Important information supplementing and updating system applied to electricity customers

Abstract

The invention, which relates to the technical field of electric power informatization, discloses an important information supplementing and updating system applied to an electricity customer, comprising an intelligent sensing module, a self-adaptive updating module, an information fusion and mining module and a security interaction module. The intelligent sensing module is used for connecting a sensor with an intelligent electric meter and a network monitoring system through the Internet of Things technology, collecting environment and power utilization parameters in real time, analyzing a power utilization behavior mode, providing data support for judging customer information change, and realizing comprehensive sensing of customer power utilization conditions and environmental influences; according to the invention, the Internet of Things technology is utilized to cooperatively work with the sensor, the intelligent electric meter and the network monitoring system, environment and power utilization parameters are collected in real time, the influence of environmental factors on the load of the power utilization equipment can be accurately analyzed through a power utilization equipment load change estimation formula, and a basis is provided for power distribution in advance; and power supply imbalance caused by information lag is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power informatization, and in particular to a system for supplementing and updating important information of electricity users. Background Art

[0002] In the power industry, electricity customer information management is crucial for power companies to provide high-quality services and ensure the stable operation of the power system. Traditional methods of managing electricity customer information mainly rely on manual operations and simple database records. In terms of information collection, basic information is usually registered once when a customer opens an account, and there is a lack of effective automation mechanism for subsequent information updates. For example, important information such as customer address changes and contact information changes often require customers to actively contact the power company for modification, and many customers may fail to update in time due to negligence or cumbersome procedures, resulting in deviations between the customer information held by the power company and the actual situation. This untimely information update will cause many problems in actual business, such as the failure to accurately deliver electricity bills, affecting customer payment, and may lead to adverse consequences such as power outages due to arrears; when repairing power failures, customers cannot be contacted in time, delaying the repair time, causing inconvenience to customers, and also reducing the service efficiency and quality of the power company;

[0003] In the traditional model, the accuracy of customer information is difficult to guarantee. Errors are prone to occur during manual information entry, and there is a lack of effective review and verification mechanisms for some important information (such as changes in customer power load). In addition, with the diversified development of power business, power companies obtain customer information from multiple channels, including marketing systems, customer service feedback, on-site surveys, etc., but this information is scattered in different subsystems and cannot be effectively integrated and managed in a unified manner. The inconsistent data formats and standards between subsystems lead to serious information duplication, conflict and redundancy. For example, the customer power type information in the marketing system may not match the data in the meter data collection system, making it impossible for power companies to obtain accurate and complete information when performing electricity bill calculations, power allocation and other businesses, which seriously affects the scientificity and accuracy of business decisions.

[0004] The existing electricity customer information management system seems to be unable to cope with the increasingly complex customer situations and frequent information changes. There is a lack of effective trigger mechanism to actively perceive changes in customer information, and it can only passively wait for customer feedback or regular manual inspections, which makes the information update lag behind. At the same time, in the process of information update, there is no scientific and reasonable evaluation system to determine which information needs to be updated first and the degree of necessity of the update. For example, in the case of a substantial increase in electricity load caused by the addition of large-scale production equipment to corporate customers, the existing system may not be able to detect and accurately evaluate its impact on power supply and billing in a timely manner, and thus cannot adjust relevant business parameters and service strategies in a timely manner. In addition, in the process of interaction with customers, information security and customer privacy protection measures are not perfect, and customers have low participation and trust in information updates, which affects the smooth progress of information updates. In summary, the traditional electricity customer information management method can no longer meet the needs of the development of the modern power industry, and there is an urgent need for a more intelligent, efficient, accurate and secure information supplement and update system.

[0005] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0006] The purpose of the present invention is to solve the problems of untimely updates, low accuracy, difficult integration and lack of effective triggering and evaluation mechanisms in traditional electricity customer information management, as well as insufficient security and privacy protection, low customer participation and trust in the information interaction process, and proposes a system for supplementing and updating important information of electricity customers.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] Applied to the important information supplement and update system for electricity users, including:

[0009] Intelligent sensing module, which is used to connect sensors with smart meters and network monitoring systems through the Internet of Things technology, collect environmental and power parameters in real time, analyze power consumption patterns, provide data support for judging changes in customer information, and achieve comprehensive perception of customer power consumption and environmental impact;

[0010] Adaptive update module, which is used to automatically determine and execute customer information updates based on information fusion and mining results and predefined strategies, and dynamically adjust relevant business system parameters and models to ensure that information is timely and accurate, so that power services can adapt to changes in customer information;

[0011] Information fusion and mining module, which is used to integrate the data of intelligent perception module and traditional channel information, and use data fusion algorithm and machine learning algorithm to mine the hidden relationships and rules in customer information, providing a basis for accurately evaluating the demand for information update;

[0012] The secure interaction module is used to follow the authorization mechanism in the collection and update of customer information to ensure information security. At the same time, it collects customer opinions and calculates satisfaction through multiple channels to feedback update information, promote service optimization, and enhance customer trust and interaction.

[0013] Furthermore, the execution process of the intelligent perception module is as follows:

[0014] Using the Internet of Things technology, it is connected to sensors installed around the customer's electrical equipment. The sensors collect environmental and power parameters in real time, and through data analysis, it determines the potential impact of environmental factors on electrical equipment and customer's power usage habits. Through the formula: Where ΔP represents the estimated load change of electrical equipment, k is the equipment load temperature coefficient, is the rate of change of ambient temperature, The rate of change of the current of the electrical equipment. By real-time monitoring of the ambient temperature and equipment current changes, the formula is used to estimate the change of the load of the electrical equipment, providing a basis for the deployment of the power system and the update of customer information in advance.

[0015] By working with smart meters and the power company's network monitoring system, we can analyze the customer's electricity consumption behavior patterns; identify the customer's peak and valley periods, electricity consumption frequency, and power change information; and combine network access data to infer changes in the customer's life or production patterns; through the formula: Where S ij represents the similarity of the electricity consumption behavior patterns of customers i and j in the preset time period, x ik and x jk are the electricity consumption characteristic values ​​of customer i and customer j at the kth time point, and is the average value of the electricity consumption characteristic value of the corresponding customer during the time period, and n is the number of time points. By calculating the similarity of the electricity consumption behavior patterns between customers, the customer group whose absolute value of the similarity difference is less than the threshold is screened to analyze the group electricity consumption trend. When the deviation between a customer's electricity consumption behavior and the group pattern is greater than the preset deviation, the existing information changes are discovered in time, thereby triggering the information update process.

[0016] Furthermore, the specific operation steps of the information fusion and mining module are as follows:

[0017] The data obtained from the intelligent sensing module is integrated with the information from traditional channels, including customer account registration information and customer service feedback records. The data fusion algorithm is used to standardize data in different formats and from different sources to eliminate data conflicts and redundancies.

[0018] When integrating information from different sources, the credibility differences of each information source are analyzed, and accurate integrated information is obtained through the following formula: Where F represents the information value after fusion, w i is the credibility weight of the ith information source, which is pre-set based on factors such as the reliability and accuracy of the information source, and I i is the information value provided by the i-th information source, including sensor measurement values, customer registration information values, etc., and m is the number of information sources;

[0019] Use machine learning algorithms to conduct in-depth mining of the integrated information to uncover the hidden relationships and patterns in customer information.

[0020] Furthermore, the specific operation steps of the adaptive update module are as follows:

[0021] According to the results of information fusion and mining, combined with the predefined update strategy, it is automatically determined whether the customer information needs to be updated and the content of the update; the difference between the current collected information and the original information is quantitatively compared, and the necessity of information update is evaluated in combination with the attribute importance weight, through the formula: Where U represents the information update necessity index, a j is the importance weight of the jth information attribute, C j is the value of the attribute in the currently collected customer information. is the original value of the attribute in the customer information database, and p is the number of information attributes; when U exceeds the preset update necessity index threshold, it is determined that the customer information needs to be updated;

[0022] The importance weight of the information attribute is a j It is set according to the power business needs and the impact of customer information on the service;

[0023] After the decision is updated, the information update operation is automatically executed to synchronize the updated information to the customer information database of the power company; at the same time, according to the nature and scope of the updated content, the parameters and models of the relevant business systems are dynamically adjusted to adapt to the new customer information status. After the customer's electricity information is updated, the unit price parameters in the electricity fee calculation system are dynamically adjusted according to the following formula Where P new is the adjusted electricity price, P old is the original electricity price, β is the adjustment coefficient, ΔE is the change in customer electricity consumption, E old is the customer’s original electricity consumption;

[0024] According to this formula, the electricity fee calculation is more in line with the customer's actual electricity consumption, ensuring that the power company's billing is accurate and reasonable. At the same time, it also allows customers to clearly understand the relationship between changes in electricity costs and information updates.

[0025] Furthermore, the importance weight a of the information attribute in the adaptive update module j The specific steps of the analysis are as follows:

[0026] The importance weight of information attribute a j Through comprehensive analysis of influencing parameters, the influencing parameters include:

[0027] Information accuracy influencing parameters include historical data accuracy score and data source reliability score. The historical data accuracy score is obtained by counting the ratio of the number of accurate records of the information attribute data in the past preset time period to the total number of records, multiplied by 100 to obtain a score between 0 and 100, which is recorded as the accuracy value; the data source reliability score is assigned according to the reliability of the information source channel and recorded as the trust value. After the obtained accuracy value and trust value are normalized, the accuracy value and trust value are used as the two right-angled sides of the right triangle respectively, and the other side of the triangle is connected. A triangular pyramid model is established with the triangle combined with the preset height, and the volume of the triangular pyramid model is calculated and recorded as the accuracy judgment value. This accuracy judgment value is used as the standard for measuring information accuracy;

[0028] The business correlation degree parameters include the direct business correlation degree and the business process dependence degree. The direct business correlation degree is assigned by the correlation judgment of the information attribute. When the information attribute directly affects the core power business, including electricity fee calculation, power distribution, etc., the value is assigned to 10; if it has an indirect impact, the value is assigned to 5; if there is no impact, the value is assigned to 1 and recorded as the correlation value ax; the business process dependence degree is calculated by counting the frequency of the information attribute being used in the business process, recorded as the frequency judgment value po; the obtained correlation value ax and frequency judgment value po are normalized and then entered into the following formula: To obtain the associated judgment value GVD, where is the correction coefficient, and the final correlation judgment value GVD is used as the standard to measure the degree of correlation between information attributes and business;

[0029] The information change sensitivity parameter includes the change frequency and the change impact range. The change frequency is obtained by counting the number of changes of the information attribute in the preset time period and dividing it by the total monitoring time to obtain the average number of changes per month, and then assigning a value according to the range of the number of changes, which is recorded as the frequency score; the change impact range is evaluated according to the impact of the information change on the power company's services, and quantified by the number of affected departments or business links, which is recorded as the change value. The obtained frequency score and change value are calibrated as gf and by, respectively, and after normalization, they are inserted into the following formula: XMZ = gf × λ1 + by × λ2 to obtain the signal sensitivity value XMZ, where λ1 and λ2 are the preset weight coefficients of the frequency score and the change value, respectively, and the obtained signal sensitivity value XMZ is used as the standard for measuring the information change sensitivity parameter;

[0030] The customer privacy importance parameters, including the privacy leakage risk level, are evaluated according to the nature of the information attributes and the degree of harm caused by the leakage. They are divided into three levels: high, medium, and low, and are assigned values ​​of 10, 6, and 2 respectively, and recorded as the customer weight value. This customer weight value is used as the standard for measuring the customer privacy importance parameters;

[0031] Then, the calculated accuracy value XCF, association value GVD, signal sensitivity value XMZ and customer weight value GZZ are normalized and inserted into the following formula: TGR = XCF × ν1 + GVD × ν2 + XMZ × ν3 + GZZ × ν4 to obtain the comprehensive score TGR, where ν1, ν2, ν3, and ν4 are the preset weight coefficients of the accuracy value XCF, association value GVD, signal sensitivity value XMZ and customer weight value GZZ, respectively, and this comprehensive score is used as the importance weight a of the information attribute. j standards;

[0032] The obtained comprehensive score TGR is compared with several preset comprehensive score intervals, and the several comprehensive score intervals correspond to different importance weights of information attributes. When the comprehensive score interval to which the comprehensive score TGR belongs is determined, the importance weight of the information attribute is determined.

[0033] Furthermore, the specific operation steps of the security interaction module are as follows:

[0034] During the collection and update process of customer authorization information, the security risk of information interaction is continuously evaluated through the following formula: Where R represents the security risk value of customer information during the interaction process, b k is the weight of the kth security risk factor, L k is the risk level of the kth security risk factor, determined through real-time monitoring or pre-assessment, and q is the number of security risk factors;

[0035] If R exceeds the set security threshold, the system automatically takes corresponding security enhancement measures to ensure the security of customer information;

[0036] In the process of collecting and updating customer information, we follow the customer authorization mechanism and ensure that customers clearly know and authorize the scope of information collection and updating when they use mobile terminals or other devices to interact with the system through secure communication protocols and encryption technology;

[0037] Before and after the information is updated, the customer will be informed of the update status through SMS, APP push, and email, informing the customer of which information has been updated, the basis for the update, and the possible impact on the customer's electricity service;

[0038] At the same time, establish customer feedback channels to facilitate customers to raise questions or objections to information updates, respond and handle them in a timely manner, and collect customer feedback to calculate customer satisfaction.

[0039] Furthermore, the specific operation steps of collecting customer feedback and calculating customer feedback satisfaction in the security interaction module are as follows:

[0040] By formula: Where S represents customer feedback satisfaction, s l is the score corresponding to the lth feedback evaluation level, N l is the number of the first type of feedback evaluation received, and r is the number of feedback evaluation levels;

[0041] By calculating customer feedback satisfaction, we can quantitatively understand the customer's satisfaction with the information update process and results;

[0042] Based on the satisfaction results, we can improve service processes and optimize information update strategies in a targeted manner to enhance customer experience.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] (1) The present invention uses the Internet of Things technology to work in conjunction with sensors, smart meters and network monitoring systems to collect environmental and power parameters in real time. Through the power equipment load change prediction formula, it can accurately analyze the impact of environmental factors on the power equipment load, provide a basis for power allocation in advance, and avoid power supply imbalance caused by information lag; at the same time, the power behavior pattern similarity calculation can accurately identify changes in customer power usage habits, timely discover information changes such as new home appliances or corporate production adjustments, quickly trigger the update process, and ensure information timeliness; the information fusion and mining module integrates multi-source information, uses data fusion algorithms and machine learning algorithms, and deeply mines customer information hiding rules to provide an accurate basis for evaluating information update needs, effectively solving the problems of low accuracy and untimely updates in traditional information management;

[0045] (2) In the present invention, in terms of power service optimization, the adaptive update module automatically determines and executes information updates based on the fusion and mining results, dynamically adjusts business system parameters, and adjusts the unit price formula of the electricity fee calculation system to make the electricity fee calculation fit the actual electricity consumption, ensure reasonable and accurate billing, and enhance customers' trust in the power company's billing; optimizes business systems such as power allocation according to the updated content, improves the adaptability of power services to changes in customer information, ensures stable and reliable power supply, and improves service efficiency and quality; the security interaction module follows a strict authorization mechanism in information collection and update, adopts secure communication protocols and encryption technology to ensure information security and privacy, monitors interactive security in real time through security risk assessment formulas, takes enhanced measures in a timely manner, and enhances customer trust. At the same time, it provides feedback on update status through multiple channels and calculates satisfaction, optimizes service processes and update strategies based on customer opinions, strengthens interaction with customers, and improves customer satisfaction and loyalty;

[0046] (3) The present invention has a positive impact on the overall operational efficiency of the power system. Through intelligent perception and adaptive update functions, power companies can reasonably allocate resources according to real-time changes in customer electricity consumption, avoid resource waste or shortages, reduce operating costs, and improve grid operation efficiency and stability. Information fusion and mining provide forward-looking decision support for grid planning, help to plan infrastructure construction in advance, and optimize long-term resource allocation. In addition, the system's comprehensive perception and timely update of customer information helps to identify risks and potential failures of power equipment in advance, reduce the incidence of power accidents, and ensure the safe operation of the power system. Accurate information analysis provides a scientific basis for power companies' decisions, enhances market competitiveness, promotes the sustainable development of the power industry, and adapts to the growing and changing needs of social and economic development for power services. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0048] Figure 1 This is the overall system block diagram of the present invention. DETAILED DESCRIPTION

[0049] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] It should be understood that the terms "include" and "comprising" used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0051] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and claims, the singular forms of "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations.

[0052] like Figure 1 As shown, it is applied to the important information supplement and update system for electricity users, including intelligent perception module, adaptive update module, information fusion and mining module and security interaction module;

[0053] The intelligent sensing module is used to connect sensors with smart meters and network monitoring systems through the Internet of Things technology, collect environmental and power usage parameters in real time, analyze power usage behavior patterns, provide data support for judging changes in customer information, and achieve comprehensive perception of customer power usage and environmental impact;

[0054] Using the Internet of Things technology, it is connected to sensors installed around the customer's electrical equipment (such as temperature sensors, humidity sensors, current and voltage sensors, etc.). These sensors collect environmental and power parameters in real time, and through data analysis, determine the potential impact of environmental factors on electrical equipment and customer power habits; through the formula: Where ΔP represents the estimated load change of the electrical equipment (unit: kilowatt), k is the equipment load temperature coefficient (different equipment types have different coefficient values, which need to be determined in advance through experiments or empirical data), is the rate of change of ambient temperature (unit: degrees Celsius / minute), The current change rate of the electrical equipment; by real-time monitoring of ambient temperature and equipment current changes, the formula is used to estimate the change in the load of the electrical equipment, providing a basis for the deployment of the power system and the update of customer information in advance

[0055] By working in collaboration with smart meters and the power company's network monitoring system, the customer's electricity consumption behavior patterns are analyzed; the peak and valley periods of the customer's electricity consumption, the frequency of electricity consumption, and the power change information are identified. At the same time, combined with network access data (if the customer conducts business inquiries and other operations through the power company's network platform), the customer's life or production law changes are inferred; through the formula: Where S ij represents the similarity of the electricity consumption behavior patterns of customers i and j in the preset time period, x ik and x jk are the electricity consumption characteristic values ​​(such as power, electricity consumption, etc.) of customer i and customer j at the kth time point, and is the average value of the electricity consumption characteristic value of the corresponding customer in the time period, and n is the number of time points;

[0056] By calculating the similarity of electricity usage behavior patterns among customers, we can find customer groups with similar electricity usage habits, which can be used to analyze group electricity usage trends. When the deviation between a customer's electricity usage behavior and the group pattern is greater than the preset deviation, we can promptly discover existing information changes (such as enterprise production adjustments, new large appliances in the home, etc.), thereby triggering the information update process.

[0057] The information fusion and mining module is used to integrate the data of the intelligent perception module and the information of traditional channels, and use the data fusion algorithm and machine learning algorithm to mine the hidden relationships and rules in customer information, provide a basis for accurately evaluating the needs of information updates, and ensure the integrity and in-depth utilization of information;

[0058] The data obtained from the intelligent perception module is integrated with the information from traditional channels, where traditional channels include customer account registration information and customer service feedback records. The data fusion algorithm is used to standardize data in different formats and from different sources to eliminate data conflicts and redundancies. When integrating information from different sources, the credibility differences of each information source are taken into account, and more accurate and reliable integrated information is obtained through the following formula: Where F represents the information value after fusion, w i is the credibility weight of the ith information source, which is pre-set based on factors such as the reliability and accuracy of the information source, and I i is the information value provided by the i-th information source, including sensor measurement values, customer registration information values, etc., and m is the number of information sources;

[0059] Use machine learning algorithms (such as cluster analysis, association rule mining, etc.) to conduct in-depth mining of the fused information. Dig out the hidden relationships and patterns in customer information, such as finding that certain types of corporate customers often have specific changes in electricity parameters and equipment purchase behaviors before expanding their production scale, thereby predicting possible important changes in customer information in advance.

[0060] The adaptive update module is used to automatically determine and execute customer information updates based on information fusion and mining results and predefined strategies, and dynamically adjust relevant business system parameters and models to ensure that information is timely and accurate, so that power services can adapt to changes in customer information;

[0061] According to the results of information fusion and mining, combined with predefined update strategies (these strategies can be flexibly adjusted according to different types of customers and business scenarios), it is automatically determined whether customer information needs to be updated and the content of the update; the difference between the current collected information and the original information is specifically quantified and compared, and combined with the attribute importance weight, the necessity of information update is accurately evaluated through the formula: Where U represents the information update necessity index, a j is the importance weight of the jth information attribute, C j is the value of the attribute in the currently collected customer information. is the original value of the attribute in the customer information database, and p is the number of information attributes; when U exceeds the preset update necessity index threshold, it is determined that the customer information needs to be updated;

[0062] The importance weight of the information attribute is a j The impact of customer information on the service is set according to the power business needs and the impact of customer information on the service. The impact parameters are comprehensively analyzed and include:

[0063] Information accuracy influencing parameters include historical data accuracy score and data source reliability score. The historical data accuracy score is obtained by counting the ratio of the number of accurate records of the information attribute data to the total number of records in the past period of time, multiplying it by 100 to obtain a score between 0 and 100, which is recorded as the accuracy value. The data source reliability score is assigned according to the reliability of the information source channel and recorded as the trust value. After the obtained accuracy value and trust value are normalized, the accuracy value and trust value are used as the two right-angled sides of the right triangle respectively, and the other side of the triangle is connected. A triangular pyramid model is established with the triangle combined with the preset height, and the volume of the triangular pyramid model is calculated and recorded as the reliability judgment value. This reliability judgment value is used as the standard for measuring information accuracy.

[0064] The business correlation degree parameters include the direct business correlation degree and the business process dependence degree. The direct business correlation degree is assigned by the correlation judgment of the information attribute. When the information attribute directly affects the core power business, including electricity fee calculation, power distribution, etc., the value is assigned to 10; if it has an indirect impact, the value is assigned to 5; if there is no impact, the value is assigned to 1 and recorded as the correlation value ax; the business process dependence degree is calculated by counting the frequency of the information attribute being used in the business process, recorded as the frequency judgment value po; the obtained correlation value ax and frequency judgment value po are normalized and then entered into the following formula: To obtain the associated judgment value GVD, where is the correction coefficient, the value is set between 2 and 6, and the final correlation judgment value GVD is used as the standard to measure the degree of correlation between information attributes and business;

[0065] The information change sensitivity parameter includes the change frequency and the change impact range. The change frequency is obtained by counting the number of changes of the information attribute in the preset time period and dividing it by the total monitoring time to obtain the average number of changes per month, and then assigning a value according to the range of the number of changes, which is recorded as the frequency score; the change impact range is evaluated according to the impact of the information change on the power company's services, and quantified by the number of affected departments or business links, which is recorded as the change value. The obtained frequency score and change value are calibrated as gf and by, respectively, and after normalization, they are inserted into the following formula: XMZ = gf × λ1 + by × λ2 to obtain the signal sensitivity value XMZ, where λ1 and λ2 are the preset weight coefficients of the frequency score and the change value, respectively, and the obtained signal sensitivity value XMZ is used as the standard for measuring the information change sensitivity parameter;

[0066] The customer privacy importance parameters, including the privacy leakage risk level, are evaluated according to the nature of the information attributes and the degree of harm that may be caused once leakage occurs. They are divided into three levels: high, medium, and low, and are assigned values ​​of 10, 6, and 2 respectively, and recorded as customer weight values. This customer weight value is used as the standard for measuring the customer privacy importance parameters;

[0067] Then, the calculated accuracy value XCF, association value GVD, signal sensitivity value XMZ and customer weight value GZZ are normalized and inserted into the following formula: TGR = XCF × ν1 + GVD × ν2 + XMZ × ν3 + GZZ × ν4 to obtain the comprehensive score TGR, where ν1, ν2, ν3, and ν4 are the preset weight coefficients of the accuracy value XCF, association value GVD, signal sensitivity value XMZ and customer weight value GZZ, respectively, and this comprehensive score is used as the importance weight a of the information attribute. j The obtained comprehensive score TGR is compared with several preset comprehensive score intervals, and the several comprehensive score intervals correspond to different importance weights of information attributes. When the comprehensive score interval to which the comprehensive score TGR belongs is determined, the importance weight of the information attribute is determined.

[0068] After the decision is updated, the information update operation is automatically executed to synchronize the updated information to the customer information database of the power company. At the same time, it can also dynamically adjust the parameters and models of related business systems (such as power dispatch system, electricity fee calculation system, etc.) according to the nature and scope of the updated content to adapt to the new customer information status. After the customer's electricity consumption information is updated (such as a significant increase in electricity consumption due to the addition of new equipment), the unit price parameters in the electricity fee calculation system are dynamically adjusted according to the following formula Where P new is the adjusted electricity price, P old is the original electricity price, β is the adjustment coefficient (determined according to the power company's pricing strategy and cost factors), ΔE is the customer's electricity consumption change (calculated based on the updated electricity consumption information), and E oldIt is the customer's original electricity consumption; according to this formula, the electricity fee calculation is more in line with the customer's actual electricity consumption, ensuring that the power company's billing is accurate and reasonable, while also allowing customers to clearly understand the relationship between changes in electricity costs and information updates.

[0069] The secure interaction module is used to follow the authorization mechanism in the collection and update of customer information to ensure information security. At the same time, it collects customer opinions and calculates satisfaction through multiple channels to feedback the update status, promote service optimization, and enhance customer trust and interaction.

[0070] During the collection and update process of customer authorization information, the security risk of information interaction is continuously evaluated through the following formula: Where R represents the security risk value of customer information during the interaction process, b k is the weight of the kth security risk factor (such as the weight of communication protocol security, data encryption strength, etc., which is set according to industry standards and expert experience), L k is the risk level of the kth security risk factor (determined through real-time monitoring or pre-assessment, with a value range of 1-5, 1 for low risk and 5 for high risk), and q is the number of security risk factors; if R exceeds the set security threshold, the system automatically takes corresponding security enhancement measures (such as upgrading encryption algorithms, prompting customers to re-authorize, etc.) to ensure customer information security, prevent information leakage and malicious tampering, and enhance customer trust in the system. In the process of collecting and updating customer information, follow the customer authorization mechanism, and use secure communication protocols (such as HTTPS, etc.) and encryption technology to ensure that customers are clearly aware of and authorize the scope of information collection and update when customers use mobile terminals or other devices to interact with the system;

[0071] Before and after the information is updated, timely feedback is provided to customers. Through SMS, APP push, email and other methods, customers are informed of which information is updated, the basis for the update and the possible impact on the customer's electricity service. At the same time, a customer feedback channel is established to facilitate customers to raise questions or objections to information updates. The system can respond and handle them in a timely manner. Customer feedback is calculated by collecting customer feedback, and the formula is: Where S represents customer feedback satisfaction, s l is the score corresponding to the first type of feedback evaluation level (specifically, very satisfied is 5 points, satisfied is 4 points, dissatisfied is 3 points, etc.), N l is the number of the th feedback evaluation received, and r is the number of feedback evaluation levels; by calculating customer feedback satisfaction, we can quantitatively understand the customer's satisfaction with the information update process and results; based on the satisfaction results, we can improve the service process and optimize the information update strategy in a targeted manner, improve customer experience, and further strengthen the good interactive relationship with customers.

[0072] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. Applied to the important information supplement and update system for electricity users, characterized by: include: Intelligent sensing module, which is used to connect sensors with smart meters and network monitoring systems through the Internet of Things technology, collect environmental and power parameters in real time, analyze power consumption patterns, provide data support for judging changes in customer information, and achieve comprehensive perception of customer power consumption and environmental impact; Adaptive update module, which is used to automatically determine and execute customer information updates based on information fusion and mining results and predefined strategies, and dynamically adjust relevant business system parameters and models to ensure that information is timely and accurate, so that power services can adapt to changes in customer information; Information fusion and mining module, which is used to integrate the data of intelligent perception module and traditional channel information, and use data fusion algorithm and machine learning algorithm to mine the hidden relationships and rules in customer information, providing a basis for accurately evaluating the needs of information update; The secure interaction module is used to follow the authorization mechanism in the collection and update of customer information to ensure information security. At the same time, it collects customer opinions and calculates satisfaction through multiple channels to feedback the update status.

2. The important information supplement and update system for electricity customers according to claim 1 is characterized in that: The execution process of the intelligent perception module is as follows: Using the Internet of Things technology, it is connected to sensors installed around the customer's electrical equipment. The sensors collect environmental and power parameters in real time, and through data analysis, it determines the potential impact of environmental factors on electrical equipment and customer's power usage habits. Through the formula: Where ΔP represents the estimated load change of electrical equipment, k is the equipment load temperature coefficient, is the rate of change of ambient temperature, is the rate of change of current of the electrical equipment; By monitoring the changes in ambient temperature and equipment current in real time, the formula is used to estimate the changes in the load of electrical equipment, providing a basis for the deployment of the power system and the update of customer information in advance; By working together with smart meters and the power company's network monitoring system, we can analyze the customer's electricity consumption behavior patterns; identify the customer's peak and valley periods, electricity consumption frequency, and power change information; and at the same time, combine network access data to infer changes in the customer's life or production patterns; through the formula: Where S ij represents the similarity of the electricity consumption behavior patterns of customers i and j in the preset time period, x ik and x jk are the electricity consumption characteristic values ​​of customer i and customer j at the kth time point, and is the average value of the electricity consumption characteristic value of the corresponding customer during the time period, and n is the number of time points. By calculating the similarity of the electricity consumption behavior patterns between customers, the customer group whose absolute value of the similarity difference is less than the threshold is screened to analyze the group electricity consumption trend. When the deviation between a customer's electricity consumption behavior and the group pattern is greater than the preset deviation, the existing information changes are discovered in time, thereby triggering the information update process.

3. The important information supplement and update system for electricity customers according to claim 1 is characterized in that: The specific operation steps of the information fusion and mining module are as follows: The data obtained from the intelligent sensing module is integrated with the information from traditional channels, including customer account registration information and customer service feedback records. The data fusion algorithm is used to standardize data in different formats and from different sources to eliminate data conflicts and redundancies. When integrating information from different sources, the credibility differences of each information source are analyzed, and accurate integrated information is obtained through the following formula: Where F represents the information value after fusion, w i is the credibility weight of the ith information source, which is pre-set based on the reliability and accuracy factors of the information source, and I i is the information value provided by the i-th information source, including sensor measurement values ​​and customer registration information values, and m is the number of information sources; Use machine learning algorithms to conduct in-depth mining of the integrated information to uncover the hidden relationships and patterns in customer information.

4. The important information supplement and update system for electricity customers according to claim 1 is characterized in that: The specific operation steps of the adaptive update module are as follows: According to the results of information fusion and mining, combined with the predefined update strategy, it is automatically determined whether the customer information needs to be updated and the content of the update; the difference between the current collected information and the original information is quantitatively compared, and the necessity of information update is evaluated in combination with the attribute importance weight, through the formula: Where U represents the information update necessity index, a j is the importance weight of the jth information attribute, C j is the value of the attribute in the currently collected customer information. is the original value of the attribute in the customer information database, and p is the number of information attributes; when U exceeds the preset update necessity index threshold, it is determined that the customer information needs to be updated; The importance weight of the information attribute is a j It is set according to the power business needs and the impact of customer information on the service; After the decision is updated, the information update operation is automatically executed to synchronize the updated information to the customer information database of the power company; at the same time, according to the nature and scope of the updated content, the parameters and models of the relevant business systems are dynamically adjusted to adapt to the new customer information status. After the customer's electricity information is updated, the unit price parameters in the electricity fee calculation system are dynamically adjusted according to the following formula Where P new is the adjusted electricity price, P old is the original electricity price, β is the adjustment coefficient, ΔE is the change in customer electricity consumption, E old is the customer’s original electricity consumption; According to this formula, the electricity fee calculation is more in line with the customer's actual electricity consumption, ensuring that the power company's billing is accurate and reasonable. At the same time, it also allows customers to clearly understand the relationship between changes in electricity costs and information updates.

5. The system for supplementing and updating important information of electricity customers according to claim 4 is characterized in that: The importance weight a of the information attribute in the adaptive update module j The specific steps of the analysis are as follows: The importance weight of information attribute a j Through comprehensive analysis of influencing parameters, the influencing parameters include: Information accuracy influencing parameters include historical data accuracy score and data source reliability score. The historical data accuracy score is obtained by counting the ratio of the number of accurate records of the information attribute data in the past preset time period to the total number of records, multiplied by 100 to obtain a score between 0 and 100, which is recorded as the accuracy value; the data source reliability score is assigned according to the reliability of the information source channel and recorded as the trust value. After the obtained accuracy value and trust value are normalized, the accuracy value and trust value are used as the two right-angled sides of the right triangle respectively, and the other side of the triangle is connected. A triangular pyramid model is established with the triangle combined with the preset height, and the volume of the triangular pyramid model is calculated and recorded as the accuracy judgment value. This accuracy judgment value is used as the standard for measuring information accuracy; The business correlation degree parameters include the direct business correlation degree and the business process dependence degree. The direct business correlation degree is assigned by the association judgment of the information attribute. When the information attribute directly affects the core power business, including electricity fee calculation and power distribution, the value is assigned to 10; if it has an indirect impact, the value is assigned to 5; if there is no impact, the value is assigned to 1 and recorded as the correlation value ax; the business process dependence degree is calculated by counting the frequency of the information attribute being used in the business process, recorded as the frequency judgment value po; the obtained association value ax and frequency judgment value po are normalized and then entered into the following formula: To obtain the associated judgment value GVD, where is the correction coefficient, and the final correlation judgment value GVD is used as the standard to measure the degree of correlation between information attributes and business; The information change sensitivity parameter includes the change frequency and the change impact range. The change frequency is obtained by counting the number of changes of the information attribute in the preset time period and dividing it by the total monitoring time to obtain the average number of changes per month, and then assigning a value according to the range of the number of changes, which is recorded as the frequency score; the change impact range is evaluated according to the impact of the information change on the power company's services, and quantified by the number of affected departments or business links, which is recorded as the change value. The obtained frequency score and change value are calibrated as gf and by, respectively, and after normalization, they are inserted into the following formula: XMZ = gf × λ1 + by × λ2 to obtain the signal sensitivity value XMZ, where λ1 and λ2 are the preset weight coefficients of the frequency score and the change value, respectively, and the obtained signal sensitivity value XMZ is used as the standard for measuring the information change sensitivity parameter; The customer privacy importance parameters, including the privacy leakage risk level, are evaluated according to the nature of the information attributes and the degree of harm caused by the leakage. They are divided into three levels: high, medium, and low, and are assigned values ​​of 10, 6, and 2 respectively, and recorded as the customer weight value. This customer weight value is used as the standard for measuring the customer privacy importance parameters; Then, the calculated accuracy value XCF, association value GVD, signal sensitivity value XMZ and customer weight value GZZ are normalized and inserted into the following formula: TGR = XCF × ν1 + GVD × ν2 + XMZ × ν3 + GZZ × ν4 to obtain the comprehensive score TGR, where ν1, ν2, ν3, and ν4 are the preset weight coefficients of the accuracy value XCF, association value GVD, signal sensitivity value XMZ and customer weight value GZZ, respectively, and this comprehensive score is used as the importance weight a of the information attribute. j standards; The obtained comprehensive score TGR is compared with several preset comprehensive score intervals, and the several comprehensive score intervals correspond to different importance weights of information attributes. When the comprehensive score interval to which the comprehensive score TGR belongs is determined, the importance weight of the information attribute is determined.

6. The important information supplement and update system for electricity customers according to claim 1 is characterized in that: The specific operation steps of the security interaction module are as follows: During the collection and update process of customer authorization information, the security risk of information interaction is continuously evaluated through the following formula: Where R represents the security risk value of customer information during the interaction process, b k is the weight of the kth security risk factor, L k is the risk level of the kth security risk factor, determined through real-time monitoring or pre-assessment, and q is the number of security risk factors; If R exceeds the set security threshold, the system automatically takes corresponding security enhancement measures to ensure customer information security; In the process of collecting and updating customer information, we follow the customer authorization mechanism and ensure that customers know and authorize the scope of information collection and updating through secure communication protocols and encryption technology when they interact with the system using mobile terminals; Before and after the information is updated, the customer will be informed of the update status through SMS, APP push, and email, informing the customer of which information has been updated, the basis for the update, and the impact on the customer's electricity service; At the same time, establish customer feedback channels to facilitate customers to raise questions or objections to information updates, respond and handle them in a timely manner, and collect customer feedback to calculate customer satisfaction.

7. The system for supplementing and updating important information of electricity customers according to claim 6 is characterized in that: The specific operation steps of collecting customer feedback and calculating customer feedback satisfaction in the security interaction module are as follows: By formula: Where S represents customer feedback satisfaction, s l is the score corresponding to the lth feedback evaluation level, N l is the number of the first type of feedback evaluation received, and r is the number of feedback evaluation levels; By calculating customer feedback satisfaction, we can quantitatively understand the customer's satisfaction with the information update process and results; Based on the satisfaction results, we can improve service processes and optimize information update strategies in a targeted manner to enhance customer experience.

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