic configuration, the first deviation of the carbon emission coal sample inspection result, and whether the carbon emission coal sample has an abnormal behavior mark.

[0045] Specifically, the sample cabinet management unit includes: The deadline adjustment module is used to generate the carbon emission coal sample evaluation coefficient according to the carbon emission coal sample evaluation data package ; and based on the carbon emission coal sample evaluation coefficient Set the storage period T of the current carbon emission coal sample 1 '; Carbon emission coal sample evaluation coefficient =Y{Q 3 *e 3 *r+Q 4 *e 4 *f+Q 5 *e 5 *h 2 (x)}; Among them, 1< <2, Q 3 The third fixed coefficient, Q 4 The fourth fixed coefficient, Q 5 is the preset fifth fixed coefficient, e 3 is the preset third weight coefficient, e 4 is the preset fourth weight coefficient, e 5 is the preset fifth weight coefficient, r is the change degree value of the basic configuration of the carbon emission coal sample, f is the first deviation of the carbon emission coal sample test result, h 2 (x) is the second selection coefficient. When the carbon emission coal sample has abnormal behavior marks, h 2 (x) = 1, otherwise h 2 (x)=0, Y{x} is used to map any real value to the interval (1,2), ; According to the carbon emission coal sample evaluation coefficient Set the storage period T of the current carbon emission coal sample 1 '; T 1 '= *T 1 , is the carbon emission coal sample evaluation coefficient, T 1 To preset the minimum carbon emission coal sample storage period.

[0046] Specifically, by presetting the third fixed coefficient, the fourth fixed coefficient and the seventh fixed coefficient, all parameters in the model are normalized so that each parameter is within the same value range.

[0047] It can be understood that in the above embodiment, the storage period of the carbon emission coal sample is dynamically adjusted according to the change degree value of the basic configuration of the carbon emission coal sample, the first deviation of the carbon emission coal sample inspection result and whether the carbon emission coal sample has an abnormal behavior mark, thereby enhancing the flexibility of the system and improving the reliability and traceability of the carbon emission coal sample management system.

[0048] In a preferred embodiment of the present application, the sample cabinet management unit includes: Sample cabinet cleaning module, preset cleaning cycle T 2 , every time T 2 Time, clean up the carbon emission coal samples in the sample cabinet; Scan the sample cabinets to determine the number of free sample cabinets c 1 ; When c 1 c 2 When the inventory cleanup instruction is not generated, c 2 To preset the minimum number of free cabinets; When c 1 <c 2 When the inventory cleaning instruction is generated, the carbon emission coal samples in the sample cabinet are cleaned up, and the number of sample cabinets to be cleaned up is determined. 3 , c 3 =c 2 -c 1 .

[0049] Specifically, the inventory clearing instructions include: Scan the sample cabinet to confirm that the storage period is greater than T 1 The number of carbon emissions from coal samples c 4 ; If c 4 <c 3 , stop executing inventory cleanup instructions If c 4 ≥c 3 , execute the inventory cleanup instruction, generate a storage period greater than T 1The comprehensive evaluation degree p of the carbon emission coal sample is used to evaluate the storage period greater than T 1 The carbon emission coal samples are arranged in descending order, and the ones ranked last are 3 The carbon emission coal samples were cleaned up.

[0050] Specifically, the cleaning cycle T 2 Can be set based on historical parameters.

[0051] It can be understood that in the above embodiment, the storage period is greater than T according to the comprehensive evaluation degree. 1 The coal samples stored in the sample cabinet are arranged in descending order, and the inventory of the coal samples stored at the end of the order is optimized to effectively utilize the storage space, reduce resource waste, and optimize resource utilization.

[0052] Specifically, the generated storage period is greater than T 1 The comprehensive evaluation degree p of the carbon emission coal sample includes: p=Q 6 *e 6 *a+Q 7 *e 7 *y; Where Q 6 The sixth fixed coefficient, Q 7 is the preset seventh fixed coefficient, e 6 is the preset sixth weight coefficient, e 7 is the preset seventh weight coefficient, a is the abnormal value of the carbon emission coal sample, and y is the importance of the carbon emission coal sample; y= *u i ; Among them, k is the number of importance evaluation indicators, is the preset weight coefficient of the i-th evaluation index, u i is the reference value of the ith evaluation index.

[0053] Specifically, by presetting the sixth fixed coefficient and the seventh fixed coefficient, all parameters in the model are normalized so that each parameter is within the same value range.

[0054] Specifically, the evaluation indicators of the importance of carbon emission coal samples should include the number of times the carbon emission coal samples are called per unit time, the scale of the unit using this coal sample, etc.

[0055] According to the first concept of the present application, the present application generates an abnormal value of the carbon emission coal sample based on the change degree value of the basic configuration of the carbon emission coal sample and the first deviation of the carbon emission coal sample inspection result, and determines whether to re-test the carbon emission coal sample based on the abnormal value of the carbon emission coal sample, and adds abnormal behavior marks to the carbon emission coal samples that fail the verification, so as to ensure the accuracy of the carbon emission coal sample inspection results.

[0056] According to the second concept of the present application, the present application dynamically adjusts the storage period of carbon emission coal samples according to the change degree value of the basic configuration of the carbon emission coal samples, the first deviation of the carbon emission coal sample inspection results and whether the carbon emission coal samples have abnormal behavior marks, thereby enhancing the flexibility of the system and improving the reliability and traceability of the carbon emission coal sample management system.

[0057] According to the third concept of the present application, the present application evaluates the storage period greater than T according to the comprehensive evaluation degree. 1 The carbon emission coal samples stored in the sample cabinet are arranged in descending order, and the carbon emission coal samples at the end of the order are cleaned up to effectively utilize the storage space, reduce resource waste, and optimize resource utilization.

[0058] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention. These improvements and substitutions should be regarded as the scope of protection of the present invention.

Claims

1. An intelligent management system for carbon emission coal samples in thermal power plants, characterized in that , including: A central control unit for establishing a carbon emission coal sample coal quality prediction model based on historical data; A report generation unit for generating a carbon emission coal sample statistical report; A sample cabinet management unit for storing and retrieving carbon emission coal samples in the sample cabinet; A data collection unit for weighing and data collection of carbon emission coal samples; The central control unit includes: A first processing module for generating a first deviation degree f of the carbon emission coal sample test result based on the predicted value of the carbon emission coal sample generated by the carbon emission coal sample coal quality prediction model and the test result of the carbon emission coal sample; A second processing module for setting the basic configuration of the carbon emission coal sample and generating a change degree value r of the basic configuration of the carbon emission coal sample; The report generation unit includes: A report verification module for verifying the carbon emission coal sample statistical report.

2. The intelligent management system for carbon emission from coal samples in thermal power plants according to claim 1, characterized in that , when generating the first deviation degree f of the carbon emission coal sample test result, including: f=[ e i *(b i -∆b i ) 2 ]; Among them, m is the number of carbon emission coal sample inspection indicators, e i is the preset weight coefficient of the i-th carbon emission coal sample inspection index, b i is the reference value of the i-th inspection index generated according to the inspection results of the carbon emission coal sample, ∆b i is the expected value of the i-th test indicator generated based on the prediction model.

3. The intelligent management system for carbon emission from coal samples in thermal power plants according to claim 2, characterized in that: , when generating the change degree value r of the basic configuration of the carbon emission coal sample, including: r= e i '*h(i); Among them, q is the number of items in the basic configuration of carbon emission coal samples, e i ' is the preset weight coefficient of the i-th carbon emission coal sample basic configuration item, h1(i) is the first selection coefficient, when the carbon emission coal sample basic configuration item changes, h(i)=1, otherwise h(i)=0.

4. The intelligent management system for carbon emission from coal samples in thermal power plants according to claim 3, characterized in that: The report verification module is further used for: Generating an abnormal value a of the carbon emission coal sample based on the change degree value r of the basic configuration of the carbon emission coal sample and the first deviation degree f of the carbon emission coal sample test result; a = Q1 * e1 * r + Q2 * e2 * f; Where, Q1 is a preset first fixed coefficient, Q2 is a preset second fixed coefficient, e1 is a preset first weight coefficient, e2 is a preset second weight coefficient, r is the change degree value of the basic configuration of the carbon emission coal sample, and f is the first deviation degree of the carbon emission coal sample test result; A preset first abnormal value threshold a1; Judging whether to generate an abnormal verification instruction according to the abnormal value a of the carbon emission coal sample; When a < a1, no abnormal verification instruction is generated; When a ≥ a1, generate an abnormal verification instruction and conduct verification according to the instruction.

5. The intelligent management system for coal samples of carbon emissions from thermal power plants according to claim 4, characterized in that: The abnormal verification instruction includes: Setting the detection times n of the carbon emission coal sample, detecting the carbon emission coal sample n times, and generating an average detection data packet according to the n - time detection data; Calculating the second deviation degree f' of the coal sample test result; f'=[ e i *(b i -b i ') 2 ]; b i ' is the average value of the i-th inspection index in the average detection data packet, b i is the reference value of the i-th test index in the test result of the original carbon emission coal sample; A preset second deviation degree standard threshold f0; If f' < f0, the original carbon emission coal sample test result is verified to be qualified, and a carbon emission coal sample statistical report is generated according to the original carbon emission coal sample test result; If f' f0, the original carbon emission coal sample test results are unqualified, an abnormal behavior mark is added to the carbon emission coal sample in the original carbon emission coal sample test results, and a carbon emission statistical report is generated based on the average test results of the carbon emission coal samples in the average test data package.

6. The intelligent management system for carbon emission from coal samples in thermal power plants according to claim 5, characterized in that: The central control unit further includes: A third processing module for generating a carbon emission coal sample evaluation data packet, and the carbon emission coal sample evaluation data packet includes the change degree value of the basic configuration of the carbon emission coal sample, the first deviation degree of the carbon emission coal sample test result, and whether the carbon emission coal sample has an abnormal behavior mark.

7. The intelligent management system for coal samples of carbon emissions from thermal power plants according to claim 6, characterized in that: The sample cabinet management unit includes: The deadline adjustment module is used to generate the carbon emission coal sample evaluation coefficient according to the carbon emission coal sample evaluation data package ; and based on the carbon emission coal sample evaluation coefficient Set the storage period T1' of the current carbon emission coal sample; Carbon emission coal sample evaluation coefficient =Y{Q3*e3*r+Q4*e4*f+Q5*e5*h2(x)}; Among them, 1< <2, Q3 is the preset third fixed coefficient, Q4 is the preset fourth fixed coefficient, Q5 is the preset fifth fixed coefficient, e3 is the preset third weight coefficient, e4 is the preset fourth weight coefficient, e5 is the preset fifth weight coefficient, r is the change degree value of the basic configuration of the carbon emission coal sample, f is the first deviation of the carbon emission coal sample inspection result, h2(x) is the second selection coefficient, when the carbon emission coal sample has an abnormal behavior mark, h2(x)=1, otherwise h2(x)=0, Y{x} is used to map any real value to the (1,2) interval, ； According to the carbon emission coal sample evaluation coefficient Set the storage period T1' of the current carbon emission coal sample; T1'= *T1, is the carbon emission coal sample evaluation coefficient, and T1 is the preset minimum carbon emission coal sample storage period.

8. The intelligent management system for coal samples of carbon emissions from thermal power plants according to claim 6, characterized in that: The sample cabinet management unit includes: A sample cabinet cleaning module, with a preset cleaning period T2, and every T2 time, cleaning the carbon emission coal samples in the sample cabinet; Scanning the sample cabinet to determine the number c1 of idle sample cabinets; When c1 When c2 is used, no inventory clearing instruction is generated. c2 is the preset minimum number of free cabinets. When c1 < c2, generating an inventory cleaning instruction to clean the carbon emission coal samples in the sample cabinet, and determining the sample cabinet cleaning quantity c3, where c3 = c2 - c1.

9. The intelligent management system for coal samples of carbon emissions from thermal power plants according to claim 8, characterized in that: The inventory cleaning instruction includes: Scanning the sample cabinet to determine the number c4 of carbon emission coal samples with a storage period greater than T1; If c4 < c3, stop executing the inventory cleaning instruction If c4≥c3, execute the inventory clearing instruction, generate the comprehensive evaluation degree p of the carbon emission coal samples with a storage period greater than T1, sort the carbon emission coal samples with a storage period greater than T1 in descending order according to the comprehensive evaluation degree p, and clear the carbon emission coal samples ranked at the last c3 positions.

10. The intelligent management system for coal samples of carbon emissions from thermal power plants according to claim 9, characterized in that: The comprehensive evaluation degree p of the carbon emission coal sample with a storage period greater than T1 includes: p=Q6*e6*a+Q7*e7*y; Wherein Q6 is the preset sixth fixed coefficient, Q7 is the preset seventh fixed coefficient, e6 is the preset sixth weight coefficient, e7 is the preset seventh weight coefficient, a is the abnormal value of the carbon emission coal sample, and y is the importance of the carbon emission coal sample; y= *u i ; Among them, k is the number of importance evaluation indicators, is the preset weight coefficient of the i-th evaluation index, u i is the reference value of the ith evaluation index.

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