Scheduling log AI generation system based on scheduling recording telephone and networked order issuing

Abstract

The invention relates to the technical field of intelligent scheduling management, voice recognition, natural language processing and artificial intelligence, in particular to a system for realizing AI intelligent scheduling log generation by scheduling recording telephones and networked order issuing, which comprises a data acquisition module, a data processing module, a data processing module and a data processing module, and is characterized in that the data acquisition module comprises recording telephone acquisition and networked order issuing acquisition; the data preprocessing module is used for preprocessing the collected recording telephone voice data; the AI processing module comprises voice recognition, natural language processing and log generation; and the log management module is used for storing, querying and retrieving log data and checking and correcting the log data. According to the method and the system, the key information in the scheduling recording telephone and the networked command issuing instruction are fused, and the standardized scheduling log is automatically and accurately generated in real time by utilizing an artificial intelligence technology, so that the efficiency and the quality of recording the scheduling log are improved, and the manual operation cost and the error rate are reduced.

Description

Technical Field

[0001] This invention relates to the fields of intelligent dispatch management, speech recognition, natural language processing and artificial intelligence, and specifically to a system that enables AI-powered intelligent generation of dispatch logs through dispatch recorded telephone calls and networked commands. Background Technology

[0002] The dispatch recording telephone system is a communication and information management system specifically designed for industries such as transportation and power, ensuring efficient communication, information recording, and emergency command during operations. The system combines telephone communication, recording storage, and dispatch management functions, serving as a crucial technical support tool for dispatch operations across various industries.

[0003] Networked command systems are a command issuance and execution management model based on computer network technology, widely used in fields such as power, transportation, and the military. Through digital and networked means, they enable rapid transmission, precise execution, and end-to-end monitoring of commands, replacing traditional paper-based or manual command issuance methods. In the power industry, networked command systems are commonly used by power dispatch centers to perform switching operations and equipment commissioning at substations, ensuring the safety and stability of the power grid.

[0004] In dispatch management within industries such as power, transportation, and energy, dispatch logs are crucial documents recording key information such as dispatch operations, instruction transmission, and event handling. Traditional dispatch log recording methods primarily rely on manual entry, which presents the following problems: 1. Inefficiency: Dispatchers manually record logs in busy work scenarios, which is easy to get distracted and consumes a lot of time.

[0005] 2. Insufficient accuracy: Manual recording is prone to errors, omissions, or misunderstandings, resulting in incomplete and inaccurate log content.

[0006] 3. Lack of real-time capability: Manual recording cannot be synchronized and updated in real time, which is not conducive to subsequent information retrieval and analysis.

[0007] 4. Low standardization: Different dispatchers have different recording habits and formats, resulting in poor log standardization and difficulty in unified management and analysis.

[0008] While some existing technologies utilize speech recognition for assisted recording, most only offer simple speech-to-text conversion. They cannot leverage existing dispatch recording telephone systems and networked command systems to deeply understand and intelligently analyze dispatch instructions based on networked command information, thus failing to meet the demand for high-quality and efficient dispatch log generation in complex dispatch scenarios. Summary of the Invention

[0009] The purpose of this invention is to provide an AI-powered dispatch log generation system based on dispatch recording calls and networked command issuance. By integrating key information from dispatch recording calls with networked command issuance instructions, the system uses artificial intelligence technology to automatically, in real time, and accurately generate standardized dispatch logs, thereby improving the efficiency and quality of dispatch log recording and reducing manual operation costs and error rates.

[0010] The technical solution of this invention is achieved through the following measures: This invention provides a scheduling log AI generation system based on scheduling recorded telephone calls and networked command issuance, including: (1) a data acquisition module: including recorded telephone call acquisition and networked command issuance acquisition; (2) a data preprocessing module: preprocessing the acquired recorded telephone call voice data; (3) an AI processing module: including speech recognition, natural language processing, and log generation; and (4) a log management module: including log data storage, query and retrieval, review and correction.

[0011] The data acquisition module specifically includes the following implementation units: ① Recording telephone acquisition unit: used to connect the acquisition unit to the dispatch telephone system, collect call voice data in daily dispatch work in real time, and encode and store it; ② Networked Command Acquisition Unit: This unit interfaces with the networked command system to acquire information issued through the system during daily dispatching operations, including text commands and equipment status. For example, it receives networked commands regarding changes in the switching status of power grid equipment during power dispatching, including the command time, command content, response time, and the equipment number involved.

[0012] The data preprocessing module preprocesses the collected recorded telephone voice data through a voice preprocessing unit, including noise reduction and endpoint detection; it uses digital signal processing algorithms to remove environmental noise interference and uses endpoint detection technology to accurately identify the start and end positions of the voice, thereby improving the accuracy of voice recognition.

[0013] The AI ​​processing module specifically includes the following implementation units: ① Speech recognition unit: A speech recognition model based on deep learning that can convert preprocessed speech data into text information; ② Natural Language Processing Unit: Performs semantic analysis, entity extraction, and relationship recognition on text information after speech recognition and text information of networked commands; for example, extracts key entities in scheduling instructions, such as device name, operation time, operation type, etc., and analyzes the logical relationships between entities; ③ Log Generation Unit: Based on the preset scheduling log template and extracted key information, it automatically generates structured scheduling logs. For example, it categorizes and integrates information according to factors such as time sequence and operation type, and automatically fills the corresponding fields in the log template.

[0014] The log management module specifically includes the following implementation units: ① Storage unit: The generated scheduling logs are named and stored in the database according to shift for easy subsequent querying and management; ② Query and Retrieval Unit: Provides various query conditions, including time range, device name, and dispatcher name, to enable quick retrieval and viewing of dispatch logs; ③ Audit and Correction Unit: Supports scheduling administrators to audit the generated scheduling logs, manually correct errors or omissions, and record the correction process.

[0015] Referring to the accompanying drawings, the following is a further detailed description of the above-mentioned technical solution: 1. Data Acquisition Phase: Real-time acquisition of two types of core data.

[0016] ① Dispatch recorded telephone voice data, including time, location, dispatch instructions, equipment status, etc.

[0017] ② Networked command text information, including scheduling instructions, parameters, equipment status, etc.

[0018] 2. Data processing stage: Improve data quality by processing the collected content.

[0019] ①Speech processing: Through noise reduction and endpoint detection, interference from the environment is removed to locate valid speech.

[0020] ②Word processing: Standardize formatting and correct typos, and standardize data formats.

[0021] 3. AI Analysis and Information Fusion Stage: ① Speech recognition: Converting pre-processed speech into text; ② Information fusion: Perform semantic analysis and entity extraction on two types of textual information, such as equipment, time or operation, as well as relationship identification and integration of key information.

[0022] 4. Automatic generation phase of scheduling logs Based on the preset scheduling log template, the integrated key information is filled into the corresponding fields to generate a structured scheduling log.

[0023] 5. Log Management Phase ① Review and Correction: The generated scheduling logs will be pushed to administrators for review. Errors can be corrected and the process will be recorded. Once the review is successful, the logs can be stored in the database.

[0024] ② Storage: Store the generated logs in the database.

[0025] ③ Retrieval: The scheduling logs stored in the database can be quickly queried based on multiple conditions such as time, device, or personnel.

[0026] ④ Output: The output can be directly used for standardized and accurate logs for scheduling management.

[0027] Compared with existing technologies, the scheduling log AI generation system based on scheduling recorded telephone calls and networked command issuance provided by this invention overcomes the shortcomings of existing technologies and has the following significant advantages: 1. Improved efficiency: No manual recording is required, which greatly shortens the time for generating scheduling logs, allowing schedulers to devote more energy to actual scheduling work.

[0028] 2. Ensure accuracy: AI technology is used to accurately process voice and text information, avoiding typos and omissions in manual recording, and ensuring the completeness and accuracy of the dispatch log content.

[0029] 3. Achieve standardization: Pre-set unified log templates and processing rules to ensure that the generated scheduling logs have standardized formats and content, facilitating unified management and analysis, and providing a reliable data foundation for subsequent data analysis and decision support. Attached Figure Description

[0030] Appendix Figure 1 This is a schematic diagram of the overall process of an embodiment of the present invention. Detailed Implementation

[0031] The present invention is not limited to the following embodiments, and specific implementation methods can be determined according to the technical solutions and actual conditions of the present invention.

[0032] The present invention will be further described below with reference to embodiments: This embodiment provides a scheduling log AI generation system based on scheduling recorded telephone calls and networked command issuance, including: (1) a data acquisition module: including recorded telephone call acquisition and networked command issuance acquisition; (2) a data preprocessing module: preprocessing the acquired recorded telephone call voice data; (3) an AI processing module: including speech recognition, natural language processing, and log generation; and (4) a log management module: including log data storage, query and retrieval, review and correction.

[0033] The data acquisition module specifically includes the following implementation units: ① Recording telephone acquisition unit: used to connect the acquisition unit to the dispatch telephone system, collect call voice data in daily dispatch work in real time, and encode and store it; ② Networked Command Acquisition Unit: This unit interfaces with the networked command system to acquire information issued through the system during daily dispatching operations, including text commands and equipment status. For example, it receives networked commands regarding changes in the switching status of power grid equipment during power dispatching, including the command time, command content, response time, and the equipment number involved.

[0034] The data preprocessing module preprocesses the collected recorded telephone voice data through a voice preprocessing unit, including noise reduction and endpoint detection; it uses digital signal processing algorithms to remove environmental noise interference and uses endpoint detection technology to accurately identify the start and end positions of the voice, thereby improving the accuracy of voice recognition.

[0035] The AI ​​processing module specifically includes the following implementation units: ① Speech recognition unit: A speech recognition model based on deep learning that can convert preprocessed speech data into text information; ② Natural Language Processing Unit: Performs semantic analysis, entity extraction, and relationship recognition on text information after speech recognition and text information of networked commands; for example, extracts key entities in scheduling instructions, such as device name, operation time, operation type, etc., and analyzes the logical relationships between entities; ③ Log Generation Unit: Based on the preset scheduling log template and extracted key information, it automatically generates structured scheduling logs. For example, it categorizes and integrates information according to factors such as time sequence and operation type, and automatically fills the corresponding fields in the log template.

[0036] The log management module specifically includes the following implementation units: ① Storage unit: The generated scheduling logs are named and stored in the database according to shift for easy subsequent querying and management; ② Query and Retrieval Unit: Provides various query conditions, including time range, device name, and dispatcher name, to enable quick retrieval and viewing of dispatch logs; ③ Audit and Correction Unit: Supports scheduling administrators to audit the generated scheduling logs, manually correct errors or omissions, and record the correction process.

[0037] Referring to the accompanying drawings, the following is a further description of the above-mentioned technical solution: 1. Data Acquisition Phase: Real-time acquisition of two types of core data.

[0038] ① Dispatch recorded telephone voice data, including time, location, dispatch instructions, equipment status, etc.

[0039] ② Networked command text information, including scheduling instructions, parameters, equipment status, etc.

[0040] 2. Data processing stage: Improve data quality by processing the collected content.

[0041] ①Speech processing: Through noise reduction and endpoint detection, interference from the environment is removed to locate valid speech.

[0042] ②Word processing: Standardize formatting and correct typos, and standardize data formats.

[0043] 3. AI Analysis and Information Fusion Stage: ① Speech recognition: Converting pre-processed speech into text; ② Information fusion: Perform semantic analysis and entity extraction on two types of textual information, such as equipment, time or operation, as well as relationship identification and integration of key information.

[0044] 4. Automatic generation phase of scheduling logs Based on the preset scheduling log template, the integrated key information is filled into the corresponding fields to generate a structured scheduling log.

[0045] 5. Log Management Phase ① Review and Correction: The generated scheduling logs will be pushed to administrators for review. Errors can be corrected and the process will be recorded. Once the review is successful, the logs can be stored in the database.

[0046] ② Storage: Store the generated logs in the database.

[0047] ③ Retrieval: The scheduling logs stored in the database can be quickly queried based on multiple conditions such as time, device, or personnel.

[0048] ④ Output: The output can be directly used for standardized and accurate logs for scheduling management.

[0049] The above technical features constitute the embodiments of the present invention, which have strong adaptability and implementation effect. Unnecessary technical features can be added or removed according to actual needs to meet the needs of different situations.

Claims

1. A scheduling log AI generation system based on scheduling recorded telephone calls and networked command issuance, characterized in that... include: (1) Data acquisition module: including recorded telephone data acquisition and networked command acquisition; (2) Data preprocessing module: preprocessing the collected recorded telephone voice data; (3) AI processing module: including speech recognition, natural language processing, and log generation; (4) Log management module: including log data storage, query and retrieval, review and correction.

2. The dispatch log AI generation system based on dispatch recorded telephone calls and networked command issuance as described in claim 1, characterized in that... The data acquisition module specifically includes the following implementation units: ① Recording telephone acquisition unit: used to connect the acquisition unit to the dispatch telephone system, collect call voice data in daily dispatch work in real time, and encode and store it; ② Networked command acquisition unit: The acquisition unit interfaces with the networked command system to acquire information issued through the networked command system during daily scheduling work, including text commands and equipment status.

3. The dispatch log AI generation system based on dispatch recorded telephone calls and networked command issuance according to claim 1 or 2, characterized in that... The data preprocessing module preprocesses the collected recorded telephone voice data through a voice preprocessing unit, including noise reduction and endpoint detection; it uses digital signal processing algorithms to remove environmental noise interference and uses endpoint detection technology to accurately identify the start and end positions of the voice, thereby improving the accuracy of voice recognition.

4. The dispatch log AI generation system based on dispatch recorded telephone calls and networked command issuance according to claim 1 or 2, characterized in that... The AI ​​processing module specifically includes the following implementation units: ① Speech recognition unit: A speech recognition model based on deep learning that can convert preprocessed speech data into text information; ②Natural Language Processing Unit: Performs semantic analysis, entity extraction, and relation recognition on the text information after speech recognition and the text information of networked commands; ③ Log generation unit: Automatically generates structured scheduling logs based on preset scheduling log templates and extracted key information.

5. The dispatch log AI generation system based on dispatch recorded telephone calls and networked command issuance according to claim 3, characterized in that... The AI ​​processing module specifically includes the following implementation units: ① Speech recognition unit: A speech recognition model based on deep learning that can convert preprocessed speech data into text information; ②Natural Language Processing Unit: Performs semantic analysis, entity extraction, and relation recognition on the text information after speech recognition and the text information of networked commands; ③ Log generation unit: Automatically generates structured scheduling logs based on preset scheduling log templates and extracted key information.

6. The dispatch log AI generation system based on dispatch recorded telephone calls and networked command issuance according to claim 1 or 2, characterized in that... The log management module specifically includes the following implementation units: ① Storage unit: The generated scheduling logs are named and stored in the database according to shift for easy subsequent querying and management; ② Query and Retrieval Unit: Provides various query conditions, including time range, device name, and dispatcher name, to enable quick retrieval and viewing of dispatch logs; ③ Audit and Correction Unit: Supports scheduling administrators to audit the generated scheduling logs, manually correct errors or omissions, and record the correction process.

7. The dispatch log AI generation system based on dispatch recorded telephone calls and networked command issuance according to claim 5, characterized in that... The log management module specifically includes the following implementation units: ① Storage unit: The generated scheduling logs are named and stored in the database according to shift for easy subsequent querying and management; ② Query and Retrieval Unit: Provides various query conditions, including time range, device name, and dispatcher name, to enable quick retrieval and viewing of dispatch logs; ③ Audit and Correction Unit: Supports scheduling administrators to audit the generated scheduling logs, manually correct errors or omissions, and record the correction process.

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