Power marketing risk management system and method based on big data

Abstract

The invention discloses an electric power marketing risk management system and method based on big data, and relates to the technical field of electric power marketing risk management, and the method comprises the steps: S10: constructing a risk map, and analyzing risk business data in business data; s20, analyzing the matching degree between the power marketing business and the user, and selecting whether to send early warning information to the user terminal or not according to an analysis result; s30, according to the electricity utilization characteristics of the user, searching an associated electricity marketing scheme of the user in an electricity marketing scheme database; and S40, sending the searched associated power marketing scheme as early warning information to a user terminal, and enabling a user to autonomously select whether to change the power marketing business or not according to the received early warning information. According to the method, whether the acquired business data is the risk business data is judged based on multiple dimensions, the complexity and diversity of the power marketing risk are fully considered, and the risk identification capability of the system on the power marketing business is further enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power marketing risk management, and particularly relates to a power marketing risk management system and method based on big data. BACKGROUND

[0002] With the continuous change of social system, power enterprise marketing risk management work is facing great challenges, and power marketing risks include electricity recovery risks, service risks, contract management risks, natural disaster risks and network payment risks. In terms of risks, power marketing management plays a very key role and can analyze the whole situation.

[0003] In the prior art, the collected power marketing data is cleaned, summarized, arranged, and the configured power marketing risk indicators are used to analyze whether the power marketing business has risks. The power marketing risk has complexity, so single data processing cannot meet the actual needs. In the prior art, after power marketing early warning, the best risk response strategy cannot be provided for users in a short time. SUMMARY

[0004] The present application aims to provide a power marketing risk management system and method based on big data to solve the problems in the prior art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a power marketing risk management method based on big data, the method comprising: S10: acquiring business data in the power marketing business, constructing a risk map based on the business index data in the power marketing business, and analyzing the risk business data in the business data; S20: collecting the power consumption characteristics of the user corresponding to the power marketing business within the distribution time of the matching risk business data, analyzing the matching degree between the power marketing business and the user corresponding to the power marketing business according to the collected information, and selecting whether to send early warning information to the user terminal of the user corresponding to the power marketing business according to the analysis result; S30: when it is necessary to send early warning information to the user terminal of the user corresponding to the power marketing business, the associated power marketing scheme of the user corresponding to the power marketing business is searched in the power marketing scheme database according to the power consumption characteristics of the user corresponding to the power marketing business; S40: the searched associated power marketing scheme is sent to the user terminal of the user corresponding to the power marketing business as early warning information, and the user corresponding to the power marketing business autonomously selects whether to change the power marketing business according to the received early warning information.

[0006] Further, the S10 comprises: S101: Obtain real-time business data in the power marketing business, the business data including electricity recovery data, power marketing service data, power marketing power supply data, and network payment data; S102: According to the business index data in the power marketing business, mark the power marketing business with abnormal power marketing power supply data, divide the time period [T, t] into several division time periods with the collection interval time d of the business data in the time period [T, t], and obtain the number of the division time periods, the numbering processing result being: i = 1, 2, …, n; n represents the total number of the division time periods, according to Calculate the risk index of the power marketing business j in the first dimension, wherein j = 1, 2, …, m, represents the numbering processing of each power marketing business, m represents the total number of the power marketing businesses, w j represents the number of times that the power marketing business j in the time period [T, t] is marked, V ij represents the power supply voltage value of the power marketing business j in the i-th division time period, E j represents the power marketing power supply index value of the power marketing business j; S103: According to Calculate the risk index of the power marketing business j in the c-th dimension, wherein c = 2, 3, represents the numbering corresponding to each dimension in the power marketing business, when c = 2, represents the power marketing service dimension in the power marketing business, X 2j , Y 2j respectively represent the power marketing service score index value and the power marketing service score value of the power marketing business j in the second dimension, when c = 3, represents the electricity trend dimension in the power marketing business, X 3j represents the network payment value of the power marketing business j in the third dimension, Y 3j represents the electricity recovery value of the power marketing business j in the third dimension, R cj represents the risk index of the power marketing business j in the c-th dimension; S104: According to the risk index of the power marketing business j in each dimension, construct the risk map of the power marketing business j, according to the area S of the constructed risk map, analyze whether the business data of the power marketing business j in the time period [T, t] is risk business data, if S ≥ the set threshold value, it is analyzed that the business data of the power marketing business j in the time period [T, t] is risk business data, if S < the set threshold value, it is analyzed that the business data of the power marketing business j in the time period [T, t] is not risk business data.

[0007] Further, the S20 includes: S201: collecting power consumption characteristics of the user corresponding to the power marketing business j within the distribution time of the matching risk business data, the power consumption characteristics including power consumption and equipment running state; When the total running time of the equipment within the distribution time is not equal to 0: the equipment running state value = the running time of the equipment at the rated voltage / the total running time of the equipment within the distribution time; When the total running time of the equipment within the distribution time is equal to 0: the equipment running state value = 0; S202: according to predicting the matching degree between the power marketing business j and the user corresponding to the power marketing business j, wherein p = 1, 2, …, q, represents the number corresponding to each equipment owned by the user corresponding to the power marketing business j, q represents the total number of numbers, K p represents the running state value of the equipment numbered p, g j represents the number of equipment owned by the user corresponding to the power marketing business j that is in the working state within the distribution time, D j represents the maximum power supply that the power enterprise can provide for the user within a unit time according to the contract specification signed with the user corresponding to the power marketing business j, G j represents the power consumption of the power marketing business j within the distribution time; S203: when U j ≥ 0, at this time, no warning information needs to be sent to the user terminal of the user corresponding to the power marketing business j, when U j < 0, at this time, warning information needs to be sent to the user terminal of the user corresponding to the power marketing business j.

[0008] Further, the S30 includes: When it is analyzed that warning information needs to be sent to the user terminal of the user corresponding to the power marketing business j, the real-time power supply reliability of the power enterprise is obtained within the distribution time, and if the obtained real-time power supply reliability < |D j -[G j / (t-T)]| / D j , the user corresponding to the power marketing business j cannot work normally within the distribution time, at this time, the time period in which the power supply reliability of the power enterprise is greater than (1+b)*{|D j -[G j / (t-T)]| / D j} is searched, wherein b represents an error coefficient; According to the coincidence degree between the searched time period and the power supply time period of each power marketing scheme in the power marketing scheme database, the associated power marketing scheme of the user corresponding to the power marketing business j is searched, and the specific searching method is: randomly selecting one power marketing scheme in the power marketing scheme database, the coincidence degree = the intersection time length value between the time period to be found and the power supply time period of the selected power marketing scheme / the time length value of the time period to be found; the power marketing scheme corresponding to the maximum coincidence degree is taken as the associated power marketing scheme of the user corresponding to the power marketing business j.

[0009] Further, the S40 includes: sending the associated power marketing scheme to be found to the user terminal of the user corresponding to the power marketing business j as early warning information, and the user corresponding to the power marketing business j autonomously selects whether to change the power marketing business according to the received early warning information, and the power marketing scheme of the changed power marketing business is the associated power marketing scheme to be found.

[0010] A power marketing risk management system based on big data, the system includes a risk business data analysis module, a matching degree analysis module, an associated power marketing scheme finding module and a power marketing risk management module; The risk business data analysis module is used to filter out the risk business data in the business data according to the constructed risk map; The matching degree analysis module is used to analyze the matching degree between the power marketing business and the user; The associated power marketing scheme finding module is used to find the associated power marketing scheme of the user in the power marketing scheme database; The power marketing risk management module is used to autonomously select whether to change the power marketing business according to the received early warning information.

[0011] Further, the risk business data analysis module includes a business data acquisition unit, a first risk index calculation unit, a second risk index calculation unit and a risk business data analysis unit; The business data acquisition unit acquires the real-time business data in the power marketing business; The first risk index calculation unit calculates the risk index of the power marketing business in the first dimension according to the business index data in the power marketing business, marks the power marketing business with abnormal power supply data, and combines the business data and the business index data of the power marketing business in the selected time period; The second risk index calculation unit calculates the risk index of the power marketing business in the second and third dimensions according to the business data and the business index data of the power marketing business in the selected time period; The risk business data analysis unit constructs the risk map of the power marketing business according to the risk index of the power marketing business in each dimension, and analyzes whether the acquired business data is risk business data according to the area of the constructed risk map.

[0012] Furthermore, the matching degree analysis module includes an electricity consumption characteristic acquisition unit, a matching degree prediction unit, and an early warning information sending unit; The electricity consumption characteristic collection unit collects the electricity consumption characteristics of users corresponding to the electricity marketing business within the distribution time of the matching risk business data. The matching degree prediction unit predicts the matching degree between the electricity marketing business and the users corresponding to the electricity marketing business based on the constructed mathematical model. The warning information sending unit selects whether to send warning information to the user terminal based on the matching degree predicted by the matching degree prediction unit.

[0013] Furthermore, the associated power marketing scheme search module includes a time period search unit and an associated power marketing scheme search unit; When the time period search unit analyzes and determines that it needs to send early warning information to the user terminal of the user corresponding to the power marketing business, it searches for the time period when the power company's power supply reliability rate is greater than the calculated value based on the real-time power supply reliability rate of the power company within the distributed time period. The associated power marketing scheme search unit searches for associated power marketing schemes for users corresponding to power marketing business j based on the overlap between the searched time period and the power supply time periods of each power marketing scheme in the power marketing scheme database.

[0014] Furthermore, the electricity marketing risk management module sends the identified related electricity marketing schemes as early warning information to the user terminals of the users corresponding to the electricity marketing business. Based on the received early warning information, the users corresponding to the electricity marketing business can choose whether to modify the electricity marketing business. The modified electricity marketing business will use the identified related electricity marketing schemes.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention calculates the risk index of each electricity marketing business across various dimensions using acquired business data, constructs a risk map based on the calculation results, analyzes whether the business data acquired by each electricity marketing business is risky business data, predicts the matching degree between electricity marketing businesses and users based on the electricity consumption characteristics of users within the distribution time of risky business data, and identifies related electricity marketing solutions. The identified related electricity marketing solutions are then used as modification plans for electricity marketing businesses. This application judges whether the acquired business data is risky business data based on multiple dimensions, fully considering the complexity and diversity of electricity marketing risks, and further enhances the system's ability to identify risks in electricity marketing businesses.

[0016] 2. This application analyzes the early warning status of users corresponding to electricity marketing business based on the electricity consumption characteristics of users during the distribution time of risk business data, and determines the compatibility between users corresponding to electricity marketing business and various electricity marketing plans. The compatibility depends on the degree of time overlap and the amount of calculation is small, thereby ensuring that the best risk response strategy is provided to users in a short period of time, and further improving the system's risk management effect on electricity marketing business.

[0017] 3. This invention identifies risk business data of electricity marketing business from multiple dimensions through business indicator data of electricity marketing business. Compared with the configured risk indicators of electricity marketing, this application can effectively identify risk business data of each electricity marketing business, and further improve the system's usability. Attached Figure Description

[0018] Fig. 1 This is a schematic diagram illustrating the workflow of a big data-based power marketing risk management system and method according to the present invention. Fig. 2 This is a schematic diagram illustrating the working principle of a big data-based power marketing risk management system and method according to the present invention. Fig. 3 This is a risk map diagram of a power marketing risk management system and method based on big data according to the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] like Figs. 1-3 As shown, this invention provides a technical solution for a power marketing risk management system and method based on big data, and a power marketing risk management method based on big data, the method comprising: S10: Obtain business data from the electricity marketing business, construct a risk map based on the business indicator data from the electricity marketing business, and analyze the risk business data in the business data; S10 includes: S101: Obtain real-time business data in the electricity marketing business. The business data includes electricity bill collection data, electricity marketing service data, electricity marketing power supply data and network payment data. The electricity marketing service data is uploaded by the user to the business data acquisition terminal through the APP. If the user does not update the electricity marketing service data in real time, the most recently uploaded electricity marketing service data will be used as the electricity marketing service data currently uploaded to the business data acquisition terminal. S102: Based on the business indicator data in the electricity marketing business, which includes electricity bill collection indicator data, electricity marketing service indicator data, electricity marketing supply indicator data, and network payment indicator data, the data values ​​of the business indicator data are determined by the contract specifications signed between the power company and the user. Electricity marketing businesses with abnormal electricity marketing supply data are marked. Abnormal electricity marketing supply data refers to real-time electricity marketing supply data in the electricity marketing business that is lower than the electricity marketing supply indicator data. Within the time period [T,t], the user corresponding to electricity marketing business j maintains a continuous electricity consumption status within the time period [T,t]. The time period [T,t] is divided into several time periods based on the data collection interval d. Each time period is numbered, and the numbering result is: i=1,2,…,n; n represents the total number of time periods. The risk index of electricity marketing business j in the first dimension is calculated. The first dimension refers to the electricity marketing supply dimension in electricity marketing business, where j=1,2,…,m, indicating that each electricity marketing business is numbered, m represents the total number of electricity marketing businesses, and w j V represents the number of times electricity marketing transaction j is marked within the time period [T,t]. ij E represents the supply voltage value of electricity marketing business j within the i-th time period. j R represents the power supply index value of power marketing business j. 1j This represents the risk index of electricity marketing business j in the first dimension; S103: According to The risk index of electricity marketing business j in dimension c is calculated, where c = 2, 3, representing the corresponding number of each dimension in the electricity marketing business. When c = 2, it represents the electricity marketing service dimension in the electricity marketing business. X 2j Y 2j These represent the electricity marketing service score index value and electricity marketing service score value of electricity marketing business j in the second dimension, respectively. When c=3, it represents the electricity price trend dimension in the electricity marketing business. X 3j This represents the network payment value of electricity marketing business j in the third dimension. The network payment value refers to the payment unit price corresponding to each kilowatt-hour of electricity consumption. 3jThis represents the electricity cost recovery value of electricity marketing business j in the third dimension. The electricity cost recovery value refers to the electricity cost recovery unit price per kilowatt-hour. R cj This represents the risk index of electricity marketing business j in the c-th dimension; S104: Based on the risk indices of electricity marketing business j across various dimensions, construct a risk map for electricity marketing business j. The risk map is a radar chart, with each dimension of electricity marketing business j serving as the axis labels and the risk indices of electricity marketing business j across each dimension serving as the data points on the corresponding dimension. Analyze the area S of the constructed risk map to determine whether the business data of electricity marketing business j within the time period [T,t] is risky. If S ≥ a set threshold, then the business data of electricity marketing business j within the time period [T,t] is considered risky. If S < a set threshold, then the business data of electricity marketing business j within the time period [T,t] is not considered risky. The risky business data of electricity marketing business j within the time period [T,t] represents the matching risky business data for the users corresponding to electricity marketing business j. The time period [T,t] is the distribution time of the matching risky business data. Fig. 3 The shaded area in the diagram represents the region S of the constructed risk map. S20: Collect electricity consumption characteristics of users corresponding to the electricity marketing business within the distribution time of matching risk business data; analyze the matching degree between the electricity marketing business and the users corresponding to the electricity marketing business based on the collected information; and select whether to send early warning information to the user terminals of the users corresponding to the electricity marketing business based on the analysis results. S20 includes: S201: During the distribution time of the matching risk business data for the user corresponding to the power marketing business j, the electricity consumption characteristics of the user corresponding to the power marketing business j are collected. The electricity consumption characteristics include electricity consumption and the operating status of electrical equipment. When the total operating time of electrical equipment within the distributed time is not equal to 0: Operating status value of electrical equipment = Operating time of electrical equipment under rated voltage / Total operating time of electrical equipment within the distributed time; When the total operating time of electrical equipment within the distributed time is equal to 0: the operating status value of electrical equipment = 0; S202: According to Predict the matching degree between electricity marketing business j and the users corresponding to electricity marketing business j, where p=1,2,…,q, representing the serial numbers of each electrical device owned by the user corresponding to electricity marketing business j, q representing the total number of serial numbers, and K… p This represents the operating status value of the electrical equipment numbered p, g j D represents the number of electrical devices owned by the user corresponding to electricity marketing business j that are in working condition during the distributed time period. jG represents the maximum amount of electricity that a power company can provide to a user within a unit of time, as specified in the contract between the power company and the user corresponding to the power marketing business j. j U represents the electricity consumption of electricity marketing business j within a given time period. j This indicates the matching degree between electricity marketing business j and the user corresponding to electricity marketing business j; S203: When U j When U ≥ 0, there is no need to send warning information to the user terminal of the user corresponding to the electricity marketing business j. j When <0, a warning message needs to be sent to the user terminal of the user corresponding to the electricity marketing business j; S30: When it is necessary to send early warning information to the user terminal of the user corresponding to the electricity marketing business, search for the relevant electricity marketing plan of the user corresponding to the electricity marketing business in the electricity marketing plan database according to the electricity consumption characteristics of the user corresponding to the electricity marketing business. S30 includes: When it is determined that an early warning message needs to be sent to the user terminal of the user corresponding to the electricity marketing business j, the real-time power supply reliability rate of the power company is obtained within the distribution time period. The power supply reliability rate = 1 - (total power supply of the power company during the statistical period / power generation of the power company during the statistical period). The power supply reliability rate ≥ 0. If the obtained real-time power supply reliability rate < |D j -[G j / (tT)]| / D j This indicates that the user corresponding to electricity marketing business j is unable to work normally within the distributed time period, and at this time the power supply reliability rate of the power company is greater than (1+b)*{|D j -[G j / (tT)]| / D j The search is performed within a time period of}, where b represents the error coefficient; Based on the overlap between the searched time period and the power supply time periods of each power marketing plan in the power marketing plan database, the associated power marketing plans for the user corresponding to power marketing business j are searched. The specific search method is as follows: Randomly select an electricity marketing plan from the electricity marketing plan database. The overlap rate is calculated as the crossover time length between the searched time period and the power supply time period of the selected electricity marketing plan / the time length of the searched time period. The electricity marketing plan corresponding to the maximum overlap is taken as the associated electricity marketing plan for the user corresponding to electricity marketing business j. S40: The found related electricity marketing schemes are sent as early warning information to the user terminals of the users corresponding to the electricity marketing business. The users corresponding to the electricity marketing business can choose whether to change the electricity marketing business based on the received early warning information. S40 includes: sending the found associated electricity marketing plan as early warning information to the user terminal of the user corresponding to the electricity marketing business j; the user corresponding to the electricity marketing business j can choose whether to change the electricity marketing business based on the received early warning information; the electricity marketing plan of the changed electricity marketing business is the found associated electricity marketing plan.

[0021] A big data-based power marketing risk management system includes a risk business data analysis module, a matching degree analysis module, a related power marketing solution search module, and a power marketing risk management module. The risk business data analysis module is used to filter out risky business data from the business data based on the constructed risk map; The risk business data analysis module includes a business data acquisition unit, a first risk index calculation unit, a second risk index calculation unit, and a risk business data analysis unit. The business data acquisition unit acquires real-time business data from the electricity marketing business; The first risk index calculation unit marks power marketing businesses with abnormal power marketing and supply data based on business indicator data in power marketing business. Combining the business data and business indicator data of power marketing business within a selected time period, it calculates the risk index of power marketing business in the first dimension. The second risk index calculation unit calculates the risk index of the power marketing business in the second and third dimensions based on the business data and business indicator data of the power marketing business within a selected time period. The risk business data analysis unit constructs a risk map of the power marketing business based on the risk index of the power marketing business in various dimensions, and analyzes whether the acquired business data is risk business data based on the area of ​​the constructed risk map. The matching degree analysis module is used to analyze the matching degree between electricity marketing business and users; The matching degree analysis module includes an electricity consumption characteristic acquisition unit, a matching degree prediction unit, and an early warning information sending unit; The electricity consumption characteristic collection unit collects the electricity consumption characteristics of users corresponding to the electricity marketing business within the distribution time of matching risk business data. The matching degree prediction unit predicts the matching degree between the electricity marketing business and the users corresponding to the electricity marketing business based on the constructed mathematical model; The early warning information sending unit selects whether to send early warning information to the user terminal based on the matching degree predicted by the matching degree prediction unit. The associated electricity marketing plan search module is used to search for users' associated electricity marketing plans in the electricity marketing plan database; The module for finding related electricity marketing solutions includes a time period search unit and a related electricity marketing solution search unit. When the time period search unit analyzes and determines that early warning information needs to be sent to the user terminals of users corresponding to the electricity marketing business, it searches for time periods in which the power company's power supply reliability rate is greater than the calculated value, based on the real-time power supply reliability rate of the power company within the distributed time period. The calculated value is (1+b)*{|D j -[G j / (tT)]| / D j}; The associated electricity marketing scheme search unit searches for associated electricity marketing schemes for users corresponding to electricity marketing business j based on the overlap between the search time period and the power supply time period of each electricity marketing scheme in the electricity marketing scheme database. The electricity marketing risk management module is used to autonomously choose whether to modify electricity marketing operations based on the received early warning information; The electricity marketing risk management module sends the identified related electricity marketing plans as early warning information to the user terminals of the users corresponding to the electricity marketing business. Based on the received early warning information, the users corresponding to the electricity marketing business can choose whether to modify the electricity marketing business. The modified electricity marketing business will use the identified related electricity marketing plans.

[0022] Example 1: Suppose that the user corresponding to electricity marketing business j has 3 electrical devices, and the operating status values ​​of the devices numbered 1, 2, and 3 are K1=0.3, K2=0.5, and K3=0, respectively. The contract signed between the power company and the user corresponding to electricity marketing business j specifies the maximum power supply D that can be provided to the user per unit time. j =40 kWh, with a unit time of 1 hour, the electricity consumption G of electricity marketing business j within the distributed time period. j =100 kWh, the number of electrical devices owned by the user corresponding to electricity marketing business j that are in working condition during the distributed time period, g. j =2, tT=2 hours, then the matching degree between electricity marketing business j and the user corresponding to electricity marketing business j is: ; Due to U j =-0.1<0, at this time it is necessary to send a warning message to the user terminal of the user corresponding to the electricity marketing business j; Assume that the real-time power supply reliability rate of the power company during the distributed time period is 0.02. Since the real-time power supply reliability rate of the power company is 0.02 < {|D}, j -[G j / (tT)]| / D j}=0.1, at this time the user corresponding to the electricity marketing business j cannot work normally during the distributed time period; Let b = 0.2. At this point, the power supply reliability for the power company is greater than (1 + 0.2) * {|D} j -[G j / (tT)]| / D j The search is conducted within a time period of 0.12.

[0023] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A big data-based method for electricity marketing risk management, characterized in that: The method includes: S10: Obtain business data from the electricity marketing business, construct a risk map based on the business indicator data from the electricity marketing business, and analyze the risk business data in the business data; S20: Collect electricity consumption characteristics of users corresponding to the electricity marketing business within the distribution time of matching risk business data; analyze the matching degree between the electricity marketing business and the users corresponding to the electricity marketing business based on the collected information; and select whether to send early warning information to the user terminals of the users corresponding to the electricity marketing business based on the analysis results. S30: When it is necessary to send early warning information to the user terminal of the user corresponding to the electricity marketing business, search for the associated electricity marketing plan of the user corresponding to the electricity marketing business in the electricity marketing plan database according to the electricity consumption characteristics of the user corresponding to the electricity marketing business. S40: The found related electricity marketing plans are sent as early warning information to the user terminals of the users corresponding to the electricity marketing business. The users corresponding to the electricity marketing business can choose whether to make changes to the electricity marketing business based on the received early warning information.

2. The method for power marketing risk management based on big data according to claim 1, characterized in that: S10 includes: S101: Obtain real-time business data in electricity marketing operations, including electricity bill collection data, electricity marketing service data, electricity marketing power supply data, and network payment data; S102: Based on the business indicator data in the power marketing business, mark the power marketing business with abnormal power marketing and power supply data. Within the time period [T,t], divide the time period [T,t] into several time periods by the data collection interval d. Based on the power supply voltage deviation coefficient of the power marketing business in each divided time period, and the marking status of the power marketing business in the [T,t] time period, the risk index of the power marketing business in the first dimension is calculated; The power supply voltage deviation coefficient is used to represent the difference between the power supply voltage value and the power supply index value of the power marketing business in each divided time period, and the ratio between the power supply voltage value and the power marketing index value of the power marketing business. S103: Determine the business data and business indicator data of the electricity marketing business in the second and third dimensions, and calculate the risk index of the electricity marketing business in the second or third dimension based on the proportion of business data in each dimension to business indicator data. S104: Based on the risk indices of the electricity marketing business in various dimensions, construct a risk map of the electricity marketing business. Analyze whether the business data of the electricity marketing business in the time period [T,t] is risky based on the area S of the constructed risk map. If S ≥ the set threshold, the business data of the electricity marketing business in the time period [T,t] is considered risky. If S < the set threshold, the business data of the electricity marketing business in the time period [T,t] is not considered risky.

3. The method for power marketing risk management based on big data according to claim 2, characterized in that: S20 includes: S201: During the distribution time of the matching risk business data, the electricity consumption characteristics of the users corresponding to the electricity marketing business are collected. The electricity consumption characteristics include electricity consumption and the operating status of electrical equipment. S202: Determine the theoretical power supply and actual power consumption of the electricity marketing business within the distributed time period, as well as the operating status values ​​of each electrical device owned by the users corresponding to the electricity marketing business, and predict the matching degree between the electricity marketing business and the users corresponding to the electricity marketing business. S203: When the matching degree is ≥ 0, there is no need to send a warning message to the user terminal of the user corresponding to the electricity marketing business. When the matching degree is < 0, it is necessary to send a warning message to the user terminal of the user corresponding to the electricity marketing business.

4. The power marketing risk management method based on big data according to claim 3, characterized in that: S30 includes: When it is determined that an early warning message needs to be sent to the user terminal of the user corresponding to the power marketing business, the real-time power supply reliability rate of the power company is obtained within the distribution time. If the obtained real-time power supply reliability rate is less than the power supply deviation coefficient of the power marketing business, it means that the user corresponding to the power marketing business cannot work normally within the distribution time. At this time, the time period in which the power company's power supply reliability rate is greater than (1+b)× the power supply deviation coefficient of the power marketing business is searched, where b represents the error coefficient. The power supply and consumption deviation coefficient of the power marketing business is used to represent the difference between the maximum power supply provided by the power company to the user per unit time and the power consumption of the power marketing business per unit time, and the ratio between the power company's maximum power supply to the user per unit time. Based on the overlap between the searched time period and the power supply time periods of each power marketing plan in the power marketing plan database, the associated power marketing plans for the user corresponding to power marketing business j are searched. The specific search method is as follows: Randomly select an electricity marketing plan from the electricity marketing plan database. The overlap rate is calculated as the length of the crossover between the searched time period and the power supply time period of the selected electricity marketing plan, divided by the length of the searched time period. The electricity marketing plan corresponding to the maximum overlap is taken as the associated electricity marketing plan for the user corresponding to electricity marketing business j.

5. The power marketing risk management method based on big data as described in claim 4, characterized in that: S40 includes: sending the found associated electricity marketing plan as early warning information to the user terminal of the user corresponding to the electricity marketing business; the user corresponding to the electricity marketing business can choose whether to change the electricity marketing business based on the received early warning information; the electricity marketing plan of the changed electricity marketing business is the found associated electricity marketing plan.

6. A big data-based power marketing risk management system applied to the big data-based power marketing risk management method according to any one of claims 1-5, characterized in that: The system includes a risk business data analysis module, a matching degree analysis module, a related power marketing scheme search module, and a power marketing risk management module. The risk business data analysis module is used to filter out risk business data from the business data based on the constructed risk map. The matching degree analysis module is used to analyze the matching degree between electricity marketing business and users; The associated electricity marketing scheme search module is used to search for users' associated electricity marketing schemes in the electricity marketing scheme database; The electricity marketing risk management module is used to autonomously choose whether to change the electricity marketing business based on the received early warning information.

7. The power marketing risk management system based on big data according to claim 6, characterized in that: The risk business data analysis module includes a business data acquisition unit, a first risk index calculation unit, a second risk index calculation unit, and a risk business data analysis unit. The business data acquisition unit acquires real-time business data in the power marketing business; The first risk index calculation unit marks the power marketing business with abnormal power marketing and supply data based on the business indicator data in the power marketing business, and calculates the risk index of the power marketing business in the first dimension by combining the business data and business indicator data of the power marketing business in the selected time period. The second risk index calculation unit calculates the risk index of the power marketing business in the second and third dimensions based on the business data and business indicator data of the power marketing business within a selected time period. The risk business data analysis unit constructs a risk map of the power marketing business based on the risk index of the power marketing business in various dimensions, and analyzes whether the acquired business data is risk business data based on the area of ​​the constructed risk map.

8. The power marketing risk management system based on big data according to claim 7, characterized in that: The matching degree analysis module includes an electricity consumption characteristic acquisition unit, a matching degree prediction unit, and an early warning information sending unit; The electricity consumption characteristic collection unit collects the electricity consumption characteristics of users corresponding to the electricity marketing business within the distribution time of the matching risk business data. The matching degree prediction unit predicts the matching degree between the electricity marketing business and the users corresponding to the electricity marketing business based on the constructed mathematical model. The warning information sending unit selects whether to send warning information to the user terminal based on the matching degree predicted by the matching degree prediction unit.

9. The power marketing risk management system based on big data according to claim 8, characterized in that: The related power marketing scheme search module includes a time period search unit and a related power marketing scheme search unit; When the time period search unit analyzes and determines that it needs to send early warning information to the user terminal of the user corresponding to the power marketing business, it searches for the time period when the power company's power supply reliability rate is greater than the calculated value based on the real-time power supply reliability rate of the power company within the distributed time period. The associated power marketing scheme search unit searches for associated power marketing schemes for users corresponding to power marketing business j based on the overlap between the searched time period and the power supply time periods of each power marketing scheme in the power marketing scheme database.

10. The power marketing risk management system based on big data according to claim 9, characterized in that: The electricity marketing risk management module sends the identified related electricity marketing schemes as early warning information to the user terminals of the users corresponding to the electricity marketing business. Based on the received early warning information, the users corresponding to the electricity marketing business can choose whether to modify the electricity marketing business. The modified electricity marketing business will use the identified related electricity marketing schemes.

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