A big data distributed processing method and system for carbon emission monitoring and prediction

Abstract

The present invention discloses a big data distributed processing method and system for carbon emission monitoring and prediction, and relates to the technical field of carbon emission monitoring. The processing method includes: real-time data acquisition: nodes acquire heterogeneous data for monitoring carbon emissions from data sources, distributed data processing: preprocessing heterogeneous data and expressing data values ​​in a unified form to obtain standard data, inputting standard data into a prediction model to obtain predicted data. The present invention introduces a sequencing combination method and positioning processing, so that data can be combined and located in an orderly manner, verifies No. 1 data through a verification code, and uses different transmission methods during the upload process, thereby reducing the risk of data loss or error. The data verification process can timely discover and correct errors in the data and replace erroneous data by comparing the verification code, thereby ensuring the reliability of the data, thereby ensuring the realization of comprehensive and reliable carbon emission monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission monitoring, and in particular to a big data distributed processing method and system for carbon emission monitoring and prediction. Background Art

[0002] Big data distributed processing is a technical method that disperses large amounts of data to multiple computer nodes for processing, aiming to improve the speed and efficiency of data processing. Traditional data processing methods have been unable to meet the needs of large-scale, high-efficiency data processing. Distributed processing technology can significantly improve data processing speed and efficiency by dispersing large amounts of data in carbon emission monitoring to multiple computer nodes for processing, meeting the needs of large-scale carbon emission data processing.

[0003] For example, the patent publication number "CN115864531A" is named "A system and method for monitoring carbon emissions from regional distributed photovoltaic power generation". The power data is calculated according to the power supply mode, and the carbon emission data is obtained and uploaded to the cloud server. The cloud server stores, counts and monitors the power data and carbon emission data. The use of the above system is conducive to the real-time management of electric energy in the region, adjusting the energy structure, and is suitable for current and future energy conservation and emission reduction development; the above system has a simple structure, is easy to operate and maintain, and has good promotion and application value.

[0004] When the above-mentioned distributed processing is adopted, the data collected by each node needs to be uploaded to the main processing station. During the uploading process, due to the large number of nodes, data transmission errors may occur during the uploading process. The erroneous data will affect the overall monitoring of carbon emissions by the main processing station. In addition, due to the large amount of data, it is inconvenient to quickly find errors and trace the source of erroneous data. For this reason, a big data distributed processing method and system for carbon emission monitoring and prediction is invented. Summary of the invention

[0005] The purpose of the present invention is to provide a big data distributed processing method and system for carbon emission monitoring and prediction to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a big data distributed processing method for carbon emission monitoring and prediction, the processing method comprising:

[0007] Real-time data acquisition: Nodes acquire heterogeneous data for monitoring carbon emissions from data sources;

[0008] Distributed data processing: pre-process heterogeneous data and express data values ​​in a unified form to obtain standard data, input standard data into the prediction model to obtain predicted data, obtain No. 1 data through a sequence combination method for standard data and predicted data, and perform location processing on No. 1 data through a location method combined with a sequence combination method to obtain location data;

[0009] Data upload and verification: The No. 1 data is verified through the verification mechanism to obtain the original verification code. The original verification code and No. 1 data are uploaded through different transmission methods respectively. The original verification code and No. 1 data are uploaded to the main processing station, and the main processing station obtains the reference data and reference verification code;

[0010] Data verification: The verification machine in the main processing station verifies the reference data to obtain a No. 1 verification code, which is then compared with the reference verification code to obtain a comparison result. If the comparison is inconsistent, the comparison difference position is obtained according to the fault location method, and the comparison difference position is combined with the No. 1 verification code to obtain a No. 1 marking code, and the comparison difference position is combined with the reference verification code to obtain a reference marking code;

[0011] The No. 1 marker code, the reference marker code and the comparison difference position are transmitted back to the node, the reference marker code is combined with the original verification code to obtain the original marker code, the original marker code is compared with the reference marker code, and if the comparison is correct, the corresponding position data in the No. 1 data is copied through the reference marker code and the position data, and the copied data is uploaded to the main site, and the copied data replaces the corresponding data in the reference data;

[0012] If there is any inconsistency, the code at the corresponding position in the reference verification code is copied using the reference verification code and the position data, and the copied code is uploaded to the main site, and the copied code replaces the corresponding part in the reference verification code;

[0013] Repeat the process of comparing and contrasting until the comparison is correct;

[0014] Data merging: The prediction data in the reference data is merged using the merging strategy method to obtain the prediction merged data.

[0015] Furthermore, the sequencing and combining method includes: obtaining the correlation between standard data and predicted data, establishing a sequence number for the carbon emission monitoring equipment, sorting the standard data according to the sequence number, and inserting the predicted data into the sorted standard data using an insertion method and correlation to obtain data No. 1.

[0016] Furthermore, the positioning processing includes sorting the No. 1 data according to the sequence number, and separating the sequence numbers to record the data at the corresponding position of each sequence number to form position data, which includes the correspondence between the sequence number of the carbon emission monitoring equipment and the data position in the No. 1 data.

[0017] Furthermore, the verification mechanism includes: converting the numerical values ​​in the same position in the first data into hash values ​​through a calculation method according to the position data, repeating the conversion process until the numerical values ​​in all positions in the first data are converted into hash values ​​to obtain a verification code.

[0018] Furthermore, the merging strategy method includes: establishing a basic weighting coefficient for the predicted data, comparing the carbon emission data actually detected in history with the corresponding predicted data in history to obtain the difference, adjusting the basic weighting coefficient based on the difference, multiplying the corresponding predicted data in history by the adjusted basic weighting coefficient to obtain the calculated predicted result, and controlling the difference between the calculated predicted result and the actually detected carbon emission data in history to decrease.

[0019] Furthermore, the fault location method includes comparing the first verification code with the reference verification code to obtain the same part and the different part, establishing a comparison difference position according to the location of the different parts, and locating the fault by comparing the difference position.

[0020] Furthermore, the insertion method includes virtually separating the sorted standard data, inserting the predicted data into the position behind the corresponding standard data according to the relevance, eliminating the virtual separation, and obtaining No. 1 data.

[0021] Furthermore, establishing serial numbers for carbon emission monitoring equipment includes formulating a set of numbering rules for generating serial numbers. The numbering rules are designed in sections according to the specific attributes of the equipment. According to the numbering rules, a serial number is designed and assigned for each identified device. The assigned serial number matches the actual identity and attributes of the carbon emission monitoring equipment, and the specific attributes include the installation location and equipment type.

[0022] A big data distributed processing system for carbon emission monitoring and prediction adopts the above-mentioned big data distributed processing method for carbon emission monitoring and prediction.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] The big data distributed processing method and system for carbon emission monitoring and prediction, through the introduction of sequencing combination method and positioning processing, enables data to be combined and located in an orderly manner, further improving the efficiency of data processing. The data upload and verification mechanism ensures the integrity and accuracy of the data. The No. 1 data is verified by the verification code, and different transmission methods are used during the upload process, reducing the risk of data loss or error. The data verification process can timely discover and correct errors in the data and replace erroneous data by comparing the verification code, thereby ensuring the reliability of the data and ensuring the realization of comprehensive and reliable carbon emission monitoring.

[0025] At the same time, the prediction data is obtained through the prediction model, and the prediction model realizes the prediction of carbon emission data in the node. The merging strategy method adjusts the basic weighting coefficient according to the difference between the historical actual carbon emission data and the prediction data, so that the prediction result is closer to the actual situation and the accuracy of the prediction is improved.

[0026] The fault location method can accurately identify the error location in the data, which is convenient for quickly locating and correcting the problem. By returning the No. 1 marker code, reference marker code and comparison difference position, the node can make comparisons based on this information, thereby copying the data at the error location and re-uploading the copied data to replace the erroneous data at the original location to ensure the correctness of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic diagram of data verification of the present invention;

[0028] Figure 2 It is a schematic diagram of carbon emission data of the present invention;

[0029] Figure 3 Schematic diagram of the processing method of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] like Figure 1 - Figure 3 As shown, the present invention provides a technical solution: a big data distributed processing method for carbon emission monitoring and prediction, the processing method comprising:

[0032] Real-time data acquisition: Nodes acquire heterogeneous data for monitoring carbon emissions from data sources;

[0033] Distributed data processing: pre-process heterogeneous data and express data values ​​in a unified form to obtain standard data, input standard data into the prediction model to obtain predicted data, obtain No. 1 data through a sequence combination method for standard data and predicted data, and perform location processing on No. 1 data through a location method combined with a sequence combination method to obtain location data;

[0034] Data upload and verification: The No. 1 data is verified through the verification mechanism to obtain the original verification code. The original verification code and No. 1 data are uploaded through different transmission methods respectively. The original verification code and No. 1 data are uploaded to the main processing station, and the main processing station obtains the reference data and reference verification code;

[0035] Data verification: The verification machine in the main processing station verifies the reference data to obtain a No. 1 verification code, which is then compared with the reference verification code to obtain a comparison result. If the comparison is inconsistent, the comparison difference position is obtained according to the fault location method, and the comparison difference position is combined with the No. 1 verification code to obtain a No. 1 marking code, and the comparison difference position is combined with the reference verification code to obtain a reference marking code;

[0036] The No. 1 marker code, the reference marker code and the comparison difference position are transmitted back to the node, the reference marker code is combined with the original verification code to obtain the original marker code, the original marker code is compared with the reference marker code, and if the comparison is correct, the corresponding position data in the No. 1 data is copied through the reference marker code and the position data, and the copied data is uploaded to the main site, and the copied data replaces the corresponding data in the reference data;

[0037] If there is any inconsistency, the code at the corresponding position in the reference verification code is copied using the reference verification code and the position data, and the copied code is uploaded to the main site, and the copied code replaces the corresponding part in the reference verification code;

[0038] Repeat the process of comparing and contrasting until the comparison is correct;

[0039] Data merging: The prediction data in the reference data is merged using the merging strategy method to obtain the prediction merged data.

[0040] The serialization and combination method includes: obtaining the correlation between standard data and predicted data, establishing the serial number of the carbon emission monitoring equipment, sorting the standard data according to the serial number, and inserting the predicted data into the sorted standard data using the insertion method and correlation to obtain No. 1 data.

[0041] The positioning processing includes sorting the No. 1 data according to the sequence number, and separating the sequence numbers to record the data at the corresponding position of each sequence number to form position data. The position data includes the correspondence between the sequence number of the carbon emission monitoring equipment and the data position in the No. 1 data.

[0042] The verification mechanism includes: converting the numerical value in the same position in the No. 1 data into a hash value by a calculation method according to the position data, repeating the conversion process until the numerical values ​​in all positions in the No. 1 data are converted into hash values ​​to obtain a verification code.

[0043] The merging strategy method includes: establishing a basic weighting coefficient for the predicted data, comparing the actual historical carbon emission data with the corresponding historical predicted data to obtain the difference, adjusting the basic weighting coefficient based on the difference, multiplying the corresponding historical predicted data by the adjusted basic weighting coefficient to obtain the calculated predicted result, and controlling the difference between the calculated predicted result and the actual historical carbon emission data to be reduced.

[0044] The fault location method includes comparing a first verification code with a reference verification code to obtain identical parts and different parts, establishing a comparison difference position according to the locations of the different parts, and locating the fault by comparing the difference position.

[0045] The insertion method includes virtually separating the sorted standard data, inserting the predicted data into the position behind the corresponding standard data according to the relevance, eliminating the virtual separation, and obtaining No. 1 data.

[0046] Establishing serial numbers for carbon emission monitoring equipment includes formulating a set of numbering rules for generating serial numbers. The numbering rules are designed in sections according to the specific attributes of the equipment. According to the numbering rules, a serial number is designed and assigned for each identified device. The assigned serial number matches the actual identity and attributes of the carbon emission monitoring equipment. The specific attributes include the installation location and equipment type.

[0047] A big data distributed processing system for carbon emission monitoring and prediction adopts the above-mentioned big data distributed processing method for carbon emission monitoring and prediction.

[0048] Under the big data distributed processing architecture, data and computing tasks are distributed to multiple nodes, which communicate and collaborate with each other through network protocols to jointly complete the data processing task. Each node performs calculations, and then coordinates and merges through the network. The main processing station receives heterogeneous data uploaded by each node. Since carbon emission data comes from a variety of sensors, monitoring equipment and systems, the data formats are different. At the same time, since the specific locations monitored by the monitoring equipment are different, carbon emission data belongs to heterogeneous data. When nodes process and calculate heterogeneous data, they first pre-process the heterogeneous data. The pre-processing includes cleaning, formatting and normalization, etc., so as to ensure the accuracy of the standard data. The standard data is input into the corresponding prediction model on each node. The subsequent situation data of the carbon emission data is predicted through the prediction model and heterogeneous data to obtain the prediction results. The prediction results and standard data are combined and uploaded to the main processing station. The main processing station verifies the No. 1 data through the verification mechanism to confirm the No. 1 data and obtain the No. 2 data. The main processing station merges the No. 2 data to obtain the final result.

[0049] The data sources include various sensors and monitoring equipment. The above equipment detects carbon emissions in various regions and links. Since the data is expressed in specific values, the unified form of data value expression refers to expressing the specific values ​​in the same format. First, convert the data in different units into the same unit. For example, all carbon emission data are converted into kilograms of carbon dioxide equivalent, such as 19.01 and 135.5 are expressed as 019.010 and 135.500. However, when it is actually displayed on the display screen, the 0 before 19 can be hidden through programming to avoid the numerical display becoming unnatural, thereby ensuring the use of the sequence combination method.

[0050] The node directly receives the carbon emission data from the data source, processes and predicts the received carbon emission data, and then uploads it to the main processing station. The main processing station mainly summarizes and merges the predicted data. The predicted data of carbon emission monitoring data needs to take into account the error accumulation of the prediction results. Since each node is located in different regions, the heterogeneous data processed will be different. At the same time, the prediction model used in the node may also be inconsistent. Due to the inconsistency of the data type in the node, the specific prediction model used will be different. There are existing technologies for prediction models and there are many types. The specific method of the prediction model is not explained here. The prediction data in each node are merged to obtain the total prediction data. Due to the error accumulation of the prediction data, an erroneous prediction conclusion will be drawn. Therefore, the merging strategy method will be used to merge the prediction data. In the merging process, the error accumulation of the prediction results is reduced, and the difference between the predicted total value and the subsequent corresponding actual monitoring value is reduced, thereby ensuring the accuracy of the prediction results.

[0051] The serialization combination method is to sort the standard data and the predicted data according to the set sorting order. The positioning method is combined with the sorting order in the serialization combination method. The positioning method locates the No. 1 data to obtain the position data. When the position data is used for subsequent verification and an error occurs, the node can be returned through the cause of the data error. The corresponding position in the No. 1 data can be quickly located through the position data. The numbering rules should ensure that each device has a unique and non-confusing serial number. The numbering can be used to understand the installation location of the device, thereby achieving comprehensive simulation and corresponding prediction of the region.

[0052] The predicted data is obtained after the standard data is input into the prediction model. Therefore, there is a correlation between the predicted data and the standard data. The sequence combination method realizes the combination between the predicted data and the standard data through the correlation, and sorts them in order according to the correlation between the standard data and the predicted data. Then, the overall sorting of each carbon emission data is realized. By establishing the sequence number of the carbon emission monitoring equipment for collecting, the overall sorting can be realized through the sequence number to obtain the No. 1 data. The sequence number of the monitoring equipment is set in advance. The sequence combination is mainly to realize the sorting between the values. The sequence number of the monitoring equipment is sorted in order. The numerical sorting between the standard data and the predicted data is realized through the sequence combination method. The sequence combination method is not only stored in the node, but also in the general processing station. The positioning method combined with the sequence combination method can realize the positioning of the numerical position of the carbon emission data in the No. 1 value, that is, the carbon emission data represented by the numerical value in the No. 1 data can be clearly understood through the position data, and the sequence number and position data of the data equipment can be combined to understand it.

[0053] Subsequently, the No. 1 data is verified through a verification mechanism, and the numerical value at the same position can be converted into a code sequence through the position data. Multiple code sequences are combined with each other according to the position data to obtain the original verification code. The No. 1 verification code is also obtained by the above method. There are existing technologies for calculating hash values, and the specific content of the calculation method is not explained here. The hash value calculation method is to convert long data into short data, thereby shortening the No. 1 data. It is mainly used in the field of data transmission and storage, and is required to have high uniqueness and anti-interference properties to ensure the integrity and correctness of the data. Common verification hash functions include cyclic redundancy check and hash-based message authentication code.

[0054] That is, data sorting between predicted data and standard data is realized, and data No. 1 is obtained after data sorting, so that data No. 1 can be subsequently located and processed, and data No. 1 can be verified, so as to obtain a verification code. The verification code verifies data No. 1, so as to determine whether data No. 1 is wrong during the upload process. At the same time, even if data No. 1 is correct but the verification code is wrong, it can also be judged in this way. When the verification result determines that there is an error, the cause of the data error can be obtained through the method of rapid fault location. At the same time, the cause of the data error can be used to locate whether the verification code is wrong during the upload process, and the verification mechanism can be used to verify data No. 1. Verification is performed to obtain the original verification code. The verification mechanism verifies the No. 1 data. The No. 1 data is expressed numerically. The verification mechanism has the same set at the node and the main site. After the No. 1 data is uploaded to the main processing station, the verification mechanism in the main processing station verifies the No. 1 data again to obtain a new No. 1 verification code for the No. 1 data. By comparing the No. 1 verification code with the original verification code, it can be determined whether the original verification code is wrong during the upload process. After the original verification code is not wrong, the original verification code or the No. 1 verification code is selected to verify the uploaded No. 1 data to obtain the verification result. The verification result can be judged later, and the judgment can be made directly through comparison.

[0055] After determining that an error has occurred, the data error location can be obtained through a method of rapid fault location. The main site transmits the data error location to the node. The node finds the corresponding data that has an error during the upload process from the data No. 1 in the node based on the data error location, and uploads the correct data again. The correct data replaces the erroneous data in the main processing station, and the main processing station verifies the replaced data No. 1 again until the verification is correct.

[0056] Since the No. 1 verification code is obtained by verifying the reference data, considering that the reference data is obtained by uploading the No. 1 data, and considering that an error occurs in the uploading of the No. 1 data, the reference verification code and the No. 1 verification code are compared. If an error occurs in the comparison, the error position is marked. The marked error position distribution is combined with the No. 1 verification code and the reference verification code to obtain the No. 1 marking code in the No. 1 verification code and the reference marking code in the reference verification code. The No. 1 marking code and the reference marking code are transmitted back to the node, considering the error situation in the transmission process. However, the No. 1 marking code and the reference verification code occupy a small amount of data. Therefore, the No. 1 marking code and the reference verification code can be transmitted back multiple times, thereby ensuring the accuracy of the No. 1 marking code and the reference verification code after transmission.

[0057] After the transmission is returned, the reference mark code and the original verification code are combined to obtain the original mark code. The original mark code and the reference mark code are compared. If the comparison is correct, the corresponding position data in the No. 1 data is copied through the reference mark code and position data, and the copied data is uploaded to the main site. The copied data replaces the corresponding data in the reference data. If they are inconsistent, the code at the corresponding position in the reference verification code is copied through the reference verification code and position data, and the copied code is uploaded to the main site, and the copied code replaces the corresponding part of the reference verification code.

[0058] The code at the corresponding position in the original verification code in the node is marked according to the No. 1 marking code to obtain the original marking code. The original marking code is combined with the position data and the data at the corresponding position in the No. 1 data is marked. The original marking code is preferably compared with the reference marking code. If the comparison is correct, it means that the No. 1 verification code is wrong. The No. 1 verification code is obtained by uploading the No. 1 data. Therefore, the corresponding position data in the No. 1 data is copied through the reference marking code, and the copied data is uploaded to the main site.

[0059] When an error occurs during the upload of data No. 1, the segment of data is copied from the corresponding error position and uploaded to the general processing station. The general processing station receives the segment of data and replaces the segment of data No. 1 originally received in the general processing station according to the corresponding position data to obtain new data No. 1. The new data No. 1 is repeatedly verified until the comparison is correct. By introducing the sequence combination method and positioning processing, the data can be combined and located in an orderly manner, further improving the efficiency of data processing. The data upload and verification mechanism ensures the integrity and accuracy of the data. The No. 1 data is verified by the verification code, and different transmission methods are used during the upload process to reduce the risk of data loss or error. The data verification process can timely discover and correct errors in the data by comparing the verification code to ensure the reliability of the data, thereby ensuring the realization of comprehensive and reliable carbon emission monitoring. The fault location method can accurately identify the error position in the data, which is convenient for rapid location and correction of problems. Through the return of the No. 1 mark code, the reference mark code and the comparison difference position, the node can compare based on this information, thereby copying the data at the error position, and re-uploading the copied data to replace the error data at the original position to ensure the correctness of the data.

[0060] If the comparison is correct, data merging is realized. If there is predicted data in the reference data, the predicted data will be extracted and merged through the merging strategy to obtain predicted merged data. The actual detected data is directly displayed and the predicted data is obtained through the prediction model. The prediction model realizes the prediction of carbon emission data in the node, and the merging strategy method adjusts the basic weighting coefficient according to the difference between the historical actual detected carbon emission data and the predicted data, so that the prediction result is closer to the actual situation and the accuracy of the prediction is improved.

[0061] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is limited by the attached embodiments and their equivalents.

Claims

1. A big data distributed processing method for carbon emission monitoring and prediction, characterized by: The processing method comprises: Real-time data acquisition: Nodes acquire heterogeneous data for monitoring carbon emissions from data sources; Distributed data processing: pre-process heterogeneous data and express data values ​​in a unified form to obtain standard data, input standard data into the prediction model to obtain predicted data, obtain No. 1 data through a sequence combination method for standard data and predicted data, and perform location processing on No. 1 data through a location method combined with a sequence combination method to obtain location data; Data upload and verification: The No. 1 data is verified through the verification mechanism to obtain the original verification code. The original verification code and No. 1 data are uploaded through different transmission methods respectively. The original verification code and No. 1 data are uploaded to the main processing station, and the main processing station obtains the reference data and reference verification code; Data verification: The verification machine in the main processing station verifies the reference data to obtain a No. 1 verification code, which is then compared with the reference verification code to obtain a comparison result. If the comparison is inconsistent, the comparison difference position is obtained according to the fault location method, and the comparison difference position is combined with the No. 1 verification code to obtain a No. 1 marking code, and the comparison difference position is combined with the reference verification code to obtain a reference marking code; The No. 1 marker code, the reference marker code and the comparison difference position are transmitted back to the node, the reference marker code is combined with the original verification code to obtain the original marker code, the original marker code is compared with the reference marker code, and if the comparison is correct, the corresponding position data in the No. 1 data is copied through the reference marker code and the position data, and the copied data is uploaded to the main site, and the copied data replaces the corresponding data in the reference data; If there is any inconsistency, the code at the corresponding position in the reference verification code is copied using the reference verification code and the position data, and the copied code is uploaded to the main site, and the copied code replaces the corresponding part in the reference verification code; Repeat the process of comparing and contrasting until the comparison is correct; Data merging: The prediction data in the reference data is merged using the merging strategy method to obtain the prediction merged data.

2. The big data distributed processing method for carbon emission monitoring and prediction according to claim 1 is characterized by: The sequencing and combining method includes: obtaining the correlation between standard data and predicted data, establishing the sequence number of the carbon emission monitoring equipment, sorting the standard data according to the sequence number, and inserting the predicted data into the sorted standard data using the insertion method and correlation to obtain No. 1 data.

3. The big data distributed processing method for carbon emission monitoring and prediction according to claim 1 is characterized by: The positioning processing includes sorting the No. 1 data according to the sequence number, and separating the sequence numbers to record the data at the corresponding position of each sequence number to form position data, which includes the correspondence between the sequence number of the carbon emission monitoring equipment and the data position in the No. 1 data.

4. The big data distributed processing method for carbon emission monitoring and prediction according to claim 1 is characterized by: The verification mechanism includes: converting the values ​​in the same position in the first data into hash values ​​by a calculation method according to the position data, repeating the conversion process until the values ​​in all positions in the first data are converted into hash values ​​to obtain a verification code.

5. The big data distributed processing method for carbon emission monitoring and prediction according to claim 1 is characterized by: The merging strategy method includes: establishing a basic weighting coefficient for the predicted data, comparing the carbon emission data actually detected in history with the corresponding predicted data in history to obtain the difference, adjusting the basic weighting coefficient based on the difference, multiplying the corresponding predicted data in history by the adjusted basic weighting coefficient to obtain the calculated predicted result, and controlling the difference between the calculated predicted result and the actually detected carbon emission data in history to decrease.

6. The big data distributed processing method for carbon emission monitoring and prediction according to claim 1 is characterized by: The fault location method includes comparing a first verification code with a reference verification code to obtain identical parts and different parts, establishing a comparison difference position according to the locations of the different parts, and locating the fault by comparing the difference position.

7. The big data distributed processing method for carbon emission monitoring and prediction according to claim 2 is characterized by: The insertion method includes virtually separating the sorted standard data, inserting the predicted data into the position behind the corresponding standard data according to the relevance, eliminating the virtual separation, and obtaining No. 1 data.

8. The big data distributed processing method for carbon emission monitoring and prediction according to claim 1 is characterized by: Establishing serial numbers for carbon emission monitoring equipment includes formulating a set of numbering rules for generating serial numbers. The numbering rules are designed in sections according to the specific attributes of the equipment. According to the numbering rules, a serial number is designed and assigned for each identified device. The assigned serial number matches the actual identity and attributes of the carbon emission monitoring equipment. The specific attributes include the installation location and equipment type.

9. A big data distributed processing system for carbon emission monitoring and prediction, characterized by: A big data distributed processing method for carbon emission monitoring and prediction as described in any one of claims 1 to 8 is adopted.

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