Intelligent detection and inspection integrated system in digital factory environment

Abstract

The invention discloses an intelligent detection and inspection integrated system in a digital factory environment, and relates to the technical field of digital factory management, and the system comprises a data integration module which is used for collecting the data of factory equipment and environment in real time, and sharing and intercommunicating the data among factory departments; the data integration module comprises a real-time monitoring unit, a data integration unit and a data sharing unit, the real-time monitoring unit is connected with the data integration unit through an interface, and the data integration unit is connected with the data sharing unit through a network. By designing the data integration module, a real-time data unified sharing function of production equipment in a digital factory environment is realized, the problems of non-uniform data formats and information isolated islands between different equipment and systems are solved, each department of a factory can know the production environment and the equipment state in time, and the production efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of digital factory management, and in particular to an intelligent detection and inspection integrated system in a digital factory environment. Background Art

[0002] As an important tool to promote industrial intelligence, the construction and development of digital factories are of great significance to enhancing the competitiveness of my country's manufacturing industry. In the digital factory environment, with the increasing level of intelligence, automation and digitalization of the production process, people's requirements for the efficiency and accuracy of equipment detection and inspection are also increasing. However, there are often many types and models of equipment in the factory, and the data formats generated by these devices during detection are different. The diversity of data formats has brought challenges to data processing and integration, affecting the sharing and utilization efficiency of data.

[0003] At the same time, traditional equipment detection data often cannot be transmitted in real time between departments, resulting in serious information islands. The delay and lag in data transmission make it difficult to adjust and optimize production management and decision-making in a timely manner based on the latest data, which may lead to delayed discovery of equipment failures and affect the production efficiency of the factory. Therefore, an integrated intelligent detection and inspection system in a digital factory environment is extremely important.

[0004] 1. Patent document CN112486127B discloses a virtual inspection system for a digital factory. The above patent realizes that the virtual inspection system directly and truly inspects the properties of the controlled objects in the factory and controls the controlled objects, and the inspection efficiency is higher. However, the above patent cannot realize the real-time data unified sharing function of production equipment in a digital factory environment.

[0005] 2. Patent document CN116045171B discloses a distributed status monitoring system for a smart factory production workshop. The above patent realizes dynamic detection of the environment of the factory production workshop, and realizes the position movement, height adjustment and angle adjustment of the second detection component, which is conducive to reducing safety hazards. However, the above patent cannot realize the intelligent management function of the production process in a digital factory environment.

[0006] 3. Patent document CN116502925B discloses a digital factory equipment inspection and evaluation method, system and medium based on big data. The above patent realizes intelligent technology for inspection and evaluation of equipment operating conditions through big data, but the above patent cannot realize the intelligent planning and dynamic management functions of inspection tasks in a digital factory environment.

[0007] 4. Patent document CN112462723B discloses a system for real-time control and visualization of digital factories in an augmented reality environment. The above patent reduces the workload of inspection personnel and improves inspection efficiency, but the above patent cannot realize the remote monitoring function of factory equipment and inspection process.

[0008] In summary, the above patents cannot realize the real-time data unified sharing function of production equipment in a digital factory environment, cannot realize the intelligent management function of the production process in a digital factory environment, cannot realize the intelligent planning and dynamic management function of the inspection tasks in a digital factory environment, and cannot realize the remote monitoring function of factory equipment and inspection processes, resulting in low data transmission efficiency among departments of the digital factory, equipment failure affecting the production process, untimely inspection causing equipment abnormalities, and failure to intuitively display equipment data; To this end, the present application proposes an integrated intelligent detection and inspection system in a digital factory environment that realizes the real-time unified data sharing function of production equipment in a digital factory environment, the intelligent planning and dynamic management function of inspection tasks in a digital factory environment, the remote monitoring function of factory equipment and inspection process, and the intelligent detection and inspection function in a digital factory environment. Summary of the invention

[0009] The purpose of the present invention is to provide an integrated intelligent detection and inspection system in a digital factory environment, so as to solve the technical problems raised in the above background technology that the real-time data unified sharing function of production equipment in a digital factory environment cannot be realized, the intelligent management function of the production process in a digital factory environment cannot be realized, the intelligent planning and dynamic management function of the inspection tasks in a digital factory environment cannot be realized, and the remote monitoring function of factory equipment and inspection processes cannot be realized, resulting in low data transmission efficiency among departments of the digital factory, equipment failure affecting the production process, untimely inspection causing equipment abnormalities, and inability to intuitively display equipment data.

[0010] To achieve the above-mentioned object, the present invention provides the following technical solutions: an integrated intelligent detection and inspection system in a digital factory environment, characterized in that: it includes a data integration module, the data integration module is used to collect data of factory equipment and environment in real time, and share and communicate data between various factory departments; The data integration module includes: a real-time monitoring unit, a data integration unit and a data sharing unit, wherein the real-time monitoring unit is connected to the data integration unit via an interface, and the data integration unit is connected to the data sharing unit via a network; The real-time monitoring unit collects data on various equipment and environmental parameters in the factory in real time through the sensor network deployed in the factory, and identifies the model types of various equipment in the factory by scanning the RFID tags of the equipment; The data integration unit has a built-in equipment information database, develops a data parsing engine based on the basic information and configuration parameters of the equipment, identifies and parses the data, converts the parsed data into a unified standard format, performs multi-modal fusion on the standard data, forms comprehensive perception data of the equipment and production environment, and performs time synchronization on the integrated data; The data sharing unit records the operation time and results of the data collection and processing process, establishes a data aggregation and sharing platform, uploads the integrated data to the data aggregation and sharing platform in real time, and transmits the data in the data aggregation and sharing platform to various departments of the factory in real time according to the timeliness of the data.

[0011] Preferably, the data integration module is connected to an intelligent decision-making module via a network, and the intelligent decision-making module is used to make and optimize production decisions; The intelligent decision-making module includes: a data analysis unit, a decision-making unit and a decision execution unit. The data analysis unit is connected to the decision-making unit through a network, the decision-making unit is connected to the decision execution unit through a network, and the data analysis unit is connected to the data sharing unit through a network; The data analysis unit receives the equipment integration data from the data aggregation and sharing platform, performs intelligent analysis on the integrated data, determines the operating status of the equipment, predicts the potential failure of the equipment, conducts risk assessment of the equipment, and identifies the production process of the factory equipment; The decision-making unit evaluates the priority of faults based on their impact on production, urgency, and difficulty of repair, and formulates instructions to prioritize faults that have a large impact on production, are urgent, and are easy to repair. It also generates decisions to adjust production tasks based on production plans and real-time production data. The decision execution unit receives fault instructions and generates equipment fault reports, including fault time, fault location and handling methods. It uploads the fault reports to the data aggregation and sharing platform and transmits them to the relevant departments of the factory in a timely manner. According to the requirements of the decision, the working status of the factory equipment is adjusted in real time.

[0012] Preferably, the data integration module is connected to an inspection planning module via a network, and the inspection planning module intelligently plans inspection tasks according to real-time data of the equipment; The inspection planning module includes: a plan making unit, a status evaluation unit and an inspection report unit. The plan making unit is connected to the status evaluation unit through a network, and the status evaluation unit is connected to the inspection report unit through a network. The planning unit analyzes the factory layout and equipment distribution based on the factory's environmental data, automatically generates inspection routes based on inspection requirements, analyzes the historical conditions of the equipment based on historical equipment failure data, increases the number of inspections of equipment that has experienced abnormal conditions, plans the inspection time points of inspection personnel and inspection robots based on the generated requirements, and generates inspection plans based on the inspection routes, frequency, and time points; The status evaluation unit receives the inspection plan, notifies the inspection personnel and controls the inspection robot to carry the intelligent inspection equipment to perform the inspection task, uploads the real-time inspection data of the inspection personnel and the inspection robot to the system, and analyzes and evaluates the data; The inspection report unit receives inspection evaluation data, organizes and summarizes the data, generates inspection reports, displays inspection task overview, inspection point details, abnormal situation records and handling suggestions, and transmits the inspection reports to the data aggregation and sharing platform and inspection personnel in real time.

[0013] Preferably, the data integration module is connected to a remote monitoring module via a network, and the inspection planning module is connected to a remote monitoring module via a network, and the remote monitoring module is used to remotely monitor the production process and inspection process of the factory equipment; The remote monitoring module includes: a model building unit, a visualization unit and an abnormality marking unit, the model building unit is connected to the visualization unit through a network, and the visualization unit is connected to the abnormality marking unit through a network; The model building unit uses digital twin technology to build a high-precision three-dimensional digital twin model based on the factory environment data and model type data in the data aggregation and sharing platform, and integrates the real-time data and inspection process data of the equipment with the digital twin model; The visualization unit displays the inspection route, inspection points, and key equipment that require attention in the three-dimensional digital twin model. It uses dynamic trajectories to display the inspection process in real time, and remotely displays the real-time inspection progress and equipment working status to management personnel. The abnormality marking unit marks the abnormal conditions of the equipment in the digital twin model, uses eye-catching colors to display abnormal equipment, divides abnormal areas, and triggers alarm information when unrelated personnel enter the abnormal area.

[0014] Preferably, the data integration module is connected to a data encryption module via a network, and the data encryption module is used to encrypt and store data in the digital factory; The data encryption module includes: an encryption determination unit, a ciphertext storage unit and a permission configuration unit, the encryption determination unit is connected to the ciphertext storage unit via a network, the ciphertext storage unit is connected to the permission configuration unit via a network, and the permission configuration unit is connected to the data sharing unit via a network; The encryption determination unit receives various types of data from the data aggregation and sharing platform, including equipment status information, environmental parameters, and inspection reports, and uses encryption algorithms and keys to encrypt the data and convert plaintext data into ciphertext data; The ciphertext storage unit stores the encrypted ciphertext data in a distributed database, performs sharding, replication and redundancy on the ciphertext data, and stores it in multiple nodes of the network; The permission configuration unit configures data access permissions according to the sensitivity and importance of the ciphertext data, sets user permissions to access and modify data, records events of attempted access and authorized access, and records encryption and decryption operations.

[0015] Preferably, the intelligent decision-making module is connected to a signal detection module via an API interface, and the signal detection module is used to detect the quality of the network signal; The signal detection module includes: a signal acquisition unit, a signal analysis unit and an alarm and notification unit, the signal acquisition unit is connected to the signal analysis unit through an optical fiber, and the signal analysis unit is connected to the alarm and notification unit through an API interface; The signal acquisition unit uses hardware sensors to collect various parameters of network signals in real time, including signal strength, packet loss rate, and delay, and identify changes in network signals; The signal analysis unit processes the collected signal data, sets the signal threshold, evaluates the signal quality, and generates an alarm message when the signal quality is lower than the threshold; The alarm and notification unit receives alarm information, prompts the staff through the display screen, informs them of poor signals, and the control system re-formulates factory production decisions.

[0016] Preferably, the inspection planning module is connected to a positioning tracking module via a network, and the positioning tracking module is used to track the inspection personnel and the inspection robot in real time; The positioning tracking module includes: a positioning unit and a deviation warning unit, and the positioning unit is connected to the deviation warning unit through a network; The positioning unit uses GPS to obtain the precise location information of the inspection equipment in real time, converts the location information into the driving trajectory of the inspection personnel and the inspection robot, and uploads the driving trajectory to the digital twin model; The deviation warning unit compares and analyzes the driving trajectory with the preset inspection path. When the inspection equipment deviates from the preset path, the alarm mechanism is triggered to remind the inspection personnel to adjust the inspection path in time.

[0017] Preferably, the method for using the RFID tag comprises the following steps: (1) Attach the RFID tag to the device, program the RFID tag, and enter the model, type, and serial number of the device; (2) The reader communicates with the RFID tag through radio frequency signals and reads the data in the RFID tag.

[0018] Preferably, the method for using the data aggregation and sharing platform comprises the following steps: (1) The data aggregation and sharing platform organizes and summarizes the integrated data, builds a unified data graph, and stores the data and data graph in a distributed database; (2) The data aggregation and sharing platform transmits data to various departments and corresponding equipment in the factory in real time and on a regular basis according to the timeliness of the data and departmental needs. Each department obtains the required data through the remote monitoring module.

[0019] Preferably, the method for using the digital twin technology comprises the following steps: (1) Determine the application goal of the model as improving production efficiency and optimizing the inspection process, and set the specific scenario of the digital twin model, including the layout of the virtual factory, equipment configuration, and production process; (2) Collect environmental parameters, equipment operation and production data from the data aggregation and sharing platform, build a three-dimensional model that includes the production line, equipment and environmental elements, and bind the three-dimensional model with real-time data.

[0020] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention realizes the real-time data unified sharing function of production equipment in the digital factory environment by designing a data integration module, solves the problem of inconsistent data formats and information islands between different devices and systems, enables various departments of the factory to understand the production environment and equipment status in a timely manner, and improves production efficiency; 2. The present invention realizes intelligent planning and dynamic management of inspection tasks in a digital factory environment by designing an intelligent decision-making module, thereby improving fault prediction and response capabilities, optimizing production decisions, and improving equipment management and maintenance levels; 3. The present invention is designed with an inspection planning module to realize intelligent planning and dynamic management of inspection tasks in a digital factory environment, solve the problems of ineffective labor and repeated work of inspection personnel, and improve inspection efficiency; 4. The present invention is designed with a remote monitoring module to achieve remote monitoring functions of factory equipment and inspection processes, solve the problem that managers cannot grasp the production situation and equipment health status of the factory at any time, and enhance the safety of factory production. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of a data integration module of the present invention; Figure 2 It is a schematic diagram of the intelligent decision-making module of the present invention; Figure 3 It is a schematic diagram of the inspection planning module of the present invention; Figure 4 It is a schematic diagram of a remote monitoring module of the present invention; Figure 5 This is a schematic diagram of a data encryption module of the present invention; Figure 6 It is a schematic diagram of a signal detection module of the present invention; Figure 7 It is a schematic diagram of the workflow of the data integration module of the present invention; Figure 8 It is a schematic diagram of the system operation of the present invention. DETAILED DESCRIPTION

[0022] 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.

[0023] See also Figure 1 , Figure 7 and Figure 8 , an embodiment provided by the present invention: an intelligent detection and inspection integrated system in a digital factory environment, comprising a data integration module, the data integration module is used to collect data of factory equipment and environment in real time, and share and communicate data between various factory departments; The data integration module includes: a real-time monitoring unit, a data integration unit and a data sharing unit, wherein the real-time monitoring unit is connected to the data integration unit via an interface, and the data integration unit is connected to the data sharing unit via a network; The real-time monitoring unit collects data on various equipment and environmental parameters in the factory in real time through the sensor network deployed in the factory, and identifies the model types of various equipment in the factory by scanning the RFID tags of the equipment; The data integration unit has a built-in equipment information database, develops a data parsing engine based on the basic information and configuration parameters of the equipment, identifies and parses the data, converts the parsed data into a unified standard format, performs multi-modal fusion on the standard data, forms comprehensive perception data of the equipment and production environment, and performs time synchronization on the integrated data; The data sharing unit records the operation time and operation results of the data collection and processing process, establishes a data aggregation and sharing platform, uploads the integrated data to the data aggregation and sharing platform in real time, and transmits the data in the data aggregation and sharing platform to various departments of the factory in real time according to the timeliness of the data; Furthermore, during the operation of factory equipment, the sensor network in the factory captures data on various equipment status parameters, equipment production processes and environmental parameters in real time. At the same time, the real-time monitoring unit uses RFID technology to scan the RFID tags of the equipment to accurately identify the model, type and location information of various equipment in the factory, which facilitates the subsequent matching of equipment with information in the information database, provides a basis for the subsequent unification of data formats, and provides basic information for data integration; The built-in equipment information database of the data integration unit stores a large amount of basic information and configuration parameters of the equipment. A data parsing engine is developed based on the basic information and configuration parameters of the equipment. After receiving the equipment data, the data integration unit compares the equipment model and type data with the data in the equipment information database. Based on the comparison results, the data parsing engine automatically identifies and parses the data of the corresponding equipment status parameters, equipment production process and environmental parameters, converts the data into a unified standard format, and performs multi-modal fusion of the unified format data, such as video, image, sound, sensor data, etc., to form comprehensive and multi-dimensional perception data of the equipment and production environment. The integrated data is time synchronized to ensure that the timestamps of all data are consistent. The data sharing unit establishes a unified data aggregation and sharing platform. The data integration unit uploads the integrated data to the platform in real time. The platform transmits the data to various departments of the factory in real time to ensure that each department can obtain the required data in time, avoid data failure, and realize centralized management and efficient use of data. Through real-time data sharing, various departments of the factory can timely understand the production environment, equipment status and equipment production process, so as to timely discover and deal with potential problems, avoid production interruptions and delays, and improve production efficiency.

[0024] See also Figure 2 , Figure 7 and Figure 8 , an embodiment provided by the present invention: an intelligent detection and inspection integrated system in a digital factory environment, wherein the data integration module is connected to an intelligent decision-making module via a network, and the intelligent decision-making module is used to make and optimize production decisions; The intelligent decision-making module includes: a data analysis unit, a decision-making unit and a decision execution unit. The data analysis unit is connected to the decision-making unit through a network, the decision-making unit is connected to the decision execution unit through a network, and the data analysis unit is connected to the data sharing unit through a network; The data analysis unit receives the equipment integration data from the data aggregation and sharing platform, performs intelligent analysis on the integrated data, determines the operating status of the equipment, predicts the potential failure of the equipment, conducts risk assessment of the equipment, and identifies the production process of the factory equipment; The decision-making unit evaluates the priority of faults based on their impact on production, urgency, and difficulty of repair, and formulates instructions to prioritize faults that have a large impact on production, are urgent, and are easy to repair. It also generates decisions to adjust production tasks based on production plans and real-time production data. The decision execution unit receives the fault instruction and generates an equipment fault report, including the fault time, fault location and treatment method. The fault report is uploaded to the data aggregation and sharing platform and transmitted to the corresponding department of the factory in a timely manner. According to the requirements of the decision, the working status of the factory equipment is adjusted in real time; Furthermore, the data analysis unit analyzes the data in the data aggregation and sharing platform, compares the historical data of the equipment with the current operating status data, evaluates the real-time status of the equipment, determines whether the equipment is in an abnormal state, and predicts the potential failure of the equipment through trend analysis. At the same time, the data analysis unit will identify the production process of the factory equipment, including production speed, efficiency, bottlenecks, etc., and compare the production progress of various departments in the factory; The decision-making unit receives the fault information detected by the data analysis unit, and firstly comprehensively evaluates the priority of the fault according to the impact of the fault on production, urgency and difficulty of repair, and formulates maintenance instructions with high priority, so as to ensure that key faults are handled first and reduce the impact on production. The decision-making unit combines the equipment fault information, production plan and real-time production progress of the equipment to make targeted decisions, including adjusting the production tasks of the equipment, reallocating resources, etc., to optimize the production process or deal with task transfer after equipment failure; The decision execution unit receives maintenance instructions from the decision-making unit. According to the fault data analyzed by the data analysis unit, the decision execution unit generates an equipment fault report, including fault time, fault location, fault phenomenon, possible causes and recommended treatment methods, and uploads the fault report to the data aggregation and sharing platform. The platform transmits the fault report to the corresponding department of the factory in real time for rapid response and processing. The decision execution unit makes decisions based on the decision. At the same time, the decision execution unit shuts down the equipment for maintenance, reduces the speed, switches to standby equipment, etc., so as to ensure the smooth progress of the production process and avoid the impact of equipment failure on production. The decision execution unit adjusts the production process of each equipment in real time according to the decision to ensure the maximization of equipment production.

[0025] See also Figure 3 , Figure 7 and Figure 8 , an embodiment provided by the present invention: an intelligent detection and inspection integrated system in a digital factory environment, wherein the data integration module is connected to an inspection planning module via a network, and the inspection planning module intelligently plans inspection tasks according to real-time data of the equipment; The inspection planning module includes: a plan making unit, a status evaluation unit and an inspection report unit. The plan making unit is connected to the status evaluation unit through a network, and the status evaluation unit is connected to the inspection report unit through a network. The planning unit analyzes the factory layout and equipment distribution based on the factory's environmental data, automatically generates inspection routes based on inspection requirements, analyzes the historical conditions of the equipment based on historical equipment failure data, increases the number of inspections of equipment that has experienced abnormal conditions, plans the inspection time points of inspection personnel and inspection robots based on the generated requirements, and generates inspection plans based on the inspection routes, frequency, and time points; The status evaluation unit receives the inspection plan, notifies the inspection personnel and controls the inspection robot to carry the intelligent inspection equipment to perform the inspection task, uploads the real-time inspection data of the inspection personnel and the inspection robot to the system, and analyzes and evaluates the data; The inspection report unit receives the inspection evaluation data, organizes and summarizes the data, generates an inspection report, displays the inspection task overview, inspection point details, abnormal situation records and handling suggestions, and transmits the inspection report to the data aggregation and sharing platform and inspection personnel in real time; Furthermore, the planning unit receives factory environment data and equipment status information from the data aggregation and sharing platform, and understands the overall layout and equipment distribution of the factory through analysis of the environment data. Based on the factory environment data and equipment status data, it generates inspection routes according to the inspection scope and inspection targets. It combines the equipment's historical fault data and the number of production inspections to increase the number of inspections for equipment that has experienced abnormal conditions. It generates inspection time points based on the factory's production process, and plans specific inspection tasks and schedules in combination with the inspection routes, inspection times and inspection time points to generate a complete inspection plan. The status assessment unit receives the inspection plan from the planning unit, notifies the inspection personnel or controls the inspection robot to carry the intelligent inspection equipment to perform the inspection task according to the plan. During the inspection process, the inspection personnel or the inspection robot transmits the real-time detected data to the status assessment unit. The status assessment unit analyzes the data, evaluates the current status of the equipment, and identifies potential problems or abnormal conditions. The status assessment unit transmits the analysis and evaluation data to the inspection reporting unit. The inspection reporting unit sends the data-collated report to the inspection personnel so that they can understand the inspection results and subsequent processing measures. At the same time, the report is transmitted to the data aggregation and sharing platform for use by other systems or departments.

[0026] See also Figure 4 , Figure 7 and Figure 8, an embodiment provided by the present invention: an intelligent detection and inspection integrated system in a digital factory environment, wherein the data integration module is connected to a remote monitoring module via a network, the inspection planning module is connected to a remote monitoring module via a network, and the remote monitoring module is used to remotely monitor the production process and inspection process of factory equipment; The remote monitoring module includes: a model building unit, a visualization unit and an abnormality marking unit, the model building unit is connected to the visualization unit through a network, and the visualization unit is connected to the abnormality marking unit through a network; The model building unit uses digital twin technology to build a high-precision three-dimensional digital twin model based on the factory environment data and model type data in the data aggregation and sharing platform, and integrates the real-time data and inspection process data of the equipment with the digital twin model; The visualization unit displays the inspection route, inspection points, and key equipment that require attention in the three-dimensional digital twin model. It uses dynamic trajectories to display the inspection process in real time, and remotely displays the real-time inspection progress and equipment working status to management personnel. The abnormality marking unit marks the abnormal conditions of the equipment in the digital twin model, uses eye-catching colors to display abnormal equipment, divides abnormal areas, and triggers alarm information when unrelated personnel enter abnormal areas; Furthermore, the three-dimensional digital twin model constructed by the model building unit directly reflects the physical space, equipment layout and operating status of the factory, and integrates real-time information such as real-time data of the equipment and inspection process data into the three-dimensional digital twin model to ensure the synchronization and consistency between the model and the actual situation. In the three-dimensional digital twin model, the visualization unit displays the inspection route and inspection points in the form of dynamic images to help managers intuitively understand the inspection process and inspection coverage. Through the guidance of three-dimensional images, inspection personnel can clearly understand the inspection route and inspection points, reduce the possibility of missed inspections and false inspections, and improve inspection efficiency. The visualization unit also displays the real-time working status of the equipment, including operating parameters, performance indicators, etc., providing managers with comprehensive monitoring information. The abnormality marking unit marks the three-dimensional digital twin model displayed by the visualization unit and uses eye-catching colors to display equipment abnormalities to quickly attract the attention of managers. The abnormality marking unit divides the abnormal area according to the abnormal situation and sets an alarm mechanism. When unrelated personnel try to enter the abnormal area, the abnormality marking unit will immediately trigger an alarm message to ensure the safety of personnel and safe production of the factory.

[0027] See also Figure 5 and Figure 8 , an embodiment provided by the present invention: an intelligent detection and inspection integrated system in a digital factory environment, wherein the data integration module is connected to a data encryption module via a network, and the data encryption module is used to encrypt and store data in the digital factory; The data encryption module includes: an encryption determination unit, a ciphertext storage unit and a permission configuration unit, the encryption determination unit is connected to the ciphertext storage unit via a network, the ciphertext storage unit is connected to the permission configuration unit via a network, and the permission configuration unit is connected to the data sharing unit via a network; The encryption determination unit receives various types of data from the data aggregation and sharing platform, including equipment status information, environmental parameters, and inspection reports, and uses encryption algorithms and keys to encrypt the data and convert plaintext data into ciphertext data; The ciphertext storage unit stores the encrypted ciphertext data in a distributed database, performs sharding, replication and redundancy on the ciphertext data, and stores it in multiple nodes of the network; The permission configuration unit configures data access permissions according to the sensitivity and importance of the ciphertext data, sets the user's permissions to access and modify the data, records the events of attempted access and authorized access, and records encryption and decryption operations; Furthermore, the encryption determination unit receives various types of data from the data aggregation and sharing platform, including equipment status information, production process, environmental parameters, and inspection reports, etc. The encryption determination unit uses a pre-set encryption algorithm and key to encrypt the received plaintext data. The plaintext data is converted into ciphertext data that cannot be directly read, thereby protecting the confidentiality of the data; The ciphertext storage unit is responsible for storing the encrypted ciphertext data in a distributed database, dispersing the data on multiple nodes of the network, thereby improving the security and reliability of the data. In order to further improve the availability of data, the ciphertext storage unit performs sharding, replication and redundancy processing on the ciphertext data to ensure that when some nodes fail, other nodes can also provide complete data copies, thereby ensuring the continuity and consistency of the data; The permission configuration unit configures corresponding data access rights for different users according to the sensitivity and importance of the data, and stipulates the user's permissions to access, modify and perform other operations on the data, thereby ensuring that only authorized users can access sensitive data. The permission configuration unit records all user access attempts and authorized access events to avoid data leakage, ensure the security and integrity of the data, and provide strong protection for the production and management of digital factories, preventing external personnel from accessing and modifying various types of data in the data aggregation and sharing platform, thereby ensuring the privacy of the data.

[0028] Embodiment 6, See also Figure 6 and Figure 8 , an embodiment provided by the present invention: an intelligent detection and inspection integrated system in a digital factory environment, wherein the intelligent decision-making module is connected to a signal detection module through an API interface, and the signal detection module is used to detect the quality of a network signal; The signal detection module includes: a signal acquisition unit, a signal analysis unit and an alarm and notification unit, the signal acquisition unit is connected to the signal analysis unit through an optical fiber, and the signal analysis unit is connected to the alarm and notification unit through an API interface; The signal acquisition unit uses hardware sensors to collect various parameters of network signals in real time, including signal strength, packet loss rate, and delay, and identify changes in network signals; The signal analysis unit processes the collected signal data, sets the signal threshold, evaluates the signal quality, and generates an alarm message when the signal quality is lower than the threshold; The alarm and notification unit receives alarm information, prompts the staff through the display screen, informs them that the signal is not good, and the control system re-formulates the factory production decision; Furthermore, the signal acquisition unit collects various key parameters of the network signal in real time through hardware sensors deployed in the network environment, providing the system with comprehensive and real-time network signal data, laying a solid data foundation for subsequent signal quality evaluation. The signal analysis unit sets a reasonable signal threshold. After receiving the data transmitted by the signal acquisition unit, the signal analysis unit compares the threshold with the real-time collected signal data to evaluate the signal quality, thereby accurately judging whether there are quality problems with the network signal, such as insufficient signal strength, high packet loss rate or excessive delay, and timely discovering potential problems in the network environment, such as signal coverage blind spots, network congestion, etc. When the signal analysis unit finds that the signal quality is lower than the preset threshold, the signal analysis unit will immediately generate corresponding alarm information. After receiving the alarm information from the signal analysis unit, the alarm and notification unit will respond quickly and take corresponding alarm and notification measures, so that management personnel can quickly understand the abnormal situation of the network signal, ensure the stability of the network status, and thus ensure the real-time data transmission, so that the system can promptly discover abnormalities or potential problems in the production process, ensure that relevant personnel can quickly obtain necessary information, improve decision-making accuracy, and reduce the risk of decision-making errors due to delayed or incomplete information.

[0029] Working principle: the data integration module uploads the collected data to the data aggregation and sharing platform in the same format; the data encryption module encrypts the data in the data aggregation and sharing platform; the data integration module transmits the data in the platform to each module of the system and each department of the digital factory in real time; during the data transmission process, the signal detection module monitors the quality of the network signal in real time to ensure the stability of the signal, thereby ensuring the real-time nature of the data; The intelligent decision-making module determines whether the equipment is abnormal based on the equipment status, production process, and inspection process data in the data aggregation and sharing platform. According to the production progress of the equipment, the production task of the abnormal equipment is transferred to other equipment, the working process of the equipment is adjusted, the production strategy is optimized, and the production efficiency is improved; The remote monitoring module establishes a three-dimensional digital twin model based on the data in the data aggregation and sharing platform. The inspection planning module formulates an inspection plan according to the data in the platform. The positioning and tracking module locates the position of the inspection personnel in real time and uploads the inspection trajectory to the digital twin model of the remote monitoring module. The inspection personnel accurately perform the inspection tasks according to the inspection plan route and real-time trajectory in the model, and transmit the inspection report to the data aggregation and sharing platform in real time. The remote monitoring module uploads the data in the platform to the model to show the real-time inspection progress, production process and equipment working status to the management personnel.

[0030] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An integrated intelligent detection and inspection system in a digital factory environment, characterized by: It includes a data integration module, which is used to collect data on factory equipment and environment in real time, and share and communicate data between various factory departments; The data integration module includes: a real-time monitoring unit, a data integration unit and a data sharing unit, wherein the real-time monitoring unit is connected to the data integration unit via an interface, and the data integration unit is connected to the data sharing unit via a network; The real-time monitoring unit collects data on various equipment and environmental parameters in the factory in real time through the sensor network deployed in the factory, and identifies the model types of various equipment in the factory by scanning the RFID tags of the equipment; The data integration unit has a built-in equipment information database, develops a data parsing engine based on the basic information and configuration parameters of the equipment, identifies and parses the data, converts the parsed data into a unified standard format, performs multi-modal fusion on the standard data, forms comprehensive perception data of the equipment and production environment, and performs time synchronization on the integrated data; The data sharing unit records the operation time and results of the data collection and processing process, establishes a data aggregation and sharing platform, uploads the integrated data to the data aggregation and sharing platform in real time, and transmits the data in the data aggregation and sharing platform to various departments of the factory in real time according to the timeliness of the data.

2. According to claim 1, the intelligent detection and inspection integrated system in a digital factory environment is characterized by: The data integration module is connected to an intelligent decision-making module via a network, and the intelligent decision-making module is used to make and optimize production decisions; The intelligent decision-making module includes: a data analysis unit, a decision-making unit and a decision execution unit. The data analysis unit is connected to the decision-making unit through a network, the decision-making unit is connected to the decision execution unit through a network, and the data analysis unit is connected to the data sharing unit through a network; The data analysis unit receives the equipment integration data from the data aggregation and sharing platform, performs intelligent analysis on the integrated data, determines the operating status of the equipment, predicts the potential failure of the equipment, conducts risk assessment of the equipment, and identifies the production process of the factory equipment; The decision-making unit evaluates the priority of faults based on their impact on production, urgency, and difficulty of repair, and formulates instructions to prioritize faults that have a large impact on production, are urgent, and are easy to repair. It also generates decisions to adjust production tasks based on production plans and real-time production data. The decision execution unit receives fault instructions and generates equipment fault reports, including fault time, fault location and handling methods. It uploads the fault reports to the data aggregation and sharing platform and transmits them to the relevant departments of the factory in a timely manner. According to the requirements of the decision, the working status of the factory equipment is adjusted in real time.

3. According to claim 1, the intelligent detection and inspection integrated system in a digital factory environment is characterized by: The data integration module is connected to the inspection planning module via a network, and the inspection planning module intelligently plans inspection tasks according to the real-time data of the equipment; The inspection planning module includes: a plan making unit, a status evaluation unit and an inspection report unit. The plan making unit is connected to the status evaluation unit through a network, and the status evaluation unit is connected to the inspection report unit through a network. The planning unit analyzes the factory layout and equipment distribution based on the factory's environmental data, automatically generates inspection routes based on inspection requirements, analyzes the historical conditions of the equipment based on historical equipment failure data, increases the number of inspections of equipment that has experienced abnormal conditions, plans the inspection time points of inspection personnel and inspection robots based on the generated requirements, and generates inspection plans based on the inspection routes, frequency, and time points; The status evaluation unit receives the inspection plan, notifies the inspection personnel and controls the inspection robot to carry the intelligent inspection equipment to perform the inspection task, uploads the real-time inspection data of the inspection personnel and the inspection robot to the system, and analyzes and evaluates the data; The inspection report unit receives inspection evaluation data, organizes and summarizes the data, generates inspection reports, displays inspection task overview, inspection point details, abnormal situation records and handling suggestions, and transmits the inspection reports to the data aggregation and sharing platform and inspection personnel in real time.

4. The intelligent detection and inspection integrated system in a digital factory environment according to claim 1 is characterized in that: The data integration module is connected to the remote monitoring module via the network, and the inspection planning module is connected to the remote monitoring module via the network, and the remote monitoring module is used to remotely monitor the production process and inspection process of factory equipment; The remote monitoring module includes: a model building unit, a visualization unit and an abnormality marking unit, the model building unit is connected to the visualization unit through a network, and the visualization unit is connected to the abnormality marking unit through a network; The model building unit uses digital twin technology to build a high-precision three-dimensional digital twin model based on the factory environment data and model type data in the data aggregation and sharing platform, and integrates the real-time data and inspection process data of the equipment with the digital twin model; The visualization unit displays the inspection route, inspection points, and key equipment that require attention in the three-dimensional digital twin model. It uses dynamic trajectories to display the inspection process in real time, and remotely displays the real-time inspection progress and equipment working status to management personnel. The abnormality marking unit marks the abnormal conditions of the equipment in the digital twin model, uses eye-catching colors to display abnormal equipment, divides abnormal areas, and triggers alarm information when unrelated personnel enter the abnormal area.

5. The intelligent detection and inspection integrated system in a digital factory environment according to claim 1 is characterized by: The data integration module is connected to a data encryption module via a network, and the data encryption module is used to encrypt and store data in the digital factory; The data encryption module includes: an encryption determination unit, a ciphertext storage unit and a permission configuration unit, the encryption determination unit is connected to the ciphertext storage unit via a network, the ciphertext storage unit is connected to the permission configuration unit via a network, and the permission configuration unit is connected to the data sharing unit via a network; The encryption determination unit receives various types of data from the data aggregation and sharing platform, including equipment status information, environmental parameters, and inspection reports, and uses encryption algorithms and keys to encrypt the data and convert plaintext data into ciphertext data; The ciphertext storage unit stores the encrypted ciphertext data in a distributed database, performs sharding, replication and redundancy on the ciphertext data, and stores it in multiple nodes of the network; The permission configuration unit configures data access permissions according to the sensitivity and importance of the ciphertext data, sets user permissions to access and modify data, records events of attempted access and authorized access, and records encryption and decryption operations.

6. The intelligent detection and inspection integrated system in a digital factory environment according to claim 2 is characterized by: The intelligent decision-making module is connected to a signal detection module via an API interface, and the signal detection module is used to detect the quality of the network signal; The signal detection module includes: a signal acquisition unit, a signal analysis unit and an alarm and notification unit, the signal acquisition unit is connected to the signal analysis unit through an optical fiber, and the signal analysis unit is connected to the alarm and notification unit through an API interface; The signal acquisition unit uses hardware sensors to collect various parameters of network signals in real time, including signal strength, packet loss rate, and delay, and identify changes in network signals; The signal analysis unit processes the collected signal data, sets the signal threshold, evaluates the signal quality, and generates an alarm message when the signal quality is lower than the threshold; The alarm and notification unit receives alarm information, prompts the staff through the display screen, informs them of poor signals, and the control system re-formulates factory production decisions.

7. The intelligent detection and inspection integrated system in a digital factory environment according to claim 3 is characterized by: The inspection planning module is connected to a positioning tracking module via a network, and the positioning tracking module is used to track inspection personnel and inspection robots in real time; The positioning tracking module includes: a positioning unit and a deviation warning unit, and the positioning unit is connected to the deviation warning unit through a network; The positioning unit uses GPS to obtain the precise location information of the inspection equipment in real time, converts the location information into the driving trajectory of the inspection personnel and the inspection robot, and uploads the driving trajectory to the digital twin model; The deviation warning unit compares and analyzes the driving trajectory with the preset inspection path. When the inspection equipment deviates from the preset path, the alarm mechanism is triggered to remind the inspection personnel to adjust the inspection path in time.

8. The intelligent detection and inspection integrated system in a digital factory environment according to claim 1 is characterized by: The method for using the RFID tag comprises the following steps: (1) Attach the RFID tag to the device, program the RFID tag, and enter the model, type, and serial number of the device; (2) The reader communicates with the RFID tag through radio frequency signals and reads the data in the RFID tag.

9. The intelligent detection and inspection integrated system in a digital factory environment according to claim 1, characterized in that: The method for using the data aggregation and sharing platform comprises the following steps: (1) The data aggregation and sharing platform organizes and summarizes the integrated data, builds a unified data graph, and stores the data and data graph in a distributed database; (2) The data aggregation and sharing platform transmits data to various departments and corresponding equipment in the factory in real time and on a regular basis according to the timeliness of the data and departmental needs. Each department obtains the required data through the remote monitoring module.

10. The intelligent detection and inspection integrated system in a digital factory environment according to claim 4, characterized in that: The method for using the digital twin technology includes the following steps: (1) Determine the application goal of the model as improving production efficiency and optimizing the inspection process, and set the specific scenario of the digital twin model, including the layout of the virtual factory, equipment configuration, and production process; (2) Collect environmental parameters, equipment operation and production data from the data aggregation and sharing platform, build a three-dimensional model that includes the production line, equipment and environmental elements, and bind the three-dimensional model with real-time data.

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