Data management system for carbon emission database
By identifying abnormal interfaces, selecting the optimal transmission path, and performing data clustering and storage, the problems of poor interface adaptability and incomplete classification management in the carbon emission data management system were solved, achieving stability in data transmission and standardization in storage, and improving management efficiency and analytical capabilities.
Patent Information
- Application Number
- CN202511296960.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-11
- Publication Date
- 2026-01-23
AI Technical Summary
Existing carbon emission data management systems suffer from poor interface compatibility due to multi-source heterogeneous interfaces, resulting in data transmission delays and format errors. They also lack effective classification and management mechanisms, affecting the real-time performance and integrity of the data and making it difficult to quickly uncover the inherent relationships between data.
Abnormal communication interfaces are identified by the asynchronous feature confirmation end, the optimal transmission path is selected by the communication logic confirmation end, and data is classified, integrated and stored at the execution end. It is then clustered and stored in conjunction with the carbon emission feature table to ensure the stability of data transmission and the standardization of storage.
It enables efficient data transmission and orderly management, improves data query and analysis efficiency, ensures data integrity and reliability, and supports refined management by enterprises and regulatory authorities.
Smart Images
Figure CN121389196A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of data management, in particular to a data management system for carbon emission database. BACKGROUND
[0002] With the promotion of the "double carbon" goal, the fine management of carbon emission data by enterprises and regulatory departments is increasingly urgent. The collection, transmission, storage and application of carbon emission data have become the core link of low-carbon management. However, there are many technical challenges in the current carbon emission data management process: on the one hand, carbon emission data comes from a wide range of sources and is heterogeneous, and needs to be collected from ERP systems, production monitoring equipment, energy metering instruments, and manually filled-in forms. Different data sources have protocol differences and format inconsistencies, which leads to poor interface adaptability, data transmission delays, packet loss or format errors, and affects the real-time and integrity of the data. On the other hand, the processing rate of different interfaces differs significantly, and some interfaces are not matched in rate, resulting in "waiting congestion" between the database and the transmission link, further reducing data flow efficiency.
[0003] At the same time, in the data storage link, massive carbon emission data often lack effective classification management mechanisms, and different types and different correlation characteristics of data are stored together, which not only increases the complexity of data query and statistics, but also makes it difficult to quickly mine the internal relations between data (such as the energy consumption corresponding relationship between a certain emission source and a production process), affecting the accuracy of carbon accounting and the efficiency of emission reduction analysis. In addition, the existing system relies on manual inspection for abnormal interfaces, lacks automated feature verification and logic optimization capabilities, and is difficult to adapt to the management needs of expanding carbon emission data size and increasing dimensions.
[0004] Therefore, how to realize the adaptive adaptation of multi-source heterogeneous interfaces, improve data transmission efficiency, and establish an intelligent data storage management mechanism based on feature correlation has become a key problem that needs to be solved for the current data management system for carbon emission database. SUMMARY
[0005] In view of the deficiencies of the prior art, the present application provides a data management system for carbon emission database, which solves the problem that massive carbon emission data often lack effective classification management mechanisms and different types and different correlation characteristics of data are stored together.
[0006] To achieve the above purpose, the present application realizes the following technical scheme: a data management system for carbon emission database, comprising: an asynchronous feature confirmation end, which confirms the features of different communication interfaces connected to the database, confirms the historical communication data of different communication interfaces from the cloud, and verifies and compares the historical communication data to confirm abnormal communication interfaces, in particular: The different communication interfaces connected by the database are marked as pending interfaces, and the processing rates of different pending interfaces for different types of data are confirmed from the cloud, and the average processing rate Jz is obtained by averaging the processing rates of a group of data associated with a type of data i where i represents different pending interfaces, and the processing rates of the corresponding databases for this type of data are confirmed and averaged as the standard rate Bz i where i represents different types of data Adopt: Cz i =|Jz i -BZ i |Confirm the difference rate Cz associated with it i Confirm whether there is a single difference rate Cz from the group of difference rates Cz confirmed by the pending interface i i Cz i >Y1, Y1 is a preset value, if it is satisfied, the corresponding pending interface is marked as an abnormal communication interface, if it is not satisfied, no marking is performed The communication logic confirmation end confirms the classification data belonging to different types in the abnormal communication interface with data to be managed, and confirms the classification characteristics associated with different classification data in the transmission process, and then locks the optimal communication logic from the confirmed classification characteristics, including: Confirm different types of data from the data to be managed, integrate data belonging to the same type, confirm the classification data associated with the corresponding type, and set the corresponding classification mark in the classification confirmation process for each group of data Confirm the processing characteristics of the abnormal communication interface, the database interface and the intermediate layer interface for the corresponding classification data: confirm the minimum processing rate and the maximum processing rate of the corresponding interface for the corresponding classification data from the cloud, and generate a rate interval, and then the generated rate interval is used as the processing characteristic associated with the corresponding interface Confirm the first communication process: confirm the total capacity R of the classification data, and based on the processing characteristics associated with the abnormal communication interface, confirm the corresponding time characteristic Q1, and based on the processing characteristics associated with the database interface, confirm the time characteristic Q2 associated with the database interface, and based on the confirmed time characteristics Q1 and Q2, confirm the difference time interval CF1 associated with the two groups of time characteristics Q1 and Q2 Confirming the second communication process: confirming the time characteristic Q3 associated with the intermediate layer interface based on the processing characteristic of the corresponding classified data and the total capacity R of the corresponding classified data, and confirming the difference time interval CY1 between Q1 and Q3 based on the confirmed time characteristic Q1, synchronously confirming the processing characteristic of the database interface to the output data of the intermediate layer interface, and confirming the time characteristic Q4 associated with the database interface according to the total capacity and the processing characteristic of the output data, confirming the difference time interval CY2 between Q3 and Q4, and combining CY1 and CY2 to confirm the combined difference interval ZH; Evaluating the combined difference interval ZH and the difference time interval CF1, and confirming the interval median ZJ of ZH and the interval median ZQ of CF1, if ZJ=ZQ, randomly selecting a group of communication processes as the execution process of the current classified data; If ZJ>ZQ, the confirmed first communication process is taken as the execution process of the current classified data; If ZJ<ZQ, the confirmed second communication process is taken as the execution process of the current classified data; The execution processes associated with different classified data in the to-be-managed data are confirmed in sequence, and the data transmission process of the corresponding to-be-managed data is completed through the corresponding execution end; The execution end confirms the execution process associated with the corresponding classified data and performs execution processing when executing the transmission process of different classified data, and after completing the transmission processing, the integration process of the classified data is completed according to the associated classification mark, so that the current to-be-managed data is transmitted to the partition characteristic processing end.
[0007] Preferably, it further comprises: The partition characteristic processing end receives the to-be-managed data transmitted by different data interfaces, and confirms the data characteristic associated with the to-be-managed data in combination with the carbon emission characteristic table, and then confirms the to-be-managed data belonging to the same classification according to the graphic verification process, and the specific method is: Limiting a group of processing periods, and sequentially receiving and confirming the to-be-managed data associated with the processing periods; According to the preset carbon emission characteristic table, the characteristic data associated with a group of characteristic items in the corresponding data is confirmed from a group of to-be-managed data associated, and the characteristic data associated with the same type of characteristic items is subjected to difference processing from a plurality of groups of different to-be-managed data, and the characteristic difference is confirmed, and the characteristic difference≥0, and then the maximum value SJ is selected from the two groups of characteristic data subjected to difference processing, and it is identified whether the confirmed characteristic difference satisfies: characteristic difference≤0.1SJ, if it satisfies, the same type of characteristic items of the corresponding two groups of to-be-managed data are marked as clustering items, if it does not satisfy, no marking is performed; The two groups of to-be-managed data with all similar features as the clustering items are divided into similar data, and the same is true for the several groups of to-be-managed data, which are sequentially confirmed two by two, and the to-be-managed data belonging to the similar data are comprehensively confirmed; And the to-be-managed data belonging to the similar data are stored through the association database, so as to be stored at the same storage location.
[0008] Preferably, the association database stores the confirmed to-be-managed data, and the to-be-managed data stored at the same storage location are all similar data.
[0009] The present application provides a carbon emission database data management system. Compared with the prior art, the following advantages are achieved: From the data transmission link, the asynchronous feature confirmation end accurately identifies abnormal interfaces by comparing the processing rates of the communication interface and the database, avoids transmission delay problems caused by rate mismatch from the source, and lays a foundation for efficient data circulation; the communication logic confirmation end further targets the abnormal interface, dynamically selects the optimal communication path (direct transmission or intermediate layer conversion) by analyzing the transmission characteristics of the classified data, and ensures the adaptability of the transmission path based on time difference interval quantitative evaluation, effectively reduces the waiting time of data transmission, improves the transmission efficiency, and guarantees the continuity and stability of data transmission; the execution end completes data integration through classification marking after transmission, avoids the confusion after multi-source data transmission, and ensures data integrity; In the data storage and management link, the partition feature processing end combines the carbon emission feature table, performs clustering division on the data through feature difference analysis, groups the similar data, and stores them in the same partition of the association database. This design not only realizes the orderly management of data, but also significantly improves the efficiency of subsequent data query, statistics and analysis — the centralized storage of similar data can reduce the time-consuming of cross-partition retrieval, facilitate the rapid extraction of specific types of carbon emission data (such as certain emission sources, certain time period associated data), and provide efficient data support for enterprise carbon accounting, emission reduction potential analysis, and compliance report generation, etc. scenarios; Through feature verification, logic optimization and clustering management in the whole process, the system considers the real-time of data transmission and the standardization of storage, solves the transmission confusion problem caused by multi-source and heterogeneous carbon emission data, and provides reliable guarantee for long-term management and deep application of data (such as carbon footprint tracking and carbon efficiency analysis) through accurate classification storage, effectively helping enterprises or regulatory departments to realize fine and efficient management of carbon emission data. BRIEF DESCRIPTION OF DRAWINGS
[0010] Figure 1 It is a schematic diagram of the principle framework of the present application. DETAILED DESCRIPTION
[0011] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described, obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work are within the protection scope of the present application.
[0012] First embodiment Please refer to Figure 1 The application provides a data management system for a carbon emission database, comprising an asynchronous feature confirmation end, a communication logic confirmation end, an execution end, a partition feature processing end, and an associated database, wherein the asynchronous feature confirmation end, the communication logic confirmation end, the execution end, the partition feature processing end, and the associated database are electrically connected in sequence from an output node to an input node. The asynchronous feature confirmation end confirms the features of different communication interfaces connected by the database, confirms the historical communication data of different communication interfaces in the cloud, and checks and compares the historical communication data to confirm abnormal communication interfaces. An abnormal communication interface is a docking port with inconsistent processing rate standards. There is a communication process between the database and the corresponding line or module, and the communication process requires corresponding communication interfaces to interwork to ensure that the corresponding data can be effectively transmitted to the corresponding database to complete the data storage process. The specific way of confirming the abnormal communication interface is as follows: Different communication interfaces connected by the database are denoted as pending interfaces, and the processing rate of different types of data for different pending interfaces is confirmed from the cloud. The average processing rate Jz of a plurality of groups of processing rates associated with a single group of type data is confirmed. i Wherein i represents different pending interfaces, and the processing rate of the corresponding database for this type of data is confirmed and averaged synchronously, which is taken as the standard rate BZ i Wherein i represents different types of data. Adopt: Cz i =|Jz i -BZ i Confirm the difference rate Cz associated with it i Confirm whether there is a single difference rate Cz i from a plurality of groups of difference rates Cz i confirmed by the pending interface i satisfies: Cz i > Y1, Y1 is a preset value, and its specific value is determined by the operator according to experience. If it is satisfied, the corresponding pending interface is marked as an abnormal communication interface. If it is not satisfied, no marking is performed. Specifically, different communication interfaces have different differentiating features. If the data features associated with the corresponding communication interfaces differ greatly from the differentiating standards of the corresponding database for the same type of data, it will cause excessive delay between the communication interfaces, resulting in database waiting or communication interface waiting. Therefore, feature adjustment is needed to ensure the communication rate between the corresponding communication interfaces, or the specific conversion rate of the data format through the intermediate layer. Among them, the communication logic confirmation end confirms the classified data of different types in the to-be-managed data of the abnormal communication interface, confirms the classification features associated with the different classified data in the transmission process, and locks the optimal communication logic from the confirmed classification features to complete the communication transmission process of the current to-be-managed data. Specifically, different classified data is associated with different transmission times when different transmission logics are executed, so there are different classification features. In order to ensure the effective transmission of the corresponding to-be-managed data, the optimal communication logic needs to be confirmed to ensure the standard transmission process of the corresponding to-be-managed data. Among them, the specific way to lock the optimal communication logic is: Confirm different types of data from the to-be-managed data, integrate data of the same type, confirm the classified data associated with the corresponding type, and set the corresponding classification mark in advance for each group of data in the classification confirmation process to facilitate subsequent data reorganization. Confirm the processing features of the abnormal communication interface, the database interface, and the intermediate layer interface for the corresponding classified data: confirm the minimum processing rate and the maximum processing rate of the corresponding interface for the corresponding classified data from the cloud, and generate a rate interval, and then use the generated rate interval as the processing feature associated with the corresponding interface. Confirm the first communication process: confirm the total capacity R of the classified data, and based on the processing feature associated with the abnormal communication interface, confirm the corresponding time feature Q1 (the time feature is a time interval, based on the total capacity R and the minimum rate and maximum rate of the processing feature, the end time of the corresponding time interval can be confirmed), and simultaneously confirm the time feature Q2 associated with the database interface based on the processing feature associated with the database interface. Based on the confirmed time features Q1 and Q2, confirm the difference time interval CF1 associated with the two groups of time features Q1 and Q2, set the time feature Q1 as [QT1, QT2] and the time feature Q2 as [QT3, QT4]. From the two time features, confirm the two groups of time values closest to each other or the two groups of time values farthest from each other. There is a time difference between each time value, so the minimum and maximum of the corresponding time difference is the associated difference time interval. Confirming the second communication process: based on the processing feature of the intermediate layer interface for the corresponding classified data and the total capacity R of the corresponding classified data, confirming the time feature Q3 associated with the intermediate layer interface, and based on the confirmed time feature Q1, confirming the difference time interval CY1 associated between Q1 and Q3, synchronously confirming the processing feature of the database interface for the output data of the intermediate layer interface, and according to the total capacity and the processing feature of the output data, confirming the time feature Q4 associated with the database interface, and confirming the difference time interval CY2 between the time features Q3 and Q4, and combining CY1 and CY2 to confirm the combined difference interval ZH; Evaluating the combined difference interval ZH and the difference time interval CF1, and confirming the interval median ZJ of ZH and the interval median ZQ of CF1, if ZJ=ZQ, randomly selecting a group of communication processes as the execution process of the current classified data; If ZJ>ZQ, it represents that the time difference value associated with the first communication process is smaller, so the confirmed first communication process is taken as the execution process of the current classified data; If ZJZQ, it represents that the time difference value associated with the second communication process is smaller, so the confirmed second communication process is taken as the execution process of the current classified data; The execution processes associated with different classified data in the to-be-managed data are confirmed in sequence, and the data transmission process of the corresponding to-be-managed data is completed through the corresponding execution end.
[0013] The execution end, when executing the transmission process of different classified data, confirms the execution process associated with the corresponding classified data and performs execution processing, and after completing the transmission processing, integrates the classified data according to the associated classification mark, so that the current to-be-managed data is transmitted to the partition feature processing end.
[0014] Specifically, the abnormal communication interface is denoted as A port, the database interface is denoted as B port, and the intermediate layer interface is denoted as C port, wherein A port and B port have corresponding time intervals for classified data, so that the classified interval can be confirmed according to the corresponding time interval, and the time difference feature between A port and C port is confirmed, and the time difference feature between C port and B port is locked, so that the time difference feature between A port-C port-B port can be confirmed, the time features associated with the current communication process and the last group of communication processes are compared and verified, and the communication logic with smaller time feature is confirmed, so as to lock the corresponding optimal communication logic to ensure the effective transmission process of the data, and sufficiently reduce the transmission time and sufficiently guarantee the transmission rate.
[0015] Second embodiment Compared with the above embodiment, the main difference of the embodiment is that the relative clustering related data is uniformly integrated and processed to be stored in the same storage partition, thereby guaranteeing the storage effect. The partition feature processing end receives the to-be-managed data transmitted by different data interfaces, confirms the data features associated with the to-be-managed data in combination with the carbon emission feature table, and then confirms the to-be-managed data belonging to the same category according to the graph verification process. The specific confirmation method is as follows: A set of processing periods is limited, and the to-be-managed data associated with the processing period is sequentially received and confirmed. According to the preset carbon emission feature table, the feature data associated with the single feature item in the corresponding data is confirmed from the associated single set of to-be-managed data (the feature data associated with the corresponding feature item is directly indexed from the to-be-managed data, and the feature data is directly confirmed). The feature data associated with the same type of feature item is subjected to difference processing from the different sets of to-be-managed data, the feature difference is confirmed, the feature difference is greater than or equal to 0, the maximum value SJ is selected from the two sets of feature data subjected to difference processing, and it is identified whether the confirmed feature difference satisfies the feature difference being less than or equal to 0.1 SJ. If it satisfies, the same type of feature item of the corresponding two sets of to-be-managed data is recorded as a clustering item. If it does not satisfy, no marking is performed. All the same type of feature is the clustering item of the two sets of to-be-managed data, and the same type of data is divided. Similarly, the to-be-managed data belonging to the same type of data is confirmed in pairs, and the to-be-managed data belonging to the same type of data is comprehensively confirmed. The to-be-managed data belonging to the same type of data is stored in the associated database, so as to be stored in the same storage location, thereby guaranteeing the storage effect. The associated database stores the confirmed to-be-managed data, and the to-be-managed data stored in the same storage location is the same type of data.
[0016] Some data in the above formula are dimensionless numerical calculations, and the contents not described in detail in the specification belong to the prior art known to those skilled in the art.
[0017] The above embodiments are only used to illustrate the technical method of the present application and are not limited. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical method of the present application.
Claims
1. A data management system for carbon emission databases, characterized in that, include: The asynchronous feature confirmation end performs feature confirmation on different communication interfaces interconnected by the database, confirms the historical communication data of different communication interfaces from the cloud, and verifies and compares the historical communication data to identify abnormal communication interfaces. The communication logic confirmation end, for abnormal communication interfaces with data to be managed, confirms that the data to be managed belongs to different types of classified data, and confirms the classification characteristics associated with different types of data in the transmission process, and then locks the optimal communication logic from the confirmed classification characteristics. On the execution end, when executing the transmission process of different categories of data, the execution process associated with the corresponding category data is identified and executed. After the transmission process is completed, the integration process of the category data is completed according to the associated classification label, so that the current data to be managed is transmitted to the partition feature processing end.
2. The data management system for carbon emission database according to claim 1, characterized in that, The asynchronous feature confirmation terminal confirms abnormal communication interfaces in the following specific way: The different communication interfaces interconnected by the databases are designated as pending interfaces. The processing rates of different pending interfaces for different types of data are confirmed from the cloud. The average processing rates of several sets of data associated with a single set of data types are then calculated to confirm the average rate Jz. i Where i represents different pending interfaces, the processing rate of the corresponding database for this type of data is synchronously averaged and used as the standard rate BZ. i , where i represents different types of data; Used: Cz i =|Jz i -BZ i |Confirm the associated differential rate Cz i Then, several sets of differential rates Cz confirmed from the undetermined interface. i Confirm the existence of a single difference rate Cz i Satisfy: Cz i >Y1, where Y1 is a preset value. If satisfied, the corresponding pending interface will be marked as an abnormal communication interface.
3. The data management system for carbon emission database according to claim 2, characterized in that, If all difference rates Cz i None of them satisfy Cz i If the value is greater than Y1, no calibration is performed.
4. The data management system for carbon emission databases according to claim 1, characterized in that, The specific methods by which the communication logic confirmation terminal locks the optimal communication logic include: Different types of data are identified from the data to be managed, and data belonging to the same type are integrated. The corresponding category data is identified, and each group of data is pre-set with a corresponding category label during the category identification process. The processing characteristics of the abnormal communication interface, database interface, and intermediate layer interface for the corresponding category data are confirmed: the minimum and maximum processing rates of the corresponding interfaces for the corresponding category data are confirmed from the cloud, and rate ranges are generated. The generated rate ranges are then used as the processing characteristics associated with the corresponding interfaces. Confirm the first communication process: Confirm the total capacity R of its classified data, and based on the processing characteristics associated with the abnormal communication interface, confirm its corresponding time characteristic Q1. Simultaneously, based on the processing characteristics associated with the database interface, confirm the time characteristic Q2 associated with the database interface. Based on the confirmed time characteristics Q1 and Q2, confirm the difference time interval CF1 associated with the two sets of time characteristics Q1 and Q2. Confirm the second communication process: Based on the processing characteristics of the intermediate layer interface for the corresponding classification data and the total capacity R of the corresponding classification data, confirm the time feature Q3 associated with the intermediate layer interface, and based on the confirmed time feature Q1, confirm the time difference interval CY1 associated with Q1 and Q3. Simultaneously confirm the processing characteristics of the database interface for the output data of the intermediate layer interface, and based on the total capacity and processing characteristics of the output data, confirm the time feature Q4 associated with the database interface, and confirm the time difference interval CY2 between time features Q3 and Q4. Combine CY1 and CY2 to confirm the combined difference interval ZH.
5. The data management system for carbon emission database according to claim 4, characterized in that, The specific method by which the communication logic confirmation terminal locks the optimal communication logic also includes: Evaluate the combined difference interval ZH and the difference time interval CF1, and confirm the interval median ZJ of ZH and the interval median ZQ of CF1. If ZJ=ZQ, then randomly select a set of communication processes as the execution process for the current classification data. If ZJ > ZQ, then the first confirmed communication process will be used as the execution process for the current classification data. If ZJ < ZQ, then the confirmed second communication process will be used as the execution process for the current classification data. The execution processes associated with different categories of data within the data to be managed are confirmed sequentially, and data transmission is processed through the corresponding execution terminals to complete the transmission process of the corresponding data to be managed.
6. The data management system for carbon emission databases according to claim 1, characterized in that, Also includes: The partition feature processing end receives the data to be managed transmitted from different data interfaces, and confirms the data characteristics associated with the data to be managed by combining the carbon emission feature table. Then, according to the graphical verification process, it confirms the data to be managed that belong to the same category.
7. The data management system for carbon emission databases according to claim 6, characterized in that, The specific method by which the partition feature processing terminal confirms the data to be managed that belongs to the same category is as follows: Define a set of processing cycles, and sequentially receive and confirm the data to be managed associated within the processing cycle; Based on the preset carbon emission characteristic table, the characteristic data associated with the corresponding single characteristic item in the associated single set of data to be managed is identified. Then, from multiple different sets of data to be managed, the characteristic data associated with the same type of characteristic item are processed by difference to confirm the characteristic difference. If the characteristic difference is ≥ 0, the maximum value SJ is selected from the two sets of characteristic data that have undergone difference processing. It is then identified whether the confirmed characteristic difference satisfies the following condition: characteristic difference ≤ 0.1SJ. If it satisfies the condition, the same type of characteristic item in the two sets of data to be managed is recorded as a cluster item. If it does not satisfy the condition, no labeling is performed. Two groups of data to be managed that all have the same clustering features are classified as data of the same type. This process is repeated, and several groups of data to be managed are confirmed one by one. Data to be managed that belong to the same type of data are then comprehensively confirmed. Furthermore, data belonging to the same category that is to be managed is stored through a relational database, ensuring that they are stored in the same storage location.
8. The data management system for carbon emission database according to claim 7, characterized in that, The associated database stores the confirmed data to be managed, and all data to be managed stored in the same storage location are of the same type.