Power plant income measuring and calculating system based on fuel indexes

Abstract

The invention discloses a power plant income measuring and calculating system based on fuel indexes, relates to the technical field of power plant income measuring and calculating, and aims to solve the problems that a traditional power plant income measuring and calculating method is often extensive and lacks real-time monitoring and accurate calculation of fault loss, and a power plant is difficult to accurately master effective electric quantity actually transmitted to a user side. According to the method, accurate preliminary and actual benefits are obtained through power transmission loss analysis, the problems of extensive measurement and calculation and large error in the prior art are effectively solved, and a reliable basis is provided for power plant operation decision making. Abnormal electric quantity loss can be timely perceived, rapid alarm and loss stop can be realized in the early stage of electricity stealing, abnormal areas can be accurately positioned through a power transmission network topological structure, the troubleshooting efficiency is greatly improved, the source can be accurately locked, conditions such as line aging, equipment faults and user electricity stealing can be efficiently handled, and normal power transmission and accurate income metering can be guaranteed. And the power plant operation benefit and management level are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power plant revenue calculation, and in particular to a power plant revenue calculation system based on fuel indicators. Background Art

[0002] With the continuous growth of global energy demand and the deepening of power market reform, the operation and management of power plants are facing many challenges and opportunities. In this context, accurately measuring power plant revenue and deeply analyzing its influencing factors have become the key to achieving efficient operation, sustainable development and standing out in market competition.

[0003] In this regard, the patent with publication number CN110060165B discloses a photovoltaic energy storage system profit calculation method and an energy management control method. The model takes into account the power generation parameter factors of the photovoltaic energy storage system to be measured when constructing the photovoltaic energy storage electricity metering, and takes into account the electricity price information factors when constructing the photovoltaic energy storage electricity profit, and takes into account the photovoltaic cost and energy storage cost when constructing the initial profit of the photovoltaic energy storage system to be measured. Subsequently, by adjusting the independent variables of the photovoltaic energy storage system profit model, the various dependent variables of at least one investment plan are obtained, and then the profit calculation results of the photovoltaic energy storage system to be measured can be determined based on the dependent variables under various investment plans, so as to provide a reliable setting basis for the photovoltaic energy storage system to be measured.

[0004] Traditional power plant revenue calculation methods are often relatively extensive, focusing only on the losses caused by basic factors such as line resistance, while ignoring the important variable of fault power loss. During the long-term operation of transmission lines, it is inevitable to encounter various faults, such as short circuits, circuit breaks, grounding, etc. These faults will not only cause a large amount of power loss instantly, but may also damage line equipment, further affecting subsequent transmission efficiency and power loss. Due to the lack of real-time monitoring and accurate calculation of fault losses, it is difficult for power plants to accurately grasp the effective power actually delivered to the user end, resulting in deviations in the calculation of actual revenue.

[0005] In view of the above problems, a power plant revenue calculation system based on fuel indicators is proposed. Summary of the invention

[0006] The purpose of the present invention is to provide a power plant revenue estimation system based on fuel index, which solves the problem of inaccurate revenue estimation in the background technology.

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: A fuel index-based power plant revenue estimation system, comprising: a data acquisition module, a data transmission module, a data processing and analysis module, an optimization decision module and a database management module;

[0008] The data acquisition unit is used to collect power plant power generation data, transmission line data and user power consumption data; the power plant power generation data includes the installed capacity, real-time power generation and power generation duration of the power plant, and the transmission line data includes line resistance, reactance, current, voltage and fault occurrence information;

[0009] A data processing and analysis unit is connected to the data collection unit, receives and processes the collected data, calculates the power loss in the power transmission process according to the power generation data of the power plant and the power consumption data of the user, and subdivides it into the power consumption of the line itself and the power loss due to the failure of the transmission network;

[0010] The preliminary profit of the power plant is calculated based on the power generation data of the power plant, and the actual profit is calculated based on the user's power consumption data. The difference between the two is obtained and defined as the missing value;

[0011] Calculation formula: E 缺 =E 理论 -E 损耗 -E 用户 , where E 理论 is the theoretical power generation of the power plant, E 损耗 is the total transmission loss, E 用户 For the actual electricity consumption of users, statistical analysis is performed on the missing value data to calculate the mean μ and standard deviation σ;

[0012] Missing Values 缺 Satisfy |E 缺 -μ>3σ is abnormal, the missing value is judged to be abnormal, and an abnormal report is generated. The report content includes the size of the abnormal missing value, the time of occurrence, the associated power plant unit number, the transmission line section, and the affected user range;

[0013] Optimize the decision-making module, set the missing value threshold, compare the actual power loss with the threshold, and when the actual loss is greater than the missing value threshold, the system triggers the alarm mechanism, sends an alarm message to the management personnel, and performs a first analysis based on the missing values. Use data mining and machine learning algorithms to analyze the missing values. If the missing values ​​are normal, the program ends. If the missing values ​​are abnormal, perform a secondary analysis on the missing values, establish the transmission network topology information, and locate the abnormal area on the power grid topology map.

[0014] Preferably, the mean μ and standard deviation σ of the missing value data are calculated;

[0015] Mean

[0016] Standard Deviation

[0017] Preferably, the K-Means algorithm is used to classify and analyze the missing value data to determine whether it belongs to the normal clustering range;

[0018] Randomly initialize k cluster centers μ1,μ2,…,μ k , for a data point x i Calculate the distance to each cluster center Where m is the data dimension, d represents the distance, x i,l Represents data point x i The value in the lth dimension, μ j,l Represents the data point μ j The value in the lth dimension, l is used to traverse each dimension of the data, and the value range is [1,m]. j is used to identify different cluster centers, and the value range is [1,k].

[0019] Preferably, initialize k cluster centers μ1, μ2, …, μ k , calculate the data point x i , calculate the cluster center μ j The distance d(x i ,μ j ), and x i Assign to the closest C j In the above example, the cluster center μ is recalculated based on the data points in the cluster. j , the calculation formula is Where |C j | is cluster C j The number of data points in C j After the distance calculation, the data points are divided into cluster sets, and the process is repeated until the cluster center no longer changes and the algorithm ends.

[0020] Preferably, the database management module adopts a distributed database architecture to store data according to the classification of power plants, transmission lines, and user numbers. The database architecture has a built-in data cleaning and verification mechanism to automatically eliminate erroneous and redundant data to ensure data quality.

[0021] Preferably, the fuel index data includes the calorific value and fuel consumption of the fuel. The calorific value of the coal is obtained by collecting coal samples, and the consumption of the coal is obtained by measuring. The theoretical power generation is calculated based on the collected fuel index data. The calculation formula is: Where Q 燃料 is the calorific value of the fuel, m 燃料 is the amount of fuel used; f is the power generation efficiency coefficient of the power plant, and its value range is [0,1].

[0022] Preferably, the transmission line data is collected, including the line resistance R 线 , current I, transmission time t and fault loss circuit E 故障 The above data is obtained through the current transformer, voltage transformer, resistance measuring device and fault monitoring device installed on the transmission line; the power consumption of the line itself is E 自身, calculated according to Joule's law, the formula is E 自身 =I 2 R 线 t, collect the actual power consumption of users and calculate the total transmission loss E 总损 =E 自身 +E 故障 , calculate the actual revenue R of the power plant 实际 =(E 理论 -E 总损 -E 用户 )×P, where P is the electricity price of the power plant.

[0023] Preferably, a data cache layer is set at the database front end, and query results are cached in the memory database when frequent data retrieval requests are made. Recent power generation data and electricity consumption data of important users are pre-fetched and cached during system idle periods.

[0024] Preferably, the data is divided into multiple data shards according to the time range and stored in different storage nodes respectively. When querying and processing the data, operations are performed on multiple shards in parallel.

[0025] Preferably, multiple active-active data centers are established in different locations. Data is synchronized between the main data center and the remote data centers through a high-speed network to achieve two-way real-time replication of data. When the main data center fails, it automatically switches to the remote data center. During normal operation, the remote data center also shares part of the data query and processing tasks. A data consistency algorithm is used between the multiple active-active data centers to ensure that data remains consistent.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1. The invention provides a fuel index-based power plant revenue calculation system, which obtains accurate preliminary and actual revenue through transmission loss analysis, effectively solves the problem of extensive traditional measurement and large errors, and provides a reliable basis for power plant operation decision-making. In the optimization decision module, the missing value threshold is scientifically set and a variety of algorithm analysis is used to detect abnormal power loss in time, quickly alarm and stop loss in the early stage of power theft, accurately locate abnormal areas through the topological structure of the transmission network, greatly improve the efficiency of investigation, accurately identify the root cause, and efficiently deal with line aging, equipment failure, or user power theft, ensuring normal transmission and accurate revenue measurement, greatly improving the operating efficiency and management level of the power plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic diagram of the process of the present invention;

[0029] Figure 2 This is the logic decision diagram 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] In order to further understand the content of the present invention, the present invention is described in detail in conjunction with the accompanying drawings.

[0032] Combination Figure 1-Figure 2 , a fuel index-based power plant revenue estimation system of the present invention comprises: a data acquisition module, a data transmission module, a data processing and analysis module, an optimization decision module and a database management module;

[0033] The data acquisition unit is used to collect power plant power generation data, transmission line data and user power consumption data; the power plant power generation data includes the installed capacity, real-time power generation and power generation duration of the power plant, and the transmission line data includes line resistance, reactance, current, voltage and fault occurrence information. When collecting power plant power generation data, a high-precision power sensor is installed at the output end of the generator, which can measure the real-time power generation at very short time intervals and is connected to the power plant's monitoring system. The monitoring system also records the power generation duration and accurately calculates the power generation duration by capturing the start and stop signals of the generator;

[0034] For data collection of transmission lines, current transformers, voltage transformers, and resistance measuring devices are installed along the transmission line intervals. Current transformers and voltage transformers monitor the current and voltage values ​​of the line in real time, and resistance measuring devices regularly measure the line resistance. These data are transmitted to the power plant data center. At the same time, the fault monitoring device monitors line faults in real time. Once a fault is detected, it immediately records the time and type of the fault, and continuously monitors the current and voltage changes during the fault to calculate the power loss caused by the fault.

[0035] The user's electricity consumption data is collected through the smart meters installed for each user. The electricity consumption is measured according to different time periods, and these data are regularly transmitted to the power plant data center through power line carrier communication or wireless communication technology.

[0036] A data processing and analysis unit is connected to the data collection unit, receives and processes the collected data, calculates the power loss in the power transmission process according to the power generation data of the power plant and the power consumption data of the user, and subdivides it into the power consumption of the line itself and the power loss due to the failure of the transmission network;

[0037] The electric energy consumed during the fault period is calculated by integrating the voltage waveform during the fault period. The specific calculation method uses different power system fault analysis algorithms according to the type of short circuit, open circuit or grounding fault, and finally the fault power loss is obtained.

[0038] The preliminary profit of the power plant is calculated based on the power generation data of the power plant, and the actual profit is calculated based on the user's power consumption data. The difference between the two is obtained and defined as the missing value;

[0039] Calculation formula: E 缺 =E 理论 -E 损耗 -E 用户 , where E 理论 is the theoretical power generation of the power plant, E 损耗 is the total transmission loss, E 用户 For the actual electricity consumption of users, statistical analysis is performed on the missing value data to calculate the mean μ and standard deviation σ;

[0040] Calculate the mean μ and standard deviation σ of missing value data;

[0041] Mean

[0042] Standard Deviation

[0043] Missing Values 缺 Satisfy |E 缺 -μ>3σ is abnormal, the missing value is judged to be abnormal, and an abnormal report is generated. The report content includes the size of the abnormal missing value, the time of occurrence, the associated power plant unit number, the transmission line section, and the affected user range;

[0044] Use K-Means algorithm to classify and analyze missing value data to determine whether it belongs to the normal clustering range;

[0045] Randomly initialize k cluster centers μ1,μ2,…,μ k , for a data point x i Calculate the distance to each cluster center Where m is the data dimension, d represents the distance, x i,l Represents data point x i The value in the lth dimension, μ j,l Represents the data point μ j The value in the lth dimension, l is used to traverse each dimension of the data, and the value range is [1,m]; j is used to identify different cluster centers, and the value range is [1,k];

[0046] Initialize k cluster centers μ1,μ2,…,μ k , calculate the data point x i , calculate the cluster center μ jThe distance d(x i ,μ j ), and x i Assign to the closest C j In the above example, the cluster center μ is recalculated based on the data points in the cluster. j , the calculation formula is Where |C j | is cluster C j The number of data points in C j After the distance calculation, the data points are divided into cluster sets, and the process is repeated until the cluster center no longer changes and the algorithm ends.

[0047] Optimize the decision module, set the missing value threshold, compare the actual power loss with the threshold, when the actual loss is greater than the missing value threshold, the system triggers the alarm mechanism, sends an alarm message to the management personnel, and performs the first analysis based on the missing value, using data mining and machine learning algorithms to analyze the missing value, the program ends if the analysis of the missing value is normal, the missing value is abnormal, the missing value is analyzed again, the transmission network topology information is established, the abnormal area is located on the power grid topology map, the transmission network topology information is established, the abnormal missing value data is mapped to the power grid topology map, the abnormal area is located, and the line equipment and user distribution around the abnormal area are analyzed. Further investigate the abnormal causes, such as whether there are serious aging of local lines, concentrated electricity theft by users, etc. Reasonable missing value threshold setting can timely detect abnormal power loss and avoid large revenue losses caused by failure to detect in time. When the electricity theft occurs in the early stage, the threshold comparison can quickly trigger the alarm, stop the electricity theft in time and reduce losses. Secondary analysis locates the abnormal area through the grid topology structure, which can more accurately determine the scope of investigation, improve the efficiency of investigation, quickly find the root cause of the abnormality and take measures to repair it, inspect the lines in the abnormal area, and focus on the suspicious users, so as to restore normal power transmission and revenue measurement;

[0048] If the abnormal missing value appears on a certain transmission line, determine the substation nodes at both ends of the line; if the abnormality is in a certain user area, find the corresponding user access point node and the upstream power supply node, take the associated node as the center, expand and mark the abnormal area according to certain rules, and take the abnormal associated node as the starting point, expand a certain electrical distance along the transmission line, and mark all nodes and lines within the range as abnormal areas according to the equivalent distance calculated based on the line resistance, reactance and voltage level;

[0049] For each transmission line in the abnormal area, analyze its line parameters in detail. Calculate the theoretical power loss range of the line. According to the line resistance, reactance, rated current and actual operating current and voltage, use the power system flow calculation method to calculate the power loss of the line under normal operation. If the actual power loss exceeds the theoretical range, further check whether the line has problems such as aging, damage or poor contact. Compare the actual resistance value of the line with the designed resistance value. If the actual resistance value increases significantly, it may be due to additional loss caused by line aging or loose joints.

[0050] Count the number of users in the abnormal area and the distribution of power load. Analyze the historical power consumption data of users, including the time series changes of power consumption and the ratio of peak to valley power consumption. If it is found that the power consumption of users in a certain area suddenly increases significantly or an abnormal power consumption pattern appears, such as a large amount of power consumption during non-production time, these users will be listed as key suspects, and further check whether their electricity meters are faulty or have electricity theft. For example, for industrial users, the matching degree between their production plan and the actual power consumption curve can be analyzed. If there are still a large number of power consumption records during the shutdown period, there may be suspicion of power theft. Secondly, check the operating status of node equipment such as substations and distribution rooms in the abnormal area. Obtain the oil temperature, oil pressure, and insulation performance parameters of the equipment to determine whether the equipment is operating normally. If there is a fault in the equipment, it may affect the power transmission efficiency and cause abnormal power loss. When the oil temperature of the transformer is too high, it may cause its internal winding resistance to increase, thereby increasing power loss. By analyzing the monitoring data of the node equipment, potential equipment problems can be discovered in a timely manner, and their impact on power loss can be evaluated;

[0051] According to the above analysis results, the root cause of the abnormality is comprehensively judged. If it is a line problem, the specific location of the fault point can be determined by using fault distance measurement technology. By measuring the propagation time and speed of the fault wave on the transmission line, the distance from the fault point to the measurement point can be determined. Combined with the line length information in the topology map, the line segment where the fault point is located can be accurately determined. For user electricity theft or meter failure, the specific suspected user or meter location can be determined. According to the fault location results, corresponding repair suggestions are generated. At the same time, the effect after the repair is estimated, and the power loss reduction of the line after the repair, the expected amount of electricity to be restored, etc. are calculated, so as to evaluate the economic benefits of the repair work.

[0052] The database management module adopts a distributed database architecture to store data according to the power plant, transmission line, and user number. The database architecture has a built-in data cleaning and verification mechanism to automatically remove errors and redundant data to ensure data quality;

[0053] A data cache layer is set up at the front end of the database. When there are frequent data retrieval requests, the query results are cached in the memory database. Recent power generation data and electricity consumption data of important users are pre-fetched and cached during system idle periods. The data is divided into multiple data shards according to the time range and stored in different storage nodes. When querying and processing data, operations are performed on multiple shards in parallel, and multi-site active data centers are established. Data synchronization is performed between the main data center and the remote data center through a high-speed network to achieve two-way real-time replication of data. When the main data center fails, it automatically switches to the remote data center. During normal operation, the remote data center also shares part of the data query and processing tasks. A data consistency algorithm is used between the multi-site active data centers to ensure data consistency.

[0054] The fuel index data includes the calorific value and fuel consumption of the fuel. The calorific value of the coal is obtained by collecting coal samples, analyzing the calorific value of the coal, and measuring the coal consumption. The theoretical power generation is calculated based on the collected fuel index data. The calculation formula is: Where Q 燃料 is the calorific value of the fuel, m 燃料 is the amount of fuel used; f is the power generation efficiency coefficient of the power plant, with a value range of [0,1]. The transmission line data is collected, including the line resistance R 线 , current I, transmission time t and fault loss circuit E 故障 The above data is obtained through the current transformer, voltage transformer, resistance measuring device and fault monitoring device installed on the transmission line; the power consumption of the line itself is E 自身 , calculated according to Joule's law, the formula is E 自身 =I 2 R 线 t, collect the actual power consumption of users and calculate the total transmission loss E 总损 =E 自身 +E 故障 , calculate the actual revenue R of the power plant 实际 =(E 理论 -E 总损 -E 用户 )×P, where P is the electricity price of the power plant.

[0055] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0056] 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 the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A fuel index-based power plant revenue calculation system, characterized by: include: Data acquisition module, data transmission module, data processing and analysis module, optimization decision module and database management module; The data acquisition unit is used to collect power plant power generation data, transmission line data and user power consumption data; the power plant power generation data includes the installed capacity, real-time power generation and power generation duration of the power plant, and the transmission line data includes line resistance, reactance, current, voltage and fault occurrence information; A data processing and analysis unit is connected to the data collection unit, receives and processes the collected data, calculates the power loss in the power transmission process according to the power generation data of the power plant and the power consumption data of the user, and subdivides it into the power consumption of the line itself and the power loss due to the failure of the transmission network; The preliminary profit of the power plant is calculated based on the power generation data of the power plant, and the actual profit is calculated based on the user's power consumption data. The difference between the two is obtained and defined as the missing value; Calculation formula: E 缺 =E 理论 -E 损耗 -E 用户 , where E 理论 is the theoretical power generation of the power plant, E 损耗 is the total transmission loss, E 用户 For the actual electricity consumption of users, statistical analysis is performed on the missing value data to calculate the mean μ and standard deviation σ; Missing Values 缺 Satisfy |E 缺 -μ>3σ is abnormal, the missing value is judged to be abnormal, and an abnormal report is generated. The report content includes the size of the abnormal missing value, the time of occurrence, the associated power plant unit number, the transmission line section, and the affected user range; Optimize the decision-making module, set the missing value threshold, compare the actual power loss with the threshold, and when the actual loss is greater than the missing value threshold, the system triggers the alarm mechanism, sends an alarm message to the management personnel, and performs a first analysis based on the missing values. Use data mining and machine learning algorithms to analyze the missing values. If the missing values ​​are normal, the program ends. If the missing values ​​are abnormal, perform a secondary analysis on the missing values, establish the transmission network topology information, and locate the abnormal area on the power grid topology map.

2. The fuel index-based power plant revenue calculation system according to claim 1 is characterized by: Calculate the mean μ and standard deviation σ of missing value data; Mean Standard Deviation 3. The fuel index-based power plant revenue calculation system according to claim 2 is characterized by: Use K-Means algorithm to classify and analyze missing value data to determine whether it belongs to the normal clustering range; Randomly initialize k cluster centers μ1,μ2,…,μ k , for a data point x i Calculate the distance to each cluster center Where m is the data dimension, d represents the distance, x i,l Represents data point x i The value in the lth dimension, μ j,l Represents the data point μ j The value in the lth dimension, l is used to traverse each dimension of the data, and the value range is [1,m]. j is used to identify different cluster centers, and the value range is [1,k].

4. The fuel index-based power plant revenue calculation system according to claim 3 is characterized by: Initialize k cluster centers μ1,μ2,…,μ k , calculate the data point x i , calculate the cluster center μ j The distance d(x i ,μ j ), and x i Assign to the closest C j In the above example, the cluster center μ is recalculated based on the data points in the cluster. j , the calculation formula is Where |C j | is cluster C j The number of data points in C j After the distance calculation, the data points are divided into cluster sets, and the process is repeated until the cluster center no longer changes and the algorithm ends.

5. The fuel index-based power plant revenue calculation system according to claim 1 is characterized by: The database management module adopts a distributed database architecture to store data according to power plants, transmission lines, and user numbers. The database architecture has a built-in data cleaning and verification mechanism to automatically eliminate errors and redundant data to ensure data quality.

6. The fuel index-based power plant revenue calculation system according to claim 1 is characterized by: The fuel index data includes the calorific value and fuel consumption of the fuel. The calorific value of the coal is obtained by collecting coal samples, analyzing the calorific value of the coal, and measuring the coal consumption. The theoretical power generation is calculated based on the collected fuel index data. The calculation formula is: Where Q 燃料 is the calorific value of the fuel, m 燃料 is the amount of fuel used; f is the power generation efficiency coefficient of the power plant, and its value range is [0,1].

7. The fuel index-based power plant revenue calculation system according to claim 6 is characterized by: Collect transmission line data, including line resistance R 线 , current I, transmission time t and fault loss circuit E 故障 The above data is obtained through the current transformer, voltage transformer, resistance measuring device and fault monitoring device installed on the transmission line; the power consumption of the line itself is E 自身 , calculated according to Joule's law, the formula is E 自身 =I 2 R 线 t, collect the actual power consumption of users and calculate the total transmission loss E 总损 =E 自身 +E 故障 , calculate the actual revenue R of the power plant 实际 =(E 理论 -E 总损 -E 用户 )×P, where P is the electricity price of the power plant.

8. The fuel index-based power plant revenue calculation system according to claim 5 is characterized by: A data cache layer is set up at the database front end. When there are frequent data retrieval requests, the query results are cached in the memory database. Recent power generation data and electricity consumption data of important users are pre-fetched and cached during system idle periods.

9. The fuel index-based power plant revenue calculation system according to claim 8 is characterized by: The data is divided into multiple data shards according to the time range and stored on different storage nodes. When querying and processing data, operations are performed on multiple shards in parallel.

10. The fuel index-based power plant revenue calculation system according to claim 9, characterized in that: Establish multiple active-active data centers in different locations. Data is synchronized between the main data center and the remote data center through a high-speed network to achieve two-way real-time data replication. When the main data center fails, it automatically switches to the remote data center. During normal operation, the remote data center also shares part of the data query and processing tasks. Data consistency algorithms are used between the multiple active-active data centers to ensure data consistency.

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