Power marketing risk early warning method and device, terminal and storage medium

Abstract

The application provides a power marketing risk early warning method, device, terminal and storage medium. The method comprises the following steps: acquiring real-time business nodes and risk business nodes of a target power marketing business; wherein the target power marketing business is a multi-business node process; different power marketing business categories have corresponding risk business nodes; determining a risk early warning time point for the target power marketing business based on the business node interval information between the real-time business nodes and the risk business nodes; and performing risk early warning on the target power marketing business based on the risk early warning time point. The application can perform risk early warning on power marketing businesses with high timeliness requirements in a timely manner, and can avoid frequent early warning on power marketing businesses with non-essential matters, thereby reasonably allocating risk early warning resources, and being suitable for various categories of power marketing businesses, saving resources while ensuring the risk early warning effect, and improving the early warning efficiency.

Description

Technical Field

[0001] This invention relates to the field of power marketing risk management technology, and in particular to a power marketing risk early warning method, device, terminal and storage medium. Background Technology

[0002] With the technological development of power systems, the types of electricity marketing business are increasing, and the risks involved are becoming more complex. To improve the quality of electricity marketing and to prevent, handle, and manage electricity marketing risks, staff use various technologies for risk management, which can be broadly categorized as follows: One type analyzes electricity marketing data to determine the existence of risk events, improving the accuracy of grid marketing risk control. This type of technology can only detect risks after they occur, making prevention difficult. Another type uses neural networks to classify electricity marketing business information into risk warning levels, achieving risk management primarily based on prevention. This type of technology only uses historical data for risk warnings and does not utilize real-time data analysis. A third type involves comprehensive analysis and processing of work order delay risks in grid marketing data to provide risk warnings for grid work completion. This type of technology only provides warnings for a fixed type of risk and has limited practicality.

[0003] Therefore, there is a current need for a power marketing risk management technology that is widely applicable and can efficiently provide risk warnings. Summary of the Invention

[0004] This invention provides a method, device, terminal, and storage medium for early warning of electricity marketing risks, in order to solve the problem of early warning of electricity marketing risks.

[0005] In a first aspect, embodiments of the present invention provide a method for early warning of electricity marketing risks, including: Obtain real-time business nodes and risk business nodes of the target electricity marketing business; the target electricity marketing business is a multi-business node process; different electricity marketing business categories have corresponding risk business nodes; Based on the business node interval information between real-time business nodes and risk business nodes, determine the risk warning time point for the target power marketing business. Risk warnings are issued for target electricity marketing businesses based on the risk warning time points.

[0006] In one possible implementation, the risky business nodes for acquiring the target electricity marketing business include: Obtain historical risk data corresponding to the business categories of the target electricity marketing business, and determine the risk business nodes of the target electricity marketing business based on the historical risk data; wherein, the historical risk data includes historical risks and the business nodes corresponding to the historical risks.

[0007] In one possible implementation, identifying risky business nodes in the target electricity marketing business based on historical risk data includes: For each business node, the proportion of the historical risk corresponding to that business node to the total historical risk is taken as the risk probability of that business node; Business nodes with a risk probability greater than a preset threshold are designated as high-risk business nodes for the target electricity marketing business.

[0008] In one possible implementation, the business node interval information includes a specific time interval; Based on the business node interval information between real-time business nodes and risk time nodes, the risk warning time points for determining the target power marketing business include: Based on the specific time interval between real-time business nodes and risk business nodes, and the risk probability corresponding to the risk business nodes, the early warning period for risk warning of the target power marketing business is determined. The risk warning time point for the target power marketing business is calculated based on real-time business nodes and warning cycle.

[0009] In one possible implementation, the early warning period for risk warning of the target electricity marketing business is determined based on the specific time interval between real-time business nodes and risk business nodes, as well as the risk probability corresponding to the risk business nodes. calculate This allows us to obtain the early warning cycle for risk assessment of the target electricity marketing business; in, Indicates the warning period. Indicates the preset period. This indicates the standard business duration corresponding to the target electricity marketing business. This indicates the specific time interval between real-time business nodes and risky business nodes. This indicates the risk probability corresponding to the risky business node.

[0010] In one possible implementation, risk warning for target electricity marketing operations based on risk warning time points includes: Acquire real-time and standard data of the target electricity marketing business at the risk warning time point; Calculate the deviation of real-time data from standard data; Determine the risk level of the target electricity marketing business based on the degree of deviation; Risk warnings are issued based on risk levels.

[0011] Secondly, embodiments of the present invention provide an electricity marketing risk early warning device, comprising: The acquisition module is used to acquire the real-time business nodes and risk business nodes of the target electricity marketing business; the target electricity marketing business is a multi-business node process; different electricity marketing business categories have corresponding risk business nodes; The early warning time determination module is used to determine the risk warning time point for the target power marketing business based on the business node interval information between real-time business nodes and risk business nodes. The early warning module is used to provide risk warnings for target electricity marketing businesses based on the risk warning time points.

[0012] Thirdly, embodiments of the present invention provide a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method as described in the first aspect or any possible implementation thereof.

[0013] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method as described in the first aspect or any possible implementation thereof.

[0014] The beneficial effects of the power marketing risk early warning method, device, terminal, and storage medium provided in this invention are as follows: This invention selects different time nodes as risk time nodes according to different categories of electricity marketing business, and determines the early warning time point based on the time interval between the current business node and the risk time node. For electricity marketing business with high timeliness requirements, it can provide timely risk warnings, and for electricity marketing business with non-essential matters, it can avoid frequent warnings, thereby rationally allocating risk warning resources. It is applicable to a variety of electricity marketing business, and can save resources and improve early warning efficiency while ensuring the effectiveness of risk warning. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating the implementation of a power marketing risk early warning method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of an electricity marketing risk early warning device provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of a terminal provided in an embodiment of the present invention. Detailed Implementation

[0017] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0018] To make the objectives, technical solutions, and advantages of the present invention clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0019] See Figure 1 The document illustrates a flowchart of the implementation of the power marketing risk early warning method provided in an embodiment of the present invention, which is described in detail below: Step 101: Obtain the real-time business nodes and risk business nodes of the target electricity marketing business; wherein, the target electricity marketing business is a multi-business node process; different electricity marketing business categories have corresponding risk business nodes.

[0020] Step 102: Based on the business node interval information between real-time business nodes and risk business nodes, determine the risk warning time point for the target power marketing business.

[0021] Step 103: Conduct risk warnings for the target electricity marketing business based on the risk warning time point.

[0022] In one possible implementation, the risky business nodes for acquiring the target electricity marketing business include: Obtain historical risk data corresponding to the business categories of the target electricity marketing business, and determine the risk business nodes of the target electricity marketing business based on the historical risk data; wherein, the historical risk data includes historical risks and the business nodes corresponding to the historical risks.

[0023] In one possible implementation, identifying risky business nodes in the target electricity marketing business based on historical risk data includes: For each business node, the proportion of the historical risk corresponding to that business node to the total historical risk is taken as the risk probability of that business node; Business nodes with a risk probability greater than a preset threshold are designated as high-risk business nodes for the target electricity marketing business.

[0024] In one possible implementation, the business node interval information includes a specific time interval; Based on the business node interval information between real-time business nodes and risk time nodes, the risk warning time points for determining the target power marketing business include: Based on the specific time interval between real-time business nodes and risk business nodes, and the risk probability corresponding to the risk business nodes, the early warning period for risk warning of the target power marketing business is determined. The risk warning time point for the target power marketing business is calculated based on real-time business nodes and warning cycle.

[0025] In one possible implementation, the early warning period for risk warning of the target electricity marketing business is determined based on the specific time interval between real-time business nodes and risk business nodes, as well as the risk probability corresponding to the risk business nodes. calculate This allows us to obtain the early warning cycle for risk assessment of the target electricity marketing business; in, Indicates the warning period. Indicates the preset period. This indicates the standard business duration corresponding to the target electricity marketing business. This indicates the specific time interval between real-time business nodes and risky business nodes. This indicates the risk probability corresponding to the risky business node.

[0026] In one possible implementation, risk warning for target electricity marketing operations based on risk warning time points includes: Acquire real-time and standard data of the target electricity marketing business at the risk warning time point; Calculate the deviation of real-time data from standard data; Determine the risk level of the target electricity marketing business based on the degree of deviation; Risk warnings are issued based on risk levels.

[0027] This invention, in its embodiments, selects different time nodes as risk time nodes based on the business category for different types of electricity marketing business, and determines the warning time point based on the time interval between the current business node and the risk time node. For electricity marketing business with high timeliness requirements, it can provide timely risk warnings, while avoiding frequent warnings for electricity marketing business with non-essential matters. This allows for the rational allocation of risk warning resources and is applicable to multiple types of electricity marketing business. It can save resources and improve warning efficiency while ensuring the effectiveness of risk warning.

[0028] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0029] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0030] Figure 2 A schematic diagram of the structure of the power marketing risk early warning device provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below: like Figure 2 As shown, the electricity marketing risk early warning device 2 includes: The acquisition module 21 is used to acquire the real-time business nodes and risk business nodes of the target electricity marketing business; wherein, the target electricity marketing business is a multi-business node process; different electricity marketing business categories have corresponding risk business nodes; The early warning time determination module 22 is used to determine the risk warning time point for the target power marketing business based on the business node interval information between real-time business nodes and risk business nodes. The early warning module 23 is used to provide risk warnings for target power marketing businesses based on the risk warning time point.

[0031] In one possible implementation, the acquisition module is specifically used for: Obtain historical risk data corresponding to the business categories of the target electricity marketing business, and determine the risk business nodes of the target electricity marketing business based on the historical risk data; wherein, the historical risk data includes historical risks and the business nodes corresponding to the historical risks.

[0032] In one possible implementation, the acquisition module is specifically used for: For each business node, the proportion of the historical risk corresponding to that business node to the total historical risk is taken as the risk probability of that business node; Business nodes with a risk probability greater than a preset threshold are designated as high-risk business nodes for the target electricity marketing business.

[0033] In one possible implementation, the business node interval information includes a specific time interval; The early warning time determination module includes: The early warning cycle determination unit is used to determine the early warning cycle for risk warning of the target power marketing business based on the specific time interval between real-time business nodes and risk business nodes, as well as the risk probability corresponding to the risk business nodes. The early warning time determination unit is used to calculate the risk warning time point for the target power marketing business based on real-time business nodes and early warning cycles.

[0034] In one possible implementation, the early warning cycle determination unit is specifically used for: calculate This allows us to obtain the early warning cycle for risk assessment of the target electricity marketing business; in, Indicates the warning period. Indicates the preset period. This indicates the standard business duration corresponding to the target electricity marketing business. This indicates the specific time interval between real-time business nodes and risky business nodes. This indicates the risk probability corresponding to the risky business node.

[0035] In one possible implementation, the early warning module is specifically used for: Acquire real-time and standard data of the target electricity marketing business at the risk warning time point; Calculate the deviation of real-time data from standard data; Determine the risk level of the target electricity marketing business based on the degree of deviation; Risk warnings are issued based on risk levels.

[0036] This invention, in its embodiments, selects different time nodes as risk time nodes based on the business category for different types of electricity marketing business, and determines the warning time point based on the time interval between the current business node and the risk time node. For electricity marketing business with high timeliness requirements, it can provide timely risk warnings, while avoiding frequent warnings for electricity marketing business with non-essential matters. This allows for the rational allocation of risk warning resources and is applicable to multiple types of electricity marketing business. It can save resources and improve warning efficiency while ensuring the effectiveness of risk warning.

[0037] Figure 3 This is a schematic diagram of a terminal provided in an embodiment of the present invention. Figure 3 As shown, the terminal 3 in this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30. When the processor 30 executes the computer program 32, it implements the steps in the various embodiments of the electricity marketing risk warning method described above, for example... Figure 2 Steps 201 to 203 are shown. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each module / unit in the above-described device embodiments.

[0038] For example, the computer program 32 can be divided into one or more modules / units, which are stored in the memory 31 and executed by the processor 30 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 32 in the terminal 3.

[0039] The terminal 3 can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will understand that... Figure 3 This is merely an example of terminal 3 and does not constitute a limitation on terminal 3. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal may also include input / output devices, network access devices, buses, etc.

[0040] The processor 30 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0041] The memory 31 can be an internal storage unit of the terminal 3, such as a hard disk or memory of the terminal 3. The memory 31 can also be an external storage device of the terminal 3, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the terminal 3. Furthermore, the memory 31 can include both internal storage units and external storage devices of the terminal 3. The memory 31 is used to store the computer program and other programs and data required by the terminal. The memory 31 can also be used to temporarily store data that has been output or will be output.

[0042] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0043] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0044] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0045] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0046] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0047] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0048] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various embodiments of the power marketing risk warning method described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0049] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for early warning of risks in electricity marketing, characterized in that, include: Obtain real-time business nodes and risk business nodes of the target electricity marketing business; wherein, the target electricity marketing business is a multi-business node process; different electricity marketing business categories have corresponding risk business nodes; Based on the business node interval information between the real-time business node and the risk business node, the risk warning time point for the target power marketing business is determined. Risk warnings are issued for the target electricity marketing business based on the aforementioned risk warning time points; Risky business nodes for acquiring target electricity marketing business include: Obtain historical risk data corresponding to the business category of the target electricity marketing business, and determine the risk business nodes of the target electricity marketing business based on the historical risk data; wherein, the historical risk data includes historical risks and the business nodes corresponding to the historical risks; The risk business nodes for determining the target electricity marketing business based on the historical risk data include: For each business node, the proportion of the historical risk corresponding to that business node to the total historical risk is taken as the risk probability of that business node; Business nodes with a risk probability greater than a preset threshold are designated as risky business nodes in the target electricity marketing business. The service node interval information includes a specific time interval; The risk warning time point for determining the risk warning for the target electricity marketing business based on the business node interval information between the real-time business node and the risk time node includes: Based on the specific time interval between the real-time business node and the risk business node, and the risk probability corresponding to the risk business node, the early warning period for risk warning of the target power marketing business is determined. Based on the real-time business node and the early warning cycle, calculate the risk warning time point for the target power marketing business; The method of determining the early warning period for risk warning of the target electricity marketing business based on the specific time interval between the real-time business node and the risk business node, and the risk probability corresponding to the risk business node, includes: calculate The early warning period for risk warning of the target electricity marketing business is obtained; in, Indicates the warning period. Indicates the preset period. This indicates the standard business duration corresponding to the target electricity marketing business. This indicates the specific time interval between the real-time service node and the risky service node. This indicates the risk probability corresponding to the risky business node.

2. The method for early warning of electricity marketing risks according to claim 1, characterized in that, The risk warning for the target electricity marketing business based on the risk warning time point includes: Obtain real-time and standard data of the target electricity marketing business at the risk warning time point; Calculate the deviation of the real-time data from the standard data; The risk level of the target electricity marketing business is determined based on the deviation. Risk warnings will be issued based on the aforementioned risk levels.

3. A power marketing risk early warning device, characterized in that, include: The acquisition module is used to acquire real-time business nodes and risk business nodes of the target electricity marketing business; wherein, the target electricity marketing business is a multi-business node process; different electricity marketing business categories have corresponding risk business nodes; The early warning time determination module is used to determine the risk warning time point for the target power marketing business based on the business node interval information between the real-time business node and the risk business node; The early warning module is used to provide risk warnings for the target electricity marketing business based on the risk warning time point. The acquisition module is specifically used for: Obtain historical risk data corresponding to the business category of the target electricity marketing business, and determine the risk business nodes of the target electricity marketing business based on the historical risk data; wherein, the historical risk data includes historical risks and the business nodes corresponding to the historical risks; The acquisition module is specifically used for: For each business node, the proportion of the historical risk corresponding to that business node to the total historical risk is taken as the risk probability of that business node; Business nodes with a risk probability greater than a preset threshold are designated as risky business nodes in the target electricity marketing business. The service node interval information includes a specific time interval; The warning time determination module includes: The early warning cycle determination unit is used to determine the early warning cycle for risk warning of the target power marketing business based on the specific time interval between the real-time business node and the risk business node, and the risk probability corresponding to the risk business node. The early warning time determination unit is used to calculate the risk warning time point for the target power marketing business based on the real-time business node and the early warning period; The early warning cycle determination unit is specifically used for: calculate The early warning period for risk warning of the target electricity marketing business is obtained; in, Indicates the warning period. Indicates the preset period. This indicates the standard business duration corresponding to the target electricity marketing business. This indicates the specific time interval between the real-time service node and the risky service node. This indicates the risk probability corresponding to the risky business node.

4. A terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 2 above.

5. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 2 above.

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