Digital employee creation and management system based on artificial intelligence large model and use method thereof

Abstract

The invention discloses a digital employee creation and management system based on an artificial intelligence large model and a use method thereof. The digital employee creation and management system comprises a data sensing module, a data transmission module, an artificial AI model, an application service module and a display module. The production environment can be monitored in real time through the data sensing module, the data are refined through the artificial AI model analysis and application service module, and the data are displayed through the display module, so that potential safety hazards and equipment faults are warned in advance, safety accidents are reduced, the safety state of personnel is known in time, and the production efficiency is improved. The method can provide guidance and help during rescue, can realize safety management of factory personnel, can collect on-site videos in real time, can facilitate interaction between operation management personnel and on-site operation personnel, can reduce dependence on manual operation, can realize man-machine collaborative operation and remote collaborative operation, can improve the standardization and management efficiency of the on-site operation personnel, and can improve the safety of the factory personnel. Meanwhile, the production efficiency and quality are improved, and personal errors are reduced.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence and process automation technology, and specifically to a digital employee creation and management system based on an artificial intelligence big model and a method of using the same. Background Art

[0002] The daily operations of petrochemical enterprises involve a large amount of complex and repetitive work. With the continuous development of the global economy and the rapid advancement of science and technology, the production efficiency and safety performance of the petrochemical industry, as an important basic industry, are increasingly valued.

[0003] The petrochemical industry faces extremely high safety risks due to its own characteristics. Frequent accidents not only cause huge economic losses, but also seriously threaten human life and environmental quality. However, the industry faces many challenges, such as high-risk working environments, increasingly complex industrial processes, and the need to improve production efficiency and reduce operating costs. The traditional operating model of traditional petrochemical companies relies heavily on manpower. Due to the limited energy of personnel, it is difficult to work for a long time, which leads to low work efficiency. In addition, personnel are prone to fatigue after working for a long time, which reduces the quality of work and is easily affected by human errors. Summary of the invention

[0004] The purpose of the present invention is to provide a digital employee creation and management system based on an artificial intelligence large model and a method of using the same, which has the advantages of being able to achieve optimal allocation of human resources, reduce dependence on manual operations, while improving production efficiency and quality, reducing human errors, and ensuring the continuity and safety of the production process. It solves the problem that the traditional operating model of traditional petrochemical enterprises relies heavily on manpower, and due to the limited energy of personnel, it is difficult to work for a long time, which leads to low work efficiency, and personnel are prone to fatigue after working for a long time, which reduces the quality of work and is easily affected by human errors.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a digital employee creation and management system based on an artificial intelligence large model, comprising a data perception module, a data transmission module, an artificial AI model, an application service module and a display module, wherein the data perception module is used to import and manage the enterprise's production data, real-time data and basic data into a data lake, the data transmission module is used to transmit image data, video stream data, organizational information data, etc. to the application service module and the artificial AI model, the artificial AI model is used to analyze and process the data, the application service module is used to support the implementation of applications related to intelligent identification and intelligent management of safety hazards in refining enterprises, and the display module is used to display identification, early warning, tracking and disposal information in multiple dimensions.

[0006] Preferably, the application service modules include human and vehicle control, on-site control, operation coordination, positioning services and basic management, and the basic management includes terminal management, locator management, base station management and map management.

[0007] Preferably, the human and vehicle control includes employee management, contractor management, visitor management and vehicle management; the on-site control includes safety control map, four-color risk map, operation monitoring, inspection supervision, risk alarm, out-of-bounds alarm, behavior management and video linkage; the operation collaboration includes emergency individual, operation knowledge, remote collaboration and SOS alarm.

[0008] Preferably, the content displayed by the display module includes human and vehicle control analysis, risk control analysis, safety alarm analysis, risk disposal coordination, security monitoring, video linkage coordination, event statistical analysis and safety trend analysis.

[0009] Preferably, the display forms of the display module include pictures, short videos, live broadcasts, reports, etc., and support export, and can be displayed using mini-programs and APPs.

[0010] Preferably, the data transmission module includes Bluetooth and 5G base stations, and the data perception module includes explosion-proof beacons, smart helmets, smart badges and vehicle-mounted terminals.

[0011] Preferably, the method of use comprises the following steps:

[0012] A. First, the data perception module is used to perceive and analyze the various business processes of petrochemical enterprises, and to identify specific tasks that can be replaced or assisted by digital employees, such as data entry, monitoring production parameters, and predicting maintenance needs.

[0013] B. Transmit historical production data, real-time data, and simulated data sets to artificial AI models for training through the data transmission module, so that they have the ability to understand complex industrial processes, decision logic, and task execution;

[0014] C. Then design the user interface (UI) and user experience (UX) of the digital worker to ensure it can integrate with existing systems and work collaboratively with human employees;

[0015] D. Deploy the trained artificial AI model to the digital employee platform and test it in a controlled environment to ensure it operates stably and meets business needs;

[0016] E. Finally, establish a real-time performance monitoring and feedback mechanism to collect data from digital employees in actual operations for continuous optimization and fine-tuning of artificial AI models;

[0017] F. When managing employees, first make the locations of internal and external employees in the factory traceable, and external risks controllable and traceable, supplementing the existing personnel safety management by using portable positioning terminals such as explosion-proof beacons, smart helmets, smart badges or vehicle-mounted terminals;

[0018] G. The explosion-proof beacons, smart helmets, smart badges or vehicle-mounted terminals carried by factory personnel can transmit positioning information in real time, and the data sensing module can sense the returned information, and the data transmission module can transmit the information collected by the data sensing module to the artificial AI model for analysis;

[0019] H. The analyzed data will be transmitted to the application service module, which will be used to support the intelligent identification and intelligent management of enterprise safety hazards;

[0020] H1. First, the intelligent identification of safety hazards in refining and chemical enterprises mainly focuses on the six typical safety hazard scenarios in refining and chemical enterprises, namely, identification of personnel violation status, door and wall perimeter safety, on-site operation safety, production area safety, equipment safety hazards, and environmental status safety hazards;

[0021] H2. Secondly, the intelligent management of safety hazards in refining and chemical enterprises mainly includes early warning processing, intelligent push, intelligent tag intelligent search, multi-dimensional statistics, i.e. smart screen, etc., to achieve the whole process management of early warning reminder, push report, analysis and management after the safety hazards are discovered;

[0022] H3. In addition, through algorithm management, the configuration of required algorithm parameters, setting of business process rules and streaming media services can be realized;

[0023] I. Finally, the display module uses the data generated by the application service module to identify, warn, track and deal with key safety hazards, and conduct situation analysis and multi-dimensional display of relevant information, so as to facilitate the online display of the location of personnel in the production area, and through functions such as abnormal alarm, personnel location search and trajectory playback, timely understand the safety status of personnel, and provide guidance and assistance during rescue, so as to achieve safety management of factory personnel.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] The present invention can monitor the production environment in real time through the data perception module, refine the data through the analysis of the artificial AI model and the application service module, and display the data through the display module, so as to give early warning of potential safety hazards and equipment failures, reduce safety accidents, timely understand the safety status of personnel, and provide guidance and assistance during rescue, so as to achieve safe management of factory personnel. At the same time, through real-time collection of on-site videos, it is convenient for operation managers to interact with on-site operators, realize human-machine collaborative operation and remote collaborative operation, improve the standardization and management efficiency of on-site operators, effectively realize the optimal allocation of human resources, reduce dependence on manual operations, and at the same time improve production efficiency and quality, reduce human errors, and ensure the continuity and safety of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of the system principle of the present invention;

[0027] Figure 2 This is a schematic diagram of the application service module of the present invention;

[0028] Figure 3 This is a schematic diagram showing a module of the present invention;

[0029] Figure 4 This is a schematic diagram of a data perception module of the present invention;

[0030] Figure 5 It is a schematic diagram of the data transmission module of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] Example 1

[0033] like Figure 1-5As shown, it is the first embodiment of the present invention, which provides a digital employee creation and management system based on an artificial intelligence large model, including a data perception module, a data transmission module, an artificial AI model, an application service module and a display module. The data perception module is used to import and manage the company's production data, real-time data and basic data into the data lake, the data transmission module is used to transmit image data, video stream data, organizational information data, etc. to the application service module and the artificial AI model, the artificial AI model is used to analyze and process the data, the application service module is used to support the implementation of related applications of intelligent identification and intelligent management of safety hazards in refining enterprises, and the display module is used to display identification, early warning, tracking and disposal information in multiple dimensions.

[0034] like Figure 1-5 As shown, the data perception module can monitor the production environment in real time, the data can be refined through the analysis of the artificial AI model and the application service module, and the data can be displayed through the display module, so as to give early warning of potential safety hazards and equipment failures, reduce safety accidents, timely understand the safety status of personnel, and provide guidance and assistance during rescue, so as to achieve safety management of factory personnel, and provide data-based decision support for enterprises, enhance the accuracy and foresight of decision-making, optimize resource allocation and market strategies, and at the same time, through real-time collection of on-site videos, it is convenient for operation managers to interact with on-site operators, realize human-machine collaborative operation and remote collaborative operation, improve the standardization and management efficiency of on-site operators, effectively realize the optimal allocation of human resources, reduce dependence on manual operations, and at the same time improve production efficiency and quality, reduce human errors, and ensure the continuity and safety of the production process.

[0035] Example 2

[0036] Reference Figure 2-5 , which is the second embodiment of the present invention, and this embodiment is based on the previous embodiment.

[0037] In this embodiment, the application service modules include human and vehicle control, site control, operation coordination, positioning services and basic management. Basic management includes terminal management, locator management, base station management and map management.

[0038] Human and vehicle control includes employee management, contractor management, visitor management and vehicle management. On-site control includes safety control map, four-color risk map, operation monitoring, inspection supervision, risk alarm, out-of-bounds alarm, behavior management and video linkage. Operation collaboration includes emergency individual, operation knowledge, remote collaboration and SOS alarm.

[0039] The contents displayed in the display module include human and vehicle control analysis, risk control analysis, safety alarm analysis, risk disposal coordination, security monitoring, video linkage coordination, event statistics analysis and safety trend analysis.

[0040] The display module can display pictures, short videos, live broadcasts, reports, etc. It supports export and can be displayed using mini programs and apps.

[0041] The data transmission module includes Bluetooth and 5G base stations, and the data perception module includes explosion-proof beacons, smart helmets, smart badges and vehicle-mounted terminals.

[0042] A method for using a digital employee creation and management system based on an artificial intelligence big model, the method comprising the following steps:

[0043] A. First, the data perception module is used to perceive and analyze the various business processes of petrochemical enterprises, and to identify specific tasks that can be replaced or assisted by digital employees, such as data entry, monitoring production parameters, and predicting maintenance needs.

[0044] B. Transmit historical production data, real-time data, and simulated data sets to artificial AI models for training through the data transmission module, so that they have the ability to understand complex industrial processes, decision logic, and task execution;

[0045] C. Then design the user interface (UI) and user experience (UX) of the digital worker to ensure it can integrate with existing systems and work collaboratively with human employees;

[0046] D. Deploy the trained artificial AI model to the digital employee platform and test it in a controlled environment to ensure it operates stably and meets business needs;

[0047] E. Finally, establish a real-time performance monitoring and feedback mechanism to collect data from digital employees in actual operations for continuous optimization and fine-tuning of artificial AI models;

[0048] F. When managing employees, first make the locations of internal and external employees in the factory traceable, and external risks controllable and traceable, supplementing the existing personnel safety management by using portable positioning terminals such as explosion-proof beacons, smart helmets, smart badges or vehicle-mounted terminals;

[0049] G. The explosion-proof beacons, smart helmets, smart badges or vehicle-mounted terminals carried by factory personnel can transmit positioning information in real time, and the data sensing module can sense the returned information, and the data transmission module can transmit the information collected by the data sensing module to the artificial AI model for analysis;

[0050] H. The analyzed data will be transmitted to the application service module, which will be used to support the intelligent identification and intelligent management of enterprise safety hazards;

[0051] H1. First, the intelligent identification of safety hazards in refining and chemical enterprises mainly focuses on the six typical safety hazard scenarios in refining and chemical enterprises, namely, identification of personnel violation status, door and wall perimeter safety, on-site operation safety, production area safety, equipment safety hazards, and environmental status safety hazards;

[0052] H2. Secondly, the intelligent management of safety hazards in refining and chemical enterprises mainly includes early warning processing, intelligent push, intelligent tag intelligent search, multi-dimensional statistics, i.e. smart screen, etc., to achieve the whole process management of early warning reminder, push report, analysis and management after the safety hazards are discovered;

[0053] H3. In addition, through algorithm management, the configuration of required algorithm parameters, setting of business process rules and streaming media services can be realized;

[0054] I. Finally, the display module uses the data generated by the application service module to identify, warn, track and deal with key safety hazards, and conduct situation analysis and multi-dimensional display of relevant information, so as to facilitate the online display of the location of personnel in the production area, and through functions such as abnormal alarm, personnel location search and trajectory playback, timely understand the safety status of personnel, and provide guidance and assistance during rescue, so as to achieve safety management of factory personnel.

[0055] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0056] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A digital employee creation and management system based on an artificial intelligence large model, comprising a data perception module, a data transmission module, an artificial AI model, an application service module and a display module, characterized in that: The data perception module is used to import and manage the company's production data, real-time data and basic data into the data lake; the data transmission module is used to transmit image data, video stream data, organizational information data, etc. to the application service module and the artificial AI model; the artificial AI model is used to analyze and process the data; the application service module is used to support the implementation of applications related to intelligent identification and intelligent management of safety hazards in refining enterprises; the display module is used to display identification, early warning, tracking and disposal information in multiple dimensions.

2. According to claim 1, a digital employee creation and management system based on an artificial intelligence big model is characterized by: The application service modules include human and vehicle management and control, on-site management and control, operation coordination, positioning services and basic management. The basic management includes terminal management, locator management, base station management and map management.

3. According to claim 1, a digital employee creation and management system based on an artificial intelligence big model is characterized by: The personnel and vehicle control includes employee management, contractor management, visitor management and vehicle management; the on-site control includes safety control map, four-color risk map, operation monitoring, inspection supervision, risk alarm, out-of-bounds alarm, behavior management and video linkage; the operation collaboration includes emergency individual, operation knowledge, remote collaboration and SOS alarm.

4. According to claim 1, a digital employee creation and management system based on an artificial intelligence big model is characterized by: The content displayed in the display module includes human and vehicle control analysis, risk control analysis, safety alarm analysis, risk disposal coordination, security monitoring, video linkage coordination, event statistics analysis and safety trend analysis.

5. According to claim 1, a digital employee creation and management system based on an artificial intelligence big model is characterized by: The display forms of the display module include pictures, short videos, live broadcasts, reports, etc., and support export, and can be displayed using mini-programs and APPs.

6. According to claim 1, a digital employee creation and management system based on an artificial intelligence big model is characterized by: The data transmission module includes Bluetooth and 5G base stations, and the data perception module includes explosion-proof beacons, smart safety helmets, smart badges and vehicle-mounted terminals.

7. A method for using a digital employee creation and management system based on an artificial intelligence big model according to any one of claims 1 to 6, characterized in that: The method of use includes the following steps: A. First, the data perception module is used to perceive and analyze the various business processes of petrochemical enterprises, and to identify specific tasks that can be replaced or assisted by digital employees, such as data entry, monitoring production parameters, and predicting maintenance needs. B. Transmit historical production data, real-time data, and simulated data sets to the artificial AI model for training through the data transmission module, so that it has the ability to understand complex industrial processes, decision logic, and task execution; C. Then design the user interface (UI) and user experience (UX) of the digital worker to ensure it can integrate with existing systems and work collaboratively with human employees; D. Deploy the trained artificial AI model to the digital employee platform and test it in a controlled environment to ensure it operates stably and meets business needs; E. Finally, establish a real-time performance monitoring and feedback mechanism to collect data from digital employees in actual operations for continuous optimization and fine-tuning of artificial AI models; F. When managing employees, first make the locations of internal and external employees in the factory traceable, and external risks controllable and traceable, supplementing the existing personnel safety management by using portable positioning terminals such as explosion-proof beacons, smart helmets, smart badges or vehicle-mounted terminals; G. The explosion-proof beacons, smart helmets, smart badges or vehicle-mounted terminals carried by factory personnel can transmit positioning information in real time, and the data sensing module can sense the returned information, and the data transmission module can transmit the information collected by the data sensing module to the artificial AI model for analysis; H. The analyzed data will be transmitted to the application service module, which will be used to support the intelligent identification and intelligent management of enterprise safety hazards; H1. First, the intelligent identification of safety hazards in refining and chemical enterprises mainly focuses on the six typical safety hazard scenarios in refining and chemical enterprises, namely, identification of personnel violation status, door and wall perimeter safety, on-site operation safety, production area safety, equipment safety hazards, and environmental status safety hazards; H2. Secondly, the intelligent management of safety hazards in refining and chemical enterprises mainly includes early warning processing, intelligent push, intelligent tag intelligent search, multi-dimensional statistics, i.e. smart screen, etc., to achieve the whole process management of early warning reminder, push report, analysis and management after the safety hazards are discovered; H3. In addition, through algorithm management, the configuration of required algorithm parameters, setting of business process rules and streaming media services can be realized; I. Finally, the display module uses the data generated by the application service module to identify, warn, track and deal with key safety hazards, and conduct situation analysis and multi-dimensional display of relevant information, so as to facilitate the online display of the location of personnel in the production area, and through functions such as abnormal alarm, personnel location search and trajectory playback, timely understand the safety status of personnel, and provide guidance and assistance during rescue, so as to achieve safety management of factory personnel.

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