Permission fine-grained management method of power transaction auxiliary decision-making system

Abstract

The invention relates to the technical field of power transaction management, and discloses a permission fine-grained management method for a power transaction auxiliary decision-making system, which comprises the following steps of: labeling all computing nodes participating in power transaction, and sequentially acquiring environment parameters and operation parameters of each computing node; the environment influence coefficient of each computing node is calculated in sequence according to the environment parameters of each computing node, the operation influence coefficient of each computing node is calculated in sequence according to the operation parameters of each computing node, and then the safety coefficient of each computing node is calculated by combining the environment influence coefficient and the operation influence coefficient of each computing node; and finally, according to the security coefficient of each computing node, the permission level of each computing node in the power transaction process is allocated, and the purpose is to improve the security and integrity in the power transaction process.

Description

Technical Field

[0001] The present invention relates to the technical field of power transaction management, and in particular to a method for fine-grained management of permissions of a power transaction auxiliary decision-making system. Background Art

[0002] Power trading refers to the activities of buying and selling power commodities in the power market through negotiation, bidding, etc. It is an important means to achieve the optimal allocation of power resources, guide power production and consumption through market-oriented mechanisms, improve power supply efficiency, promote the development of renewable energy, and promote the transformation and upgrading of the power industry.

[0003] With the continuous development of the power market, the security and integrity of data in the power trading system have become increasingly important. The power trading process involves many procedures, including the market preparation stage, the transaction declaration stage, the transaction matching stage, the contract signing stage, and the implementation and settlement stage. In order to ensure the smoothness of the power trading process, multiple network computing nodes are required to participate together, and each network computing node has different permissions in the transaction process. The higher the security level of the computing node, the higher the permission level assigned to the computing node, and the more important the corresponding stage in the power trading process. The existing power trading auxiliary decision-making system lacks the management of the permissions of these computing nodes. Therefore, the present invention provides a fine-grained permission management method for a power trading auxiliary decision-making system. Summary of the invention

[0004] The purpose of the present invention is to provide a method for fine-grained management of permissions in an electric power trading auxiliary decision-making system to solve the above technical problems.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A method for fine-grained management of permissions in an electric power trading auxiliary decision-making system, the method comprising:

[0007] Step S1: Obtain all computing nodes participating in power trading in the management area and number them, the numbers are: 1, 2, ..., n;

[0008] Step S2: Obtain the influencing parameters of all computing nodes;

[0009] Step S3, analyzing the safety factor of each computing node according to the operating parameters of all computing nodes;

[0010] Step S4: Allocate transaction rights to each computing node based on the security factor of each node.

[0011] As a further description of the solution of the present invention, the specific process of step S2 includes:

[0012] Step S21, obtaining the environmental impact parameter of each computing node in turn;

[0013] Step S22: Obtain the operation influencing parameters of each computing node in turn.

[0014] As a further description of the solution of the present invention, the specific process of step S3 includes:

[0015] Step S31, obtaining the environmental impact coefficient of each computing node according to the environmental impact parameter of each computing node obtained in step S21;

[0016] Step S32, obtaining an operation influence coefficient of each computing node according to the operation influence parameter of each computing node obtained in step S21;

[0017] Step S33: construct a mathematical calculation model of the safety factor of each calculation node according to the environmental impact coefficient and the operation impact coefficient obtained in step S31 and step S32.

[0018] As a further description of the solution of the present invention, the specific process of step S31 includes:

[0019] The environmental impact coefficient σ of the i-th computing node is calculated by the following formula: i :

[0020]

[0021] In the formula, σ i is the environmental impact coefficient of the i-th computing node, M is the number of detected environmental parameters, where j belongs to M, E ij is the jth environmental impact parameter of the ith computing node, E ij0 is the jth environmental impact parameter standard value of the i-th computing node preset by the system, k ij is the weight coefficient corresponding to the jth environmental impact parameter of the ith computing node, G ij Reference value of the j-th environmental impact parameter range of the ith computing node.

[0022] As a further description of the solution of the present invention, the specific process of step S32 includes:

[0023] First, the operation impact coefficient of the i-th computing node during the peak trading period is calculated by the following formula:

[0024]

[0025] Then, the operation impact coefficient of the i-th computing node during the trading trough period is calculated by the following formula:

[0026]

[0027] Finally, the operation influence coefficient ρ of the i-th computing node is calculated by the following formula: i :

[0028]

[0029] In the formula, ρ i is the operation impact coefficient of the i-th computing node, is the operation impact coefficient of the i-th computing node during the peak trading period, is the operation impact coefficient of the i-th computing node during the trading trough period, y is the number of detected operation parameters, where x belongs to y, A is the cumulative change of the xth operation influencing parameter during the peak trading period of the i-th computing node, ix0 The standard value of the cumulative change of the xth operation influencing parameter during the trading peak period of the i-th computing node preset by the system, is the cumulative change of the xth operation influencing parameter during the trading trough period of the i-th computing node, B ix0 k is the standard value of the cumulative change of the xth operation influencing parameter during the trading trough period of the i-th computing node preset by the system, ix is the weight coefficient corresponding to the xth operation influencing parameter of the ith computing node, and ω is the conversion coefficient.

[0030] As a further description of the solution of the present invention, the specific process of step S33 includes:

[0031] Construct the mathematical calculation model of the safety factor of the i-th calculation node, the expression is

[0032]

[0033] In the formula, σ thi is the standard value of the environmental impact coefficient of the i-th computing node preset by the system, ρ thi is the standard value of the operation influence coefficient of the i-th computing node preset by the system, δ and ε are the proportional coefficients, μ i is the safety factor of the i-th calculation node;

[0034] Calculate the safety factors of n computational nodes in sequence.

[0035] As a further description of the solution of the present invention, the specific process of step S4 includes:

[0036] The safety factors of n computing nodes are arranged in descending order. According to the importance of each authority in the power trading process, the authority calculation at each stage of the power trading process is allocated to each computing node in descending order according to the safety factor.

[0037] Beneficial effects of the present invention:

[0038] The present invention labels all computing nodes participating in power trading, obtains the environmental parameters and operating parameters of each computing node in turn, calculates the environmental impact coefficient of each computing node in turn according to the environmental parameters of each computing node, calculates the operating impact coefficient of each computing node in turn according to the operating parameters of each computing node, and then calculates the safety factor of each computing node in combination with the environmental impact coefficient and the operating impact coefficient of each computing node. Finally, according to the size of the safety factor of each computing node, the authority level of each computing node in the power trading process is allocated, aiming to improve the security and integrity of the power trading process. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The present invention will be further described below in conjunction with the accompanying drawings.

[0040] Figure 1 It is a partial flow chart of the method for fine-grained authority management of the power trading decision support system provided by the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] See also Figure 1 As shown, the present invention is a method for fine-grained management of permissions of an auxiliary decision-making system for power trading, the method comprising:

[0043] Step S1: Obtain all computing nodes participating in power trading in the management area and number them, the numbers are: 1, 2, ..., n;

[0044] Step S2: Obtain the influencing parameters of all computing nodes;

[0045] Step S3, analyzing the safety factor of each computing node according to the operating parameters of all computing nodes;

[0046] Step S4: Allocate transaction rights to each computing node based on the security factor of each node.

[0047] Through the above technical scheme, the present invention will label all computing nodes participating in power trading, obtain the environmental parameters and operating parameters of each computing node in turn, calculate the environmental impact coefficient of each computing node in turn according to the environmental parameters of each computing node, calculate the operating impact coefficient of each computing node in turn according to the operating parameters of each computing node, and then combine the environmental impact coefficient and the operating impact coefficient of each computing node to calculate the safety factor of each computing node. Finally, according to the size of the safety factor of each computing node, the authority level of each computing node in the power trading process is allocated, aiming to improve the security and integrity of the power trading process.

[0048] As a further description of the solution of the present invention, the specific process of step S2 includes:

[0049] Step S21, obtaining the environmental impact parameter of each computing node in turn;

[0050] Step S22: Obtain the operation influencing parameters of each computing node in turn.

[0051] As a further description of the solution of the present invention, the specific process of step S3 includes:

[0052] Step S31, obtaining the environmental impact coefficient of each computing node according to the environmental impact parameter of each computing node obtained in step S21;

[0053] Step S32, obtaining an operation influence coefficient of each computing node according to the operation influence parameter of each computing node obtained in step S21;

[0054] Step S33: construct a mathematical calculation model of the safety factor of each calculation node according to the environmental impact coefficient and the operation impact coefficient obtained in step S31 and step S32.

[0055] As a further description of the solution of the present invention, the specific process of step S31 includes:

[0056] The environmental impact coefficient σ of the i-th computing node is calculated by the following formula: i :

[0057]

[0058] In the formula, σ i is the environmental impact coefficient of the i-th computing node, M is the number of detected environmental parameters, where j belongs to M, E ij is the jth environmental impact parameter of the ith computing node, E ij0 is the jth environmental impact parameter standard value of the i-th computing node preset by the system, k ij is the weight coefficient corresponding to the jth environmental impact parameter of the ith computing node, Gij Reference value of the j-th environmental impact parameter range of the ith computing node.

[0059] Through the above technical solution, this embodiment provides a method for calculating the environmental impact coefficient of a computing node participating in power trading, obtains the environmental impact parameter of the i-th computing node, and then substitutes it into the formula Calculate the environmental impact coefficient σ of the i-th computing node i , where M is the number of detected environmental parameters, and j belongs to M, E ij is the jth environmental impact parameter of the ith computing node, E ij0 is the jth environmental impact parameter standard value of the i-th computing node preset by the system, k ij is the weight coefficient corresponding to the jth environmental impact parameter of the ith computing node, G ij Reference value of the j-th environmental impact parameter range of the ith computing node.

[0060] As a further description of the solution of the present invention, the specific process of step S32 includes:

[0061] First, the operation impact coefficient of the i-th computing node during the peak trading period is calculated by the following formula:

[0062]

[0063] Then, the operation impact coefficient of the i-th computing node during the trading trough period is calculated by the following formula:

[0064]

[0065] Finally, the operation influence coefficient ρ of the i-th computing node is calculated by the following formula: i :

[0066]

[0067] In the formula, ρ i is the operation impact coefficient of the i-th computing node, is the operation impact coefficient of the i-th computing node during the peak trading period, is the operation impact coefficient of the i-th computing node during the trading trough period, y is the number of detected operation parameters, where x belongs to y, A is the cumulative change of the xth operation influencing parameter during the peak trading period of the i-th computing node, ix0 The standard value of the cumulative change of the xth operation influencing parameter during the trading peak period of the i-th computing node preset by the system, is the cumulative change of the xth operation influencing parameter during the trading trough period of the i-th computing node, B ix0k is the standard value of the cumulative change of the xth operation influencing parameter during the trading trough period of the i-th computing node preset by the system, ix is the weight coefficient corresponding to the xth operation influencing parameter of the ith computing node, and ω is the conversion coefficient.

[0068] Through the above technical solution, this embodiment provides a method for calculating the operation influence coefficient of the computing node participating in the power transaction, obtaining the operation influence parameter of the i-th computing node, and using the formula Calculate the cumulative change of the xth operation-influencing parameter during the peak trading period of the i-th computing node. Calculate the cumulative change of the xth operation influencing parameter during the trading trough period of the i-th computing node, and then substitute it into the formula and Calculate the operation impact coefficient of the i-th computing node during the peak trading period and the operation impact coefficient of the i-th computing node during the low trading period respectively. Finally, through the formula Get the operation impact coefficient ρ of the i-th computing node i .

[0069] As a further description of the solution of the present invention, the specific process of step S33 includes:

[0070] Construct the mathematical calculation model of the safety factor of the i-th calculation node, the expression is

[0071]

[0072] In the formula, σ thi is the standard value of the environmental impact coefficient of the i-th computing node preset by the system, ρ thi is the standard value of the operation influence coefficient of the i-th computing node preset by the system, δ and ε are the proportional coefficients, μ i is the safety factor of the i-th calculation node;

[0073] Calculate the safety factors of n computational nodes in sequence.

[0074] As a further description of the solution of the present invention, the specific process of step S4 includes:

[0075] The safety factors of n computing nodes are arranged in descending order. According to the importance of each authority in the power trading process, the authority calculation at each stage of the power trading process is allocated to each computing node in descending order according to the safety factor.

[0076] Through the above technical solution, this embodiment constructs a mathematical calculation model of the safety factor of the i-th computing node Then, according to the calculation model, the safety factors of the n computing nodes are calculated in turn, and the safety factors of the n computing nodes are arranged in descending order. According to the importance of each authority in the power trading process, the authority calculation at each stage of the power trading process is allocated to each computing node in descending order according to the safety factor.

[0077] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A fine-grained authority management method for an electric power trading decision-making support system, characterized in that: The method comprises: Step S1: Obtain all computing nodes participating in power trading in the management area and number them, the numbers are: 1, 2, ..., n; Step S2: Obtain the influencing parameters of all computing nodes; Step S3, analyzing the safety factor of each computing node according to the operating parameters of all computing nodes; Step S4: Allocate transaction rights to each computing node based on the security factor of each node.

2. According to claim 1, a method for fine-grained management of permissions in an auxiliary decision-making system for power trading, characterized in that: The specific process of step S2 includes: Step S21, obtaining the environmental impact parameter of each computing node in turn; Step S22: Obtain the operation influencing parameters of each computing node in turn.

3. According to claim 1, a method for fine-grained management of permissions in an auxiliary decision-making system for power trading, characterized in that: The specific process of step S3 includes: Step S31, obtaining the environmental impact coefficient of each computing node according to the environmental impact parameter of each computing node obtained in step S21; Step S32, obtaining an operation influence coefficient of each computing node according to the operation influence parameter of each computing node obtained in step S21; Step S33: construct a mathematical calculation model of the safety factor of each calculation node according to the environmental impact coefficient and the operation impact coefficient obtained in step S31 and step S32.

4. According to claim 3, a method for fine-grained management of permissions in an auxiliary decision-making system for power trading, characterized in that: The specific process of step S31 includes: The environmental impact coefficient σ of the i-th computing node is calculated by the following formula: i : In the formula, σ i is the environmental impact coefficient of the i-th computing node, M is the number of detected environmental parameters, where j belongs to M, E ij is the jth environmental impact parameter of the ith computing node, E ij0 is the jth environmental impact parameter standard value of the i-th computing node preset by the system, k ij is the weight coefficient corresponding to the jth environmental impact parameter of the ith computing node, G ij Reference value of the j-th environmental impact parameter range of the ith computing node.

5. According to claim 3, a method for fine-grained management of permissions in an auxiliary decision-making system for power trading, characterized in that: The specific process of step S32 includes: First, the operation impact coefficient of the i-th computing node during the peak trading period is calculated by the following formula: Then, the operation impact coefficient of the i-th computing node during the trading trough period is calculated by the following formula: Finally, the operation influence coefficient ρ of the i-th computing node is calculated by the following formula: i : In the formula, ρ i is the operation impact coefficient of the i-th computing node, is the operation impact coefficient of the i-th computing node during the peak trading period, is the operation impact coefficient of the i-th computing node during the trading trough period, y is the number of detected operation parameters, where x belongs to y, A is the cumulative change of the xth operation impact parameter during the peak trading period of the i-th computing node, ix0 The standard value of the cumulative change of the xth operation influencing parameter during the trading peak period of the i-th computing node preset by the system, is the cumulative change of the xth operation influencing parameter during the trading trough period of the i-th computing node, B ix0 k is the standard value of the cumulative change of the xth operation influencing parameter during the trading trough period of the i-th computing node preset by the system, ix is the weight coefficient corresponding to the xth operation influencing parameter of the ith computing node, and ω is the conversion coefficient.

6. According to claim 5, a method for fine-grained management of permissions in an auxiliary decision-making system for power trading, characterized in that: The specific process of step S33 includes: Construct the mathematical calculation model of the safety factor of the i-th calculation node, the expression is In the formula, σ thi is the standard value of the environmental impact coefficient of the i-th computing node preset by the system, ρ thi is the standard value of the operation influence coefficient of the i-th computing node preset by the system, δ and ε are proportional coefficients, μ i is the safety factor of the i-th calculation node; Calculate the safety factors of n computational nodes in sequence.

7. The method for fine-grained authority management of an electric power trading decision-making support system according to claim 1 is characterized in that: The specific process of step S4 includes: The safety factors of n computing nodes are arranged in descending order. According to the importance of each authority in the power trading process, the authority calculation at each stage of the power trading process is allocated to each computing node in descending order according to the safety factor.

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