Artificial intelligence service system and method for electric power measurement digital master station

Abstract

The invention discloses an artificial intelligence service system and method for an electric power metering digital master station, and relates to the technical field of intelligent language services, the system comprises three core modules of telephone traffic access, a virtual telephone operator and an interrogation assistant, a user call is received through an IMS administrative telephone line, the user demand is analyzed by using an intelligent voice interaction technology, and the intelligent voice interaction technology is used for receiving the user call. And a structured instruction is generated to drive an RPA automatic script to execute operations such as remote interrogation and the like. In terms of technical implementation, the ASR voice recognition unit is adopted, and high-precision voice recognition is achieved. Meanwhile, in cooperation with ACD dynamic routing and dual-channel redundancy design, it is guaranteed that the system has the high-concurrency service capacity. In addition, an SM4 encryption tunnel and an operation auditing chain are further constructed, and it is ensured that the whole business process is safe and credible. Through the design, the electric power metering service response speed is effectively improved, the service processing accuracy is improved, the manual operation and maintenance cost is remarkably reduced, and an intelligent solution is provided for digital transformation of electric power enterprises.

Description

Technical Field

[0001] The present invention relates to the field of intelligent language service technology, and mainly to an artificial intelligence service system and method for a digital power metering master station. Background Art

[0002] Current digital power metering master station services generally rely on human operators answering user calls and manually operating the system. This leads to issues such as delayed response times, high operational errors, and insufficient service capacity in high-concurrency scenarios. Although AI voice interaction technology has seen initial application in areas such as power grid dispatching, its functionality is primarily focused on voice Q&A and work order generation, making it difficult to directly drive the automation of core power metering services (such as remote data recall and meter parameter debugging). Furthermore, it lacks end-to-end protection mechanisms tailored to the high security requirements of the power industry.

[0003] Prior art, such as Chinese invention patent application publication number CN111416914B, discloses an artificial intelligence voice interaction service system, comprising an external telephone, a switch module, a central processing unit, and a gateway monitoring module. The external telephone is connected to the switch module, which is in turn connected to the gateway monitoring module. The central processing unit and the gateway monitoring module enable bidirectional information transmission. The central processing unit includes a cloud database module, which stores a language database for voice analysis and a database of historical decision-making processes for problem resolution. A natural language understanding module intelligently analyzes and understands voice data. An AI application module uses artificial intelligence to monitor the analyzed voice data and make intelligent processing decisions. A cloud public service module provides interactive services to users. This patent connects to an external telephone through a switch module and combines the natural language understanding module with a cloud database to implement voice interaction decision-making. However, the AI ​​application module in this patent only supports general problem resolution and work order generation, and is not deeply integrated with the business interfaces of the power metering system (such as data call monitoring and parameter transparent reading). This results in user requests still requiring manual secondary parsing and system operation, leaving efficiency bottlenecks unresolved. Furthermore, the traffic access layer in this patent relies on a single switch module and lacks a dynamic routing allocation strategy, making it prone to channel congestion due to sudden high-concurrency calls.

[0004] To address the above issues, there is an urgent need for an intelligent service system that can accurately map voice commands into executable operations for the power metering system and support flexible call routing. Summary of the Invention

[0005] In order to solve the above-mentioned problems existing in the prior art, the present application provides an artificial intelligence service system and method for a digital master station for electric power metering.

[0006] The technical solution of this application is as follows: In one aspect, an artificial intelligence service system for a digital power metering master station is provided, the system comprising a traffic access module, a virtual operator module, and a call and test assistant module, wherein: The traffic access module receives external user call requests through the IMS administrative telephone line and establishes a communication channel with the public switched telephone network PSTN; The virtual operator module is connected to the call input module and uses intelligent voice interaction technology to analyze the user voice or key input in the external user call request and generate a structured operation instruction containing the terminal asset number and service type code; The call and test assistant module is connected to the virtual operator module, and is used to trigger the power metering system to perform at least one of the corresponding service operations of remote data call and test, parameter setting and process management according to the structured operation instructions, and return the execution result to the user terminal.

[0007] Preferably, the traffic access module includes a CTI computer telephone integration unit, an ACD automatic call distribution unit and an IVR interactive voice response unit, wherein: The CTI computer telephone integration unit is used to convert telephone switch signals in the PSTN into TCP / IP protocol data packets for communication with the business system server. The ACD automatic call distribution unit is deployed in the business system server and is used to dynamically distribute external user call requests based on the current concurrent call volume. The IVR interactive voice response unit is connected to the ACD automatic call distribution unit and guides users to enter the terminal asset number and business type code through a preset voice navigation menu, and performs format verification on the input data.

[0008] Preferably, the virtual operator module includes an ASR speech recognition unit, a command mapping unit and a TTS speech synthesis unit, wherein: The ASR speech recognition unit is used to convert the user's speech into text and extract keywords; The instruction mapping unit is connected to the ASR speech recognition unit to obtain keywords, perform keyword matching based on a preset business rule library, and generate a structured operation instruction containing the terminal asset number, business type code and timestamp; The TTS speech synthesis unit is used to convert the operation confirmation information corresponding to the structured operation instruction into speech feedback to the user.

[0009] Preferably, the test call assistant module includes an RPA script execution engine, a task solution matcher, and a result feedback interface, wherein: The RPA script execution engine is deployed in the power metering system, and automatically calls the data call interface, work order query interface and parameter penetration reading interface of the power metering system through pre-compiled RPA scripts, and calls the pre-compiled RPA script according to the business type code in the structured operation instruction to execute at least one of the data call service, penetration verification service, process debugging service or timing statistics service; the task plan matcher is pre-set with a call task plan for matching call tasks based on the business type code, generating an operation instruction set including the terminal asset number, task plan identifier and call parameters, and driving the power metering system to execute non-broadcast task call, penetration meter reading or work order status triggering operation through the operation instruction set; the result feedback interface encapsulates the execution result of the operation into a JSON format message and sends it to the user terminal.

[0010] Preferably, the ACD automatic call distribution unit dynamically distributes external user call requests according to the current concurrent call volume as follows: At least two IMS administrative telephone lines are set as access channels in the ACD automatic call distribution unit. The IMS administrative telephone lines include a main line and at least one backup line. When the current concurrent traffic volume of the main line reaches the preset maximum concurrent traffic volume, the ACD automatic call distribution unit triggers the routing overflow rule and automatically transfers external user call requests that exceed the maximum concurrent traffic volume to the backup line queue.

[0011] Preferably, the system also includes a security protection unit, which establishes an encrypted tunnel between the call access module, the virtual operator module and the call assistant module, implements SM4 algorithm encryption on the structured operation instructions and execution results in transmission, and generates an operation audit chain containing a timestamp.

[0012] Preferably, the system also includes a business assistant module, which is connected to the call assistant module and consists of an anomaly detection unit and a log association unit. The anomaly detection unit is used to count the number of consecutive failed operations and trigger SMS alarms; the log association unit is used to establish an association index of the terminal asset number, operation time and fault code.

[0013] On the other hand, the present invention also provides an artificial intelligence service method for a digital power metering master station, the method comprising: S1. Receive external user call requests through the IMS administrative telephone line and distribute the external user call requests to the IVR interactive voice response unit based on the dynamic routing policy of the ACD automatic call distribution unit. The IVR interactive voice response unit guides the user to enter the terminal asset number and service type code by voice or keystroke through a preset voice navigation menu; S2. The ASR speech recognition unit converts user voice or key input into text and extracts keywords. The instruction mapping unit matches the preset business rule library to generate structured operation instructions containing the terminal asset number, business type code, and timestamp. The TTS speech synthesis unit converts the operation confirmation information into voice feedback to the user. S3. Based on the business type code in the structured operation instruction, the task solution matcher calls the preset call test task solution to generate an operation instruction set. This drives the RPA script execution engine to automatically call the data call test interface, work order query interface, or parameter transparent reading interface of the power metering system. Based on the operation instruction set, it performs remote data call test, penetrating verification, process debugging, or scheduled statistics services to obtain the execution results. S4. Encapsulate the execution result into a JSON format message through the result feedback interface and return it to the user terminal.

[0014] On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, it implements an artificial intelligence service method for a digital master station for electric power metering as described in the present invention.

[0015] On the other hand, the present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the program is executed by a processor, it implements an artificial intelligence service method for a digital master station for electric power metering as described in the present invention.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1) This invention provides an artificial intelligence service system and method for a digital power metering master station. This system uses an ASR voice recognition unit to accurately analyze user voice and keystroke input. It then generates structured instructions containing the terminal asset number and service type code based on a business rule library. This avoids manual translation errors, improves instruction generation accuracy, and enables non-technical personnel to trigger complex services using natural language or simple keystrokes. 2) This invention provides an artificial intelligence service system and method for a digital master station for electric power metering. The call and test assistant module automatically calls interfaces such as data call and test and penetration verification of the electric power metering system through pre-compiled RPA scripts. It dynamically matches task plans based on structured instructions, eliminating manual operation delays and achieving millisecond-level response for remote services. It also supports seamless switching and execution of multiple types of services (such as meter synchronization and data statistics). 3) The present invention provides an artificial intelligence service system and method for a digital master station for electric power metering. Based on IMS dual-channel redundancy and an ACD dynamic allocation strategy, the system ensures high-concurrency call traffic carrying through a primary-backup line overflow mechanism. Simultaneously, the system employs an SM4 encrypted tunnel and an operation audit chain to encrypt the entire instruction transmission link, resolving the issues of easy congestion and low security associated with telephone access. This system ensures stable processing of 200+ concurrent calls and prevents tampering with operation instructions and data leakage. DETAILED DESCRIPTION

[0017] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0018] The present invention provides the following technical solution: an artificial intelligence service system and method for a digital power metering master station.

[0019] Example 1: This embodiment provides an artificial intelligence service system for a digital power metering master station, the system comprising: A1. Traffic access module: The call access module receives external user call requests through the IMS administrative telephone line and establishes a communication channel with the public switched telephone network (PSTN). Furthermore, the call access module includes a CTI computer telephone integration unit, an ACD automatic call distribution unit, and an IVR interactive voice response unit, wherein: The CTI computer telephone integration unit is used to convert telephone switch signals in the PSTN into TCP / IP protocol packets for communication with the business system server. The ACD automatic call distribution unit is deployed in the business system server and is used to dynamically distribute external user call requests based on the current concurrent call volume. Specifically: At least two IMS administrative telephone lines are set up in the ACD automatic call distribution unit as access channels. The IMS administrative telephone lines include a primary line and at least one backup line. When the current concurrent call volume of the primary line reaches a preset maximum concurrent call volume, the ACD automatic call distribution unit triggers a routing overflow rule and automatically transfers external user call requests exceeding the maximum concurrent call volume to the backup line queue. For example, a company applies for two IMS administrative telephone numbers and deploys a primary and backup line structure. When the primary line reaches the maximum concurrent call volume of 10 lines, the ACD automatic call distribution unit triggers a routing overflow rule and transfers the excess calls to the backup line queue. The IVR interactive voice response unit is connected to the ACD automatic call distribution unit, and guides the user to enter the terminal asset number and service type code through a preset voice navigation menu, and performs format verification on the input data. For ease of understanding, the preset voice navigation menu is given as an example: First level menu: "1 Data collection, 2 Parameter setting, 3 Process management"; Secondary menu (after selecting 1): "1 Supplementary recruitment data, 2 Execution status of call-up and test tasks, 3 Penetration meter"; Level 3 menu (after selecting 1): "1 Supplementary recruitment of metering data, 2 Supplementary recruitment of transportation and procurement data" Level 4 menu (after selecting 1): "Prompt the user to enter the terminal asset number or collection point number" Step 5: Enable the secondary confirmation function. After the user enters the terminal asset number, the terminal will automatically broadcast the last digit verification. A2. Virtual operator module: The virtual operator module is connected to the call input module and uses intelligent voice interaction technology to analyze user voice or key input in external user call requests and generate structured operation instructions containing the terminal asset number and service type code. Furthermore, the virtual operator module includes an ASR speech recognition unit, a command mapping unit, and a TTS speech synthesis unit, wherein: The ASR speech recognition unit is used to convert the user's voice into text and extract keywords. For example, if the user inputs "supplementary recruitment and cross-purchase data" by voice, the keywords "supplementary recruitment" and "cross-purchase" are extracted. If the user inputs "call test concentrator version" by voice, the keywords "call test" and "concentrator" are extracted. The instruction mapping unit is connected to the ASR speech recognition unit to obtain keywords, perform keyword matching based on a preset business rule library, and generate a structured operation instruction containing the terminal asset number, business type code and timestamp; The TTS speech synthesis unit is used to convert the operation confirmation information corresponding to the structured operation instruction into speech feedback to the user; For ease of understanding, let's take an example: the preset business rule library contains keyword matching rules such as "Terminal Asset Number" and "Business Type Code." When a user enters "Supplementary Metering Data," the ASR speech recognition unit recognizes the keyword, triggers the data recall process, and announces through the TTS speech synthesis unit: "Please confirm that the terminal asset number you entered is XXXXXXXX. Press 1 to confirm and 2 to re-enter." A3. Test assistant module: The call assistant module is connected to the virtual operator module and is used to trigger the power metering system to perform at least one of the corresponding service operations of remote data call, parameter setting, and process management according to structured operation instructions, and return the execution results to the user terminal. Furthermore, the call assistant module includes an RPA script execution engine, a task solution matcher, and a result feedback interface, wherein: The RPA script execution engine is deployed in the power metering system, and automatically calls the data call interface, work order query interface and parameter penetration reading interface of the power metering system through pre-compiled RPA scripts, and calls the pre-compiled RPA script according to the business type code in the structured operation instruction to execute at least one of the data call service, penetration verification service, process debugging service or timing statistics service covered by the corresponding service operation; the task plan matcher is pre-set with a call task plan for matching call tasks based on the business type code, generating an operation instruction set including the terminal asset number, task plan identifier and call parameters, and driving the power metering system to execute non-broadcast task call, penetration meter reading or work order status triggering operation through the operation instruction set; the result feedback interface encapsulates the execution result of the operation into a JSON format message and sends it to the user terminal; To facilitate understanding, the following examples explain the data call test service, penetration verification service, process debugging service, and scheduled statistics service: The RPA script execution engine automatically calls the data call interface of the power metering system through pre-compiled RPA scripts to execute the data call service, including supplementary meter data, supplementary exchange data, meter call, exchange call, and meter penetration: In the supplementary meter data scenario, enter the terminal asset number to obtain terminal information. If the terminal type in the terminal information is concentrator, the message "Supplementary metering function only supports dedicated transformer terminals" is returned. If the terminal type in the terminal information is dedicated transformer terminal, the following operation is performed: the database is checked to see if there is data for the meter on that day. If there is data, the message "Meter data has been stored" is returned. Otherwise, the supplementary data operation is performed: the call task plan ID is 6E15, the plan number is 600, and the supplementary call is performed. The supplementary call result is fed back to the user terminal.

[0020] In the supplementary procurement data scenario, enter the terminal asset number to obtain terminal information and query the database to see if there is data for the procurement day. If there is data, the message "Procurement data has been stored" will be displayed. Otherwise, a supplementary data operation will be performed: the call task plan ID is 6E15, the plan number is 500, and supplementary recruitment will be performed. The supplementary recruitment results will be fed back to the user terminal.

[0021] In the meter test call task scenario, enter the terminal asset number to obtain terminal information. If the terminal type in the terminal information is a concentrator, call the task plan matcher to select the task plan 100 with the task plan identifier 6E20 for the test call. If the terminal type in the terminal information is a dedicated transformer terminal, call the task plan matcher to select the task plan 600 with the task plan identifier 6E20 for the test call. The task plan test result, combined with the terminal information, is fed back to the user terminal. When in the call test and procurement scenario, enter the terminal asset number to obtain the terminal information, then call the task plan matcher to select the task plan 500 with the task plan identifier 6E20 for the call test, and feed back the task plan call test result combined with the terminal information to the user terminal; In the meter penetration scenario, enter the terminal asset number (which is actually the meter asset number) and enter the penetration reading page to check whether the meter asset number is an IoT meter. If it is, the message "IoT meter does not support penetration" will be returned. If it is a non-physical meter, select real-time forward active energy penetration reading, record the penetration reading results, and encapsulate them into a JSON message with the meter asset number for feedback. A4. Business Assistant Module: The service assistant module is connected to the call and test assistant module and consists of an anomaly detection unit and a log association unit. The anomaly detection unit is used to count the number of consecutive failed operations and trigger a text message alarm. For example, if the same terminal fails to call and test three times, an interface type alarm will be sent to determine that the system is operating abnormally. The log association unit is used to establish an association index between the terminal asset number, operation time and fault code. Furthermore, the service assistant module is also used for information management, including call information storage and user demand storage. In this embodiment, the call information storage field includes the caller number, access time, navigation path, and service type, and the storage period is 6 months; the user demand storage field includes the terminal asset number, task solution code, and operation timestamp. If the user also interacts with the system of this embodiment through WeChat, an associated index is established with the WeChat message record. A5. Safety protection unit: The security protection unit establishes an encrypted tunnel between the call access module, the virtual operator module and the call test assistant module, implements SM4 algorithm encryption on the structured operation instructions and execution results in transmission, and generates an operation audit chain containing a timestamp.

[0022] Example 2: This embodiment provides an alternative implementation of the ASR speech recognition unit described in Example 1, specifically: The model structure of the ASR speech recognition unit is an end-to-end design based on the attention mechanism, adopting an encoder-decoder framework. The encoder receives the original speech signal corresponding to the user's speech (such as a Mel spectrogram), extracts acoustic features through the Transformer layer, and the decoder generates a text sequence based on the attention weights and directly outputs a complete sentence (such as "Supplemental recruitment procurement data"); the specific attention mechanism is to dynamically allocate weights during the decoding process and focus on the most relevant speech segments at the current moment. For example, when the user says "Supplemental recruitment procurement data", the model will focus on the speech segments corresponding to "Supplemental recruitment" and "Procurement". Furthermore, different from traditional ASR multi-modules (acoustic model + language model + pronunciation dictionary), the model in the ASR speech recognition unit of this embodiment completes the mapping from speech to text through a single neural network, reducing the accumulation of intermediate errors and improving the recognition efficiency; To improve the recognition accuracy of the ASR speech recognition unit, the model in this embodiment not only learns the mapping from speech to text during training, but also annotates keywords through multi-task learning. For example, both the complete text and keywords (such as "Supplemental recruitment", "Procurement") are annotated in the training data, enabling the model to learn to directly output key information; Since the application scenario of this embodiment is power metering, the model is fine-tuned using power industry speech data (such as customer service call recordings) to improve the recognition accuracy of professional terms (such as "6E15 task plan", "Penetrating reading").

[0023] Embodiment 3: This embodiment provides a method for power metering digital master station artificial intelligence service, and the method includes: S1. Receive an external user call request through the IMS administrative telephone line, and allocate the external user call request to the IVR interactive voice response unit based on the dynamic routing strategy of the ACD automatic call distribution unit. The IVR interactive voice response unit guides the user to input the terminal asset number and service type code through voice or key presses through a preset voice navigation menu; S2. Use the ASR speech recognition unit to convert the user's speech or key press input into text and extract keywords, match the preset service rule library through the instruction mapping unit to generate a structured operation instruction containing the terminal asset number, service type code and timestamp, and convert the operation confirmation information into voice by the TTS speech synthesis unit and feedback it to the user; S3. According to the service type code in the structured operation instruction, call the preset measurement task plan through the task plan matcher to generate an operation instruction set, drive the RPA script execution engine to automatically call the data measurement interface, work order query interface or parameter penetration reading interface of the power metering system, and perform remote data measurement, penetration verification, process debugging or timing statistics services based on the operation instruction set to obtain the execution result; S4. Encapsulate the execution result into a JSON format message through the result feedback interface and return it to the user terminal.

[0024] It is worth noting that the method described in this embodiment is executed based on the system described in Example 1, and the principles are the same, so they will not be repeated here.

[0025] Example 4: The present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, an artificial intelligence service method for a digital master station for electric power metering as described in any embodiment of the present invention is implemented.

[0026] Example 5: The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements an artificial intelligence service method for a digital master station for electric power metering as described in any embodiment of the present invention.

[0027] It is worth noting that the electronic device and computer-readable storage medium described in the present invention are based on the same inventive concept as the method described in the present invention, and will not be described in detail here.

[0028] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An artificial intelligence service system for digital power metering master station, characterized in that: The system includes a traffic access module, a virtual operator module and a call and test assistant module, wherein: The traffic access module receives external user call requests through the IMS administrative telephone line and establishes a communication channel with the public switched telephone network PSTN; The virtual operator module is connected to the call input module and uses intelligent voice interaction technology to analyze the user voice or key input in the external user call request and generate a structured operation instruction containing the terminal asset number and service type code; The call and test assistant module is connected to the virtual operator module, and is used to trigger the power metering system to perform at least one of the corresponding service operations of remote data call and test, parameter setting and process management according to the structured operation instructions, and return the execution result to the user terminal.

2. The artificial intelligence service system for digital power metering master station according to claim 1 is characterized in that: The traffic access module includes a CTI computer telephone integration unit, an ACD automatic call distribution unit and an IVR interactive voice response unit, wherein: The CTI computer telephone integration unit is used to convert telephone switch signals in the PSTN into TCP / IP protocol data packets for communication with the business system server. The ACD automatic call distribution unit is deployed in the business system server and is used to dynamically distribute external user call requests based on the current concurrent call volume. The IVR interactive voice response unit is connected to the ACD automatic call distribution unit and guides users to enter the terminal asset number and business type code through a preset voice navigation menu, and performs format verification on the input data.

3. The artificial intelligence service system for digital power metering master station according to claim 1 is characterized in that: The virtual operator module includes an ASR speech recognition unit, a command mapping unit and a TTS speech synthesis unit, wherein: The ASR speech recognition unit is used to convert user speech into text and extract keywords; The instruction mapping unit is connected to the ASR speech recognition unit to obtain keywords, perform keyword matching based on a preset business rule library, and generate a structured operation instruction containing the terminal asset number, business type code and timestamp; The TTS speech synthesis unit is used to convert the operation confirmation information corresponding to the structured operation instruction into speech feedback to the user.

4. The artificial intelligence service system for digital power metering master station according to claim 1 is characterized in that: The test call assistant module includes an RPA script execution engine, a task solution matcher, and a result feedback interface, where: The RPA script execution engine is deployed in the power metering system, and automatically calls the data call interface, work order query interface and parameter penetration reading interface of the power metering system through pre-compiled RPA scripts, and calls the pre-compiled RPA script according to the business type code in the structured operation instruction to execute at least one of the data call service, penetration verification service, process debugging service or timing statistics service; the task plan matcher is pre-set with a call task plan for matching call tasks based on the business type code, generating an operation instruction set including the terminal asset number, task plan identifier and call parameters, and driving the power metering system to execute non-broadcast task call, penetration meter reading or work order status triggering operation through the operation instruction set; the result feedback interface encapsulates the execution result of the operation into a JSON format message and sends it to the user terminal.

5. The artificial intelligence service system for digital power metering master station according to claim 2 is characterized in that: The ACD automatic call distribution unit dynamically distributes external user call requests based on the current concurrent call volume. Specifically: At least two IMS administrative telephone lines are set as access channels in the ACD automatic call distribution unit. The IMS administrative telephone lines include a main line and at least one backup line. When the current concurrent traffic volume of the main line reaches the preset maximum concurrent traffic volume, the ACD automatic call distribution unit triggers the routing overflow rule and automatically transfers external user call requests that exceed the maximum concurrent traffic volume to the backup line queue.

6. The artificial intelligence service system for digital power metering master station according to claim 1 is characterized in that: The system also includes a security protection unit, which establishes an encrypted tunnel between the call access module, the virtual operator module and the call and test assistant module, implements SM4 algorithm encryption on the structured operation instructions and execution results in transmission, and generates an operation audit chain containing a timestamp.

7. The artificial intelligence service system for digital power metering master station according to claim 1 is characterized in that: The system also includes a business assistant module, which is connected to the call and test assistant module and consists of an anomaly detection unit and a log association unit. The anomaly detection unit is used to count the number of consecutive failed operations and trigger SMS alarms; the log association unit is used to establish an association index between the terminal asset number, operation time and fault code.

8. An artificial intelligence service method for a digital power metering master station, implemented based on the artificial intelligence service system for a digital power metering master station according to any one of claims 1 to 7, characterized in that: The method comprises: S1. Receive external user call requests through the IMS administrative telephone line and distribute the external user call requests to the IVR interactive voice response unit based on the dynamic routing policy of the ACD automatic call distribution unit. The IVR interactive voice response unit guides the user to enter the terminal asset number and service type code by voice or keystroke through a preset voice navigation menu; S2. The ASR speech recognition unit converts user voice or key input into text and extracts keywords. The instruction mapping unit matches the preset business rule library to generate structured operation instructions containing the terminal asset number, business type code, and timestamp. The TTS speech synthesis unit converts the operation confirmation information into voice feedback to the user. S3. Based on the business type code in the structured operation instruction, the task solution matcher calls the preset call test task solution to generate an operation instruction set. This drives the RPA script execution engine to automatically call the data call test interface, work order query interface, or parameter transparent reading interface of the power metering system. Based on the operation instruction set, it performs remote data call test, penetrating verification, process debugging, or scheduled statistics services to obtain the execution results. S4. Encapsulate the execution result into a JSON format message through the result feedback interface and return it to the user terminal.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, an artificial intelligence service method for a digital master station for electric power metering is implemented as described in claim 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, an artificial intelligence service method for a digital master station for electric power metering as described in claim 8 is implemented.