A power spot market power purchase transaction data sharing system

Abstract

The present invention discloses a power purchase transaction data sharing system in the power spot market, which belongs to a data processing system dedicated to the power system. The power purchase transaction data sharing system includes: a power consumption data acquisition module, a power consumption information analysis module, a data storage sharing module, an intelligent power purchase suggestion module, a visualization display module, and a system monitoring module. The present invention realizes data sharing and transparency by extracting key power consumption characteristics and future load forecast results and future electricity price forecast results generated by the load forecasting unit, integrating the data resources of various market entities; it can provide more accurate power purchase suggestions that meet the specific needs of each user and are in line with their power consumption habits; it converts complex data into intuitive chart forms, so that different market entities can more easily understand and compare each other's data, and promote the transparency and circulation of market information.

Description

Technical Field

[0001] The invention relates to a data processing system dedicated to an electric power system, and in particular to a power spot market electricity purchase transaction data sharing system. Background Art

[0002] The electricity spot market refers to a market where electricity is bought and sold based on the real-time electricity price. In the electricity spot market, electricity transactions are immediate, usually in cycles of hours or less, and prices change in real time based on supply and demand.

[0003] However, there are still the following problems in actual operation:

[0004] In the traditional electricity market, due to the limitations of data collection, processing and analysis, there is often information asymmetry between market players, resulting in a lack of basis for decision-making, affecting market efficiency and fairness. At the same time, individual users or small businesses often find it difficult to formulate scientific and reasonable electricity purchasing strategies due to the lack of professional market analysis capabilities and sufficient data support. The lack of an effective information sharing mechanism between different market players leads to poor market information flow and affects market efficiency. Summary of the invention

[0005] The present invention aims to provide a power spot market electricity purchase transaction data sharing system to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A power spot market power purchase transaction data sharing system, comprising:

[0008] The power consumption data collection module is used to collect power consumption information of power users in real time through smart meters and sensors, clean the collected raw data of power consumption information, perform data standardization and formatting, and store the cleaned data in a temporary database; the power consumption information includes power, voltage, current and power factor;

[0009] The power consumption information analysis module is used to perform distributed processing on the power consumption data in the temporary database based on the big data framework, build a data analysis model to conduct in-depth analysis on the power consumption data, extract power consumption patterns and load forecast information, and perform market electricity price forecasts, and store the analysis and forecast results in the data storage sharing module;

[0010] The data storage sharing module is used to build a distributed database system. Based on the distributed database storage and the analysis results of the power consumption information analysis module, a data synchronization mechanism is implemented to update data in real time among multiple nodes. At the same time, a standardized data interface is provided to provide different market entities with access and query data functions based on the data interface.

[0011] The intelligent electricity purchase suggestion module is used to integrate user data and electricity price forecast results based on the analysis results of the electricity consumption information analysis module, and provide users with personalized electricity purchase suggestions based on the user's historical electricity consumption data and electricity price forecast factors;

[0012] Visual display module, used to display the processed data in the form of charts and provide interactive query and export functions;

[0013] The system monitoring module is used to monitor the system operation status in real time, establish an alarm mechanism, automatically send alarm information to the administrator when the system is abnormal, and perform regular system maintenance and upgrades.

[0014] Furthermore, the electricity consumption information analysis module includes:

[0015] A data analysis modeling unit is used to construct a data analysis model, select various power consumption characteristics as analysis variables, use historical power consumption data to train the model, adjust model parameters, conduct in-depth analysis on the pre-processed power consumption data, and extract key power consumption characteristics, wherein the key power consumption characteristics include peak power consumption time and average power consumption;

[0016] The load forecasting unit is used to forecast the power load for a period of time in the future based on historical power consumption data and external factors, wherein the external factors include weather information and holiday information, construct input features of the forecasting model, wherein the input features include historical load values, temperature, humidity, and holiday information, conduct model training, input the model according to the latest data, and generate future load forecast results;

[0017] The market electricity price prediction unit is used to extract and convert the features of the historical data of the market electricity price based on the demand for electricity price prediction, construct characteristic variables that affect the electricity price, and the characteristic variables include the supply-demand ratio, policy indicators, and historical electricity price fluctuations. The electricity price prediction model is trained using historical data based on the characteristic variables, the latest data is input into the model, the future electricity price prediction results are generated, and the prediction results are evaluated using a test set.

[0018] Furthermore, the electricity consumption information analysis module further includes:

[0019] The data integration and optimization unit is used to integrate key electricity consumption characteristics, load forecast results and electricity price forecast results, generate electricity consumption analysis reports, and build user portraits based on users' historical electricity consumption data and electricity price sensitivity information.

[0020] Furthermore, the data storage sharing module includes:

[0021] A distributed storage unit, used to configure a distributed database and perform distributed storage on the power consumption information data and the analysis results of the power consumption information analysis module;

[0022] The data synchronization unit is used to implement the data synchronization mechanism, synchronize data between multiple nodes in real time based on the active replication synchronization strategy, capture, transmit and apply data in real time, monitor the synchronization process, and regularly evaluate the synchronization performance.

[0023] Furthermore, the data storage sharing module further includes:

[0024] The data interface unit is used to encapsulate and isolate data and provide a standardized data interface through which different market players can access, query and obtain data;

[0025] The data security management unit is used to implement data encryption, encrypt and store sensitive data, implement access control mechanisms, verify user identities when accessing distributed databases, authorize access to verified users, and regularly audit data security.

[0026] Furthermore, the intelligent electricity purchase suggestion module includes:

[0027] A user portrait construction unit is used to obtain the user's electricity consumption behavior portrait constructed by the electricity consumption information analysis module, extract the user's electricity consumption behavior characteristics, and divide the user into different electricity consumption behavior groups;

[0028] The electricity price forecast fusion unit is used to obtain market electricity price forecast data from the electricity consumption information analysis module, predict the user's electricity demand in the future based on the user portrait and historical electricity consumption data, conduct comprehensive analysis and forecast based on the market electricity price forecast results and the user's electricity demand, and obtain the best time and amount of electricity purchase for the user in a specific time period through dynamic planning.

[0029] Furthermore, the intelligent electricity purchase suggestion module further includes:

[0030] The power purchase strategy optimization unit is used to generate a power purchase strategy plan based on the output of the power price prediction fusion unit, and generate implementation steps based on the strategy;

[0031] The notification push unit is used to generate power purchase suggestion content according to the suggestion of the power purchase strategy optimization unit, and automatically send the power purchase suggestion notification to the user at a predetermined time by selecting a push channel according to the user's contact information.

[0032] Furthermore, the visual display module includes:

[0033] The chart generation unit is used to extract the data to be displayed from the data storage sharing module according to user requests, convert the cleaned data into the format required for chart display, generate charts based on the processed data, and the charts include line charts, bar charts, pie charts and scatter charts. The processed data is bound to the chart and a data export function is provided.

[0034] Furthermore, the visual display module further includes:

[0035] A real-time monitoring module, used for real-time monitoring of the operating parameters of the chart generation unit, wherein the operating parameters include the chart generation processing time, CPU utilization, the number of concurrent chart processing threads and the chart generation data export success rate;

[0036] The first operation evaluation coefficient acquisition module is used to acquire the first operation evaluation coefficient by using the chart generation processing time and the CPU utilization, wherein the first operation evaluation coefficient is acquired by the following formula:

[0037]

[0038] Wherein, Q1 represents the first operation evaluation coefficient; T p represents the average value of the processing time for chart generation; T z Indicates the intermediate value corresponding to the processing time of chart generation; P c Indicates the CPU utilization corresponding to the current chart generation unit;

[0039] A first coefficient comparison module, used for comparing the first operation evaluation coefficient with a preset first operation evaluation coefficient threshold;

[0040] The operation quality determination module is used to determine the operation quality of the chart generation unit by using the number of concurrent chart processing threads and the chart generation data export success rate combined with the first operation evaluation coefficient when the first operation evaluation coefficient exceeds a preset first operation evaluation coefficient threshold.

[0041] Furthermore, the operation quality determination module includes:

[0042] A parameter extraction module, used for retrieving the number of concurrent chart processing threads and the chart generation data export success rate in the operation parameters when the first operation evaluation coefficient exceeds a preset first operation evaluation coefficient threshold;

[0043] A second operation evaluation coefficient acquisition module, used to obtain a second operation evaluation coefficient by combining the number of concurrent chart processing threads and the chart generation data export success rate with the first operation evaluation coefficient;

[0044] The second operation evaluation coefficient is obtained by the following formula:

[0045]

[0046] Wherein, Q2 represents the second operation evaluation coefficient; Q1 represents the first operation evaluation coefficient; n represents the number of concurrent chart processing threads of the current chart generation unit; P s Indicates the success rate of exporting chart generation data of the current chart generation unit;

[0047] A second coefficient comparison module, used for comparing the second operation evaluation coefficient with a preset second operation evaluation coefficient threshold;

[0048] The abnormality determination and alarm module is used to determine that the operation quality of the chart generating unit is abnormal and issue an abnormality alarm when the second operation evaluation coefficient is lower than a preset second operation evaluation coefficient threshold.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] (1) By extracting key electricity consumption characteristics and future load forecasting results generated by the load forecasting unit, the present invention enables market entities to more accurately predict their own electricity demand and market load changes, reduce operational risks, and improve economic benefits. By generating future electricity price forecast results, the present invention provides market entities with predictive information on electricity price changes, integrates data resources of various market entities, realizes data sharing and transparency, helps to break information asymmetry, promotes fair competition in the electricity market, and improves market efficiency. By predicting electricity load and market electricity price, market entities can better manage electricity consumption costs and electricity sales risks.

[0051] (ii) Through the user portrait construction unit, the present invention enables the system to deeply understand the electricity usage behavior characteristics of each user and divide them into different electricity usage behavior groups. It can provide more accurate electricity purchase suggestions that are in line with the electricity usage habits of each user based on their specific needs. By combining market electricity price forecast data and the user's historical electricity usage data, through comprehensive analysis and prediction, it can derive the best time and amount of electricity purchase for the user in the future, which can help the user avoid purchasing electricity during peak electricity prices and reduce electricity costs, while ensuring the stability of power supply.

[0052] (III) The present invention converts complex data into intuitive charts to help users quickly understand the information behind the data. Users can see the dynamic changes of the power market more intuitively, which helps users better grasp market trends and formulate more scientific and reasonable power purchase strategies and sales plans, providing more powerful support for decision-making. It not only serves individual users, but also supports information sharing among multiple market players. By generating standardized charts, different market players can more easily understand and compare each other's data, thereby promoting the transparency and circulation of market information. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a module schematic diagram of the power spot market power purchase transaction data sharing system of the present invention. DETAILED DESCRIPTION

[0054] 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.

[0055] In order to solve the problem of information asymmetry between market players due to the limitations of data collection, processing and analysis in the traditional electricity market, which leads to a lack of basis for decision-making and affects market efficiency and fairness. At the same time, individual users or small enterprises often find it difficult to formulate scientific and reasonable power purchasing strategies due to the lack of professional market analysis capabilities and sufficient data support. There is a lack of effective information sharing mechanism between different market players, which leads to poor market information flow and affects market efficiency. Please refer to Figure 1 , the present invention provides the following technical solutions:

[0056] A power spot market power purchase transaction data sharing system, comprising:

[0057] The power consumption data collection module is used to collect power consumption information of power users in real time through smart meters and sensors based on the Internet of Things, clean the collected raw data of power consumption information, perform data standardization and formatting, and store the cleaned data in a temporary database; the power consumption information includes power, voltage, current and power factor;

[0058] The power consumption information analysis module is used to perform distributed processing on the power consumption data in the temporary database based on the big data framework, build a data analysis model to conduct in-depth analysis on the power consumption data, extract power consumption patterns and load forecast information, and perform market electricity price forecasts, and store the analysis and forecast results in the data storage sharing module;

[0059] The data storage sharing module is used to build a distributed database system. Based on the distributed database storage and the analysis results of the power consumption information analysis module, a data synchronization mechanism is implemented to update data in real time among multiple nodes. At the same time, a standardized data interface is provided to provide different market entities with access and query data functions based on the data interface.

[0060] The intelligent electricity purchase suggestion module is used to integrate user data and electricity price forecast results based on the analysis results of the electricity consumption information analysis module, and provide users with personalized electricity purchase suggestions based on the user's historical electricity consumption data and electricity price forecast factors;

[0061] Visual display module, used to display the processed data in the form of charts and provide interactive query and export functions;

[0062] The system monitoring module is used to monitor the system operation status in real time, establish an alarm mechanism, automatically send alarm information to the administrator when the system is abnormal, and perform regular system maintenance and upgrades.

[0063] In the above embodiment, the power consumption information of power users is collected in real time through the Internet of Things technology and smart meters and sensors, ensuring the timeliness and accuracy of the data. The real-time update and synchronization mechanism of data enables various market entities to quickly obtain the latest and most accurate data, providing strong support for decision-making. The use of the big data framework to conduct in-depth analysis of the processed data can dig out hidden power consumption patterns and load forecast information, as well as the changing trend of market electricity prices. It has important reference value for participants in the power market and helps them formulate more scientific and reasonable power purchase and sales strategies.

[0064] In the above embodiment, the use of a distributed database system not only improves the storage efficiency of data, but also ensures the security and privacy of data. At the same time, the real-time data synchronization mechanism between multiple nodes makes data access more efficient and reliable, meeting the high requirements of the power market for data real-time performance.

[0065] In the above embodiment, the intelligent electricity purchase suggestion module provides users with personalized electricity purchase suggestions based on the user's historical electricity consumption data and electricity price forecast results, which can help users optimize electricity costs, reduce electricity consumption risks, and improve user satisfaction. The visualization display module intuitively presents complex data in the form of charts, etc., which is convenient for users to compare and analyze. At the same time, the interactive query and export functions provided further enhance the user experience and convenience of use.

[0066] In the above embodiment, the system realizes data sharing and transparency by integrating the data resources of various market players, which helps to break the information asymmetry and promote fair competition in the electricity market, which not only improves the efficiency of electricity trading, but also promotes the healthy development of the electricity market.

[0067] The electricity consumption information analysis module includes:

[0068] A data analysis modeling unit is used to construct a data analysis model, select various power consumption characteristics as analysis variables, use historical power consumption data to train the model, adjust model parameters, conduct in-depth analysis on the pre-processed power consumption data, and extract key power consumption characteristics, wherein the key power consumption characteristics include peak power consumption time and average power consumption;

[0069] The load forecasting unit is used to forecast the power load for a period of time in the future based on historical power consumption data and external factors, wherein the external factors include weather information and holiday information, construct input features of the forecasting model, wherein the input features include historical load values, temperature, humidity, and holiday information, conduct model training, input the model according to the latest data, and generate future load forecast results;

[0070] A market electricity price prediction unit is used to extract and convert the features of the historical data of the market electricity price based on the demand for electricity price prediction, construct characteristic variables that affect the electricity price, and the characteristic variables include the supply-demand ratio, policy indicators, and historical electricity price fluctuations. Based on the characteristic variables, the electricity price prediction model is trained using the historical data, the latest data is input into the model, the future electricity price prediction results are generated, and the prediction results are evaluated using the test set;

[0071] The data integration and optimization unit is used to integrate key electricity consumption characteristics, load forecast results and electricity price forecast results, generate electricity consumption analysis reports, and build user portraits based on users' historical electricity consumption data and electricity price sensitivity information.

[0072] In the above embodiment, by extracting key electricity consumption characteristics and future load forecast results generated by the load forecasting unit, market entities can more accurately predict their own electricity demand and market load changes, thereby formulating more scientific and reasonable electricity purchase and sales strategies, reducing operational risks, and improving economic benefits. By extracting and converting the features of historical electricity price data, building and training an electricity price forecast model, and generating future electricity price forecast results, market entities are provided with predictive information on electricity price changes, which helps them adjust electricity production and consumption plans according to the electricity price forecast results, optimize resource allocation, and reduce the uncertainty caused by price fluctuations.

[0073] In the above embodiment, by integrating the data resources of various market entities, including electricity consumption information of power generation enterprises, power sales companies and electricity customers, data sharing and transparency are achieved, which helps to break information asymmetry, promote fair competition in the power market, and improve market efficiency. By predicting power load and market electricity prices, market entities can better manage electricity costs and power sales risks. For electricity users, they can choose more economical electricity purchase periods based on electricity price forecast results to reduce electricity costs; for power sales companies and power generation companies, they can adjust power generation plans and sales strategies based on load forecast results to avoid losses caused by surplus or shortages.

[0074] Data storage shared modules include:

[0075] A distributed storage unit, used to configure a distributed database and perform distributed storage on the power consumption information data and the analysis results of the power consumption information analysis module;

[0076] The data synchronization unit is used to implement the data synchronization mechanism, synchronize data between multiple nodes in real time based on the active replication synchronization strategy, capture, transmit and apply data in real time, monitor the synchronization process, and regularly evaluate the synchronization performance;

[0077] The data interface unit is used to encapsulate and isolate data and provide a standardized data interface through which different market players can access, query and obtain data;

[0078] The data security management unit is used to implement data encryption, encrypt and store sensitive data, implement access control mechanisms, verify user identities when accessing distributed databases, authorize access to verified users, and regularly audit data security.

[0079] In the above embodiment, by configuring a distributed database through distributed storage units, reliable storage of massive electricity consumption information data and analysis results can be achieved, and it can automatically switch to other nodes when a single node fails to ensure data continuity and availability. At the same time, as the amount of data increases, the storage nodes can be flexibly expanded to improve the scalability of the system.

[0080] In the above embodiment, the data synchronization unit implements a data synchronization mechanism, and synchronizes data between multiple nodes in real time based on an active replication synchronization strategy, which can ensure the consistency of data between multiple nodes and avoid decision-making errors caused by inconsistent data. At the same time, real-time synchronization can ensure the real-time nature of the data and meet the high requirements of the power market for data real-time nature.

[0081] In the above embodiment, by providing a standardized data interface, different market players can equally access and query data, thereby achieving data transparency and fairness, helping to break down information barriers, promote fair competition in the electricity market, and improve market efficiency.

[0082] Smart electricity purchase suggestion module, including:

[0083] A user portrait construction unit is used to obtain the user's electricity consumption behavior portrait constructed by the electricity consumption information analysis module, extract the user's electricity consumption behavior characteristics, and divide the user into different electricity consumption behavior groups;

[0084] The electricity price forecast fusion unit is used to obtain the market electricity price forecast data from the electricity consumption information analysis module, and predict the user's electricity demand in the future based on the user portrait and historical electricity consumption data. It combines the market electricity price forecast results with the user's electricity demand to conduct comprehensive analysis and forecasting, and obtains the user's optimal electricity purchase time and electricity quantity in a specific time period through dynamic planning;

[0085] The power purchase strategy optimization unit is used to generate a power purchase strategy plan based on the output of the power price prediction fusion unit, and generate implementation steps based on the strategy;

[0086] The notification push unit is used to generate power purchase suggestion content according to the suggestion of the power purchase strategy optimization unit, and automatically send the power purchase suggestion notification to the user at a predetermined time by selecting a push channel according to the user's contact information.

[0087] In the above embodiment, through the user portrait construction unit, the system can deeply understand the electricity consumption behavior characteristics of each user, divide them into different electricity consumption behavior groups, and provide more accurate electricity purchase suggestions that are in line with their electricity consumption habits according to the specific needs of each user. By combining the market electricity price forecast data and the user's historical electricity consumption data, through comprehensive analysis and prediction, the best time and amount of electricity purchase for the user in the future can be obtained, which can help users avoid purchasing electricity during peak electricity prices and reduce electricity costs, while ensuring the stability of power supply.

[0088] In the above embodiment, based on the output of the electricity price forecast fusion unit, a specific electricity purchase strategy is generated and implementation steps are provided. This enables users to clearly understand how to adjust the electricity purchase plan according to the changes in market electricity prices and their own electricity demand, realize intelligent electricity purchase management, make the electricity price information and electricity purchase strategy of the power market more transparent, and users can more clearly understand the changing trend of market electricity prices and the optimization direction of electricity purchase strategies, so as to make more reasonable electricity purchase decisions.

[0089] Visual display module, including:

[0090] The chart generation unit is used to extract the data to be displayed from the data storage sharing module according to user requests, convert the cleaned data into the format required for chart display, generate charts based on the processed data, and the charts include line charts, bar charts, pie charts and scatter charts. The processed data is bound to the chart and a data export function is provided.

[0091] Specifically, the visual display module further includes:

[0092] A real-time monitoring module, used for real-time monitoring of the operating parameters of the chart generation unit, wherein the operating parameters include the chart generation processing time, CPU utilization, the number of concurrent chart processing threads and the chart generation data export success rate;

[0093] The first operation evaluation coefficient acquisition module is used to acquire the first operation evaluation coefficient by using the chart generation processing time and the CPU utilization, wherein the first operation evaluation coefficient is acquired by the following formula:

[0094]

[0095] Wherein, Q1 represents the first operation evaluation coefficient; T p represents the average value of the processing time for chart generation; T z Indicates the intermediate value corresponding to the processing time of chart generation; P c Indicates the CPU utilization corresponding to the current chart generation unit;

[0096] A first coefficient comparison module, used for comparing the first operation evaluation coefficient with a preset first operation evaluation coefficient threshold;

[0097] The operation quality determination module is used to determine the operation quality of the chart generation unit by using the number of concurrent chart processing threads and the chart generation data export success rate combined with the first operation evaluation coefficient when the first operation evaluation coefficient exceeds a preset first operation evaluation coefficient threshold.

[0098] The technical effect of the above technical solution is: through the real-time monitoring module, the key operating parameters of the chart generation unit, such as the chart generation processing time, CPU utilization, the number of concurrent chart processing threads and the chart generation data export success rate, can be obtained in real time. Real-time monitoring of these parameters helps to timely discover potential performance bottlenecks or abnormal conditions. The first operation evaluation coefficient acquisition module uses the two key parameters of chart generation processing time and CPU utilization to calculate the first operation evaluation coefficient through the above formula. This coefficient can comprehensively reflect the performance of the chart generation unit in terms of processing speed and resource utilization, and provide a quantitative basis for subsequent evaluation. The first coefficient comparison module compares the calculated first operation evaluation coefficient with the preset first operation evaluation coefficient threshold. Through the setting of dynamic thresholds, the evaluation criteria for the operation status of the chart generation unit can be flexibly adjusted according to different application scenarios and requirements. When the first operation evaluation coefficient exceeds the preset threshold, the operation quality determination module will combine the two parameters of the number of concurrent chart processing threads and the chart generation data export success rate to make a more comprehensive determination of the operation quality of the chart generation unit. This helps to identify the key factors affecting the operation quality and provide guidance for subsequent optimization measures. Through the above technical solution, potential problems in the operation of the chart generation unit can be discovered and handled in a timely manner, thereby effectively improving the stability and reliability of the system. This is of great significance for ensuring the accuracy and real-time performance of data visualization. By real-time monitoring and evaluating the operating status of the chart generation unit, problems that affect the user experience, such as slow chart generation and data export failure, can be discovered and resolved in a timely manner. This helps to improve user satisfaction and trust in the data visualization system.

[0099] In summary, this technical solution achieves comprehensive monitoring and evaluation of the operating status of the chart generation unit through real-time monitoring, comprehensive operation evaluation, dynamic threshold comparison and judgment, and operation quality judgment and optimization, which helps to improve the stability and reliability of the system and optimize the user experience.

[0100] Specifically, the operation quality determination module includes:

[0101] A parameter extraction module, used for retrieving the number of concurrent chart processing threads and the chart generation data export success rate in the operation parameters when the first operation evaluation coefficient exceeds a preset first operation evaluation coefficient threshold;

[0102] A second operation evaluation coefficient acquisition module, used to obtain a second operation evaluation coefficient by combining the number of concurrent chart processing threads and the chart generation data export success rate with the first operation evaluation coefficient;

[0103] The second operation evaluation coefficient is obtained by the following formula:

[0104]

[0105] Wherein, Q2 represents the second operation evaluation coefficient; Q1 represents the first operation evaluation coefficient; n represents the number of concurrent chart processing threads of the current chart generation unit; P s Indicates the success rate of exporting chart generation data of the current chart generation unit;

[0106] A second coefficient comparison module, used for comparing the second operation evaluation coefficient with a preset second operation evaluation coefficient threshold;

[0107] The abnormality determination and alarm module is used to determine that the operation quality of the chart generating unit is abnormal and issue an abnormality alarm when the second operation evaluation coefficient is lower than a preset second operation evaluation coefficient threshold.

[0108] The technical effect of the above technical solution is: when the first operation evaluation coefficient exceeds the preset threshold, the parameter extraction module will retrieve more detailed operation parameters (the number of concurrent processing threads of the chart and the success rate of exporting the chart generation data) for a more in-depth evaluation. This refined evaluation helps to more accurately identify the key factors affecting the operation quality of the chart generation unit. The second operation evaluation coefficient acquisition module uses the three parameters of the first operation evaluation coefficient, the number of concurrent processing threads of the chart and the success rate of exporting the chart generation data to calculate the second operation evaluation coefficient through the above formula. This coefficient can comprehensively reflect the overall performance of the chart generation unit in terms of processing speed, resource utilization, concurrent processing capability and data export.

[0109] The second coefficient comparison module compares the calculated second operation evaluation coefficient with the preset second operation evaluation coefficient threshold. By setting the dynamic threshold, the evaluation criteria for the operation quality of the chart generation unit can be flexibly adjusted according to different application scenarios and requirements. When the second operation evaluation coefficient is lower than the preset threshold, the abnormality determination and alarm module determines that the operation quality of the chart generation unit is abnormal and performs an abnormal alarm. This timely abnormality determination and alarm mechanism helps to quickly discover and solve potential problems and prevent the problem from further deteriorating. Through the above technical solution, potential problems in the operation process of the chart generation unit can be discovered and handled in a timely manner, thereby effectively improving the stability and reliability of the system. This is of great significance for ensuring the accuracy and real-time performance of data visualization. By monitoring and evaluating the number of concurrent processing threads of the chart and the success rate of exporting chart generation data, resource allocation can be optimized to ensure that the chart generation unit can still maintain stable performance in high concurrency scenarios. Through the automated abnormality determination and alarm mechanism, the time for manual intervention and troubleshooting can be reduced, thereby reducing the maintenance cost of the system.

[0110] In summary, this technical solution achieves comprehensive monitoring and evaluation of the operating quality of the chart generation unit through measures such as refined assessment, comprehensive operation evaluation, dynamic threshold comparison, abnormality judgment and alarm, which helps to improve the stability and reliability of the system, optimize resource allocation, and reduce maintenance costs.

[0111] In the above embodiment, complex data is converted into an intuitive chart form, which can clearly show the trend, proportion and distribution of the data, help users quickly understand the information behind the data, and improve the efficiency of data interpretation. Through visual display, users can more intuitively see the dynamic changes in the power market, such as electricity price fluctuations, load changes, etc., which helps users to better grasp market trends, formulate more scientific and reasonable power purchasing strategies and sales plans, and provide more powerful support for decision-making.

[0112] In the above embodiment, in the power spot market power purchase transaction data sharing system, the visualization display module not only serves individual users, but also supports information sharing between multiple market entities. By generating standardized charts, different market entities can more easily understand and compare each other's data, promoting the transparency and circulation of market information.

[0113] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A power spot market power purchase transaction data sharing system, characterized in that: include: The electricity consumption data collection module is used to collect electricity consumption information of power users in real time through smart meters and sensors, clean the collected raw data of electricity consumption information, perform data standardization and formatting, and store the cleaned data in a temporary database; The power consumption information includes power, voltage, current and power factor; The power consumption information analysis module is used to perform distributed processing on the power consumption data in the temporary database, build a data analysis model to perform in-depth analysis on the power consumption data, extract power consumption patterns and load forecast information, make market electricity price forecasts, and store the analysis and forecast results in the data storage sharing module; The data storage sharing module is used to build a distributed database system. Based on the distributed database storage and the analysis results of the power consumption information analysis module, a data synchronization mechanism is implemented to update data in real time among multiple nodes, and a standardized data interface is provided. Based on the data interface, different market entities are provided with access and query data functions; The intelligent electricity purchase suggestion module is used to integrate user data and electricity price forecast results based on the analysis results of the electricity consumption information analysis module, and provide users with personalized electricity purchase suggestions based on the user's historical electricity consumption data and electricity price forecast factors; Visual display module, used to display the processed data in the form of charts and provide interactive query and export functions; The system monitoring module is used to monitor the system operation status in real time, establish an alarm mechanism, automatically send alarm information to the administrator when the system is abnormal, and perform regular system maintenance and upgrades.

2. A power spot market power purchase transaction data sharing system as claimed in claim 1, characterized in that: The power consumption information analysis module comprises: A data analysis modeling unit is used to construct a data analysis model, select various power consumption characteristics as analysis variables, use historical power consumption data to train the model, adjust model parameters, conduct in-depth analysis on the pre-processed power consumption data, and extract key power consumption characteristics; the key power consumption characteristics include peak power consumption time and average power consumption; The load forecasting unit is used to forecast the power load in the future based on the historical power consumption data and external factors; the external factors include weather information and holiday information, construct the input features of the forecasting model, the input features include historical load values, temperature, humidity, holiday information, conduct model training, input the model according to the latest data, and generate the future load forecast results; The market electricity price prediction unit is used to extract and convert the features of the historical data of the market electricity price based on the demand for electricity price prediction, and construct characteristic variables that affect the electricity price; the characteristic variables include the supply-demand ratio, policy indicators, and historical electricity price fluctuations. The electricity price prediction model is trained using historical data based on the characteristic variables, the latest data is input into the model, the future electricity price prediction results are generated, and the prediction results are evaluated using a test set.

3. A power spot market power purchase transaction data sharing system as claimed in claim 2, characterized in that: The power consumption information analysis module further includes: The data integration and optimization unit is used to integrate key electricity consumption characteristics, load forecast results and electricity price forecast results, generate electricity consumption analysis reports, and build user portraits based on users' historical electricity consumption data and electricity price sensitivity information.

4. A power spot market power purchase transaction data sharing system as claimed in claim 1, characterized in that: The data storage sharing module includes: A distributed storage unit, used to configure a distributed database and perform distributed storage on the power consumption information data and the analysis results of the power consumption information analysis module; The data synchronization unit is used to implement the data synchronization mechanism, synchronize data between multiple nodes in real time based on the active replication synchronization strategy, capture, transmit and apply data in real time, monitor the synchronization process, and regularly evaluate the synchronization performance.

5. A power spot market power purchase transaction data sharing system as claimed in claim 4, characterized in that: The data storage sharing module further includes: The data interface unit is used to encapsulate and isolate data and provide a standardized data interface through which different market players can access, query and obtain data; The data security management unit is used to implement data encryption, encrypt and store sensitive data, implement access control mechanisms, verify user identities when accessing distributed databases, authorize access to verified users, and regularly audit data security.

6. A power spot market power purchase transaction data sharing system as claimed in claim 1, characterized in that: The intelligent electricity purchase suggestion module includes: A user portrait construction unit is used to obtain the user's electricity consumption behavior portrait constructed by the electricity consumption information analysis module, extract the user's electricity consumption behavior characteristics, and divide the user into different electricity consumption behavior groups; The electricity price forecast fusion unit is used to obtain market electricity price forecast data from the electricity consumption information analysis module, predict the user's electricity demand in the future based on the user portrait and historical electricity consumption data, conduct comprehensive analysis and forecast based on the market electricity price forecast results and the user's electricity demand, and obtain the best time and amount of electricity purchase for the user in a specific time period through dynamic planning.

7. A power spot market power purchase transaction data sharing system as claimed in claim 6, characterized in that: The intelligent electricity purchase suggestion module further includes: The power purchase strategy optimization unit is used to generate a power purchase strategy plan based on the output of the power price prediction fusion unit, and generate implementation steps based on the strategy; The notification push unit is used to generate power purchase suggestion content according to the suggestion of the power purchase strategy optimization unit, and automatically send the power purchase suggestion notification to the user at a predetermined time by selecting a push channel according to the user's contact information.

8. The power spot market power purchase transaction data sharing system according to claim 1, characterized in that: The visual display module includes: The chart generation unit is used to extract the data to be displayed from the data storage sharing module according to user requests, convert the cleaned data into the format required for chart display, generate charts based on the processed data, and the charts include line charts, bar charts, pie charts and scatter charts. The processed data is bound to the chart and a data export function is provided.

9. A power spot market power purchase transaction data sharing system as claimed in claim 8, characterized in that: The visual display module further includes: A real-time monitoring module, used for real-time monitoring of the operating parameters of the chart generation unit, wherein the operating parameters include the chart generation processing time, CPU utilization, the number of concurrent chart processing threads and the chart generation data export success rate; The first operation evaluation coefficient acquisition module is used to acquire the first operation evaluation coefficient by using the chart generation processing time and the CPU utilization, wherein the first operation evaluation coefficient is acquired by the following formula: Wherein, Q1 represents the first operation evaluation coefficient; T p represents the average value of the processing time for chart generation; T z Indicates the intermediate value corresponding to the processing time of chart generation; P c Indicates the CPU utilization corresponding to the current chart generation unit; A first coefficient comparison module, used for comparing the first operation evaluation coefficient with a preset first operation evaluation coefficient threshold; The operation quality determination module is used to determine the operation quality of the chart generation unit by using the number of concurrent chart processing threads and the chart generation data export success rate combined with the first operation evaluation coefficient when the first operation evaluation coefficient exceeds a preset first operation evaluation coefficient threshold.

10. A power spot market power purchase transaction data sharing system as claimed in claim 9, characterized in that: The operation quality determination module comprises: A parameter extraction module, used for retrieving the number of concurrent chart processing threads and the chart generation data export success rate in the operation parameters when the first operation evaluation coefficient exceeds a preset first operation evaluation coefficient threshold; A second operation evaluation coefficient acquisition module, used to obtain a second operation evaluation coefficient by combining the number of concurrent chart processing threads and the chart generation data export success rate with the first operation evaluation coefficient; The second operation evaluation coefficient is obtained by the following formula: Wherein, Q2 represents the second operation evaluation coefficient; Q1 represents the first operation evaluation coefficient; n represents the number of concurrent chart processing threads of the current chart generation unit; P s Indicates the success rate of exporting chart generation data of the current chart generation unit; A second coefficient comparison module, used for comparing the second operation evaluation coefficient with a preset second operation evaluation coefficient threshold; The abnormality determination and alarm module is used to determine that the operation quality of the chart generating unit is abnormal and issue an abnormality alarm when the second operation evaluation coefficient is lower than a preset second operation evaluation coefficient threshold.

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