Real-time operation and maintenance monitoring and early warning system for electric power transaction platform

Abstract

The invention discloses a real-time operation and maintenance monitoring and early warning system for a power transaction platform, which relates to the technical field of operation and maintenance monitoring, and comprises the step of analyzing previous transaction information to identify a mark of a transaction peak segment and a dynamic trend of a power transaction market so as to identify power transaction information and a transaction peak segment condition in future time. The resource demand level is evaluated in combination with the basic information of the transaction peak segment and the risk condition, a resource allocation strategy in the future target time is generated according to the resource demand level, and the risk condition in the transaction and the transaction peak segment condition are combined to jointly analyze the resource demand in the future time. The operation and maintenance monitoring and early warning of the power transaction platform are realized by integrating the transaction information difference and the actual operation state information, the potential unstable condition reflected by the transaction information difference is combined with the operation state information, the operation and maintenance condition of the platform is reflected through the monitoring index, the adaptability and accuracy of the operation and maintenance monitoring are improved, and the operation and maintenance efficiency is improved. And safe and stable operation of the power transaction platform is ensured.

Description

Technical Field

[0001] The present invention relates to the field of operation and maintenance monitoring technology, and in particular to a real-time operation and maintenance monitoring and early warning system for an electric power trading platform. Background Art

[0002] Due to the current demand for efficient, safe and stable operation in the power industry, with the rapid development of advanced information technologies such as cloud computing, big data, and the Internet of Things, the power industry is gradually realizing digital transformation. In this context, the power trading platform needs to integrate advanced operation and maintenance monitoring and early warning systems to grasp the operating status of the trading platform in real time and promptly discover and deal with potential problems. This solution collects and analyzes various types of data on the power trading platform, including transaction information, system performance, network status, etc., and uses intelligent algorithms for prediction and early warning to ensure the safety and smooth progress of power trading. At the same time, through the visual management interface, operation and maintenance personnel can intuitively understand the platform status, quickly respond to various abnormal situations, and improve operation and maintenance efficiency and the overall stability of the power trading platform.

[0003] In the existing technology, the changes in the electricity market are not taken into account, and the platform allocates resources in a fixed manner, resulting in the operating status information not being able to fully reflect the operation and maintenance situation, which reduces the adaptability and accuracy of operation and maintenance monitoring and is not conducive to platform early warning management.

[0004] Therefore, how to improve the adaptability and accuracy of operation and maintenance monitoring is a technical problem that needs to be solved. Summary of the invention

[0005] The purpose of the present invention is to solve the problem of reduced adaptability and accuracy of operation and maintenance monitoring in the prior art, and to propose a real-time operation and maintenance monitoring and early warning system for a power trading platform, which includes:

[0006] The analysis module is used to obtain the past transaction information of the power trading platform, analyze the past transaction information to identify the signs of the transaction peak and the dynamic trend of the power trading market, and predict the standard transaction information of the power trading platform at the future target time through the dynamic trend of the power trading market;

[0007] An evaluation module is used to confirm the basic information of the trading peak segment in the standard trading information by using the mark of the trading peak segment, determine the risk situation involved in the standard trading information, and evaluate the resource demand level in combination with the basic information of the trading peak segment and the risk situation;

[0008] A generation module is used to generate a resource allocation strategy at a future target time according to the resource demand level, and to configure resources for the power trading platform at the future target time through the resource allocation strategy;

[0009] The monitoring module is used to obtain the actual transaction information and actual operating status information of the power trading platform at the target time, compare the actual transaction information with the standard transaction information to determine the transaction information difference, and comprehensively integrate the transaction information difference and the actual operating status information to realize the operation and maintenance monitoring and early warning of the power trading platform.

[0010] In some embodiments of the present application, the analysis module includes a first identification unit and a second identification unit. The first identification unit is used to analyze previous transaction information to identify the signs of transaction peaks, which are specifically:

[0011] The transaction peaks include high peaks, mid-peaks, and low peaks. Past transaction information includes transaction volume and transaction price.

[0012] Plotting the changes of trading volume and trading price over time to obtain a trading volume curve graph and a trading price curve graph, evenly splitting the curves in the trading volume curve graph and the trading price curve graph into multiple partial curve graphs, marking the three time periods of the peak section, the middle peak section and the low peak section in the partial curve graph according to the preset trading volume and trading price, calculating the slope average value, trading volume average value, and trading price average value in the three time periods of the peak section, the middle peak section and the low peak section of each partial curve graph, comprehensively determining the overall slope average value in the three time periods of the peak section, the middle peak section and the low peak section of each partial curve graph, and determining the volatility coefficients of the trading volume and trading price in the three time periods according to the overall slope average value in the three time periods of the peak section, the middle peak section and the low peak section;

[0013] For trading volume, the landmark trading volume in the peak period, mid-peak period and low-peak period is determined by the average trading volume and its fluctuation coefficient.

[0014] For the transaction price, the mark transaction price in the peak period, mid-peak period and low-peak period is determined by the average transaction price and its fluctuation coefficient, and the change degree of the mark transaction price in the three periods is obtained according to the mapping of the fluctuation coefficient of the transaction price;

[0015] The landmark transaction volume, landmark transaction price and landmark transaction price change are used as the symbols of the three time periods: peak period, mid-peak period and low-peak period.

[0016] In some embodiments of the present application, the second identification unit is used to analyze previous transaction information to identify the dynamic trend of the power transaction market, which is specifically:

[0017] In the past, transaction information also included macroeconomic indicator data, weather data and electricity market data. The power demand was analyzed based on the macroeconomic indicator data and weather data, and the supply and demand relationship of the power market was analyzed based on the power market data.

[0018] The macroeconomic indicator data, weather data, electricity market data, electricity demand, and supply and demand relationship of the electricity market are integrated to form a comprehensive electricity trading market analysis data set, and a dynamic trend prediction model for the electricity trading market is established based on the comprehensive electricity trading market analysis data set.

[0019] In some embodiments of the present application, the evaluation module includes a determination unit, a risk unit and a demand unit. The determination unit is used to confirm the basic information of the transaction peak segment in the standard transaction information by means of the mark of the transaction peak segment, which is specifically:

[0020] The characteristics of the power trading market on the power trading platform are analyzed through the power trading market dynamic trend prediction model, and the priorities of the symbol trading volume, symbol trading price and symbol trading price change are allocated according to the characteristics of the power trading market. The basic information of the trading peak segment is identified in the standard trading information through the symbols of the three time periods of peak segment, medium peak segment and low peak segment and the priority corresponding to each symbol;

[0021] The basic information of trading peaks includes peak type, peak time, peak price and peak trading volume.

[0022] In some embodiments of the present application, the risk unit is used to determine the risk situation involved in the standard transaction information, which is specifically:

[0023] Analyze the standard transaction information to generate multiple transaction risks, the risk level of each transaction risk, and the matching relationship between the standard transaction information timestamp and the transaction risk; establish a standard transaction information curve that changes over time based on the standard transaction information; and mark the transaction risk types and the risk levels of the transaction risks at corresponding positions on the standard transaction information curve through the matching relationship between the standard transaction information timestamp and the transaction risk.

[0024] In some embodiments of the present application, the demand unit is used to evaluate the resource demand level in combination with the basic information of the transaction peak segment and the risk situation, which is specifically:

[0025] Calculate the total value of peak segment transaction volume, total duration of peak segment, and change degree of peak segment transaction price under each peak segment type on the standard transaction information curve, and evaluate the resource demand level corresponding to each peak segment based on the total value of peak segment transaction volume, total duration of peak segment, change degree of peak segment transaction price, the type of transaction risk corresponding to the peak segment, and the risk level of transaction risk;

[0026]

[0027] Among them, R i is the resource demand level of the ith peak segment on the standard transaction information curve, Q1 i 、Q2 i 、Q3 iare the total value of peak transaction volume, the total duration of the peak, and the change of peak transaction price, respectively. δ1 and δ2 are the resource conversion coefficients corresponding to the total value of peak transaction volume and the total duration of the peak, respectively. β1 and β2 are the conversion coefficients of the risk level of transaction risk and the change of peak transaction price, respectively. n is the number of transaction risk types of the i-th peak. α j is the weight corresponding to the jth transaction risk type, E i，j is the risk level of the jth transaction risk type under the i-th peak segment, k is a preset constant, L i is the peak segment type under the i-th peak segment, τ i is a constant obtained by mapping the peak segment type under the i-th peak segment.

[0028] In some embodiments of the present application, the generation module is used to generate a resource allocation strategy at a future target time according to the resource demand level, which is specifically:

[0029] The various resources on the power trading platform are classified, and the combination of the quantity of each type of resources is determined according to the trading peak type and the resource demand level. The combination of the quantity of each type of resources is used as the resource allocation strategy for each trading peak at the future target time.

[0030] In some embodiments of the present application, the monitoring module is used to compare the actual transaction information with the standard transaction information to determine the transaction information difference, which is specifically:

[0031] The transaction elements are determined in the transaction information, and the difference between the actual transaction information and the standard transaction information under each transaction element is calculated. The differences of these transaction elements are weighted and averaged to obtain the transaction information difference index, and the transaction information difference index is used to describe the degree of difference in the transaction information.

[0032] In some embodiments of the present application, the monitoring module is used to realize operation and maintenance monitoring and early warning of the power trading platform by integrating the transaction information difference and the actual operation status information, which is specifically:

[0033] The actual operation status information is a variety of operation status indicators, and monitoring indicators are generated based on the multiple operation status indicators and transaction information difference indicators;

[0034]

[0035] Among them, M is the monitoring index, m is the number of operating status indicators, γ a is the reflection weight of the ath operating status indicator, T a is the size of the ath operation status indicator, G is the transaction information difference indicator, and b is a preset constant;

[0036] The operation and maintenance monitoring and early warning of the power trading platform can be realized by analyzing the monitoring indicators.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] 1. Analyze past transaction information to identify the signs of transaction peaks and the dynamic trends of the power trading market, so as to identify future power transaction information and transaction peaks, laying a solid foundation for subsequent monitoring. Combine the basic information of the transaction peak with the risk situation to assess the resource demand level, generate the resource allocation strategy for the future target time according to the resource demand level, combine the risk situation in the transaction with the transaction peak situation to jointly analyze the resource demand in the future, improve the optimization of resource allocation, and ensure the stable operation of the platform.

[0039] 2. Comprehensively integrate transaction information differences and actual operating status information to realize operation and maintenance monitoring and early warning of the power trading platform. Combine the potential instability reflected by transaction information differences with operating status information to generate monitoring indicators. Under appropriate resource allocation, the operation and maintenance status of the platform can be reflected through monitoring indicators, which improves the adaptability and accuracy of operation and maintenance monitoring and ensures the safe and stable operation of the power trading platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a structural diagram of a real-time operation and maintenance monitoring and early warning system for an electric power trading platform proposed by the present invention;

[0041] Figure 2 This is a structural schematic diagram of an analysis module in a real-time operation and maintenance monitoring and early warning system for an electric power trading platform proposed by the present invention;

[0042] Figure 3 This is a structural schematic diagram of an evaluation module in a real-time operation and maintenance monitoring and early warning system for an electric power trading platform proposed by the present invention. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0044] Reference Figure 1 ,A real-time operation and maintenance monitoring and early warning system for power trading platforms. With the continuous development of the power market, the power trading platform, as an important part of the power market, its stability and efficiency are directly related to the fairness, transparency and sustainable development of the market. In order to ensure the normal operation of the power trading platform, it is necessary to establish a real-time operation and maintenance monitoring system. By analyzing the previous transaction information, the transaction peak signs and the dynamic trends of the power trading market are identified, and then the standard transaction information at the future target time is predicted, and resource allocation and operation and maintenance monitoring are carried out accordingly.

[0045] The system includes the following modules,

[0046] The analysis module is used to obtain past transaction information of the power trading platform, analyze the past transaction information to identify the signs of transaction peaks and the dynamic trends of the power trading market, and predict the standard transaction information of the power trading platform at a future target time through the dynamic trends of the power trading market.

[0047] In this embodiment, the past transaction information includes transaction price, transaction volume, time, transaction information of both parties, etc. By analyzing the past transaction information, the signs of transaction peaks can be identified. Transaction peaks are usually characterized by a substantial increase in transaction volume and significant price fluctuations. At the same time, the dynamic trend of the power trading market can also be analyzed in combination with macroeconomic indicators, weather conditions and other factors. For example, in the case of rapid economic growth and extreme temperatures, electricity demand may increase significantly, thereby affecting the trend of the power trading market.

[0048] In some embodiments of the present application, the analysis module includes a first identification unit and a second identification unit. The first identification unit is used to analyze previous transaction information to identify the signs of transaction peaks, which are specifically:

[0049] The transaction peaks include high peaks, mid-peaks, and low peaks. Past transaction information includes transaction volume and transaction price.

[0050] Plotting the changes of trading volume and trading price over time to obtain a trading volume curve graph and a trading price curve graph, evenly splitting the curves in the trading volume curve graph and the trading price curve graph into multiple partial curve graphs, marking the three time periods of the peak section, the middle peak section and the low peak section in the partial curve graph according to the preset trading volume and trading price, calculating the slope average value, trading volume average value, and trading price average value in the three time periods of the peak section, the middle peak section and the low peak section of each partial curve graph, comprehensively determining the overall slope average value in the three time periods of the peak section, the middle peak section and the low peak section of each partial curve graph, and determining the volatility coefficients of the trading volume and trading price in the three time periods according to the overall slope average value in the three time periods of the peak section, the middle peak section and the low peak section;

[0051] For trading volume, the landmark trading volume in the peak period, mid-peak period and low-peak period is determined by the average trading volume and its fluctuation coefficient.

[0052] For the transaction price, the mark transaction price in the peak period, mid-peak period and low-peak period is determined by the average transaction price and its fluctuation coefficient, and the change degree of the mark transaction price in the three periods is obtained according to the mapping of the fluctuation coefficient of the transaction price;

[0053] The landmark transaction volume, landmark transaction price and landmark transaction price change are used as the symbols of the three time periods: peak period, mid-peak period and low-peak period.

[0054] In this embodiment, the average slope of the curve reflects the fluctuation, and different average slopes correspond to different fluctuation coefficients. The mark trading volume is determined by the average trading volume and its fluctuation coefficient, and the mark trading volume is obtained by multiplying the average trading volume and its fluctuation coefficient. The calculation process of other marks is similar. The transaction price change refers to the comprehensive change of the price fluctuation range and frequency. The price fluctuations in the peak period are usually large and frequent, that is, the transaction price change is large.

[0055] It should be noted that the three signs are the thresholds of three parameters, which are used to determine the type of transaction peak segment.

[0056] In some embodiments of the present application, the second identification unit is used to analyze previous transaction information to identify the dynamic trend of the power transaction market, which is specifically:

[0057] In the past, transaction information also included macroeconomic indicator data, weather data and electricity market data. The power demand was analyzed based on the macroeconomic indicator data and weather data, and the supply and demand relationship of the power market was analyzed based on the power market data.

[0058] The macroeconomic indicator data, weather data, electricity market data, electricity demand, and supply and demand relationship of the electricity market are integrated to form a comprehensive electricity trading market analysis data set, and a dynamic trend prediction model for the electricity trading market is established based on the comprehensive electricity trading market analysis data set.

[0059] In this embodiment, macroeconomic indicator data: collect macroeconomic indicator data such as GDP growth rate, industrial added value, and fixed asset investment. The data source can be official data released by the National Bureau of Statistics, government departments, or professional economic research institutions. Weather condition data: collect historical weather data, including temperature, humidity, precipitation, etc. Obtain future weather forecast data to predict the impact of future weather on electricity demand. The data source can be a meteorological department, a professional weather forecast agency, or a public weather data API. Power market data: collect power market-related data such as power generation, transmission capacity, and power reserves. Obtain real-time data such as prices and trading volumes in the power trading market. The data source can be a power regulatory department, a power exchange, or a professional power market research institution. Use statistical software or data analysis tools to analyze the relationship between macroeconomic indicators and power demand, analyze the relationship between historical weather data and power demand, and analyze the supply and demand relationship in the power market, including the matching degree between power generation and demand, and the limitation of transmission capacity. Select appropriate time series analysis, regression analysis, and other methods to establish a dynamic trend prediction model for the power trading market. You can consider introducing machine learning algorithms, such as support vector machine (SVM), random forest or neural network, to improve the accuracy and stability of prediction.

[0060] The evaluation module is used to confirm the basic information of the transaction peak segment in the standard transaction information based on the transaction peak segment mark, determine the risk situation involved in the standard transaction information, and evaluate the resource demand level in combination with the basic information of the transaction peak segment and the risk situation.

[0061] In this embodiment, when the above three signs are used to identify the peak segment type, the identification is successful only when all or part of the three signs are met. This content can be formulated according to the actual situation. These standard transaction information include the expected transaction volume, transaction price, etc., which provide a basis for subsequent resource allocation. Analyze the risks involved in the standard transaction information, such as price fluctuation risk, supply and demand imbalance risk, etc. According to the basic information and risk of the transaction peak segment, evaluate the resource demand level of the power trading platform at the future target time.

[0062] In some embodiments of the present application, the evaluation module includes a determination unit, a risk unit and a demand unit. The determination unit is used to confirm the basic information of the transaction peak segment in the standard transaction information by means of the mark of the transaction peak segment, which is specifically:

[0063] The characteristics of the power trading market on the power trading platform are analyzed through the power trading market dynamic trend prediction model, and the priorities of the symbol trading volume, symbol trading price and symbol trading price change are allocated according to the characteristics of the power trading market. The basic information of the trading peak segment is identified in the standard trading information through the symbols of the three time periods of peak segment, medium peak segment and low peak segment and the priority corresponding to each symbol;

[0064] The basic information of trading peaks includes peak type, peak time, peak price and peak trading volume.

[0065] In this embodiment, different electricity trading markets have different characteristics, such as market size, supply and demand, seasonal changes, etc. These characteristics will affect the importance of each sign in determining the peak segment type. For example, in a market with tight supply and demand, the weight of trading volume may be higher; while in a market with large price fluctuations, the weight of price variability may be more important, so some priority rules are set. The three signs may conflict when identifying the peak segment type. For example, a certain period of time may meet the trading volume and price standards of the peak segment at the same time, but the price variability is closer to the mid-peak segment. In this case, the priorities of the sign trading volume, the sign trading price, and the sign trading price variability are set (different for the three peak segment types).

[0066] In some embodiments of the present application, the risk unit is used to determine the risk situation involved in the standard transaction information, which is specifically:

[0067] Analyze the standard transaction information to generate multiple transaction risks, the risk level of each transaction risk, and the matching relationship between the standard transaction information timestamp and the transaction risk; establish a standard transaction information curve that changes over time based on the standard transaction information; and mark the transaction risk types and the risk levels of the transaction risks at corresponding positions on the standard transaction information curve through the matching relationship between the standard transaction information timestamp and the transaction risk.

[0068] In this embodiment, the standard transaction information is analyzed to identify various transaction risks, including price fluctuation risk, supply and demand imbalance risk, transaction volume risk, user reputation risk, etc. The matching relationship between the standard transaction information timestamp and the transaction risk is the transaction risk analyzed through the transaction information at which time points.

[0069] In some embodiments of the present application, the demand unit is used to evaluate the resource demand level in combination with the basic information of the transaction peak segment and the risk situation, which is specifically:

[0070] Calculate the total value of peak segment transaction volume, total duration of peak segment, and change degree of peak segment transaction price under each peak segment type on the standard transaction information curve, and evaluate the resource demand level corresponding to each peak segment based on the total value of peak segment transaction volume, total duration of peak segment, change degree of peak segment transaction price, the type of transaction risk corresponding to the peak segment, and the risk level of transaction risk;

[0071]

[0072] Among them, R i is the resource demand level of the ith peak segment on the standard transaction information curve, Q1 i 、Q2 i 、Q3 i are the total value of peak transaction volume, the total duration of the peak, and the change of peak transaction price, respectively. δ1 and δ2 are the resource conversion coefficients corresponding to the total value of peak transaction volume and the total duration of the peak, respectively. β1 and β2 are the conversion coefficients of the risk level of transaction risk and the change of peak transaction price, respectively. n is the number of transaction risk types of the i-th peak. α j is the weight corresponding to the jth transaction risk type, E i，j is the risk level of the jth transaction risk type under the i-th peak segment, k is a preset constant, L i is the peak segment type under the i-th peak segment, τ i is a constant obtained by mapping the peak segment type under the i-th peak segment.

[0073] In this embodiment, Indicates that the peak transaction price change and risk level correct the resource value converted from the total value of peak transaction volume and the total duration of the peak. The constants under the scores of different peak types are different.

[0074] Understandably, in general, the greater the risk, the more additional resources may be required.

[0075] The generation module is used to generate a resource allocation strategy at a future target time according to the resource demand level, and configure resources for the power trading platform at the future target time through the resource allocation strategy.

[0076] In some embodiments of the present application, the generation module is used to generate a resource allocation strategy at a future target time according to the resource demand level, which is specifically:

[0077] The various resources on the power trading platform are classified, and the combination of the quantity of each type of resources is determined according to the trading peak type and the resource demand level. The combination of the quantity of each type of resources is used as the resource allocation strategy for each trading peak at the future target time.

[0078] In this embodiment, the resource demand level of the power trading platform at the future target time is evaluated based on the basic information and risk situation of the trading peak period, and the corresponding resource allocation strategy is generated according to the resource demand level, including the configuration of computing resources, network resources, etc.

[0079] The monitoring module is used to obtain the actual transaction information and actual operating status information of the power trading platform at the target time, compare the actual transaction information with the standard transaction information to determine the transaction information difference, and comprehensively integrate the transaction information difference and the actual operating status information to realize the operation and maintenance monitoring and early warning of the power trading platform.

[0080] In this embodiment, after the target time arrives, the actual transaction information and actual operation status information of the power trading platform at the target time are obtained. Then, this information is compared with the previously predicted standard transaction information to determine the difference in transaction information (this difference also reflects the potential operation and maintenance safety situation). At the same time, it is also necessary to pay attention to the actual operation status information, such as system load, response time, etc., to comprehensively evaluate the operation of the power trading platform.

[0081] In some embodiments of the present application, the monitoring module is used to compare the actual transaction information with the standard transaction information to determine the transaction information difference, which is specifically:

[0082] The transaction elements are determined in the transaction information, and the difference between the actual transaction information and the standard transaction information under each transaction element is calculated. The differences of these transaction elements are weighted and averaged to obtain the transaction information difference index, and the transaction information difference index is used to describe the degree of difference in the transaction information.

[0083] In this embodiment, the transaction elements include transaction electricity, transaction price, transaction volume, etc.

[0084] In some embodiments of the present application, the monitoring module is used to realize operation and maintenance monitoring and early warning of the power trading platform by integrating the transaction information difference and the actual operation status information, which is specifically:

[0085] The actual operation status information is a variety of operation status indicators, and monitoring indicators are generated based on the multiple operation status indicators and transaction information difference indicators;

[0086]

[0087] Among them, M is the monitoring index, m is the number of operating status indicators, γ a is the reflection weight of the ath operating status indicator, T a is the size of the ath operation status indicator, G is the transaction information difference indicator, and b is a preset constant;

[0088] The operation and maintenance monitoring and early warning of the power trading platform can be realized by analyzing the monitoring indicators.

[0089] In this embodiment, the operation status information includes multiple aspects such as system load, response time, failure rate, resource utilization, etc., which directly reflect the stability, reliability and performance level of the platform. By monitoring and analyzing this status information, problems and potential risks of the platform can be discovered in a timely manner, providing strong support for operation and maintenance decisions. It indicates the correction of the transaction information difference index to the sum of the running status information, and then takes the class average value (a little larger than the average value) as the monitoring index. When the monitoring index exceeds the preset warning threshold, the warning signal is triggered. The warning signal can be notified to relevant personnel through email, SMS, system pop-up window, etc. According to the level of the warning signal, formulate corresponding emergency response plans, such as starting troubleshooting, adjusting system configuration, optimizing trading strategies, etc.

[0090] Compared with the prior art, the present invention has the following beneficial effects:

[0091] 1. Analyze past transaction information to identify the signs of transaction peaks and the dynamic trends of the power trading market, so as to identify future power transaction information and transaction peaks, laying a solid foundation for subsequent monitoring. Combine the basic information of the transaction peak with the risk situation to assess the resource demand level, generate the resource allocation strategy for the future target time according to the resource demand level, combine the risk situation in the transaction with the transaction peak situation to jointly analyze the resource demand in the future, improve the optimization of resource allocation, and ensure the stable operation of the platform.

[0092] 2. Comprehensively integrate transaction information differences and actual operating status information to realize operation and maintenance monitoring and early warning of the power trading platform. Combine the potential instability reflected by transaction information differences with operating status information to generate monitoring indicators. Under appropriate resource allocation, the operation and maintenance status of the platform can be reflected through monitoring indicators, which improves the adaptability and accuracy of operation and maintenance monitoring and ensures the safe and stable operation of the power trading platform.

[0093] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.

[0094] Those skilled in the art will appreciate that the accompanying drawings are merely schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily required for implementing the present invention.

[0095] Those skilled in the art will appreciate that the modules in the system in the implementation scenario can be distributed in the system of the implementation scenario according to the implementation scenario description, or can be changed accordingly and located in one or more systems different from the implementation scenario. The modules in the above implementation scenario can be combined into one module, or can be further split into multiple submodules.

[0096] 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 real-time operation and maintenance monitoring and early warning system for an electric power trading platform, characterized in that: include, The analysis module is used to obtain the past transaction information of the power trading platform, analyze the past transaction information to identify the signs of the transaction peak and the dynamic trend of the power trading market, and predict the standard transaction information of the power trading platform at the future target time through the dynamic trend of the power trading market; An evaluation module is used to confirm the basic information of the trading peak segment in the standard trading information by using the mark of the trading peak segment, determine the risk situation involved in the standard trading information, and evaluate the resource demand level in combination with the basic information of the trading peak segment and the risk situation; A generation module is used to generate a resource allocation strategy at a future target time according to the resource demand level, and to configure resources for the power trading platform at the future target time through the resource allocation strategy; The monitoring module is used to obtain the actual transaction information and actual operating status information of the power trading platform at the target time, compare the actual transaction information with the standard transaction information to determine the transaction information difference, and comprehensively integrate the transaction information difference and the actual operating status information to realize the operation and maintenance monitoring and early warning of the power trading platform.

2. The real-time operation and maintenance monitoring and early warning system of the power trading platform according to claim 1 is characterized in that: The analysis module includes a first identification unit and a second identification unit. The first identification unit is used to analyze previous transaction information to identify the signs of transaction peaks, which are specifically: The transaction peaks include high peaks, mid-peaks, and low peaks. Past transaction information includes transaction volume and transaction price. Plotting the changes of trading volume and trading price over time to obtain a trading volume curve graph and a trading price curve graph, evenly splitting the curves in the trading volume curve graph and the trading price curve graph into multiple partial curve graphs, marking the three time periods of the peak section, the middle peak section and the low peak section in the partial curve graph according to the preset trading volume and trading price, calculating the slope average value, trading volume average value, and trading price average value in the three time periods of the peak section, the middle peak section and the low peak section of each partial curve graph, comprehensively determining the overall slope average value in the three time periods of the peak section, the middle peak section and the low peak section of each partial curve graph, and determining the volatility coefficients of the trading volume and trading price in the three time periods according to the overall slope average value in the three time periods of the peak section, the middle peak section and the low peak section; For trading volume, the landmark trading volume in the peak period, mid-peak period and low-peak period is determined by the average trading volume and its fluctuation coefficient. For the transaction price, the mark transaction price in the peak period, mid-peak period and low-peak period is determined by the average transaction price and its fluctuation coefficient, and the change degree of the mark transaction price in the three periods is obtained according to the mapping of the fluctuation coefficient of the transaction price; The landmark transaction volume, landmark transaction price and landmark transaction price change are used as the symbols of the three time periods: peak period, mid-peak period and low-peak period.

3. The real-time operation and maintenance monitoring and early warning system of the power trading platform according to claim 2 is characterized in that: The second identification unit is used to analyze past transaction information to identify the dynamic trend of the power transaction market, which is specifically: In the past, transaction information also included macroeconomic indicator data, weather data and electricity market data. The power demand was analyzed based on the macroeconomic indicator data and weather data, and the supply and demand relationship of the power market was analyzed based on the power market data. The macroeconomic indicator data, weather data, electricity market data, electricity demand, and supply and demand relationship of the electricity market are integrated to form a comprehensive electricity trading market analysis data set, and a dynamic trend prediction model for the electricity trading market is established based on the comprehensive electricity trading market analysis data set.

4. The real-time operation and maintenance monitoring and early warning system of the power trading platform according to claim 3 is characterized in that: The evaluation module includes a determination unit, a risk unit and a demand unit. The determination unit is used to confirm the basic information of the transaction peak segment in the standard transaction information by means of the mark of the transaction peak segment, which is specifically: The characteristics of the power trading market on the power trading platform are analyzed through the power trading market dynamic trend prediction model, and the priorities of the symbol trading volume, symbol trading price and symbol trading price change are allocated according to the characteristics of the power trading market. The basic information of the trading peak segment is identified in the standard trading information through the symbols of the three time periods of peak segment, medium peak segment and low peak segment and the priority corresponding to each symbol; The basic information of trading peaks includes peak type, peak time, peak price and peak trading volume.

5. The real-time operation and maintenance monitoring and early warning system of the power trading platform according to claim 4 is characterized in that: The risk unit is used to determine the risk involved in the standard transaction information, which is specifically: Analyze the standard transaction information to generate multiple transaction risks, the risk level of each transaction risk, and the matching relationship between the standard transaction information timestamp and the transaction risk; establish a standard transaction information curve that changes over time based on the standard transaction information; and mark the transaction risk types and the risk levels of the transaction risks at corresponding positions on the standard transaction information curve through the matching relationship between the standard transaction information timestamp and the transaction risk.

6. The real-time operation and maintenance monitoring and early warning system for the power trading platform according to claim 5 is characterized in that: The demand unit is used to evaluate the resource demand level in combination with the basic information of the transaction peak segment and the risk situation, which is specifically: Calculate the total value of peak segment transaction volume, total duration of peak segment, and change degree of peak segment transaction price under each peak segment type on the standard transaction information curve, and evaluate the resource demand level corresponding to each peak segment based on the total value of peak segment transaction volume, total duration of peak segment, change degree of peak segment transaction price, the type of transaction risk corresponding to the peak segment, and the risk level of transaction risk; Among them, R i is the resource demand level of the ith peak segment on the standard transaction information curve, Q1 i 、Q2 i 、Q3 i are the total value of peak transaction volume, the total duration of the peak, and the change of peak transaction price, respectively. δ1 and δ2 are the resource conversion coefficients corresponding to the total value of peak transaction volume and the total duration of the peak, respectively. β1 and β2 are the conversion coefficients of the risk level of transaction risk and the change of peak transaction price, respectively. n is the number of transaction risk types of the i-th peak. α j is the weight corresponding to the jth transaction risk type, E i，j is the risk level of the jth transaction risk type under the i-th peak segment, k is a preset constant, L i is the peak segment type under the i-th peak segment, τ i is a constant obtained by mapping the peak segment type under the i-th peak segment.

7. The real-time operation and maintenance monitoring and early warning system for the power trading platform according to claim 1 is characterized in that: The generation module is used to generate a resource allocation strategy at a future target time according to the resource demand level, which is specifically: The various resources on the power trading platform are classified, and the combination of the quantity of each type of resources is determined according to the trading peak type and the resource demand level. The combination of the quantity of each type of resources is used as the resource allocation strategy for each trading peak at the future target time.

8. The real-time operation and maintenance monitoring and early warning system for the power trading platform according to claim 1 is characterized in that: The monitoring module is used to compare the actual transaction information with the standard transaction information to determine the transaction information difference, which is specifically: The transaction elements are determined in the transaction information, and the difference between the actual transaction information and the standard transaction information under each transaction element is calculated. The differences of these transaction elements are weighted and averaged to obtain the transaction information difference index, and the transaction information difference index is used to describe the degree of difference in the transaction information.

9. The real-time operation and maintenance monitoring and early warning system for the power trading platform according to claim 8 is characterized in that: The monitoring module is used to realize the operation and maintenance monitoring and early warning of the power trading platform by integrating the transaction information difference and the actual operation status information, which is specifically: The actual operation status information is a variety of operation status indicators, and monitoring indicators are generated based on the multiple operation status indicators and transaction information difference indicators; Among them, M is the monitoring index, m is the number of operating status indicators, γ a is the reflection weight of the ath operating status indicator, T a is the size of the ath operation status indicator, G is the transaction information difference indicator, and b is a preset constant; The operation and maintenance monitoring and early warning of the power trading platform can be realized by analyzing the monitoring indicators.

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