CN109308631B - Modeling method for electric power market decision analysis - Google Patents
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Abstract
The invention discloses a modeling method of electric power market decision analysis, which comprises an information interaction device used for collecting electric power market information and output information, a strategy generator used for analyzing various condition transactions and investments of the electric power market information and an electric power market main body, an electric power market decision analyzer used for analyzing the quality of commodity transaction strategies of the electric power market main body, the multi-intelligent-agent user behavior generator is matched with the transaction and investment strategy generator, the electric power market decision analyzer and the scheduling and operation judging device, so that the multi-intelligent-agent user behavior generator generates a plurality of user behavior sets to reflect real market member conditions, and the electric power market information is subjected to decision analysis.
Description
Technical Field
The invention relates to the field of electric power market analysis and research, in particular to a modeling method for electric power market decision analysis.
Background
At present, electric power market simulation software and simulation platforms exist at home and abroad, can assist decision of an electric power market and evaluate the quality of market rules to a certain extent, but do not have decision analysis capability of comprehensively analyzing various commodities of each main body, and analysis results are often inaccurate. The existing formed electric power market software and platforms do not have a complete market member behavior simulation function, the strategy making and decision analysis are obtained only by rigorous mathematical reasoning, the factors such as differentiated behaviors of market main body members and random impulsivity are not considered, the decision making is not considered completely, and the result often cannot really bring positive effects to the benefit maximization and rule correction of each market main body. Existing electric power market decision generation, analysis and evaluation software is usually based on own electric power market simulation software, embedded decision analysis software cannot combine the advantages of each software, an optimization result is often given only to a certain specific situation, however, market behaviors are changeable, a modeling method which is independent of special strategy generation and analysis of each electric power market simulation software is needed, the advantages of each software are integrated, and a decision and optimization scheme is provided through credible market member simulation.
Disclosure of Invention
The embodiment of the invention aims to provide a modeling method for electric power market decision analysis, which can perform specialized processing aiming at different kinds of strategy formulation and optimization of different market subjects, and simultaneously, reflects the real market member condition and performs decision analysis on electric power market information through the mutual cooperation of an information interaction device, a trading and investment strategy generator, an electric power market decision analyzer, an operation information storage device, a scheduling and operation evaluation device and a multi-intelligent agent user behavior generator.
A modeling method of electric power market decision analysis comprises an information interactor for collecting electric power market information and output information, a strategy generator for analyzing various condition transactions and investments of the electric power market information and an electric power market main body, an electric power market decision analyzer for analyzing the quality of commodity transaction strategies of the electric power market main body, an operation information storage for storing various data of electric power market decisions, a scheduling and operating operation evaluator and a multi-intelligent agent user behavior generator, wherein the multi-intelligent agent user behavior generator is matched with the transaction and investment strategy generator, the electric power market decision analyzer and the scheduling and operating operation evaluator, and the method comprises the following steps:
the method comprises the steps that firstly, electric power market information of an external space is sent to an information interaction device, the information interaction device judges the electric power market information, and the electric power market information is selectively sent to a trading and investment strategy generator, an electric power market decision analyzer and a scheduling and operation judging device;
secondly, the investment strategy formulated by the trading and investment strategy generator is sent to a multi-intelligent-agent user behavior generator; the electric power market decision analyzer analyzes the strategy collected by the information interactor and the investment strategy formulated by the trading and investment strategy generator, judges whether the strategy is optimal or not, if not, gives an optimal strategy in a defined range, and sends the optimal strategy to the intelligent agent user behavior generator; the scheduling and operating operation evaluation device evaluates the finished scheduling and operating operation, calculates the economic and technical deviation and the correction path between the actual operating behavior and the theoretical optimal operating behavior, gives out the future trend prejudgment and the corresponding operating instruction suggestion, and sends the operating instruction suggestion to the multi-intelligent agent user behavior generator;
thirdly, the multi-intelligent-agent user behavior generator receives information simulation and generates a market user behavior set, and the investment strategy generator, the electric power market decision analyzer and the scheduling and operating operation evaluation device use a plurality of user behavior sets generated by the multi-intelligent-user behavior generator to judge most accurately;
and fourthly, storing the electric power market information collected by the information interactor, the strategy formulated by the trading and investment strategy generator, the optimal strategy analyzed by the electric power market decision analyzer and the operation instruction suggestion of the scheduling and operating operation judger in an operation information memory.
Preferably, the information interactor comprises: the system comprises an information intake module, an information analysis and classification module and an information transmission module, wherein the information intake module sends the electric power market information to the information analysis and classification module, and the information analysis and classification module sends the information to the information transmission module.
Preferably, the trading and investment strategy generator comprises: the system comprises a power transaction strategy generation module, a Monte Carlo random fault generation module and a power investment strategy generation module, wherein the Monte Carlo random fault generation module automatically generates a random fault set according to model conditions, and the random fault set is sent to the power transaction strategy generation module and the power investment strategy generation module.
Preferably, the power market information includes: short-term and ultra-short-term system load prediction, new energy prediction, maintenance plans of power transmission and transformation equipment and units in different periods, actual system load, tie line power, actual maintenance capacity, load power, system standby, medium and long-term trading contract and plan information, power grid safety constraint information, and clearing results, check results and settlement results of historical spot markets (day-ahead, day-in and real-time markets).
Preferably, the electricity market body includes: power generation enterprises, power selling (distribution) enterprises, power transmission (grid) enterprises, power market transaction operating organizations, power market scheduling organizations, power market monitoring organizations, and power financial market participants; the power generation enterprises and the power selling enterprises can belong to the same community of interest.
Preferably, the investment strategy comprises: new construction investment strategy, technical transformation investment strategy and purchasing investment strategy.
Preferably, the information interactor, the trading and investment strategy generator, the electric power market decision analyzer, the operation information storage, the scheduling and operation judger can be communicated and matched with each other, and can also be operated and operated simultaneously.
Preferably, the algorithm of the multi-intelligent-agent user behavior generator is adaptive, and can be automatically evolved and replaced according to needs.
Compared with the prior art, the modeling method of the electric power market decision analysis disclosed by the invention comprises the steps of providing an information interaction device, a trading and investment strategy generator, an electric power market decision analyzer and an operation information storage, the scheduling and operation judging device and the multiple intelligent agent user behavior generator are matched with each other, the information interaction device judges the electric power market information, and selectively transmits the information to the transaction and investment strategy generator, the electric power market decision analyzer and the scheduling and operation judging device, the multiple intelligent agent user behavior generator is matched with the transaction and investment strategy generator, the electric power market decision analyzer and the scheduling and operation judging device, the obtained user behavior set considers the difference behaviors of market members, the real market member condition is simulated, and the decision analysis of the electric power market information is most accurate.
The operation information storage is used as a database to store all information and strategies, so that the extraction and the application are convenient.
The electric power market decision analyzer analyzes and judges whether the transaction strategy is globally or locally optimal, if not, the optimal strategy in a defined range is given, and the electric power market information is conveniently decided.
The algorithm of the multi-intelligent agent user behavior generator has self-adaptability, and can be automatically evolved and replaced according to the needs.
Drawings
Fig. 1 is a schematic diagram of an electric power market decision analysis system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A modeling method of electric power market decision analysis comprises an information interactor for collecting electric power market information and output information, a strategy generator for analyzing various condition transactions and investments of the electric power market information and an electric power market main body, an electric power market decision analyzer for analyzing the quality of commodity transaction strategies of the electric power market main body, an operation information storage for storing various data of electric power market decisions, a scheduling and operating operation evaluator and a multi-intelligent agent user behavior generator, wherein the multi-intelligent agent user behavior generator is matched with the transaction and investment strategy generator, the electric power market decision analyzer and the scheduling and operating operation evaluator, and the method comprises the following steps:
the method comprises the steps that firstly, electric power market information of an external space is sent to an information interaction device, the information interaction device judges the electric power market information, and the electric power market information is selectively sent to a trading and investment strategy generator, an electric power market decision analyzer and a scheduling and operation judging device;
secondly, the investment strategy formulated by the trading and investment strategy generator is sent to a multi-intelligent-agent user behavior generator; the electric power market decision analyzer analyzes the strategy collected by the information interactor and the investment strategy formulated by the trading and investment strategy generator, judges whether the strategy is optimal or not, if not, gives an optimal strategy in a defined range, and sends the optimal strategy to the intelligent agent user behavior generator; the scheduling and operating operation evaluation device evaluates the finished scheduling and operating operation, calculates the economic and technical deviation and the correction path between the actual operating behavior and the theoretical optimal operating behavior, gives out the future trend prejudgment and the corresponding operating instruction suggestion, and sends the operating instruction suggestion to the multi-intelligent agent user behavior generator;
thirdly, the multi-intelligent-agent user behavior generator receives information simulation and generates a market user behavior set, and the investment strategy generator, the electric power market decision analyzer and the scheduling and operating operation evaluation device use a plurality of user behavior sets generated by the multi-intelligent-user behavior generator to judge most accurately;
and fourthly, storing the electric power market information collected by the information interactor, the strategy formulated by the trading and investment strategy generator, the optimal strategy analyzed by the electric power market decision analyzer and the operation instruction suggestion of the scheduling and operating operation judger in an operation information memory.
As shown in fig. 1, the information interaction device is used for collecting various electric power market information and various situations of each electric power market main body, and also for outputting various information, and is an input port and an output port of an electric power market decision analysis platform based on multiple intelligent agents, as an input port of the platform, and is connected with an input end of a trading and investment strategy generator, an electric power market decision analyzer, an operation information memory, and a scheduling and operation evaluation device, and can recognize input information and send the input information to a specified target module, and also as an output port of the platform, and help to output various kinds of consultation to various target platforms.
The information interaction device comprises: the system comprises an information acquisition module, an information analysis and classification module and an information transmission module. The information acquisition module can acquire various information from an actual running system and also can acquire various information from compatible simulation software or a simulation platform. The method comprises the following steps: short-term and ultra-short-term load conditions and load prediction from an actual system operation system or a simulation operation platform, new energy participation conditions in one year, new energy output prediction conditions in the next day, maintenance plans of power transmission and transformation equipment and units in different periods, tie line power, blocking conditions, actual maintenance capacity, actual load power and load power prediction, actual standby, medium and long-term trading contract and plan information, power grid safety constraint information, and clearing results, checking results and calculation results of historical spot markets (day ahead, day in and real-time markets); various conditions of each market entity are also included.
The information analysis and classification module is connected with the information intake module and is used for analyzing various information intake by the information intake module, screening and classifying the acquired various information according to further operation types; and the information transmission module is connected with the information transmission module to assist in information transmission.
And the information transmission module is connected with the information analysis and classification module, is a sending port of the information interaction device and is used for sending various types of information to the target sub-platform or the analyzer.
And the trading and investment strategy generator is used for analyzing various electric power market information and various conditions of each electric power market main body, formulating each commodity trading strategy of each electric power market main body by utilizing the electric power market information analysis result, and formulating various investment strategies according with the benefits of each electric power market main body based on the electric power market information analysis result and in combination with various conditions of each electric power market main body.
The trading and investment strategy generator comprises a power trading strategy generation module, a Monte Carlo random fault generation module and a power investment strategy generation module. The module has two major functions:
1. for generating per-commodity trading strategies for each market subject; each market body includes: power generation enterprises, power selling (distribution) enterprises, power transmission (grid) enterprises, power market transaction operating organizations, power market scheduling organizations, power market monitoring organizations, and power financial market participants; each commodity transaction strategy includes: the system comprises an electric energy trading strategy, an auxiliary service trading strategy, a capacity market trading strategy, a power transmission right auction strategy and a medium and long term contract trading strategy.
2. Each investment strategy for generating each market entity; each market body includes: power generation enterprises, power selling (distribution) enterprises, power transmission (grid) enterprises, power market transaction operating organizations, power market scheduling organizations, power market monitoring organizations, and power financial market participants; each investment strategy includes: the method comprises the steps of creating, decommissioning, upgrading, overhauling and arranging a set of a power generation enterprise, establishing an electricity selling company strategy, a distribution network investment strategy of the electricity selling enterprise, and a power transmission network planning and investment strategy of a power transmission (power grid) enterprise.
For the transaction policy generated by the module, specifically:
(1) the electric energy trading strategy is that target users are power generation enterprises and power selling enterprises; in the form of electricity and price plans submitted to a trading institution; the generation method comprises the steps that multiple intelligent agents are adopted to simulate user behaviors of other power generation enterprises and power selling enterprises according to historical data, individual income maximization is taken as a target according to load prediction, maintenance conditions, weather information and power grid blocking conditions, and the power selling amount and price of the power generation enterprises, the power buying amount and price of the power selling enterprises and the demand side response declaration price of the power selling enterprises are generated in the day-ahead and day-ahead market declaration; the user behaviors of other power generation enterprises and power selling enterprises comprise the predicted declaration amount and price of other power generation enterprises in the same time period and the predicted declaration amount and price of other power selling enterprises in the same time period.
(2) The auxiliary service trading strategy is that the target user is a power generation enterprise or a power selling enterprise which belongs to the same community as the power generation enterprise and is a interest community; if the auxiliary service trading strategy is only aimed at the power generation enterprises, the auxiliary service trading strategy is in the form of market quotas and prices before and in the day of the auxiliary service; if the auxiliary service strategy is in the form of self-balance amount of the auxiliary service and market application amount and price strategy before and in the day aiming at the interest community simultaneously comprising the electricity selling enterprises; the generation method is that a plurality of intelligent agents are adopted to simulate the user behaviors of other power generation enterprises and power selling enterprises, the auxiliary service declaration amount and price of day-ahead and day-in market declaration of the power generation enterprises and the day-ahead and day-in declaration amount and price of combined benefits of the power generation enterprises and the power selling enterprises and the self-supply auxiliary service declaration amount (reservation amount) are generated according to load prediction, maintenance conditions, weather information and power grid blocking conditions and the random fault condition generated by the Monte Carlo fault generation module. The types of auxiliary service commodities which can be compatible include synchronous standby auxiliary service, asynchronous standby auxiliary service, fast frequency modulation auxiliary service, slow frequency modulation auxiliary service, peak modulation auxiliary service, deep peak modulation auxiliary service, reactive auxiliary service and black start auxiliary service. The function can be interacted with the previous function and can also be acquired from the information interactor, so that the optimization with the electric energy trading strategy together with the goal of maximizing the income is realized.
(3) A capacity market trading strategy, wherein target users are power generation enterprises, power market trading operation mechanisms, power market scheduling mechanisms and power market supervision mechanisms; when the target user is a power generation enterprise, the capacity market trading strategy is in the form of capacity agreement bidding amount, secondary market trading amount and limit price; when the target user is an electric power market transaction operator, an electric power market scheduling mechanism and an electric power market monitoring mechanism, the capacity market transaction strategy is in the form of a capacity quota strategy and a capacity market bidder reward and punishment strategy; the method for generating the capacity market agreement bidding amount and the secondary market trading amount for the power generation enterprise users comprises the steps of simulating agreement bidding expectations and secondary market trading behaviors of other power generation enterprises by using multiple intelligent agents, considering local and local economic development indexes collected by an information interactor according to capacity market quota, reward and punishment policies, and generating the capacity market agreement bidding amount of the power generation enterprise users by taking profit maximization as a target, and an entering price index, a trading amount and a trading cut-off price index in a secondary market; when capacity market strategies are generated for electric power market trading operation mechanisms, scheduling mechanisms and supervision mechanisms, the method is that multiple intelligent agents are used for simulating purchase willingness and performance conditions of power generation enterprises and power selling enterprises in the whole network, and capacity trading limit, price and reward and punishment mechanism strategies of the capacity market are generated by taking stable market operation and sufficient electric power supply as optimization targets according to national and local economic development indexes collected by an information interaction device.
(4) The power transmission right auction strategy comprises the following steps: a power transmission right auction rule strategy which is generated for an electric power market transaction operation mechanism, an electric power market scheduling mechanism and an electric power market supervision mechanism by taking transaction benefits maximization as a target; the bidding position, price and the amount of the final bid are generated for the capacity market participant by taking the maximum individual profit as the target.
(5) The medium-long term contract transaction strategy comprises a medium-long term physical contract strategy and a medium-long term financial contract strategy; the target users are power generation enterprises and power utilization enterprises; in the form of intermediate and long term endorsement amounts and prices; the method is to simulate the expected transaction situation of the spot market through multiple intelligent agents, and generate medium and long term contract signing amount and price on the basis of the principle of maximizing the comprehensive benefits of the medium and long term and spot market.
The electric power market decision analyzer is used for analyzing the advantages and disadvantages of each commodity trading strategy of each electric power market main body, is connected with the information interactor, the trading and investment strategy generator and the operation information storage, can receive each commodity trading strategy of each market main body collected by the information interactor, and also can receive each commodity trading strategy of each market main body generated by the trading and investment strategy generator, analyzes whether each commodity trading strategy of each market main body is globally or locally optimal or not, and if not, gives an optimal strategy in a defined range.
The method for analyzing the quality of each commodity trading strategy of each electric power market main body comprises the steps of simulating other user behaviors by using an intelligent agent, putting the strategy of an analyzed user into compatible electric power market simulation software or a platform, comparing deviation between simulation actual income conditions and ideal income conditions through multi-time scale simulation and calculation, considering the strategy to be the optimal one to use if certain judgment indexes are met, considering the strategy to be re-optimized if the judgment indexes are not met, and optimizing the strategy by using a certain optimization method. The decision index can be automatically generated by the decision analysis, or can be defined and input by the user. The optimization method is that the information of the target user, the information of the original strategy and the information of the response behavior of other market members to the strategy are transmitted to the trading and investment strategy generator by the aid of the information interaction device, and the trading and investment strategy generator comprehensively analyzes and provides a more optimized strategy.
The operation information memory is used for storing various data and information of the electric power market decision analysis platform, is a data storage center of the whole platform, data can be directly transmitted on the platform or called from the memory, and all operations, simulation and emulation can be recorded in the storage center.
The scheduling and operating operation evaluation device is used for evaluating the finished scheduling and operating operation, calculating the economic and technical deviation and the correction path between the actual operating behavior and the theoretical optimal operating behavior, analyzing the operating trends of several times, evaluating and correcting, giving future trend prejudgment and corresponding operating instruction suggestions, connecting the information interaction device and the operating information storage device, collecting the operating information by means of the information interaction device, and having the capability of reading the information in the operating information storage device; the theoretical optimal operation is the operation which is performed according to the habit of each market member and the simulated power market scheduling and operation by using an intelligent agent. The above economic and technical deviations are deviations, expressed as percentages, of the dispatchers, market operators, operating according to existing rules or personal experience, from the optimal operation in terms of profitability, economy and technology. The profit economy can be the profitability of any market subject, and can also be the safety stability and the profitability of power market scheduling, operation and supervision authorities.
The multi-intelligent-agent user behavior generator simulates and generates market user behaviors by using a multi-intelligent-agent technology, and the intelligent agent technology in the multi-intelligent-agent user behavior generator has the capabilities of self evolution and algorithm upgrading and can simulate the user behaviors more accurately along with the frequent use.
The electric power market information includes: short-term and ultra-short-term system load prediction, new energy prediction, maintenance plans of power transmission and transformation equipment and units in different periods, actual system load, tie line power, actual maintenance capacity, load power, system standby, medium and long-term trading contract and plan information, power grid safety constraint information, and clearing results, check results and settlement results of historical spot markets (day-ahead, day-in and real-time markets).
The electric power market body includes: power generation enterprises, power selling (distribution) enterprises, power transmission (grid) enterprises, power market transaction operating organizations, power market scheduling organizations, power market monitoring organizations, and power financial market participants; the power generation enterprises and the power selling enterprises can belong to the same community of interest.
Various conditions of the electricity market body include: the financial income and expenditure status of the last five years, the income expectation of the next five years and the market force level of the subject.
Each of the articles comprises: electric energy, auxiliary service, capacity, power transmission right, medium and long term physical contract and financial price difference contract.
The investment strategy comprises: new construction investment strategy, technical transformation investment strategy and purchasing investment strategy.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (6)
1. A modeling method for electric power market decision analysis is characterized by comprising an information interactor for collecting electric power market information and output information, a strategy generator for analyzing various condition transactions and investments of the electric power market information and an electric power market main body, an electric power market decision analyzer for analyzing the goods transaction strategy advantages and disadvantages of the electric power market main body, an operation information storage for storing various data of electric power market decisions, a scheduling and operation evaluator and a multi-intelligent agent user behavior generator, wherein the multi-intelligent agent user behavior generator is matched with the transaction and investment strategy generator, the electric power market decision analyzer and the scheduling and operation evaluator, and the method comprises the following steps:
the method comprises the steps that firstly, electric power market information of an external space is sent to an information interaction device, the information interaction device judges the electric power market information, and the electric power market information is selectively sent to a trading and investment strategy generator, an electric power market decision analyzer and a scheduling and operation judging device;
secondly, the investment strategy formulated by the trading and investment strategy generator is sent to a multi-intelligent-agent user behavior generator; the electric power market decision analyzer analyzes the strategy collected by the information interactor and the investment strategy formulated by the trading and investment strategy generator, judges whether the strategy is optimal or not, if not, gives an optimal strategy in a defined range, and sends the optimal strategy to the intelligent agent user behavior generator; the scheduling and operating operation evaluation device evaluates the finished scheduling and operating operation, calculates the economic and technical deviation and the correction path between the actual operating behavior and the theoretical optimal operating behavior, gives out the future trend prejudgment and the corresponding operating instruction suggestion, and sends the operating instruction suggestion to the multi-intelligent agent user behavior generator;
thirdly, the multi-intelligent-agent user behavior generator receives information simulation and generates a market user behavior set, and the investment strategy generator, the electric power market decision analyzer and the scheduling and operating operation evaluation device use a plurality of user behavior sets generated by the multi-intelligent-user behavior generator to judge most accurately;
fourthly, the electric power market information collected by the information interactor, the strategy formulated by the trading and investment strategy generator, the optimal strategy analyzed by the electric power market decision analyzer and the operation instruction suggestion of the scheduling and operating operation judger are all stored in an operation information memory;
wherein the trading and investment strategy generator comprises: the system comprises a power transaction strategy generation module, a Monte Carlo random fault generation module and a power investment strategy generation module, wherein the Monte Carlo random fault generation module automatically generates a random fault set according to model conditions, and the random fault set is sent to the power transaction strategy generation module and the power investment strategy generation module;
the information interaction device comprises: the system comprises an information acquisition module, an information analysis and classification module and an information transmission module, wherein the information acquisition module sends the electric power market information to the information analysis and classification module, and the information analysis and classification module sends the information to the information transmission module; wherein,
the information acquisition module is used for acquiring various information from an actual operating system and also used for acquiring various information from compatible simulation software or a simulation platform;
the information analysis and classification module is connected with the information intake module and used for analyzing various types of information acquired by the information intake module, screening and classifying the acquired various types of information according to further operation types, and is connected with the information transmission module to assist information transmission.
2. The modeling method of electric power market decision analysis according to claim 1, wherein the electric power market information comprises: short-term and ultra-short-term system load prediction, new energy prediction, maintenance plans of power transmission and transformation equipment and units in different periods, actual system load, tie line power, actual maintenance capacity, load power, system standby, medium and long-term trading contract and plan information, power grid safety constraint information, and clearing results, check results and settlement results of historical spot markets.
3. The modeling method of electric power market decision analysis according to claim 1, wherein the electric power market subject comprises: power generation enterprises, power selling enterprises, power transmission enterprises, power market transaction operating organizations, power market scheduling organizations, power market monitoring organizations and power financial market participants; the power generation enterprises and the power selling enterprises belong to a same interest community.
4. The modeling method of electric power market decision analysis of claim 1, wherein the investment strategy comprises: new construction investment strategy, technical transformation investment strategy and purchasing investment strategy.
5. The modeling method of electric power market decision analysis according to claim 1, wherein the information interactor, the trading and investment strategy generator, the electric power market decision analyzer, the operation information storage, the scheduling and operation judger can communicate with and cooperate with each other, and can also operate simultaneously.
6. The modeling method of electric power market decision analysis of claim 1, wherein the algorithm of the multiple intelligent agent user behavior generator is adaptive and can evolve automatically and be replaced as needed.
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