Oil depot storage data management integration and AI decision support integrated system

Abstract

The invention discloses an oil depot storage data management integration and AI decision support integrated system, and relates to the technical field of AI decision support integrated systems, the oil depot storage data management integration and AI decision support integrated system comprises a client, the client comprises a data module, an infrastructure module, an application module and an AI engine module, the data module is composed of a data resource module, a data governance module and a data service module, the output end of the data resource module is connected with the data governance module, and fragmented data dispersed in the warehouse management system, the control system and the environmental protection monitoring equipment are integrated. Standardized processing and consistency verification of data are achieved through the data management module, the problems that real-time verification of inventory data and metering data lags behind, equipment fault early warning and maintenance plans are disjointed and the like are solved, the data can be efficiently circulated and utilized, a unified and accurate data basis is provided for oil depot storage operation, and then the overall operation efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of integrated AI decision support systems, and particularly to an integrated system for oil depot storage data governance and AI decision support. Background Technology

[0002] In the wave of global energy industry transformation and intelligent upgrading of supply chains, oil depots, as the core hub connecting oil and gas production, refining and processing and end-consumer, directly affect the stability and economy of the energy industry chain through their storage operation efficiency, safety management level and decision-making accuracy. With the continuous expansion of oil and gas trade, the surge in demand for storage of various oil products (such as gasoline, diesel, aviation kerosene, and special solvent oils), and the increasingly stringent national regulatory requirements for safe production, environmental emissions, and emergency response, oil depot storage management is facing unprecedented complex challenges.

[0003] Currently, most oil depots' operational data remains fragmented. Inventory data is scattered across warehouse management systems, equipment status data is stored in SCADA control systems, and the flow of inbound and outbound documents relies on paper records or isolated ERP modules. Environmental data, on the other hand, is collected separately by environmental monitoring equipment. This data silo phenomenon leads to difficulties in data integration and poor consistency. For example, there is a lag in real-time verification of inventory and metering data, a disconnect between equipment failure warnings and maintenance plans, and difficulty in quickly tracing related operational steps when environmental indicators exceed standards. This severely restricts operational efficiency and risk management capabilities. In view of this, we propose an integrated system for oil depot warehouse data governance and AI decision support. Summary of the Invention

[0004] The purpose of this invention is to provide an integrated system for oil depot storage data governance and AI decision support to solve the problems mentioned in the background.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an integrated system for oil depot storage data governance and AI decision support, comprising a client, which includes a data module, an infrastructure module, an application module, and an AI engine module. The data module consists of a data resource module, a data governance module, and a data service module. The output end of the data resource module is connected to the data governance module, and the output end of the data governance module is signal-connected to the data service module. The data resource module consists of IoT data, a transmission module, and a database. The AI ​​engine module includes an inference application module, an algorithm support module, and a model management module. The signal output ends of the application module, the algorithm support module, and the model management module are all signal-connected to the signal input end of the AI ​​engine module.

[0006] Preferably, the signal output terminal of the model management module is connected to the algorithm support module, and the signal output terminal of the algorithm support module is connected to the signal input terminal of the application module.

[0007] Preferably, the infrastructure module includes a hardware support module, a sensing and acquisition module, and a computing resource module.

[0008] Preferably, the sensing and acquisition component consists of a liquid level and temperature sensor and a monitoring device. The application module includes a business application module, a user interaction module, and a system support module. The signal output terminal of the acquisition component is connected to the signal input terminal of the database.

[0009] Preferably, the business application module includes an inventory management module, an equipment management module, and a security control module.

[0010] Preferably, the equipment management module includes a fault early warning module and a status monitoring module.

[0011] Preferably, the IoT data is transmitted to the database and AI engine module through the transmission module. The signal output terminals of the database module and the IoT data module are both connected to the AI ​​engine module. The signal output terminal of the sensing and acquisition module is connected to the signal input terminal of the database through the transmission module. The signal output terminal of the computing resource module is also connected to the signal input terminal of the AI ​​engine module.

[0012] Compared with the prior art, the beneficial effects of the present invention are: 1. This integrated system for oil depot storage data governance and AI decision support integrates fragmented data scattered across storage management systems, control systems, and environmental monitoring equipment. Through the data governance module, it achieves standardized data processing and consistency verification, solving problems such as the lag in real-time verification of inventory and measurement data, and the disconnect between equipment failure warnings and maintenance plans. This enables efficient data flow and utilization, providing a unified and accurate data foundation for oil depot storage operations, thereby improving overall operational efficiency.

[0013] 2. This integrated system for oil depot storage data governance and AI decision support, leveraging the predictive algorithms and inference application modules of the AI ​​engine module, can analyze real-time collected sensor data and historical data. It enables intelligent early warning, risk assessment, and strategy recommendations in business scenarios such as inventory management, equipment management, and safety control. For example, the intelligent early warning module and dynamic monitoring module, in conjunction with the AI ​​engine, provide replenishment strategy suggestions. By combining violation identification and leakage detection with the AI ​​engine, risk assessment and early warning are conducted, effectively strengthening the risk control capabilities of oil depot storage and improving the accuracy and timeliness of decision-making.

[0014] 3. This integrated system for oil depot storage data governance and AI decision support, through centralized integration of environmental data and in-depth analysis by an AI engine, can accurately track the changing trends of various environmental indicators, identify potential environmental compliance risks in advance, assist in the formulation of targeted emission reduction and environmental optimization plans, and help oil depots better meet the increasingly stringent national environmental regulatory requirements and reduce the risk of environmental violations.

[0015] 4. This integrated system for oil depot storage data governance and AI decision support, through the standardized report generation and data capture and analysis functions provided by the system's data service module, enables management to quickly obtain key data and in-depth insights into all aspects of oil depot operations. This reduces the time cost of manually compiling and analyzing data, making the decision-making process more efficient and providing the oil depot with a faster response capability to dynamic market changes. Attached Figure Description

[0016] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a schematic diagram of the integrated system for oil depot storage data governance and AI decision support of the present invention; Figure 2 This is a flowchart of the AI ​​engine module of the present invention; Figure 3 This is a flowchart of the data resource module of the present invention; Figure 4 This is a flowchart of the business application module of the present invention. Detailed Implementation

[0017] This section will describe in detail specific embodiments of the present invention. Preferred embodiments of the present invention are shown in the accompanying drawings. The purpose of the drawings is to supplement the textual description with graphics, so that people can intuitively and vividly understand each technical feature and overall technical solution of the present invention, but they should not be construed as limiting the scope of protection of the present invention.

[0018] Example 1, please refer to Figures 1-4This invention provides a technical solution: an integrated system for oil depot storage data governance and AI decision support, including a client-side module. The client-side module comprises a data module, an infrastructure module, an application module, and an AI engine module. The data module consists of a data resource module, a data governance module, and a data service module. The signal output terminals of the data resource module and the data governance module are connected. The data resource module integrates business system data, IoT data, and external data. The data service module provides functions such as real-time query services, standardized report generation, data capture and analysis, and customized operational risks. The data governance module handles data model standardization, anomaly handling, consistency verification, real-time monitoring and alarms, and operational audit tracking. By achieving standardized data processing and consistency verification through the data governance module, problems such as delayed real-time verification of inventory and measurement data, and the disconnect between equipment fault warnings and maintenance plans are solved. This enables efficient data flow and utilization, providing a unified and accurate data foundation for oil depot storage operations, thereby improving overall operational efficiency.

[0019] Example 2: Based on Example 1: The integrated system for oil depot storage data governance and AI decision support includes a client-side module, which comprises a data module, an infrastructure module, an application module, and an AI engine module. The data module consists of a data resource module, a data governance module, and a data service module. The signal output of the data resource module is connected to the signal output of the data governance module, and the signal output of the data governance module is connected to the signal input of the data service module. The data resource module integrates business system data, IoT data, and external data. The data service module provides real-time query services, standardized report generation, data capture and analysis, and custom operational risk management. The AI ​​engine module includes an inference application module, an algorithm support module, and a model management module, used for overall decision-making. The signal output of the model management module is connected to the algorithm support module, and the signal output of the algorithm support module is connected to the signal input of the application module. This system integrates fragmented data scattered across the storage management system, control system, and environmental monitoring equipment.

[0020] The infrastructure module includes a hardware support module, a sensing and acquisition module, and a computing resource module. It provides the basic hardware for data storage, transmission, and computation. The data governance module handles functions such as data model standardization, anomaly handling, consistency verification, real-time monitoring and alarms, and operational auditing. Through the data governance module, standardized data processing and consistency verification are achieved, resolving issues such as the lag in real-time verification of inventory and metering data, and the disconnect between equipment failure warnings and maintenance plans. This enables efficient data flow and utilization, providing a unified and accurate data foundation for oil depot operations, thereby improving overall operational efficiency. The sensing and acquisition components consist of level and temperature sensors, monitoring equipment, etc. The system is structured as follows: the application module consists of a business application module, a user interaction module, and a system support module. The business application module includes an inventory management module, an equipment management module, and a security control module. The security control module consists of a violation identification module and a leakage detection module. The signal output terminals of both the violation identification module and the leakage detection module are connected to the AI ​​engine module, which performs risk assessment and early warning. Meanwhile, the inventory management module consists of an intelligent early warning module and a dynamic monitoring module. The signal output terminals of both the intelligent early warning module and the dynamic monitoring module are also connected to the signal input terminal module of the AI ​​engine. Through the AI ​​engine and the data module, analysis is performed and replenishment strategy suggestions are provided.

[0021] Furthermore, the equipment management module consists of a fault early warning module and a status monitoring module, used to manage and warn about equipment. The data resource module consists of IoT data, a transmission module, and a database. IoT data is transmitted to the database and AI engine module through the transmission module. The signal outputs of the database module and the IoT data module are connected to the signal input of the AI ​​engine module. The signal output of the sensing and acquisition module is connected to the signal input of the database through the transmission module. The signal output of the computing resource module is also connected to the signal input of the AI ​​engine module. With the help of the AI ​​engine module's prediction algorithms and inference application modules, the system can analyze real-time sensor data and historical data, and realize intelligent early warning, risk assessment, and strategy suggestions in business scenarios such as inventory management, equipment management, and safety control. For example, the intelligent early warning module and dynamic monitoring module, together with the AI ​​engine, provide replenishment strategy suggestions. By combining violation identification and leakage detection with the AI ​​engine for risk assessment and early warning, the risk control capabilities of oil depot storage are effectively strengthened, and the accuracy and timeliness of decision-making are improved.

[0022] Working Principle: The receiving line collects real-time IoT data from the oil depot through the material acquisition module, such as tank status, equipment operating parameters, and security information. This data is then transmitted to the database of the data resource module via a multi-protocol transmission module. Simultaneously, business system data and external data are integrated and sent to the database using the same method, forming a comprehensive data source. The raw data from the data resource module then enters the data governance module, where it is processed according to preset data model specifications. This includes cleaning outliers and missing values, using interpolation algorithms to complete missing equipment operating parameters, verifying data consistency, ensuring data quality through real-time monitoring and alarm mechanisms, and tracking and recording the modification trajectory of each data entry through operation auditing to ensure data traceability and security. Ultimately, standardized, high-quality data is generated. This standardized data is then transmitted to the data service module, generating real-time inventory ledgers, equipment health reports, etc., which can be accessed by managers at any time through the user interaction module. Reports can be generated on a daily, weekly, or monthly basis to meet different management needs. Simultaneously, the data service module transforms the processed data into directly accessible services, including real-time inventory and equipment status queries, and standardized report generation, providing data support for the AI ​​engine module and business application modules.

[0023] The AI ​​engine module, based on the algorithm support module's prediction and optimization algorithms, receives real-time sensor data and historical data from the database. It trains and iterates the model through the model management module, optimizing the model using an accuracy monitoring mechanism. Subsequently, the inference application module performs real-time inference for equipment fault warnings and batch inference for inventory trend analysis, generating decision results. The business application module receives the AI ​​engine's decision results and, through dynamic monitoring and intelligent early warning, uses historical outbound volume and market demand trend data to derive a 7-day oil demand forecast through batch inference. Based on this, the intelligent early warning module issues replenishment orders 3 days before inventory falls below the safety line. The system provides delivery reminders and generates purchase recommendations. The equipment management module, based on status monitoring and fault warnings, manages equipment health. Meanwhile, the safety control module analyzes violation identification videos and leak detection sensor data, combining video stream data from monitoring equipment with a violation identification model. When personnel are detected not wearing protective equipment or engaging in illegal operations, an audible and visual alarm is immediately triggered, and the violation footage is captured and archived for safety assessment. Finally, an AI engine conducts risk assessment and provides early warnings. The decision information from all modules is then aggregated to provide intelligent support for the entire oil depot operation process, achieving intelligent control of the entire oil depot operation chain.

[0024] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. An integrated system for oil depot storage data governance and AI decision support, comprising a client-side module, which includes a data module, an infrastructure module, an application module, and an AI engine module, characterized by: The data module consists of a data resource module, a data governance module, and a data service module. The output end of the data resource module is connected to the data governance module, and the output end of the data governance module is connected to the data service module. The data resource module consists of IoT data, a transmission module, and a database. The AI ​​engine module includes an inference application module, an algorithm support module, and a model management module. The signal output ends of the application module, the algorithm support module, and the model management module are all connected to the signal input end of the AI ​​engine module.

2. The integrated system for oil depot storage data governance and AI decision support according to claim 1, characterized in that: The signal output terminal of the model management module is connected to the algorithm support module, and the signal output terminal of the algorithm support module is connected to the signal input terminal of the application module.

3. The integrated system for oil depot storage data governance and AI decision support according to claim 2, characterized in that: The infrastructure module includes a hardware support module, a sensing and acquisition module, and a computing resource module.

4. The integrated system for oil depot storage data governance and AI decision support according to claim 3, characterized in that: The sensing and acquisition component consists of liquid level and temperature sensors and monitoring equipment. The application module includes a business application module, a user interaction module, and a system support module. The signal output terminal of the acquisition component is connected to the signal input terminal of the database.

5. The integrated system for oil depot storage data governance and AI decision support according to claim 4, characterized in that: The business application modules include an inventory management module, an equipment management module, and a security control module.

6. The integrated system for oil depot storage data governance and AI decision support according to claim 5, characterized in that: The equipment management module includes a fault early warning module and a status monitoring module.

7. The integrated system for oil depot storage data governance and AI decision support according to claim 6, characterized in that: The IoT data is transmitted to the database and AI engine modules via the transmission module. The signal output terminals of both the database module and the IoT data module are connected to the AI ​​engine module. The signal output terminal of the sensing and acquisition module is connected to the signal input terminal of the database via the transmission module. The signal output terminal of the computing resource module is also connected to the signal input terminal of the AI ​​engine module.

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