A method and system for multidimensional risk identification and early warning in the power market

Abstract

A method and system for multidimensional risk identification and early warning in the power market, belonging to the field of multidimensional risk identification and early warning technology, involves the following steps after the processing terminal is triggered: constructing a customer account based on login messages; constructing an initial customer attribute table; constructing a multidimensional credit assessment index for the power market after initial denoising; constructing automatic customer attributes; constructing information obtained through query and monitoring; constructing customer subsidiary attributes; inputting them into an attribute balance estimation model; constructing balance identification codes associated with each attribute; constructing a multidimensional credit assessment index for the power market after further denoising; and performing denoising control based on cyclic values ​​and traceability reconstruction nodes. This improves the performance and functionalities of constructing a credit assessment model for energy interconnection entities in the digital power market.

Description

Technical Field

[0001] This invention belongs to the field of multidimensional risk identification and early warning technology, specifically relating to a method and system for multidimensional risk identification and early warning in the power market. Background Technology

[0002] The electricity market is a natural monopoly market. As the power grid is an infrastructure, to prevent resource waste caused by redundant construction, the transmission network is usually allowed to operate as a monopoly, thus realizing its natural monopoly potential. However, due to the monopoly position of the power grid, the grid company has sufficient market power to vertically integrate and merge upstream (generation side) and downstream (sales side), thereby further expanding its monopoly position.

[0003] Regarding the multi-dimensional credit assessment indicators in the electricity market, as mentioned in the prior art solution with patent publication number "CN114663219B", a credit assessment model for energy interconnection entities is constructed based on the multi-dimensional credit assessment indicators in the electricity market.

[0004] Before constructing a credit assessment model for energy interconnection entities, it is necessary to denoise the multi-dimensional credit assessment indicators of the electricity market. Then, based on the denoised multi-dimensional credit assessment indicators of the electricity market, the credit assessment model for energy interconnection entities is constructed. Currently, the denoising methods for the multi-dimensional credit assessment indicators of the electricity market rely on unique attributes to perform denoising, making it difficult to accurately and effectively denoise the specific requirements of customers and the multi-dimensional credit assessment indicators of the electricity market. This results in the denoised multi-dimensional credit assessment indicators of the electricity market being inaccurate and lacking in early warning capabilities, which is not suitable for constructing a credit assessment model for energy interconnection entities and has shortcomings in terms of performance and customer experience.

[0005] Invention Information

[0006] To address the shortcomings of existing technologies, this invention proposes a multi-dimensional risk identification and early warning system and method for the electricity market. This system overcomes the limitations of existing multi-dimensional credit assessment indicator denoising methods, which rely on unique attributes to perform denoising, making it difficult to accurately and effectively denoise customer-specific requirements and the multi-dimensional credit assessment indicators of the electricity market. This results in denoised multi-dimensional credit assessment indicators that are inaccurate, lack early warning capabilities, and are unsuitable for constructing a credit assessment model for energy interconnection entities, thus compromising performance and customer experience. The invention achieves the advantage of improving the performance and functionality of constructing a credit assessment model for energy interconnection entities in the digital electricity market.

[0007] The present invention employs the following technical solution.

[0008] A multi-dimensional risk identification and early warning method for the electricity market, running on a processing terminal, includes:

[0009] The multi-dimensional credit assessment indicators of the electricity market are denoised, and then a credit assessment model for energy interconnection entities is constructed based on the denoised multi-dimensional credit assessment indicators of the electricity market.

[0010] Methods for denoising multi-dimensional credit assessment indicators in the electricity market include:

[0011] After the processing terminal is triggered, a customer account is constructed based on the login message;

[0012] The numerical data collection program is started to collect the customer's input information, the input information is parsed, and the initial customer attribute table is constructed.

[0013] Using the aforementioned initial customer attribute table as standard denoising information, the multi-dimensional credit assessment index of the electricity market is denoised to construct a denoised multi-dimensional credit assessment index of the electricity market.

[0014] A multi-dimensional risk identification and early warning system for the electricity market, comprising:

[0015] The customer account construction module is used to construct a customer account based on the login message after the processing terminal is triggered. Here, the customer account is equipped with the customer's initial review and reconstruction frequency identification code.

[0016] The module for constructing a multi-dimensional credit assessment index for the electricity market after denoising is used to start a numerical collection program to collect customer input information, parse the input information, construct an initial customer attribute table, use the initial customer attribute table as standard denoising information, perform multi-dimensional credit assessment index denoising for the electricity market, and construct a multi-dimensional credit assessment index for the electricity market after denoising. Here, the multi-dimensional credit assessment index for the electricity market after denoising is a professional introduction to the multi-dimensional credit assessment index for the electricity market after denoising.

[0017] The customer automatic attribute construction module is used to perform response detection on the customer, construct the customer's automatic response detection, refresh the initial customer attribute table with the automatic response detection, and construct the customer's automatic attributes.

[0018] The beneficial effects of the present invention are as follows: Compared with the prior art, the technical effects of the present invention include:

[0019] After the processing terminal is triggered, a customer account is constructed based on the login message; an initial customer attribute table is constructed, and a multi-dimensional credit assessment index for the electricity market after initial denoising is constructed; automatic customer attributes are constructed; information obtained from query information is constructed; customer subsidiary attributes are constructed and entered into the attribute balance estimation model; balance identification codes associated with each attribute are constructed; a multi-dimensional credit assessment index for the electricity market after further denoising is constructed; and denoising control is performed based on the cyclic value and traceability reconstruction node. This overcomes the limitations of existing multi-dimensional credit assessment index denoising in the electricity market, which relies on unique attributes to perform multi-dimensional credit assessment index denoising, making it difficult to accurately and effectively denoise customer-specific requirements and the multi-dimensional credit assessment index of the electricity market. This makes the multi-dimensional credit assessment index of the electricity market inaccurate, lacks early warning capabilities, and is not suitable for constructing the credit assessment model of the energy interconnection entity, thus improving the performance and user experience of the digital electricity market's construction of the credit assessment model for the energy interconnection entity. Attached Figure Description

[0020] Figure 1 This is a partial flowchart of the multi-dimensional risk identification and early warning method for the power market described in this invention;

[0021] Figure 2 This is a partial structural schematic diagram of the multi-dimensional risk identification and early warning system for the power market described in this invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, any other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0023] like Figure 1 As shown, the multi-dimensional risk identification and early warning method for the power market described in this invention runs on a processing terminal and includes:

[0024] The multi-dimensional credit assessment indicators of the electricity market are denoised, and then a credit assessment model for energy interconnection entities is constructed based on the denoised multi-dimensional credit assessment indicators of the electricity market; the processing terminal can be a computer.

[0025] Methods for denoising multi-dimensional credit assessment indicators in the electricity market include:

[0026] After the processing terminal is triggered, a customer account is constructed based on the login message;

[0027] In a preferred but non-limiting embodiment of the present invention, the customer account is provided with a customer's initial review frequency identification code.

[0028] The numerical data collection program is started to collect the customer's input information, the input information is parsed, and the initial customer attribute table is constructed.

[0029] Using the aforementioned initial customer attribute table as standard denoising information, the multi-dimensional credit assessment index of the electricity market is denoised to construct a denoised multi-dimensional credit assessment index of the electricity market.

[0030] In a preferred but non-limiting embodiment of the present invention, the denoising multi-dimensional credit assessment index of the electricity market is a multi-dimensional credit assessment index of the electricity market after denoising using the K-means algorithm.

[0031] In a preferred but non-limiting embodiment of the present invention, the method for denoising multi-dimensional credit assessment indicators in the electricity market further includes:

[0032] The system performs response detection on the customer, constructs automatic response detection for the customer, refreshes the initial customer attribute table using the automatic response detection, and constructs automatic customer attributes. It triggers a behavior monitoring component, uses the behavior monitoring component to monitor the customer's query information, and constructs information obtained from the query information monitoring. It constructs customer subsidiary attributes using the information obtained from the query information, inputs the automatic customer attributes and customer subsidiary attributes into an attribute balance estimation model, and constructs balance identification codes associated with each attribute. Based on the balance identification codes, automatic customer attributes, and customer subsidiary attributes, it performs denoising on the multi-dimensional credit assessment indicators of the electricity market, constructs a further denoised multi-dimensional credit assessment indicator of the electricity market, performs customer mobility detection based on the further denoised multi-dimensional credit assessment indicator of the electricity market, uses the mobility detection value to perform a loop on the further denoised multi-dimensional credit assessment indicator of the electricity market, and constructs a traceability reconstruction node using the initial review reconstruction frequency identification code. It then performs denoising control based on the loop value and the traceability reconstruction node.

[0033] In a preferred but non-limiting embodiment of the present invention, the cyclic value is a multi-dimensional credit assessment indicator for the electricity market after denoising the motor information.

[0034] In a preferred but non-limiting embodiment of the present invention, the method for performing noise control based on the cycle value and the traceability reconstruction node specifically includes: detecting the node triggering of the traceability reconstruction node and constructing a trigger response coefficient; constructing a customer behavior training pattern, using the detected customer behavior information as time-point recording information, performing customer behavior prediction based on the behavior training pattern, and constructing a behavior prediction value; allocating a response refresh queue using the behavior prediction value, performing node looping of the traceability reconstruction node based on the response refresh queue and the trigger response coefficient, and performing noise control using the node loop value.

[0035] In a preferred but non-limiting embodiment of the present invention, the method of using the behavior listening component to perform customer query information listening and constructing query information listening information specifically includes: obtaining the customer's access interface, determining whether the dwell time of the access interface meets a predefined time threshold; if the dwell time meets the predefined time threshold, registering the access interface as a base interface; performing interface information recognition on the access interface, and constructing additional listening information one using the interface information recognition value; obtaining the access sub-area of ​​the access interface, using the base interface as the interface base point, and constructing additional listening information two according to the triggering order of the access sub-area; constructing query information listening information based on the additional listening information one, the additional listening information two, and the dwell time.

[0036] In a preferred but non-limiting embodiment of the present invention, the method for constructing query information listening information based on the additional listening information one, the additional listening information two, and the dwell time specifically includes: extracting the area information of the access sub-region of the additional listening information two, and registering the trigger time of the access sub-region; constructing standard area listening attributes based on the area information and the trigger time; activating the attribute distribution importance of the additional listening information one and the triggering order of the additional listening information two, generating a time segmentation key quantity coefficient based on the attribute distribution importance and the triggering order; segmenting the dwell time based on the time segmentation key quantity coefficient, and then revising the standard area listening attributes to form query information listening information.

[0037] In a preferred but non-limiting embodiment of the present invention, the method for denoising the multi-dimensional credit assessment indicators of the electricity market based on the balance identification code, automatic customer attributes, and auxiliary customer attributes, and constructing a further denoised multi-dimensional credit assessment indicator of the electricity market, specifically includes: constructing a market information table, wherein the market information table has an information reliability identification code, the information reliability identification code being constructed through a time-point shrinkage coefficient, information source, and information completeness; constructing a joint denoising attribute using the balance identification code, automatic customer attributes, and auxiliary customer attributes, performing adaptive denoising based on the reliability identification code on the market information table, and constructing a further denoised multi-dimensional credit assessment indicator of the electricity market using the adapted denoised multi-dimensional credit assessment indicator of the electricity market.

[0038] In a preferred but non-limiting embodiment of the present invention, the method of inputting the automatic customer attributes and auxiliary customer attributes into the attribute balance estimation mode and constructing balance identification codes associated with each attribute specifically includes: starting an information content parsing program for the attribute balance estimation mode; performing information content ratio parsing of the automatic customer attributes and auxiliary customer attributes using the information content parsing program; constructing balance coefficient one using the information content ratio parsing values; starting a primary and secondary parsing program for the attribute balance estimation mode; performing attribute margin estimation of the automatic customer attributes and auxiliary customer attributes respectively according to the primary and secondary parsing program; constructing balance coefficient two with automatic-auxiliary identification codes based on the attribute margin estimation values; starting an attribute parsing program for the attribute balance estimation mode; performing attribute parsing of the automatic customer attributes and auxiliary customer attributes according to the attribute parsing program; constructing balance coefficient three; and constructing balance identification codes associated with each attribute based on balance coefficient one, balance coefficient two, and balance coefficient three.

[0039] In a preferred but non-limiting embodiment of the present invention, the method for denoising the multi-dimensional credit assessment indicators of the electricity market based on information from motor vehicles specifically includes: determining whether the denoised multi-dimensional credit assessment indicators of the electricity market meet a predefined denoising threshold; if they do not meet the predefined denoising threshold, generating an early warning command; and using the early warning command to perform additional customer information sampling to reconstruct the denoised multi-dimensional credit assessment indicators of the electricity market from the additional information collection values.

[0040] like Figure 2 As shown, the multi-dimensional risk identification and early warning system for the power market according to the present invention includes:

[0041] The customer account construction module is used to construct a customer account based on the login message after the processing terminal is triggered. Here, the customer account is equipped with the customer's initial review and reconstruction frequency identification code.

[0042] The module for constructing a multi-dimensional credit assessment index for the electricity market after denoising is used to start a numerical collection program to collect customer input information, parse the input information, construct an initial customer attribute table, use the initial customer attribute table as standard denoising information, perform multi-dimensional credit assessment index denoising for the electricity market, and construct a multi-dimensional credit assessment index for the electricity market after denoising. Here, the multi-dimensional credit assessment index for the electricity market after denoising is a professional introduction to the multi-dimensional credit assessment index for the electricity market after denoising.

[0043] The customer automatic attribute construction module is used to perform response detection on the customer, construct the customer's automatic response detection, refresh the initial customer attribute table with the automatic response detection, and construct the customer's automatic attributes.

[0044] In a preferred but non-limiting embodiment of the present invention, the multi-dimensional risk identification and early warning system for the electricity market further includes:

[0045] The system includes a query information listening module for constructing customer-related information, a behavior listening component for listening to customer query information, and a balance identification code construction module for constructing customer-related attributes using the query information listening module. The module also includes a balance identification code construction module for constructing customer-related attributes using the query information listening module, inputting the automatic customer attributes and related attributes into an attribute balance estimation model, and constructing balance identification codes associated with each attribute. Finally, a multi-dimensional credit assessment index construction module for the denoising power market is used to perform denoising of the multi-dimensional credit assessment index for the power market based on the balance identification code, automatic customer attributes, and related attributes. This module constructs the denoising multi-dimensional credit assessment index for the power market, performs customer mobility detection based on the denoising multi-dimensional credit assessment index, uses the mobility detection value to perform a loop of the denoising multi-dimensional credit assessment index for the power market, and constructs a traceability reconstruction node using the initial review reconstruction frequency identification code. Denoising control is performed based on the loop value and the traceability reconstruction node. The loop value is the multi-dimensional credit assessment index for the power market after denoising the mobility information.

[0046] The beneficial effects of the present invention are as follows: Compared with the prior art, the technical effects of the present invention include:

[0047] After the processing terminal is triggered, a customer account is constructed based on the login message; an initial customer attribute table is constructed, and a multi-dimensional credit assessment index for the electricity market after initial denoising is constructed; automatic customer attributes are constructed; information obtained from query information is constructed; customer subsidiary attributes are constructed and entered into the attribute balance estimation model; balance identification codes associated with each attribute are constructed; a multi-dimensional credit assessment index for the electricity market after further denoising is constructed; and denoising control is performed based on the cyclic value and traceability reconstruction node. This overcomes the limitations of existing multi-dimensional credit assessment index denoising in the electricity market, which relies on unique attributes to perform multi-dimensional credit assessment index denoising, making it difficult to accurately and effectively denoise customer-specific requirements and the multi-dimensional credit assessment index of the electricity market. This makes the multi-dimensional credit assessment index of the electricity market inaccurate, lacks early warning capabilities, and is not suitable for constructing the credit assessment model of the energy interconnection entity, thus improving the performance and user experience of the digital electricity market's construction of the credit assessment model for the energy interconnection entity.

[0048] This disclosure may be a system, method, and / or computer program product. A computer program product may include a computer-readable backup medium having computer-readable program instructions loaded thereon for causing a processor to achieve various aspects of the overhead disclosed.

[0049] Computer-readable backup media can be a physical circuit capable of maintaining and backing up instructions executed by the circuit. Computer-readable backup media can be, but is not limited to, electrical backup circuits, magnetic backup circuits, optical backup circuits, electromagnetic backup circuits, semiconductor backup circuits, or any suitable combination thereof. Further examples of computer-readable backup media (a non-exhaustive list) include: portable computer disks, hard disks, random access backup devices (RAM), read-only backup devices (ROM), erasable programmable read-only backup devices (EPROM or flash memory), static random access backup devices (SRAM), portable reduced disk read-only backup devices (HD-ROM), digital multipurpose disks (DXD), memory sticks, floppy disks, mechanically encoded circuits, punch cards or recessed protrusions such as those with backed-up instructions, or any suitable combination thereof. The computer-readable backup media used herein are not to be interpreted as momentary signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (like light pulses through power transmission cables), or electrical signals transmitted through wires.

[0050] The computer-readable program instructions described herein can be downloaded from computer-readable backup media to various computing / processing power lines, or downloaded via wireless networks such as the Internet, local area networks, wide area networks, and / or wireless networks to external computers or external backup power lines. Wireless networks can include copper transmission cables, transmission lines, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. Wireless network adapters or wireless network interfaces in each computing / processing power line receive computer-readable program instructions from the wireless network and forward them to the computer-readable backup media in each computing / processing power line.

[0051] The computer program instructions used to execute the operations of this disclosure can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-associative instructions, microcode, firmware instructions, conditional setting values, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Standard A, H++, etc., and conventional procedural programming languages ​​such as the "H" language or similar programming languages. The computer-readable program instructions can be executed entirely on the client computer, partially on the client computer, as a standalone software package, partially on the client computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the client computer via any type of wireless network—including a local area network (LAb) or a wide area network (WAb), or can be connected to an external computer (such as using an Internet service provider to connect via the Internet). In some embodiments, electronic circuits are personalized by employing status values ​​of computer-readable program instructions, such as programmable logic circuits, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), which can execute computer-readable program instructions to achieve various aspects of overhead disclosure.

[0052] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific embodiments of the present invention without departing from the spirit and scope of the present invention. All such modifications or equivalent substitutions should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for multi-dimensional risk identification and early warning in the electricity market, characterized in that, Running on a processing terminal, including: The multi-dimensional credit assessment indicators of the electricity market are denoised, and then a credit assessment model for energy interconnection entities is constructed based on the denoised multi-dimensional credit assessment indicators of the electricity market. Methods for denoising multi-dimensional credit assessment indicators in the electricity market include: After the processing terminal is triggered, a customer account is constructed based on the login message; The numerical data collection program is started to collect the customer's input information, the input information is parsed, and the initial customer attribute table is constructed. Using the aforementioned initial customer attribute table as standard denoising information, the multi-dimensional credit assessment index of the electricity market is denoised to construct a denoised multi-dimensional credit assessment index of the electricity market.

2. The method for multi-dimensional risk identification and early warning in the power market according to claim 1, characterized in that, The customer account has a customer's initial review frequency identification code.

3. The method for multi-dimensional risk identification and early warning in the power market according to claim 2, characterized in that, The aforementioned denoising multi-dimensional credit assessment index for the electricity market is a multi-dimensional credit assessment index for the electricity market after denoising using the K-means algorithm.

4. The method for multi-dimensional risk identification and early warning in the power market according to claim 3, characterized in that, Methods for denoising multi-dimensional credit assessment indicators in the electricity market also include: The system performs response detection on the customer, constructs automatic response detection for the customer, refreshes the initial customer attribute table using the automatic response detection, and constructs automatic customer attributes. It triggers a behavior monitoring component, uses the behavior monitoring component to monitor the customer's query information, and constructs information obtained from the query information monitoring. It constructs customer subsidiary attributes using the information obtained from the query information, inputs the automatic customer attributes and customer subsidiary attributes into an attribute balance estimation model, and constructs balance identification codes associated with each attribute. Based on the balance identification codes, automatic customer attributes, and customer subsidiary attributes, it performs denoising on the multi-dimensional credit assessment indicators of the electricity market, constructs a further denoised multi-dimensional credit assessment indicator of the electricity market, performs customer mobility detection based on the further denoised multi-dimensional credit assessment indicator of the electricity market, uses the mobility detection value to perform a loop on the further denoised multi-dimensional credit assessment indicator of the electricity market, and constructs a traceability reconstruction node using the initial review reconstruction frequency identification code. It then performs denoising control based on the loop value and the traceability reconstruction node.

5. The method for multi-dimensional risk identification and early warning in the power market according to claim 4, characterized in that, The cycle value is a multi-dimensional credit assessment indicator for the electricity market after noise reduction of motor information.

6. The method for multi-dimensional risk identification and early warning in the power market according to claim 5, characterized in that, The method for performing noise reduction control based on the loop value and the source reconstruction node specifically includes: detecting the node trigger of the source reconstruction node and constructing a trigger response coefficient; constructing a customer behavior training pattern, using the detected customer behavior information as time-point entry information, performing customer behavior prediction based on the behavior training pattern, and constructing a behavior prediction value; allocating a response refresh queue using the behavior prediction value, performing node looping of the source reconstruction node based on the response refresh queue and the trigger response coefficient, and performing noise reduction control using the node loop value; The method for using the behavior listening component to listen to customer query information and construct the information obtained from the query information listening specifically includes: obtaining the customer's access interface and determining whether the dwell time of the access interface meets a predefined time threshold; if the dwell time meets the predefined time threshold, registering the access interface as a base interface; performing interface information recognition on the access interface and constructing additional listening information one using the interface information recognition value; obtaining the access sub-area of ​​the access interface, using the base interface as the interface base point, and constructing additional listening information two according to the triggering order of the access sub-area; and constructing the query information listening information based on the additional listening information one, the additional listening information two, and the dwell time.

7. The method for multi-dimensional risk identification and early warning in the power market according to claim 6, characterized in that, The method for constructing query information obtained through listening based on the additional listening information 1, the additional listening information 2, and the dwell time specifically includes: extracting the area information of the access sub-area of ​​the additional listening information 2, and registering the trigger time of the access sub-area; constructing standard area listening attributes based on the area information and the trigger time; activating the attribute distribution importance of the additional listening information 1 and the triggering order of the additional listening information 2, and generating a time segmentation key quantity coefficient based on the attribute distribution importance and the triggering order; segmenting the dwell time based on the time segmentation key quantity coefficient, and then revising the standard area listening attributes to form query information obtained through listening.

8. The method for multi-dimensional risk identification and early warning in the power market according to claim 7, characterized in that, The method for denoising the multi-dimensional credit assessment indicators of the electricity market based on the balance identification code, automatic customer attributes, and auxiliary customer attributes, and constructing a further denoised multi-dimensional credit assessment indicator of the electricity market, specifically includes: constructing a market information table, wherein the market information table has an information reliability identification code, the information reliability identification code being constructed through a time point shrinkage coefficient, information source, and information completeness; constructing a joint denoising attribute using the balance identification code, automatic customer attributes, and auxiliary customer attributes, performing adaptive denoising based on the reliability identification code on the market information table, and constructing a further denoised multi-dimensional credit assessment indicator of the electricity market using the adapted denoised multi-dimensional credit assessment indicator of the electricity market; The method for inputting the automatic customer attributes and auxiliary customer attributes into the attribute balance estimation model and constructing balance identification codes associated with each attribute specifically includes: starting the information content parsing program of the attribute balance estimation model, performing information content ratio parsing of the automatic customer attributes and auxiliary customer attributes using the information content parsing program, and constructing balance coefficient one using the information content ratio parsing values; starting the main and auxiliary parsing program of the attribute balance estimation model, performing attribute margin estimation of the automatic customer attributes and auxiliary customer attributes respectively according to the main and auxiliary parsing program, and constructing balance coefficient two with automatic-auxiliary identification codes based on the attribute margin estimation values; starting the attribute parsing program of the attribute balance estimation model, performing attribute parsing of the automatic customer attributes and auxiliary customer attributes according to the attribute parsing program, and constructing balance coefficient three; and constructing balance identification codes associated with each attribute based on balance coefficient one, balance coefficient two, and balance coefficient three. The method for denoising the multi-dimensional credit assessment indicators of the electricity market based on mobile information specifically includes: determining whether the denoised multi-dimensional credit assessment indicators of the electricity market meet the predefined denoising threshold; if they do not meet the predefined denoising threshold, generating an early warning command; and using the early warning command to sample additional customer information to reconstruct the denoised multi-dimensional credit assessment indicators of the electricity market from the additional information collected values.

9. A multi-dimensional risk identification and early warning system for the electricity market, characterized in that, include: The customer account construction module is used to construct a customer account based on the login message after the processing terminal is triggered. Here, the customer account is equipped with the customer's initial review and reconstruction frequency identification code. The module for constructing a multi-dimensional credit assessment index for the electricity market after denoising is used to start a numerical collection program to collect customer input information, parse the input information, construct an initial customer attribute table, use the initial customer attribute table as standard denoising information, perform multi-dimensional credit assessment index denoising for the electricity market, and construct a multi-dimensional credit assessment index for the electricity market after denoising. Here, the multi-dimensional credit assessment index for the electricity market after denoising is a professional introduction to the multi-dimensional credit assessment index for the electricity market after denoising. The customer automatic attribute construction module is used to perform response detection on the customer, construct the customer's automatic response detection, refresh the initial customer attribute table with the automatic response detection, and construct the customer's automatic attributes.

10. The multi-dimensional risk identification and early warning system for the power market according to claim 9, characterized in that, The power market multidimensional risk identification and early warning system also includes: The system includes a query information listening module for constructing customer-related information, a behavior listening component for listening to customer query information, and a balance identification code construction module for constructing customer-related attributes using the query information listening module. The module also includes a balance identification code construction module for constructing customer-related attributes using the query information listening module, inputting the automatic customer attributes and related attributes into an attribute balance estimation model, and constructing balance identification codes associated with each attribute. Finally, a multi-dimensional credit assessment index construction module for the denoising power market is used to perform denoising of the multi-dimensional credit assessment index for the power market based on the balance identification code, automatic customer attributes, and related attributes. This module constructs the denoising multi-dimensional credit assessment index for the power market, performs customer mobility detection based on the denoising multi-dimensional credit assessment index, uses the mobility detection value to perform a loop of the denoising multi-dimensional credit assessment index for the power market, and constructs a traceability reconstruction node using the initial review reconstruction frequency identification code. Denoising control is performed based on the loop value and the traceability reconstruction node. The loop value is the multi-dimensional credit assessment index for the power market after denoising the mobility information.

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