CN116912003A - Multi-transaction variety-oriented power resource scheduling method and system - Google Patents
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Abstract
The application discloses a power resource scheduling method and a power resource scheduling system for multiple transaction varieties, which are applied to the technical field of data processing, wherein the method comprises the following steps: and acquiring a transaction variety feature set by acquiring a power transaction variety library. And inputting the transaction variety feature set into a transaction risk assessment model to carry out transaction risk assessment. And connecting the power resource scheduling system, and outputting a dynamic scheduling resource library according to the historical resource scheduling information of the power resource scheduling system. And carrying out conditional probability calculation by using the N transaction risks and the dynamic scheduling resource library, and outputting N resource deletion probabilities. And carrying out balance algorithm identification on each transaction variety in the electric power transaction variety library according to the N resource deletion probabilities, and outputting a balance decision result. And controlling the power resources in the dynamic scheduling resource library according to the balanced decision result. The method and the device solve the technical problems that in the prior art, the scheduling stability of the power resources is poor and the total scheduling amount is inaccurate when the power resources are scheduled.
Description
Technical Field
The application relates to the field of data processing, in particular to a power resource scheduling method and system for multiple transaction varieties.
Background
The scheduling of the power resources is a management means adopted for ensuring the safe and stable operation of the power grid and orderly carrying out the power production work. However, in the prior art, the power resource scheduling is relatively fixed for different power transaction varieties, and when power resource scheduling is performed, the problem that the power resource scheduling stability is poor and the scheduling total amount cannot be ensured exists.
Therefore, in the prior art, the scheduling stability of the power resources is poor, and the total scheduling amount cannot be ensured.
Disclosure of Invention
The application solves the technical problems that the scheduling stability of the power resources is poor and the total scheduling amount cannot be ensured in the scheduling of the power resources in the prior art by providing the power resource scheduling method and the power resource scheduling system for multiple transaction varieties.
The application provides a power resource scheduling method for multiple transaction varieties, which comprises the following steps: acquiring a power trade variety library, and extracting features according to each trade variety in the power trade variety library to acquire a trade variety feature set; inputting the transaction variety feature set into a transaction risk assessment model to carry out transaction risk assessment and outputting N transaction risks, wherein the transaction risk assessment model comprises transaction variety demand level, transaction flow maturity and variety market rarity; connecting a power resource scheduling system, and outputting a dynamic scheduling resource library according to historical resource scheduling information of the power resource scheduling system; carrying out conditional probability calculation by using the N transaction risks and the dynamic scheduling resource library, and outputting N resource deletion probabilities, wherein the resource deletion probabilities are power resource deletion probabilities for marking transaction varieties; performing balance algorithm identification on each transaction variety in the electric power transaction variety library according to the N resource deletion probabilities, and outputting a balance decision result; and controlling the power resources in the dynamic scheduling resource library according to the balance decision result.
The application also provides a power resource scheduling system for multiple transaction varieties, which comprises: the variety feature acquisition module is used for acquiring a power trade variety library, and extracting features according to each trade variety in the power trade variety library to acquire a trade variety feature set; the risk assessment module is used for inputting the transaction variety feature set into a transaction risk assessment model to carry out transaction risk assessment and outputting N transaction risks, wherein the transaction risk assessment model comprises transaction variety demand level, transaction flow maturity and variety market rarity; the dynamic scheduling resource library acquisition module is used for connecting the power resource scheduling system and outputting a dynamic scheduling resource library according to the historical resource scheduling information of the power resource scheduling system; the deletion probability acquisition module is used for carrying out conditional probability calculation on the N transaction risks and the dynamic scheduling resource library and outputting N resource deletion probabilities, wherein the resource deletion probabilities are power resource deletion probabilities for marking the transaction varieties; the decision result acquisition module is used for carrying out balance algorithm identification on each transaction variety in the electric power transaction variety library according to the N resource deletion probabilities and outputting a balance decision result; and the scheduling control module is used for controlling the power resources in the dynamic scheduling resource library according to the balance decision result.
The application also provides an electronic device, comprising:
a memory for storing executable instructions;
and the processor is used for realizing the power resource scheduling method for multiple transaction varieties when executing the executable instructions stored in the memory.
The embodiment of the application provides a computer readable storage medium which stores a computer program, and when the program is executed by a processor, the power resource scheduling method for multiple transaction varieties provided by the embodiment of the application is realized.
The power resource scheduling method and the power resource scheduling system for the multi-transaction varieties, which are proposed by the application, are used for acquiring the characteristics set of the transaction varieties by acquiring the power transaction variety library. And inputting the transaction variety feature set into a transaction risk assessment model to carry out transaction risk assessment. And connecting the power resource scheduling system, and outputting a dynamic scheduling resource library according to the historical resource scheduling information of the power resource scheduling system. And carrying out conditional probability calculation by using the N transaction risks and the dynamic scheduling resource library, and outputting N resource deletion probabilities. And carrying out balance algorithm identification on each transaction variety in the electric power transaction variety library according to the N resource deletion probabilities, and outputting a balance decision result. And controlling the power resources in the dynamic scheduling resource library according to the balanced decision result. And by acquiring the balanced decision result, the scheduling control is performed based on the power resources in the dynamic scheduling resource library, so that the scheduling stability and the accuracy of the total scheduling amount of the power resources are improved. The method and the device solve the technical problems that in the prior art, the scheduling stability of the power resources is poor and the total scheduling amount is inaccurate when the power resources are scheduled.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following description will briefly explain the drawings of the embodiments of the present disclosure. It is apparent that the figures in the following description relate only to some embodiments of the present disclosure and are not limiting of the present disclosure.
FIG. 1 is a flow chart of a power resource scheduling method for multiple transaction varieties, which is provided by an embodiment of the application;
FIG. 2 is a schematic flow chart of a dynamic scheduling resource library output by the power resource scheduling method for multiple transaction varieties provided by the embodiment of the application;
fig. 3 is a schematic flow chart of performing equalization algorithm identification in the power resource scheduling method for multiple transaction varieties provided by the embodiment of the application;
fig. 4 is a schematic structural diagram of a system of a power resource scheduling method for multiple transaction varieties according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a system electronic device of a power resource scheduling method for multiple transaction varieties according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a variety characteristic acquisition module 11, a risk assessment module 12, a dynamic scheduling resource library acquisition module 13, a deletion probability acquisition module 14, a decision result acquisition module 15, a scheduling control module 16, a processor 31, a memory 32, an input device 33 and an output device 34.
Detailed Description
Example 1
The present application will be further described in detail with reference to the accompanying drawings, for the purpose of making the objects, technical solutions and advantages of the present application more apparent, and the described embodiments should not be construed as limiting the present application, and all other embodiments obtained by those skilled in the art without making any inventive effort are within the scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is to be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with one another without conflict.
In the following description, the terms "first", "second", "third" and the like are merely used to distinguish similar objects and do not represent a particular ordering of the objects, it being understood that the "first", "second", "third" may be interchanged with a particular order or sequence, as permitted, to enable embodiments of the application described herein to be practiced otherwise than as illustrated or described herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the application only.
While the present application makes various references to certain modules in a system according to embodiments of the present application, any number of different modules may be used and run on a user terminal and/or server, the modules are merely illustrative, and different aspects of the system and method may use different modules.
A flowchart is used in the present application to describe the operations performed by a system according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in order precisely. Rather, the various steps may be processed in reverse order or simultaneously, as desired. Also, other operations may be added to or removed from these processes.
As shown in fig. 1, an embodiment of the present application provides a power resource scheduling method for multiple transaction varieties, where the method includes:
s10: acquiring a power trade variety library, and extracting features according to each trade variety in the power trade variety library to acquire a trade variety feature set;
s20: inputting the transaction variety feature set into a transaction risk assessment model to carry out transaction risk assessment and outputting N transaction risks, wherein the transaction risk assessment model comprises transaction variety demand level, transaction flow maturity and variety market rarity;
s30: connecting a power resource scheduling system, and outputting a dynamic scheduling resource library according to historical resource scheduling information of the power resource scheduling system;
specifically, a power trade variety library is obtained, feature extraction is performed on each trade variety in the power trade variety library, and features of each trade variety are extracted to obtain features of the trade variety, including but not limited to: transaction amount data, power transmission line completion, specific category of transaction power. And acquiring a transaction variety feature set. Inputting the transaction variety feature set into a transaction risk assessment model for transaction risk assessment, and outputting N transaction risks, wherein the N transaction risks are transaction risks existing in N power transaction varieties respectively, the power transaction varieties are power varieties of medium-and-long-term electric quantity transaction developed by market subjects such as power generation enterprises, power selling companies, power large users and the like through bilateral negotiation, centralized competition and the like, and the method comprises the following steps: thermal power, photovoltaic power generation, wind power, water power and other trade varieties. The transaction risk assessment model comprises transaction variety demand level, transaction flow maturity and variety market rarity. When the risk assessment model is assessed, the feature set of the transaction variety is utilized to judge whether each feature in the feature set of the transaction variety meets the preset condition, such as whether the demand degree of the transaction variety meets the preset transaction amount data, and the maturity of the transaction flow is that whether the line for power transmission is perfect after the transaction is completed, so as to judge the power transmission condition. The variety market rarity is whether a specific category of the corresponding power is a rare variety, and when the power is transmitted in a rare variety, the stability of the power may be low, and the power needs to be correspondingly processed in subsequent processing. And then, connecting a power resource scheduling system, and outputting a dynamic scheduling resource library according to the historical resource scheduling information of the power resource scheduling system.
The method S10 provided by the embodiment of the application further comprises the following steps:
s11: through carrying out trade user connection on each trade variety in the electric power trade variety library;
s12: identifying user attributes and user quantity of transaction users corresponding to each transaction variety, and outputting quantitative indexes for identifying the user large-scale degree;
s13: and screening all trade varieties in the electric power trade variety library according to the quantization index to obtain N trade varieties larger than a preset quantization index.
Specifically, when feature extraction is performed according to each transaction variety in the electric power transaction variety library, transaction users corresponding to each transaction variety are obtained by performing transaction user connection on each transaction variety in the electric power transaction variety library. And then, identifying user attributes and user quantity of the transaction users corresponding to each transaction variety, and outputting quantitative indexes for identifying the user large-scale degree. The quantization index for identifying the large degree of the user is a quantization index parameter for reflecting the power demand of the user. Further, screening all trade varieties in the electric power trade variety library according to the quantization indexes to obtain N trade varieties larger than a preset quantization index. Wherein N is a plurality of transaction varieties which are larger than a preset quantization index after screening.
As shown in fig. 2, the method S30 provided by the embodiment of the present application further includes:
s31: connecting the power resource scheduling system, acquiring a first power supply backbone network, and determining a sub-network controlled by the first power supply backbone network;
s32: identifying control authorities of the sub-networks, and determining M sub-networks meeting authority conditions, wherein the authority conditions are preset authority levels capable of controlling sub-network resource scheduling;
s33: and acquiring historical resource scheduling information by the M sub-networks, and outputting the dynamic scheduling resource library.
Specifically, the connection resource library comprises obtaining a first power supply backbone network, and determining a sub-network controlled by the first power supply backbone network, wherein the first power resource scheduling system outputs a backbone network for dynamically scheduling the power supply backbone network to supply power according to historical resource scheduling information of the power resource scheduling system, and the sub-network controlled by the first power supply backbone network is a sub-network controlled and connected with the backbone network. And identifying the control authority of the sub-network, and identifying whether the control authority of the sub-network meets the preset resource scheduling authority. And determining M sub-networks meeting the permission condition, wherein the permission condition is a preset permission level capable of controlling the scheduling of the sub-network resources. And finally, acquiring historical resource scheduling information by the M sub-networks, acquiring the historical resource scheduling information of the M sub-networks, and outputting the dynamic scheduling resource library.
S40: carrying out conditional probability calculation by using the N transaction risks and the dynamic scheduling resource library, and outputting N resource deletion probabilities, wherein the resource deletion probabilities are power resource deletion probabilities for marking transaction varieties;
s50: performing balance algorithm identification on each transaction variety in the electric power transaction variety library according to the N resource deletion probabilities, and outputting a balance decision result;
s60: and controlling the power resources in the dynamic scheduling resource library according to the balance decision result.
Specifically, the N transaction risks and the dynamic scheduling resource library are used for carrying out conditional probability calculation, and N resource deletion probabilities are output, wherein the resource deletion probabilities are power resource deletion probabilities for identifying the transaction varieties. When a certain electric power product is low in risk and stable in product transaction, and when the electric power demand is large, the corresponding supply stability must meet the requirement, otherwise, the probability of resource deficiency is more likely to occur. Further, according to the N resource deletion probabilities, carrying out balance algorithm identification on each transaction variety in the electric power transaction variety library, wherein the balance algorithm is specifically a Nash balance algorithm, and outputting a balance decision result. And controlling the power resources in the dynamic scheduling resource library according to the balance decision result, so as to ensure the stability of power transaction scheduling.
The method S40 provided by the embodiment of the application further comprises the following steps:
s41: and carrying out conditional probability calculation by using the N transaction risks and the dynamic scheduling resource library, and outputting N resource deletion probabilities, wherein the calculation formula of the resource deletion probabilities is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>For the corresponding duty factor of the ith transaction risk, +.>Corresponding transaction risk for the ith transaction variety>Is a sub-network power resource under the condition of->Probability of absence.
Specifically, when the resource deletion probability is acquired, the calculation is performed by a calculation formula of the resource deletion probability, and N resource deletion probabilities are output. Resource(s)The calculation formula of the source deletion probability is as follows:. Wherein (1)>For the corresponding duty factor of the ith transaction risk, +.>Corresponding transaction risk for the ith transaction variety>Is a sub-network power resource under the condition of->Probability of absence. The probability of the power resource deficiency is the power deficiency possibly existing in the power transaction of the corresponding risk variety, and the power consumption stability is ensured by acquiring the probability of the power resource deficiency and further carrying out extra power resource scheduling based on the probability of the power resource deficiency.
As shown in fig. 3, the method S50 provided by the embodiment of the present application further includes:
s51: generating a first probability policy combination according to the N resource deletion probabilities;
s52: defining a second probability policy combination according to the N resource deletion probabilities;
s53: and calculating cost functions of N transaction varieties by using an equalization algorithm and using the first probability strategy combination and the second probability strategy combination, and outputting equalization decision results according to the cost functions, wherein the equalization decision results comprise N resource scheduling parameters corresponding to the N transaction varieties.
Specifically, when the equalization algorithm identification is performed on each transaction variety in the electric power transaction variety library according to the N resource deletion probabilities, a first probability policy combination is generated according to the acquired N resource deletion probabilities. And defining a second probability strategy combination according to the N resource deletion probabilities. And calculating cost functions of N transaction varieties by using an equalization algorithm and using the first probability strategy combination and the second probability strategy combination, and outputting equalization decision results according to the cost functions, wherein the equalization decision results comprise N resource scheduling parameters corresponding to the N transaction varieties.
The method S53 provided by the embodiment of the application further comprises the following steps:
s531: the cost function has the following expression:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>For trade variety->Relative to->Is a cost function of (2);
for the following，/>Combining for a first probability policy; />Is a second probability policy combination which is a lack probability combination and +.>；/>Resource scheduling parameter set for controllable selection of individual trade varieties +.>；/>Is indicated at->Lower trade variety->Select->Probability of->For trade variety->Scheduling parameter set based on said resources>Creating a lost payment cost.
The method S33 provided by the embodiment of the application further comprises the following steps:
s331: analyzing the historical resource scheduling information to obtain power load data;
s332: and separating according to the power load data, outputting power grid load data and user load data, and generating the dynamic scheduling resource library by taking the user load data as dynamic power resources.
Specifically, the historical resource scheduling information is analyzed, and power load data is obtained based on the resource scheduling information, wherein the power load data comprises user power load and power grid load data. And then, separating according to the power load data, outputting power grid load data and user load data, and generating the dynamic scheduling resource library by taking the user load data as dynamic power resources.
According to the technical scheme provided by the embodiment of the application, the characteristics of the trade varieties are extracted by acquiring the electric power trade variety library according to each trade variety in the electric power trade variety library, so that the characteristic set of the trade variety is acquired. And inputting the transaction variety feature set into a transaction risk assessment model to carry out transaction risk assessment, and outputting N transaction risks, wherein the transaction risk assessment model comprises transaction variety demand level, transaction flow maturity and variety market rarity. And connecting the power resource scheduling system, and outputting a dynamic scheduling resource library according to the historical resource scheduling information of the power resource scheduling system. And carrying out conditional probability calculation by using the N transaction risks and the dynamic scheduling resource library, and outputting N resource deletion probabilities, wherein the resource deletion probabilities are power resource deletion probabilities for identifying the transaction varieties. And carrying out balance algorithm identification on each transaction variety in the electric power transaction variety library according to the N resource deletion probabilities, and outputting a balance decision result. And controlling the power resources in the dynamic scheduling resource library according to the balance decision result. And by acquiring the balanced decision result, the scheduling control is performed based on the power resources in the dynamic scheduling resource library, so that the scheduling stability and the accuracy of the total scheduling amount of the power resources are improved. The method and the device solve the technical problems that in the prior art, the scheduling stability of the power resources is poor and the total scheduling amount is inaccurate when the power resources are scheduled.
Example two
Based on the same inventive concept as the power resource scheduling method facing multiple trade varieties in the foregoing embodiments, the present application also provides a system of the power resource scheduling method facing multiple trade varieties, which can be implemented by hardware and/or software, and can be generally integrated in an electronic device, for executing the method provided by any embodiment of the present application. As shown in fig. 4, the system includes:
the variety feature acquisition module 11 is used for acquiring a power trade variety library, and extracting features according to each trade variety in the power trade variety library to acquire a trade variety feature set;
the risk assessment module 12 is configured to input the transaction variety feature set into a transaction risk assessment model for transaction risk assessment, and output N transaction risks, where the transaction risk assessment model includes a transaction variety demand level, a transaction flow maturity, and a variety market rarity;
a dynamic scheduling resource library acquisition module 13, configured to connect to a power resource scheduling system, and output a dynamic scheduling resource library according to historical resource scheduling information of the power resource scheduling system;
the deletion probability obtaining module 14 is configured to perform conditional probability calculation according to the N transaction risks and the dynamic scheduling resource library, and output N resource deletion probabilities, where the resource deletion probability is a power resource deletion probability that identifies a transaction variety to be supplied;
the decision result obtaining module 15 is configured to perform balance algorithm identification on each transaction variety in the power transaction variety library according to the N resource deficiency probabilities, and output a balance decision result;
and the scheduling control module 16 is used for controlling the power resources in the dynamic scheduling resource library according to the balanced decision result.
Further, the dynamic scheduling repository obtaining module 13 is further configured to:
connecting the power resource scheduling system, acquiring a first power supply backbone network, and determining a sub-network controlled by the first power supply backbone network;
identifying control authorities of the sub-networks, and determining M sub-networks meeting authority conditions, wherein the authority conditions are preset authority levels capable of controlling sub-network resource scheduling;
and acquiring historical resource scheduling information by the M sub-networks, and outputting the dynamic scheduling resource library.
Further, the decision result obtaining module 15 is further configured to:
generating a first probability policy combination according to the N resource deletion probabilities;
defining a second probability policy combination according to the N resource deletion probabilities;
and calculating cost functions of N transaction varieties by using an equalization algorithm and using the first probability strategy combination and the second probability strategy combination, and outputting equalization decision results according to the cost functions, wherein the equalization decision results comprise N resource scheduling parameters corresponding to the N transaction varieties.
The cost function has the following expression:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>For trade variety->Relative to->Is a cost function of (2);
for the following，/>Combining for a first probability policy; />Is a second probability policy combination which is a lack probability combination and +.>；/>Resource scheduling parameter set for controllable selection of individual trade varieties +.>；/>Is indicated at->Lower trade variety->Select->Probability of->For trade variety->Scheduling parameter set based on said resources>Creating a lost payment cost.
Further, the deletion probability obtaining module 14 is further configured to:
and carrying out conditional probability calculation by using the N transaction risks and the dynamic scheduling resource library, and outputting N resource deletion probabilities, wherein the calculation formula of the resource deletion probabilities is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>For the corresponding duty factor of the ith transaction risk, +.>Corresponding transaction risk for the ith transaction variety>Is a sub-network power resource under the condition of->Probability of absence.
Further, the breed feature acquisition module 11 is further configured to:
through carrying out trade user connection on each trade variety in the electric power trade variety library;
identifying user attributes and user quantity of transaction users corresponding to each transaction variety, and outputting quantitative indexes for identifying the user large-scale degree;
and screening all trade varieties in the electric power trade variety library according to the quantization index to obtain N trade varieties larger than a preset quantization index.
Further, the dynamic scheduling repository obtaining module 13 is further configured to:
analyzing the historical resource scheduling information to obtain power load data;
and separating according to the power load data, outputting power grid load data and user load data, and generating the dynamic scheduling resource library by taking the user load data as dynamic power resources.
The included units and modules are only divided according to the functional logic, but are not limited to the above-mentioned division, so long as the corresponding functions can be realized; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present application.
Example III
Fig. 5 is a schematic structural diagram of an electronic device provided in a third embodiment of the present application, and shows a block diagram of an exemplary electronic device suitable for implementing an embodiment of the present application. The electronic device shown in fig. 5 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present application. As shown in fig. 5, the electronic device includes a processor 31, a memory 32, an input device 33, and an output device 34; the number of processors 31 in the electronic device may be one or more, in fig. 5, one processor 31 is taken as an example, and the processors 31, the memory 32, the input device 33 and the output device 34 in the electronic device may be connected by a bus or other means, in fig. 5, by bus connection is taken as an example.
The memory 32 is used as a computer readable storage medium for storing software programs, computer executable programs and modules, such as program instructions/modules corresponding to the power resource scheduling method for multiple transaction varieties in the embodiment of the present application. The processor 31 executes various functional applications and data processing of the computer device by running software programs, instructions and modules stored in the memory 32, i.e. implements the above-described multi-transaction variety oriented power resource scheduling method.
Note that the above is only a preferred embodiment of the present application and the technical principle applied. It will be understood by those skilled in the art that the present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the application. Therefore, while the application has been described in connection with the above embodiments, the application is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the application, which is set forth in the following claims.
Claims (10)
1. The power resource scheduling method for the multi-transaction variety is characterized by comprising the following steps:
acquiring a power trade variety library, and extracting features according to each trade variety in the power trade variety library to acquire a trade variety feature set;
inputting the transaction variety feature set into a transaction risk assessment model to carry out transaction risk assessment and outputting N transaction risks, wherein the transaction risk assessment model comprises transaction variety demand level, transaction flow maturity and variety market rarity;
connecting a power resource scheduling system, and outputting a dynamic scheduling resource library according to historical resource scheduling information of the power resource scheduling system;
carrying out conditional probability calculation by using the N transaction risks and the dynamic scheduling resource library, and outputting N resource deletion probabilities, wherein the resource deletion probabilities are power resource deletion probabilities for marking transaction varieties;
performing balance algorithm identification on each transaction variety in the electric power transaction variety library according to the N resource deletion probabilities, and outputting a balance decision result;
and controlling the power resources in the dynamic scheduling resource library according to the balance decision result.
2. The method of claim 1, wherein outputting a dynamic scheduling repository based on historical resource scheduling information of the power resource scheduling system, the method further comprising:
connecting the power resource scheduling system, acquiring a first power supply backbone network, and determining a sub-network controlled by the first power supply backbone network;
identifying control authorities of the sub-networks, and determining M sub-networks meeting authority conditions, wherein the authority conditions are preset authority levels capable of controlling sub-network resource scheduling;
and acquiring historical resource scheduling information by the M sub-networks, and outputting the dynamic scheduling resource library.
3. The method of claim 1, wherein the equalization algorithm identification is performed for each transaction category in the power transaction category library according to the N resource deficiency probabilities, the method further comprising:
generating a first probability policy combination according to the N resource deletion probabilities;
defining a second probability policy combination according to the N resource deletion probabilities;
and calculating cost functions of N transaction varieties by using an equalization algorithm and using the first probability strategy combination and the second probability strategy combination, and outputting equalization decision results according to the cost functions, wherein the equalization decision results comprise N resource scheduling parameters corresponding to the N transaction varieties.
4. A method as claimed in claim 3, wherein the cost function has the following expression:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>For trade variety->Relative to->Is a cost function of (2);
for the following，/>Combining for a first probability policy; />Is a second probability policy combination which is a lack probability combination and +.>；/>Resource scheduling parameter set for controllable selection of individual trade varieties +.>；/>Is indicated at->Lower trade variety->Select->Probability of->For trade varietiesScheduling parameter set based on said resources>Creating a lost payment cost.
5. The method of claim 4, wherein the N transaction risks and the dynamic state are usedAnd (3) carrying out conditional probability calculation on the scheduling resource library, and outputting N resource deletion probabilities, wherein the calculation formula of the resource deletion probabilities is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>For the corresponding duty factor of the ith transaction risk, +.>Corresponding transaction risk for the ith transaction variety>Is a sub-network power resource under the condition of->Probability of absence.
6. The method of claim 1, wherein the feature extraction is performed based on each transaction category in the power transaction category library, the method comprising:
through carrying out trade user connection on each trade variety in the electric power trade variety library;
identifying user attributes and user quantity of transaction users corresponding to each transaction variety, and outputting quantitative indexes for identifying the user large-scale degree;
and screening all trade varieties in the electric power trade variety library according to the quantization index to obtain N trade varieties larger than a preset quantization index.
7. The method of claim 1, wherein outputting a dynamic scheduling repository based on historical resource scheduling information of the power resource scheduling system, the method further comprising:
analyzing the historical resource scheduling information to obtain power load data;
and separating according to the power load data, outputting power grid load data and user load data, and generating the dynamic scheduling resource library by taking the user load data as dynamic power resources.
8. A multi-transaction variety oriented power resource scheduling system, the system comprising:
the variety feature acquisition module is used for acquiring a power trade variety library, and extracting features according to each trade variety in the power trade variety library to acquire a trade variety feature set;
the risk assessment module is used for inputting the transaction variety feature set into a transaction risk assessment model to carry out transaction risk assessment and outputting N transaction risks, wherein the transaction risk assessment model comprises transaction variety demand level, transaction flow maturity and variety market rarity;
the dynamic scheduling resource library acquisition module is used for connecting the power resource scheduling system and outputting a dynamic scheduling resource library according to the historical resource scheduling information of the power resource scheduling system;
the deletion probability acquisition module is used for carrying out conditional probability calculation on the N transaction risks and the dynamic scheduling resource library and outputting N resource deletion probabilities, wherein the resource deletion probabilities are power resource deletion probabilities for marking the transaction varieties;
the decision result acquisition module is used for carrying out balance algorithm identification on each transaction variety in the electric power transaction variety library according to the N resource deletion probabilities and outputting a balance decision result;
and the scheduling control module is used for controlling the power resources in the dynamic scheduling resource library according to the balance decision result.
9. An electronic device, the electronic device comprising:
a memory for storing executable instructions;
a processor for implementing the multi-transaction variety oriented power resource scheduling method of any one of claims 1 to 7 when executing the executable instructions stored in the memory.
10. A computer readable medium having stored thereon a computer program, which when executed by a processor implements a multi-transaction variety oriented power resource scheduling method according to any of claims 1-7.
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