Electric power TMS communication intelligent management detection emergency alarm method

Abstract

An electric power TMS communication intelligent management detection emergency alarm method relates to an electric power communication intelligent management alarm method, and is characterized in that an electric power data center is provided with an abnormal customer module, and an abnormal customer list listed by a law enforcement department is recorded in the abnormal customer module of the electric power data center; a normal electricity consumption value, abnormal electricity consumption exceeding the normal electricity consumption and an abnormal electricity consumption time period are set in an abnormal customer module, an electric power data center encrypts collected abnormal customer electricity consumption data, encrypts abnormal customer real-time electricity consumption data and uploads the encrypted abnormal customer real-time electricity consumption data to a TMS communication management system; the electric power data center sends a detected sudden abnormal customer alarm to a law enforcement department node; the power data center further pushes the detected sudden abnormal power utilization alarm information to a system manager, and the system manager executes power failure or power limiting measures on the client; according to the invention, the TMS communication management system communicates with the law enforcement department in time, so that the law enforcement department can find and process abnormal customers in time.

Description

Technical Field

[0001] This invention relates to an alarm method for intelligent management of power communication, and more specifically, to an alarm method for detecting sudden emergencies in intelligent management of power TMS communication. Background Technology

[0002] The main task of the TMS communication management system in the power industry is to utilize the synchronization between communication and the events that occur. Currently, in the alarm monitoring link of the TMS communication management system in the power industry, it still heavily relies on the traditional manual duty mode. The duty personnel need to complete alarm confirmation and classification within the specified time according to strict work specifications. However, in actual operations, it is interfered by problems such as the suddenness, large volume, repetitiveness, and invalidity of alarm events, especially during extreme periods when a large number of them break out concentratedly, making it difficult for the duty personnel to ensure timely response and handling of all alarms. This situation directly leads to a serious lag in the alarm defect elimination work, greatly reducing the work efficiency and quality. The excessive reliance on manpower results in inefficient resource allocation and high labor costs. The current alarm monitoring of the TMS communication management system completely depends on manual duty. As the scale of the power grid becomes increasingly large and the coverage of the communication network continues to expand, the generation frequency and total volume of alarm information also continue to rise. To ensure the continuity and effectiveness of alarm monitoring, a large amount of manpower and material resources need to be invested in basic and repetitive alarm monitoring work, resulting in inefficient resource allocation. During extreme alarm periods, the duty personnel often feel overwhelmed, further exacerbating the consumption of labor costs. Low work efficiency, long operation time, and prone to misjudgment: The traditional manual duty mode requires the duty personnel to distinguish valid information from a large amount of alarm information and complete alarm confirmation and classification according to strict work specifications. Its mechanical operation has many handling links and takes a long time. Especially when the volume of alarm data is large, the duty personnel need to spend a long time to complete the work of distinguishing, confirming, and classifying alarms, with low work efficiency. In addition, manual judgment is easily affected by subjective factors, resulting in misjudgment of alarm information and further reducing the overall quality and efficiency of alarm handling. Delayed alarm response, risk of lag in defect elimination work: Due to the suddenness, large volume, invalidity, and repetitiveness of alarm information, especially during extreme periods when they break out concentratedly, it is difficult for the duty personnel to effectively respond to all alarms in a short time, resulting in a lag in the alarm defect elimination work. It not only affects the normal development of power communication services but may also trigger a chain reaction, leading to a further expansion of the fault range and seriously affecting the stability and reliability of the power grid communication network. Insufficient supervision and feedback mechanism, lack of closed-loop information transmission and processing: The traditional mode has obvious shortcomings in the real-time supervision and feedback mechanism. The entire alarm handling process completely relies on the autonomy of the duty personnel. Information on key nodes such as alarm occurrence, confirmation, and elimination cannot be timely and automatically informed to relevant business personnel, restricting the ability to discover and handle alarm information in a timely manner. At the same time, there is a lack of a complete process recording mechanism, resulting in a management blind spot of "uncontrollable process and difficult to trace the result". There are abnormal customers that need to be supervised in government functional regulatory departments. Shutting down production and business operations when being punished by supervision is also one of the important means. Affected by too many approval links and insufficient enforcement, abnormal customers deliberately produce secretly when supervision is lax. Only cutting off water and power is the most effective method.According to the "Action Plan for Accelerating the Construction of a New Power System (2024-2027)" released in August 2024, it is clearly required to improve the digitalization and intelligence level of the power grid; and to lead breakthroughs in power digitalization technology through technological innovation. Under the guidance of the national innovation-driven development strategy, the intelligent level of the power system will be continuously improved, traditional technological bottlenecks will be broken, the industry's "strengths" will be forged, and the independent innovation capability of my country's power industry will be enhanced. Summary of the Invention

[0003] The purpose of this invention is to address the shortcomings of existing methods by disclosing a power TMS communication intelligent management and detection method for sudden alarms. This method enables timely communication between the TMS communication management system and law enforcement agencies, facilitating the timely detection and handling of abnormal customers by law enforcement agencies.

[0004] In order to achieve the objective of this invention, this application discloses the following technical solution: A method for detecting sudden alarms in power TMS communication intelligent management, the method comprising the following steps: The power data center has an abnormal customer module. This module records customers listed as abnormal by law enforcement agencies. By setting normal electricity consumption values, abnormal electricity consumption exceeding normal levels, and abnormal electricity consumption periods, the power data center encrypts the collected abnormal customer electricity consumption data and uploads this encrypted real-time data to the TMS communication management system. The abnormal customer file fingerprint hash value is also uploaded to the data consortium blockchain. The abnormal customer file fingerprint hash value is calculated by the TMS communication management system based on the file fingerprint hash value of the encrypted power data. Simultaneously, the TMS communication management system notifies the relevant law enforcement agencies of the collected abnormal customer electricity consumption data daily via SMS, email, and voice call, and the law enforcement agencies then handle the subsequent processing. The training consortium blockchain enables law enforcement agencies with various data needs to send abnormal customer power data requests and their own public key PK to the power data center; The power data center uses its private key SK and the public key PK of the data requester to generate a proxy re-encryption key RK, and provides the power data request and the proxy re-encryption key RK to the law enforcement node; The law enforcement node verifies the correctness of the power data request based on the file fingerprint hash value downloaded from the data consortium chain; and uses the proxy re-encryption key RK to re-encrypt the encrypted abnormal customer power data downloaded from the TMS communication management system, and sends the re-encrypted power data to the law enforcement data requester in the training consortium chain, so that the law enforcement data requester can use its own private key to decrypt the re-encrypted power data and obtain the encrypted abnormal customer power data. The training consortium blockchain enables law enforcement data requesters to train local models using encrypted electricity data. After updating the local model, reputation opinions are generated for the law enforcement data requesters, and the local model update and reputation opinions are sent to the aggregate consortium blockchain, so that the aggregate consortium blockchain can send the reputation opinions to the electricity data center. The power data center sends reputational feedback and new rounds of power data requests from law enforcement agencies to law enforcement nodes. These nodes then allow law enforcement agencies to choose to approve or reject the data request based on the data requester's identity information and previous reputational feedback. Law enforcement agencies can also provide feedback via telephone. The power data center will also push the detected sudden abnormal power consumption alarm information to the system administrators, who will then implement power outage or power restriction measures for the customers, and the power customer service center will explain the situation to the customers.

[0005] The aforementioned power TMS communication intelligent management and detection emergency alarm method involves the law enforcement department entering an abnormal customer list after obtaining approval from the power department. The abnormal customer module's viewing permissions are limited to abnormal customers reported by a specific law enforcement department.

[0006] The aforementioned power TMS communication intelligent management and detection emergency alarm method includes law enforcement departments such as market supervision departments, environmental protection departments, fire departments, special industry management departments, safety inspection departments, or quality supervision departments.

[0007] The power TMS communication intelligent management and detection emergency alarm method, wherein the process of encrypting the power data of abnormal customers collected by the power data center includes: the power data center encrypting the collected power data of abnormal customers using a homomorphic encryption algorithm.

[0008] The aforementioned power TMS communication intelligent management and detection emergency alarm method, wherein the process by which each law enforcement department in the training consortium blockchain sends a power data request and its own public key PK to the power data center includes: Each law enforcement agency that requests data generates a public-private key pair, i.e., KeyGen(P) → {PKi, SKI}, and uses its own private key SKI to sign the data request information R, H = hash(R); After the CA authenticates its public key PKI, it sends the request R and the public key PKI to the power data center. The power data center verifies the authenticity of the public key PKI, calculates whether H′=H=hash(R) is true, and confirms that it is a power data request sent by a data requester from a law enforcement agency.

[0009] The aforementioned power TMS communication intelligent management and detection emergency alarm method selects several law enforcement data request nodes during the training of the local model, and uses smart contracts to select the effective local model for updating.

[0010] The power TMS communication intelligent management and detection emergency alarm method includes a power data center, and a TMS communication management system, law enforcement department nodes, a data alliance chain, a training alliance chain, and an aggregation alliance chain, all of which interact with the power data center. The power data center encrypts the collected power data and uploads the encrypted power data to the TMS communication management system. The TMS communication management system calculates the file fingerprint hash value of the encrypted power data and sends the file fingerprint hash value back to the power data center; The power data center uploads the file fingerprint hash value to the data alliance chain; In the training consortium blockchain, each data requester sends a power data request and its own public key PK to the power data center. The power data center uses its private key SK and the public key PK of the data requester to generate a proxy re-encryption key RK, and provides the power data request and the proxy re-encryption key RK to the law enforcement node; The law enforcement node verifies the correctness of the power data request based on the file fingerprint hash value downloaded from the data consortium chain; and uses the proxy re-encryption key RK to re-encrypt the encrypted power data downloaded from the TMS communication management system, and sends the re-encrypted power data to the data requester in the training consortium chain. The law enforcement data requester uses its own private key to decrypt the re-encrypted power data and obtain the encrypted power data. The data demanders in the training consortium blockchain use encrypted electricity data to train local models, generate reputation opinions for the data demanders after the local models are updated, and send the local model update and reputation opinions to the aggregate consortium blockchain. The consortium blockchain sends reputational opinions to the power data center; The power data center sends reputational feedback and new power data requests from data demanders to law enforcement nodes; Law enforcement nodes choose to approve or reject a data request based on the requester's identity information and reputation from the previous round.

[0011] Based on the above disclosure, the beneficial effects of the present invention are: The power TMS communication intelligent management and detection emergency alarm method described in this invention intelligently manages requests from law enforcement agencies through the TMS communication management system. After law enforcement agencies issue and issue work stoppage / production shutdown notices, the method promptly collaborates with the power sector. The power sector utilizes the real-time power consumption monitoring of the abnormal customer module set up in the power data center. Upon detecting abnormal power consumption, it promptly notifies the law enforcement agency via SMS, email, and voice call, providing evidence for law enforcement agencies to promptly identify and stop problematic enterprises. This invention, by jointly identifying the real-time power consumption of abnormal customers with law enforcement agencies and promptly handling sudden illegal power consumption alarms from abnormal customers, facilitates the timely detection of customers illegally operating production by law enforcement agencies. Attached Figure Description

[0012] Figure 1 This is a diagram of the TMS communication intelligent management detection and sudden alarm handling method of the present invention; Figure 2 This is the customer intelligent processing diagram of the present invention; Detailed Implementation

[0013] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, so as to better understand the inventive objectives, features, and advantages of the present invention. It should be understood that the embodiments shown in the drawings are not intended to limit the scope of the present invention, but are merely illustrative of possible implementations of the technical solutions of the present invention.

[0014] Combined with appendix Figure 1 Alternatively, the power TMS communication intelligent management and detection emergency alarm method described in section 2, the method comprising the following steps: The power data center has an abnormal customer module. This module records customers listed as abnormal by law enforcement agencies, including market supervision departments, environmental protection departments, fire departments, special industry management departments, safety inspection departments, and quality supervision departments. By setting normal electricity consumption values, abnormal electricity consumption exceeding normal consumption values, and abnormal electricity consumption periods in the abnormal customer module, the power data center encrypts the collected abnormal customer electricity consumption data. This encryption process includes: the power data center encrypting the collected abnormal customer electricity data using a homomorphic encryption algorithm, uploading the encrypted real-time electricity consumption data of abnormal customers to the TMS communication management system, and uploading the abnormal customer file fingerprint hash value to the data consortium blockchain. The abnormal customer file fingerprint hash value is the file fingerprint hash value of the encrypted electricity data calculated by the TMS communication management system. Simultaneously, the TMS communication management system notifies the corresponding law enforcement agencies of the collected abnormal customer electricity consumption data daily via SMS, email, and voice call, and the law enforcement agencies then handle the subsequent processing. The training consortium blockchain enables law enforcement agencies with various data needs to send abnormal customer power data requests and their own public key PK to the power data center; The power data center uses its private key SK and the public key PK of the data requester to generate a proxy re-encryption key RK, and provides the power data request and the proxy re-encryption key RK to the law enforcement node; The law enforcement node verifies the correctness of the power data request based on the file fingerprint hash value downloaded from the data consortium chain; and uses the proxy re-encryption key RK to re-encrypt the encrypted abnormal customer power data downloaded from the TMS communication management system, and sends the re-encrypted power data to the law enforcement data requester in the training consortium chain, so that the law enforcement data requester can use its own private key to decrypt the re-encrypted power data and obtain the encrypted abnormal customer power data. The training consortium blockchain enables law enforcement data requesters to train local models using encrypted electricity data. After updating the local model, reputation opinions are generated for the law enforcement data requesters, and the local model update and reputation opinions are sent to the aggregate consortium blockchain, so that the aggregate consortium blockchain can send the reputation opinions to the electricity data center. The power data center sends reputational feedback and new rounds of data requests from law enforcement agencies to law enforcement nodes, allowing the law enforcement nodes to choose to approve or reject the data request based on the data requester's identity information and the previous round of reputational feedback. Law enforcement agencies can also provide feedback by phone. After obtaining approval from the power department, the law enforcement agencies enter the list of abnormal customers. The abnormal customer module's viewing permissions for abnormal customers are limited to abnormal customers reported by a specific law enforcement agency. The power data center will also push the detected sudden abnormal power consumption alarm information to the system administrators, who will then implement power outage or power restriction measures for the customers. At the same time, the system administrators will notify the power customer service center, which will then explain the situation to the customers.

[0015] The aforementioned power TMS communication intelligent management and detection emergency alarm method, wherein the process by which each law enforcement department in the training consortium blockchain sends a power data request and its own public key PK to the power data center includes: Each law enforcement agency that requests data generates a public-private key pair, i.e., KeyGen(P) → {PKi, SKI}, and uses its own private key SKI to sign the data request information R, H = hash(R); After the CA authenticates its public key PKI, it sends the request R and the public key PKI to the power data center. The power data center verifies the authenticity of the public key PKI, calculates whether H′=H=hash(R) is true, and confirms that it is a power data request sent by a data requester from a law enforcement agency.

[0016] The aforementioned power TMS communication intelligent management and detection emergency alarm method selects several law enforcement data request nodes during the training of the local model, and uses smart contracts to select the effective local model for updating.

[0017] The power TMS communication intelligent management and detection emergency alarm method includes a power data center, and a TMS communication management system, law enforcement department nodes, a data alliance chain, a training alliance chain, and an aggregation alliance chain, all of which interact with the power data center. The power data center encrypts the collected power data and uploads the encrypted power data to the TMS communication management system. The TMS communication management system calculates the file fingerprint hash value of the encrypted power data and sends the file fingerprint hash value back to the power data center; The power data center uploads the file fingerprint hash value to the data alliance chain; In the training consortium blockchain, each data requester sends a power data request and its own public key PK to the power data center. The power data center uses its private key SK and the public key PK of the data requester to generate a proxy re-encryption key RK, and provides the power data request and the proxy re-encryption key RK to the law enforcement node; The law enforcement node verifies the correctness of the power data request based on the file fingerprint hash value downloaded from the data consortium chain; and uses the proxy re-encryption key RK to re-encrypt the encrypted power data downloaded from the TMS communication management system, and sends the re-encrypted power data to the data requester in the training consortium chain. The law enforcement data requester uses its own private key to decrypt the re-encrypted power data and obtain the encrypted power data. The data demanders in the training consortium blockchain use encrypted electricity data to train local models, generate reputation opinions for the data demanders after the local models are updated, and send the local model update and reputation opinions to the aggregate consortium blockchain. The consortium blockchain sends reputational opinions to the power data center; The power data center sends reputational feedback and new power data requests from data demanders to law enforcement nodes; Law enforcement nodes choose to approve or reject a data request based on the requester's identity information and reputation from the previous round.

[0018] The preferred embodiments of the present invention have been described in detail above. However, it should be understood that after reading the above teachings, those skilled in the art can make various alterations or modifications to the scope of protection of the present invention. These equivalent forms also fall within the scope defined by the appended claims.

[0019] The parts of this invention not described in detail are prior art.

Claims

1. A method for detecting sudden alarms in power TMS communication intelligent management, characterized by: The method comprises the following steps: The power data center sets an abnormal customer module, and abnormal customer information in the abnormal customer module is input by a law enforcement department; the abnormal customer module is set with normal power consumption value, abnormal power consumption value after exceeding the normal power consumption, and abnormal power consumption period; the power data center encrypts the collected abnormal customer power consumption data, and uploads the real-time power consumption data of the abnormal customer to a TMS communication management system and a data alliance chain; the abnormal customer file fingerprint hash value is a file fingerprint hash value of encrypted power data calculated by the TMS communication management system; the collected abnormal customer power consumption data is notified to the corresponding law enforcement department by the TMS communication management system through short message, email and voice call every day, and subsequent processing is performed by the law enforcement department; The law enforcement department in the training alliance chain sends an abnormal customer power data request and a public key PK of the law enforcement department to the power data center; The power data center generates a proxy re-encryption key RK by using a private key SK of the power data center and the public key PK of the data demand side, and provides the power data request and the proxy re-encryption key RK to the law enforcement department node; The law enforcement department node verifies the correctness of the power data demand according to the file fingerprint hash value downloaded from the data alliance chain; The law enforcement department node performs secondary encryption on the encrypted abnormal customer power data downloaded from the TMS communication management system by using the proxy re-encryption key RK, and sends the secondary encrypted power data to the law enforcement department data demand side in the training alliance chain, so that the law enforcement department data demand side decrypts the secondary encrypted power data by using a private key of the law enforcement department data demand side to obtain the encrypted abnormal customer power data; The law enforcement department data demand side in the training alliance chain trains a local model by using the encrypted power data, generates a reputation opinion for the law enforcement department data demand side after updating the local model, and sends the local model update and the reputation opinion to the aggregation alliance chain; the aggregation alliance chain sends the reputation opinion to the power data center; The power data center sends the reputation opinion and a new round of detection of the law enforcement department data demand side to the law enforcement department node, so that the law enforcement department node selects to agree or refuse the request of the data demand side according to the identity information of the data demand side and the reputation opinion of the last round; the law enforcement department can also give feedback by telephone; The power data center also pushes the detected sudden abnormal power alarm information to the system management personnel, and the system management personnel executes power-off or power-limit measures on the customer, and the power customer service center implements explanation work on the customer.

2. The method of claim 1, wherein the method further comprises: The law enforcement department inputs the abnormal customer list after being approved by reporting in the power department, and the abnormal customer viewing permission of the abnormal customer module is limited to the abnormal customers reported by a certain law enforcement department.

3. The method of claim 1, wherein the method further comprises: The law enforcement department comprises a market supervision department, an environmental protection department, a fire department, a special industry management department, a security check department or a quality supervision department.

4. The method of claim 1, wherein the method further comprises: The process of encrypting the power data of the abnormal customer by the power data center comprises: the power data center encrypts the collected abnormal customer power data by using a homomorphic encryption algorithm.

5. The method of claim 1, wherein the method further comprises: The process of sending a power data request and a public key PK of each law enforcement department data demander to the power data center in the training alliance chain comprises: Each law enforcement department data demander generates a public and private key pair, i.e., KeyGen(P)→{PKi, SKi}, and signs data request information R using the private key SKi, H=hash(R); After the CA authenticates the public key PKi, the request R and the public key PKi are transmitted to the power data center, the power data center verifies the authenticity of the public key PKi, calculates whether H'=H=hash(R) is true, and compares to confirm that the power data request is sent by a law enforcement department data demander.

6. The method of claim 1, wherein the method further comprises: When training the local model, several law enforcement department data demander nodes are selected to update the effective local model through a smart contract.

7. The method of claim 1, wherein the method further comprises: detecting a burst alarm event; and transmitting a signal to the TMS device to indicate the burst alarm event. The power data center, the TMS communication management system, the law enforcement department node, the data alliance chain, the training alliance chain, and the aggregation alliance chain all interact with the power data center; The power data center encrypts the collected power data and uploads the encrypted power data to the TMS communication management system; The TMS communication management system calculates the file fingerprint hash value of the encrypted power data and returns the file fingerprint hash value to the power data center; The power data center uploads the file fingerprint hash value to the data alliance chain; Each data demander in the training alliance chain sends a power data request and a public key PK to the power data center; The power data center generates a proxy re-encryption key RK using its private key SK and the public key PK of the data demander, and provides the power data request and the proxy re-encryption key RK to the law enforcement department node; The law enforcement department node verifies the correctness of the power data demand according to the file fingerprint hash value downloaded from the data alliance chain, and uses the proxy re-encryption key RK to perform secondary encryption on the encrypted power data downloaded from the TMS communication management system, and sends the secondary encrypted power data to the data demander in the training alliance chain. The law enforcement department data demander decrypts the secondary encrypted power data using its private key to obtain the encrypted power data; The data demander in the training alliance chain trains the local model using the encrypted power data, generates a reputation opinion for the data demander after updating the local model, and sends the local model update and the reputation opinion to the aggregation alliance chain; The aggregation alliance chain sends the reputation opinion to the power data center; The power data center sends the reputation opinion and the power data request of the new round of data demanders to the law enforcement department node; The law enforcement department node selects to agree or reject the request of the data demander according to the identity information of the data demander and the reputation opinion of the last round.

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