Method suitable for whole-process traceability management of large-scale mechanical equipment

Abstract

The invention discloses a method suitable for whole-process traceability management of large-scale mechanical equipment, and belongs to the technical field of traceability management of mechanical equipment. The problem that a closed-loop data link is not realized in the whole process due to equipment identity counterfeiting and part replacement is solved. According to the technical scheme, the method comprises the following steps that S1, an NFC electronic lead seal composite identity recognition method is adopted; s2, a whole-process closed-loop tracing data link construction method; s3, establishing a multi-party collaborative authority level-to-level management model; and S4, the intelligent early warning algorithm for state monitoring is utilized to carry out safety level judgment. The method has the beneficial effects that the problems of equipment identity counterfeiting and part replacement can be solved; the full-process closed-loop data link realizes full-life-cycle transparent management; the intelligent early warning algorithm improves the equipment safety risk prevention and control capability.

Description

Technical Field

[0001] The present invention relates to the technical field of mechanical equipment traceability management, and in particular to a method suitable for full-process traceability management of large-scale mechanical equipment. Background Art

[0002] Large-scale machinery at construction sites, such as tower cranes and construction elevators, are all special operation equipment. Once safety problems occur with such equipment, it is very easy to cause major accidents with mass casualties, causing huge losses to society and people's lives and property.

[0003] The existing technology currently has the following problems: (1) Equipment identity fraud and component replacement issues; (2) A closed-loop data chain was not implemented throughout the entire process, and some construction equipment did not even have data established; (3) There is no transparent connection between the device life cycle; (4) The authority is confusing and hierarchical management is not implemented; (5) Weak ability to prevent and control equipment safety risks. Summary of the Invention

[0004] In order to solve the above-mentioned problems, the present invention provides a method for the full-process traceability management of large-scale mechanical equipment. It adopts a composite identity recognition method to establish a large-scale mechanical equipment management and control file to achieve "identity verification" of the equipment; constructs a full-process closed-loop traceability data link to control large-scale mechanical equipment from the source to ensure "entry is qualified"; designs a multi-party collaborative authority hierarchical management model to achieve a closed-loop management and control of large-scale mechanical equipment to ensure "full process is under control and controlled"; uses an intelligent early warning algorithm for status monitoring to provide real-time early warning and alarm to ensure the safety and stability of the equipment during use. The specific steps include: S1: NFC electronic seal composite identity recognition method; This method uses an NFC electronic signature and a physical seal composite identity recognition method to establish a large-scale mechanical equipment management and control file to achieve "identity verification" of the equipment; in order to achieve full-process traceability of large-scale mechanical equipment at the construction site, it is first necessary to solve the "identity identification" of key components and parts. It is necessary to consider the uniqueness, durability, and long-term nature of the identity recognition method, while also preventing problems such as intentional substitution during the process. In response to the above problems, the present invention proposes an NFC electronic seal composite identity recognition method. This method innovatively adopts an industrial-grade NFC tag and a physical seal integrated design, combining the dual functions of environmental tolerance and information storage.

[0005] S2: A method for constructing a full-process closed-loop traceability data link; constructing a full-process closed-loop traceability data link to control large-scale mechanical equipment from the source to ensure that "entry is qualified". The present invention proposes a method for constructing a full-process closed-loop traceability data link. The core of the method is to use NFC electronic seals as a unique identity identification carrier, combined with the integrity, real-time and tamper-proof nature of the data link, covering the entire process of equipment production, installation, operation and maintenance, and scrapping.

[0006] S3: Establish a multi-party collaborative authority hierarchical management model; design a multi-party collaborative authority hierarchical management model to achieve a closed-loop control of large-scale mechanical equipment and ensure that "the entire process is under control and is controlled"; the present invention proposes a multi-party collaborative authority hierarchical management model, which aims to solve the authority management problem during the collaborative operation of multiple parties (manufacturers, leasing companies, lessees, and regulatory units) involved in the entire life cycle of large-scale mechanical equipment, and ensure data traceability, operation legality and information security through dynamic authority classification and multi-party verification mechanism.

[0007] S4: Utilize an intelligent early warning algorithm based on condition monitoring to determine safety levels; this algorithm can provide real-time early warnings and alarms, ensuring the safety and stability of equipment during use. This paper proposes an intelligent early warning algorithm based on condition monitoring, constructing a multi-dimensional equipment health assessment system that integrates heterogeneous data from multiple sources, including equipment physical characteristics, operating data, and maintenance records. By analyzing historical maintenance frequency, component assembly and disassembly times, and accumulated usage time, it analyzes the fatigue level of key components and assigns corresponding safety levels. This system then issues timely early warnings and alarms in the event of abnormal data.

[0008] Step S1 specifically includes: S11: Integrate industrial-grade NFC tags and physical seals. Different colors, shapes, and wire lengths of seals are designed to differentiate key components to facilitate process management. The S11's industrial-grade NFC tags comply with the ISO 14443 standard.

[0009] S12: Design unique logos, QR code information, and serial number content through laser engraving to increase the difficulty of imitation; Step S12 laser engraving the anti-counterfeiting layer is: laser engraving the QR code + serial number on the surface of the seal, satisfying the following relationship: Anti-counterfeiting strength formula: S = K 1 Lc + K 2 ·Qd Where: S : Anti-counterfeiting strength coefficient; Lc : Laser marking depth μm; QD: QR code information density bit / mm²; K 1 、K 2 is the material constant The anti-counterfeiting strength of the laser engraved anti-counterfeiting layer meets S≥8.2.

[0010] S13: Using a dedicated read / write device, design a corresponding encryption algorithm, write tag information to the corresponding storage space of the NFC (Near Field Communication) tag, enable the read / write password protection function, and bind the serial number and the NFC tag's UID and user identification to ensure the uniqueness of the electronic seal. Enter the information into the system to prevent abnormal cards from entering the system and ensure the legitimacy of the seal. The UID binding in step S13 is performed by binding the NFC tag UID with the component serial number and the device code and writing them into the encrypted storage area.

[0011] S14: According to the project construction progress, NFC electronic seals are applied through the software and distributed by the seal management personnel to ensure that they are traceable; a dedicated application: APP (Application: APP) is used to associate information S15: For the parts that have passed the acceptance, select several locations and bind several corresponding seals; NFC electronic seals are installed in a non-destructive manner and can only be removed by destruction after one-time use. Through a dedicated APP, the key component codes are identified and the binding of each seal and component is completed; Except for the serial numbers, all other information on the seals is the same.

[0012] S16: If the NFC electronic seal is abnormally damaged, submit the relevant review materials, and the person in charge will approve it and replace the seal. At the same time, copy the seal information in other locations to ensure the continuity of information and avoid the problem of historical information loss caused by a single seal.

[0013] The number of parts is 2-3.

[0014] Step S2 specifically includes: S21: Manufacturer inputs information: The equipment manufacturer enters the system as a production role and enters the production information of the components according to the coding of the key components, including the name, date, and serial number. It also uploads the factory product certificate information and generates a supervision record for filing; S22: The leasing manufacturer binds NFC to enter entry and exit information and maintenance information: The leasing company enters the system as the leasing manufacturer and enters entry and exit information and maintenance information according to the key component codes, including entry and exit time, maintenance content, maintenance results, maintenance personnel information, third-party monitoring results, and person in charge information; Complete the binding of NFC electronic seals on new equipment, replace abnormally damaged NFC electronic seals, and destroy NFC electronic seals on scrapped equipment; S23: The lessee accepts the equipment and enters the entry and exit records: The lessee enters the system as the lessee and enters the equipment acceptance, installation, process maintenance, and exit information according to the process management method, including entry and exit project time, acceptance results, acceptance personnel, acceptance process photos, equipment number, component number in the equipment, process maintenance information, and connects with the equipment operation monitoring system to obtain equipment operation data and register maintenance information including equipment operation process data, equipment early warning alarm data, and surrounding environment information data.

[0015] If the information in step S23 is abnormal, replace the NFC tag; if the device is scrapped, destroy the NFC tag S24: Create files for installation information to record daily maintenance, connect to the operation monitoring system, and upload operation data; S25: Regulatory authorities enter data: Regulatory authorities enter the system as equipment supervisors and enter data on relevant policies and regulations, industry requirements, and management measures.

[0016] Step S3 specifically includes: S31: Define roles and responsibilities matrix: Clarify the roles and responsibilities of construction site participants The roles are general contractor, subcontractor, supervisor, and operator: The system administrator's responsibilities are divided into global authority allocation, the equipment administrator's responsibilities are single-machine parameter control, the operator's responsibilities are basic operations, and the supervisor's responsibilities are monitoring without operation rights; By binding roles and permissions, we ensure that rights and responsibilities match to avoid unauthorized behavior.

[0017] S32: Design hierarchical authority structure horizontally and vertically: Authorization is hierarchically implemented vertically, from general contractor to subcontractor to equipment group. Superiors can view subordinate data, and subordinates need to synchronize logs. Horizontally isolate subcontracting unit data with organizational codes, and temporarily share and approve them during collaboration to achieve a balance between centralized supervision and data security.

[0018] S33: Dynamic Permission Allocation Mechanism: Permissions are dynamically adjusted based on the device lifecycle: Entry is limited to data entry, manufacturer modification is permitted during commissioning, core parameters are locked during operation, and exit requires supervisor authorization. Emergency scenarios support temporary overriding operations, with the system recovering and recording the device's trajectory within a limited time.

[0019] S34: Set stage permissions: entry, commissioning, exit. Emergency permissions: temporary overriding operations S35: Identity authentication and operation traceability: Operators need "work number + biometric identification" to log in, and operations are authorized by security officers by scanning the code; External personnel are forced to use "dynamic token + face recognition"; Operation logs are encrypted and uploaded to the cloud, supporting traceability by device, time, and personnel. Tampering triggers alarms and leaves signed evidence.

[0020] S36: Design a closed-loop control process: Permission applications require initial review by the equipment manager, review by the supervisor, and spot checks by the auditor; Real-time monitoring of unauthorized behavior, immediate blocking and alarm, unblocking requires a three-party electronic agreement; To form a complete closed loop of "authorization-execution-supervision-accountability".

[0021] Step S4 specifically includes: S41: Multi-source heterogeneous data collection: Real-time collection of equipment physical parameters (vibration, temperature, pressure), operating data (number of starts and stops, load changes), maintenance records (repair time, replacement parts), and environmental data (humidity, wind speed, temperature) through IoT sensors, equipment logs, and manual input. S42: Data Cleaning and Integration: Use data cleaning techniques to remove noise, establish a time series database with unified timestamps, and use ETL tools to achieve standardized mapping of structured and unstructured data, ensuring that multi-dimensional data can be correlated and analyzed within a unified platform; S42: Key Component Feature Extraction: Key Component Fatigue Quantification Modeling: For core components of equipment, extract historical maintenance frequency, disassembly and assembly times, and cumulative operating time characteristic parameters, and establish a degradation model based on material physical properties (metal fatigue coefficient, design life); S43: Fatigue quantification modeling: Use Weibull distribution or Markov chain algorithm to calculate the remaining life probability of components, quantify the fatigue index through cumulative damage theory, divide the safety level (normal / caution / dangerous), and form a component-level health assessment indicator system; S44: Dynamic threshold adaptive warning mechanism: Build a normal operating status baseline based on historical equipment operating data, use sliding window technology to calculate the mean, variance, and trend slope of characteristic parameters (such as vibration spectrum amplitude and temperature change rate) in real time, and design a dynamic threshold algorithm; When the monitoring data deviates from the baseline by more than 3σ When the range or trend is abnormal continuously, a primary warning is triggered; at the same time, the equipment load factor is introduced to dynamically correct the threshold to avoid false alarms under high load conditions.

[0022] S45: Multimodal data fusion decision model: Using random forest and XGBoost machine learning algorithms, we rank the importance of physical parameters, operation logs, and maintenance records, and screen out factors with strong correlation with maintenance cycles and component disassembly and assembly intervals; By integrating quantitative monitoring data with qualitative maintenance experience through a fuzzy logic system, a comprehensive equipment health scoring model is constructed, which outputs a real-time safety assessment value of 0-100 points and associates it with the warning level (yellow / orange / red) to achieve collaborative decision-making across data sources.

[0023] S46: Closed-loop feedback and model iterative optimization: Establish a knowledge base for early warning events to record the equipment status, handling measures and subsequent operating performance for each alarm; red warnings require emergency braking; orange warnings require shutdown inspections; and yellow warnings require inspections by operation and maintenance personnel.

[0024] By adopting incremental learning technology, newly generated maintenance records and component replacement data are regularly fed back to the health assessment model, and the prediction accuracy is improved through adaptive parameter adjustment. At the same time, digital twin technology is used to simulate equipment behavior under extreme working conditions, verify the effectiveness of the early warning algorithm under boundary conditions, and form a closed-loop mechanism for continuous optimization of algorithm performance.

[0025] In summary, the present invention has the following beneficial technical effects: (1) Composite identity recognition method solves the problems of device identity fraud and component replacement Guaranteed uniqueness: The NFC tag's UID is bound to the serial number and laser-engraved, ensuring the unique digital identity of each key component, preventing counterfeiting or forgery. Tamper-proof: The physical seal is integrated with the NFC tag, requiring destructive removal and single-use to prevent malicious substitution. Multiple seals (2-3) are redundantly bound to prevent single-point data loss. Rapid verification: Scanning the NFC tag via a dedicated app allows real-time verification of component legitimacy, improving on-site acceptance efficiency and reducing manual verification errors.

[0026] (2) Full-process closed-loop data link realizes transparent management of the entire life cycle Data connectivity: Covering the entire equipment production, leasing, construction, and retirement process, connecting the manufacturing side (production information), operations and maintenance (maintenance records), user side (operational data), and regulatory side (policy requirements). Tamper-proof: Blockchain technology ensures the integrity of the data chain, with traceable operations at every stage. Standardized management: A unified coding system (such as key component coding) enables cross-system data interoperability and supports automatic verification of equipment compliance upon entry ("qualified upon entry").

[0027] (3) The hierarchical authority management model solves the trust problem of multi-party collaboration Dynamic permission control: Permissions are dynamically adjusted according to the device lifecycle stage (entry → commissioning → operation → exit) to prevent parameter tampering (e.g., locking core parameters during operation). Secure cross-organizational collaboration: Vertical hierarchical authorization (general contractor → subcontractor) and horizontal data isolation (organizational coding) balance oversight and privacy, and temporary sharing requires approval. Strong identity authentication: Biometrics + dynamic tokens enable two-factor authentication. Key operations require security personnel to scan and authorize to ensure the legality of the operation. Fully traceable: Operation logs are encrypted and uploaded to the cloud, supporting multi-dimensional traceability by device / personnel / time. Tampering triggers alarms and retains evidence.

[0028] (4) Intelligent early warning algorithms improve equipment safety risk prevention and control capabilities Accurate health assessment: Integrating data from multiple sources, such as vibration and temperature, the system quantifies component fatigue using Weibull distribution / Markov chains, and categorizes safety levels (normal / caution / dangerous). Adaptive early warning: A dynamic threshold algorithm adjusts alarm trigger conditions based on operating load (e.g., relaxing temperature thresholds under high load), reducing false alarms and missed alerts. Predictive maintenance: Based on a remaining life probability model, component replacement cycles are planned in advance to reduce the risk of unplanned downtime. Knowledge closed-loop optimization: Leveraging incremental learning and digital twin technology to continuously optimize algorithms, early warning accuracy increases as data accumulates.

[0029] (5) The present invention systematically solves the core problems existing in the large-scale machinery industry, such as identity fraud, data silos, inefficient collaboration, and security out of control, through the four-layer technical architecture of "digital identity-data connectivity-authority management-intelligent early warning", and provides a credible, controllable, and traceable digital solution for equipment management in the construction industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a schematic diagram of the architecture of a method for full-process traceability management of large-scale mechanical equipment according to the present invention; Figure 2 This is a schematic diagram of the NFC electronic seal composite identity recognition method of the present invention; Figure 3 Schematic diagram of the method for constructing a closed-loop traceability data link for the entire process of the present invention; Figure 4 Schematic diagram of the hierarchical authority management model for multi-party collaboration of the present invention; Figure 5 This is a schematic diagram of the intelligent early warning algorithm based on condition monitoring of the present invention. DETAILED DESCRIPTION

[0031] The present invention will be further described in detail below with reference to the accompanying drawings.

[0032] Example 1 Reference Figure 1 This embodiment is a method for full-process traceability management of large-scale machinery and equipment. It adopts a composite identity recognition method to establish a large-scale machinery and equipment management and control file to achieve "identity verification" of the equipment; it builds a full-process closed-loop traceability data link to control large-scale machinery and equipment from the source to ensure "qualified upon entry"; it designs a multi-party collaborative authority hierarchical management model to achieve a closed-loop management and control of large-scale machinery and equipment to ensure "full process control and control"; it uses an intelligent early warning algorithm based on condition monitoring to provide real-time early warning and alarm to ensure the safety and stability of the equipment during use. The specific steps include: S1: NFC electronic seal composite identity recognition method; This method uses an NFC electronic signature and a physical seal composite identity recognition method to establish a large-scale mechanical equipment management and control file to achieve "identity verification" of the equipment; in order to achieve full-process traceability of large-scale mechanical equipment at the construction site, it is first necessary to solve the "identity identification" of key components and parts. It is necessary to consider the uniqueness, durability, and long-term nature of the identity recognition method, while also preventing problems such as intentional substitution during the process. In response to the above problems, the present invention proposes an NFC electronic seal composite identity recognition method. This method innovatively adopts an industrial-grade NFC tag and a physical seal integrated design, combining the dual functions of environmental tolerance and information storage.

[0033] S2: A method for constructing a full-process closed-loop traceability data link; constructing a full-process closed-loop traceability data link to control large-scale mechanical equipment from the source to ensure that "entry is qualified". The present invention proposes a method for constructing a full-process closed-loop traceability data link. The core of the method is to use NFC electronic seals as a unique identity identification carrier, combined with the integrity, real-time and tamper-proof nature of the data link, covering the entire process of equipment production, installation, operation and maintenance, and scrapping.

[0034] S3: Establish a multi-party collaborative authority hierarchical management model; design a multi-party collaborative authority hierarchical management model to achieve a closed-loop control of large-scale mechanical equipment and ensure that "the entire process is under control and is controlled"; the present invention proposes a multi-party collaborative authority hierarchical management model, which aims to solve the authority management problem of collaborative operations of multiple parties involved in the entire life cycle of large-scale mechanical equipment (such as manufacturers, leasing companies, lessees, and regulatory units), and ensure data traceability, operation legality and information security through dynamic authority classification and multi-party verification mechanisms.

[0035] S4: Utilize an intelligent early warning algorithm based on condition monitoring to determine safety levels; this algorithm can provide real-time early warnings and alarms, ensuring the safety and stability of equipment during use. This paper proposes an intelligent early warning algorithm based on condition monitoring, constructing a multi-dimensional equipment health assessment system that integrates heterogeneous data from multiple sources, including equipment physical characteristics, operating data, and maintenance records. By analyzing historical maintenance frequency, component assembly and disassembly times, and accumulated usage time, it analyzes the fatigue level of key components and assigns corresponding safety levels. This system then issues timely early warnings and alarms in the event of abnormal data.

[0036] refer to Figure 2 , step S1 specifically includes: S11: Integrate industrial-grade NFC tags and physical seals. Different colors, shapes, and wire lengths of seals are designed to differentiate key components to facilitate process management. The S11's industrial-grade NFC tags comply with the ISO 14443 standard.

[0037] S12: Design unique logos, QR code information, and serial number content through laser engraving to increase the difficulty of imitation; Step S12 laser engraving the anti-counterfeiting layer is: laser engraving the QR code + serial number on the surface of the seal, satisfying the following relationship: Anti-counterfeiting strength formula: S = K 1 Lc + K 2 ·Qd ( S : Anti-counterfeiting strength coefficient; Lc : Laser marking depth μm; QD : QR code information density bit / mm²; K 1 、K 2 is the material constant) The anti-counterfeiting strength of the laser engraved anti-counterfeiting layer meets S≥8.2.

[0038] S13: Using a dedicated read / write device, design a corresponding encryption algorithm, write tag information to the corresponding storage space of the NFC (Near Field Communication) tag, enable the read / write password protection function, and bind the serial number and the NFC tag's UID and user identification to ensure the uniqueness of the electronic seal. Enter the information into the system to prevent abnormal cards from entering the system and ensure the legitimacy of the seal. The UID binding in step S13 is performed by binding the NFC tag UID with the component serial number and device code and writing them into the encrypted storage area. In this example, AES-256 encryption is used.

[0039] S14: According to the project construction progress, NFC electronic seals are applied through the software and distributed by the seal management personnel to ensure that they are traceable; a dedicated application: APP (Application: APP) is used to associate information S15: For the parts that have passed the acceptance, select several locations and bind several corresponding seals; NFC electronic seals are installed in a non-destructive manner and can only be removed by destruction after one-time use; Through a dedicated APP, the key component codes are identified and the binding of each seal and component is completed; Except for the serial numbers, all other information on the seals is the same.

[0040] S16: If the NFC electronic seal is abnormally damaged, submit the relevant review materials, and the person in charge will approve it and replace the seal. At the same time, copy the seal information in other locations to ensure the continuity of information and avoid the problem of historical information loss caused by a single seal.

[0041] In this embodiment, the number of components is 2-3 to achieve the above purpose.

[0042] refer to Figure 3 , step S2 specifically includes: S21: Manufacturer inputs information: The equipment manufacturer enters the system as a production role and enters the production information of the components according to the coding of the key components, including the name, date, and serial number. It also uploads the factory product certificate information and generates a supervision record for filing; S22: The leasing manufacturer binds NFC to enter entry and exit information and maintenance information: The leasing company enters the system as the leasing manufacturer and enters entry and exit information and maintenance information according to the key component codes, including entry and exit time, maintenance content, maintenance results, maintenance personnel information, third-party monitoring results, and person in charge information; Complete the binding of NFC electronic seals on new equipment, replace abnormally damaged NFC electronic seals, and destroy NFC electronic seals on scrapped equipment; S23: The lessee accepts the equipment and enters the entry and exit records: The lessee enters the system as the lessee and enters the equipment acceptance, installation, process maintenance, and exit information according to the process management method, including entry and exit project time, acceptance results, acceptance personnel, acceptance process photos, equipment number, component number in the equipment, process maintenance information, and connects with the equipment operation monitoring system to obtain equipment operation data and register maintenance information including equipment operation process data, equipment early warning alarm data, and surrounding environment information data.

[0043] In this embodiment, the lessee refers to the construction unit.

[0044] If the information in step S23 is abnormal, replace the NFC tag; if the device is scrapped, destroy the NFC tag S24: Create files for installation information to record daily maintenance, connect to the operation monitoring system, and upload operation data; S25: Regulatory authorities enter data: Regulatory authorities enter the system as equipment supervisors and enter data on relevant policies and regulations, industry requirements, and management measures.

[0045] refer to Figure 4 , step S3 specifically includes: S31: Define roles and responsibilities matrix: Clarify the roles and responsibilities of construction site participants In this embodiment, the roles are general contractor, subcontractor, supervisor, and operator: The system administrator's responsibilities are divided into global authority allocation, the equipment administrator's responsibilities are single-machine parameter control, the operator's responsibilities are basic operations, and the supervisor's responsibilities are monitoring without operation rights; By binding roles and permissions (in this embodiment, operators are prohibited from modifying security parameters), we ensure that rights and responsibilities match to avoid unauthorized behavior.

[0046] S32: Design hierarchical authority structure horizontally and vertically: Authorization is hierarchically implemented vertically, from general contractor to subcontractor to equipment group. Superiors can view subordinate data, and subordinates need to synchronize logs. Horizontally isolate subcontracting unit data with organizational codes, and temporarily share and approve them during collaboration to achieve a balance between centralized supervision and data security.

[0047] S33: Dynamic Permission Allocation Mechanism: Permissions are dynamically adjusted based on the device lifecycle: Entry is limited to data entry, manufacturer modification is permitted during commissioning, core parameters are locked during operation, and exit requires supervisor authorization. Emergency scenarios support temporary overriding operations, with the system recovering and recording the device's trajectory within a limited time.

[0048] S34: Set stage permissions: entry, commissioning, exit. Emergency permissions: temporary overriding operations S35: Identity authentication and operation traceability: Operators need "work number + biometric identification" to log in, and operations are authorized by security officers by scanning the code; External personnel are forced to use "dynamic token + face recognition"; Operation logs are encrypted and uploaded to the cloud, supporting traceability by device, time, and personnel. Tampering triggers alarms and leaves signed evidence.

[0049] S36: Design a closed-loop control process: Permission applications require initial review by the equipment manager, review by the supervisor, and spot checks by the auditor; Real-time monitoring of unauthorized behavior (overload in this embodiment), immediate locking and alarm, unblocking requires a three-party electronic agreement; To form a complete closed loop of "authorization-execution-supervision-accountability".

[0050] refer to Figure 5 , step S4 specifically includes: S41: Multi-source heterogeneous data collection: Real-time collection of equipment physical parameters (in this embodiment, the physical parameters are vibration, temperature, and pressure), operating data (in this embodiment, the number of starts and stops, and load changes), maintenance records (in this embodiment, repair time and replacement parts), and environmental data (in this embodiment, humidity, wind speed, and temperature) through multiple channels such as IoT sensors, equipment logs, and manual input; S42: Data Cleaning and Integration: Use data cleaning techniques to remove noise, establish a time series database with unified timestamps, and use ETL tools to achieve standardized mapping of structured and unstructured data, ensuring that multi-dimensional data can be correlated and analyzed within a unified platform; S42: Key component feature extraction: Key component fatigue quantitative modeling: For core components of the equipment, extract the characteristic parameters of historical maintenance frequency, number of disassembly and assembly, and cumulative operating time, and establish a degradation model based on the material physical properties (in this embodiment, the characteristics are metal fatigue coefficient and design life); S43: Fatigue quantification modeling: Use Weibull distribution or Markov chain algorithm to calculate the remaining life probability of components, quantify the fatigue index through cumulative damage theory, divide the safety level (normal / caution / dangerous), and form a component-level health assessment indicator system; S44: Dynamic threshold adaptive warning mechanism: Build a normal operating status baseline based on historical equipment operating data, use sliding window technology to calculate the mean, variance, and trend slope of characteristic parameters (in this embodiment, the characteristic parameters are vibration spectrum amplitude and temperature change rate) in real time, and design a dynamic threshold algorithm; When the monitoring data deviates from the baseline by more than 3 σWhen the range or trend is abnormal continuously, a primary warning is triggered; at the same time, the equipment load factor is introduced to dynamically correct the threshold to avoid false alarms under high load conditions.

[0051] S45: Multimodal data fusion decision model: Using random forest and XGBoost machine learning algorithms, we rank the importance of physical parameters, operation logs, and maintenance records, and screen out factors with strong correlation with maintenance cycles and component disassembly and assembly intervals; By integrating quantitative monitoring data with qualitative maintenance experience through a fuzzy logic system, a holistic equipment health scoring model is constructed, which outputs a real-time safety assessment value from 0 to 100 and associates it with an early warning level (yellow, orange, and red in this example) to achieve collaborative decision-making across data sources.

[0052] S46: Closed-loop feedback and model iterative optimization: Establish a warning event knowledge base to record the equipment status, handling measures and subsequent operating performance of each alarm; in this embodiment, a red warning is for emergency braking; an orange warning is for shutdown inspection, and a yellow warning is for inspection by operation and maintenance personnel.

[0053] By adopting incremental learning technology, newly generated maintenance records and component replacement data are regularly fed back to the health assessment model, and the prediction accuracy is improved through adaptive parameter adjustment. At the same time, digital twin technology is used to simulate equipment behavior under extreme working conditions, verify the effectiveness of the early warning algorithm under boundary conditions, and form a closed-loop mechanism for continuous optimization of algorithm performance.

[0054] The above are all preferred embodiments of the present invention, and are not intended to limit the scope of protection of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for full-process traceability management of large-scale mechanical equipment, characterized in that: The following steps are involved: S1: NFC electronic seal composite identification method; S2: Full-process closed-loop traceability data link construction method; S3: Establish a multi-party collaborative authority hierarchical management model; S4: Use the intelligent early warning algorithm of condition monitoring to determine the safety level.

2. A method for full-process traceability management of large-scale mechanical equipment according to claim 1, characterized in that: Step S1 specifically includes: S11: Integrate industrial-grade NFC tags and physical seals, and design seals with different colors, shapes, and wire rope lengths to differentiate the types of traceable parts. S12: Design unique logo, QR code information, and serial number content through laser engraving; S13: Using a dedicated read / write device, design a corresponding encryption algorithm, write tag information to the corresponding storage space of the NFC tag, enable the read / write password protection function, and bind the serial number and the user identity certificate (UID) of the NFC tag to ensure the uniqueness of the electronic seal, and enter the information into the system; S14: According to the project construction progress, NFC electronic seals are applied through the software and distributed by the seal management personnel; information is associated using a dedicated application: APP; S15: For the parts that have passed the acceptance, select several locations and bind several corresponding seals; S16: If the NFC electronic seal is abnormally damaged, submit the relevant review materials, the person in charge will approve it, replace the seal, and copy the seal information in other locations.

3. A method for full-process traceability management of large-scale mechanical equipment according to claim 1, characterized in that: Step S2 specifically includes: S21: Manufacturer inputs information: The equipment manufacturer enters the system as a production role, enters the production information of the parts according to the part code, uploads the factory product certificate information, and generates a supervision record for filing; S22: The leasing manufacturer binds NFC to enter the entry and exit information and maintenance information: The leasing company enters the system as the leasing manufacturer and enters the entry and exit information and maintenance information according to the component code; Complete the binding of NFC electronic seals on new equipment, replace abnormally damaged NFC electronic seals, and destroy NFC electronic seals on scrapped equipment; S23: The lessee accepts the equipment and enters the entry and exit records: The lessee enters the system as the lessee and performs equipment acceptance, installation, process maintenance, and exit information according to the process management method. It also connects to the equipment operation monitoring system to obtain equipment operation data and register maintenance information. S24: Create files for installation information to record daily maintenance, connect to the operation monitoring system, and upload operation data; S25: Regulatory authorities enter data: Regulatory authorities enter the system as equipment supervisors and enter data on relevant policies and regulations, industry requirements, and management measures.

4. A method for full-process traceability management of large-scale mechanical equipment according to claim 1, characterized in that: Step S3 specifically includes: S31: Define the roles and responsibilities matrix: Clarify the roles of construction site participants and set responsibilities, and ensure that rights and responsibilities match by binding roles and permissions. S32: Design hierarchical authority structure horizontally and vertically: Authorization is hierarchically implemented vertically, from general contractor to subcontractor to equipment group. Superiors can view subordinate data, and subordinates need to synchronize logs. Horizontally isolate subcontractor data by organizational code, temporarily share and approve during collaboration; S33: Dynamic permission allocation mechanism: Dynamically adjust permissions according to the device life cycle; S34: Set stage permissions: entry, commissioning, exit, and emergency permissions: temporary overriding operations; S35: Identity authentication and operation traceability: Operators need "work number + biometric identification" to log in, and operations are authorized by security officers by scanning the code; External personnel are forced to use "dynamic token + face recognition"; Operation logs are encrypted and uploaded to the cloud, supporting traceability by device, time, and personnel. Tampering triggers alarms and leaves signed evidence. S36: Design a closed-loop control process: Permission applications require initial review by the equipment manager, review by the supervisor, and spot checks by the auditor; Real-time monitoring of unauthorized behavior, immediate locking and alarming, unblocking requires a three-party electronic agreement.

5. The method for full-process traceability management of large-scale mechanical equipment according to claim 1 is characterized in that: Step S4 specifically includes: S41: Multi-source heterogeneous data collection: Real-time collection of equipment physical parameters, operating data, maintenance records, and environmental data through IoT sensors, equipment logs, and manual input; S42: Data Cleaning and Integration: Use data cleaning techniques to remove noise, establish a time series database with unified timestamps, and use ETL tools to achieve standardized mapping of structured and unstructured data, ensuring that multi-dimensional data can be correlated and analyzed within a unified platform; S42: Key Component Feature Extraction: Key Component Fatigue Quantification Modeling: For core components of equipment, extract historical maintenance frequency, disassembly and assembly times, and cumulative operating time characteristic parameters, and establish a degradation model based on the physical properties of the material; S43: Fatigue quantification modeling: Use Weibull distribution or Markov chain algorithm to calculate the probability of remaining life of components, quantify fatigue index through cumulative damage theory, divide safety levels, and form a component-level health assessment index system; S44: Dynamic threshold adaptive warning mechanism: Build a normal operating status baseline based on historical equipment operation data, use sliding window technology to calculate the mean, variance, and trend slope of characteristic parameters in real time, and design a dynamic threshold algorithm; S45: Multimodal data fusion decision model: Using random forest and XGBoost machine learning algorithms, the model ranks the importance of physical parameters, operation logs, and maintenance records, and selects factors with strong correlations with maintenance cycles and component disassembly and assembly intervals. By integrating quantitative monitoring data with qualitative maintenance experience through a fuzzy logic system, a comprehensive equipment health scoring model is constructed, which outputs a real-time safety assessment value from 0 to 100 and associates it with an early warning level, enabling collaborative decision-making across data sources. S46: Closed-loop feedback and model iterative optimization: Establish a warning event knowledge base to record the equipment status, handling measures and subsequent operating performance of each alarm; By adopting incremental learning technology, newly generated maintenance records and component replacement data are regularly fed back to the health assessment model, and the prediction accuracy is improved through adaptive parameter adjustment. At the same time, digital twin technology is used to simulate equipment behavior under extreme working conditions, verify the effectiveness of the early warning algorithm under boundary conditions, and form a closed-loop mechanism for continuous optimization of algorithm performance.

6. A method for full-process traceability management of large-scale mechanical equipment according to claim 2, characterized in that: The S11's industrial-grade NFC tags comply with the ISO 14443 standard.

7. The method for full-process traceability management of large-scale mechanical equipment according to claim 2, characterized in that: Step S12 laser engraving the anti-counterfeiting layer is: laser engraving the QR code + serial number on the surface of the seal, satisfying the following relationship: Anti-counterfeiting strength formula: S = K 1 Lc + K 2 ·Qd Where: S : Anti-counterfeiting strength coefficient; Lc : Laser marking depth μm; QD : QR code information density bit / mm²; K 1 、K 2 is the material constant.

8. The method for full-process traceability management of large-scale mechanical equipment according to claim 2 is characterized in that: The UID binding in step S13 is performed by binding the NFC tag UID with the component serial number and the device code and writing them into the encrypted storage area.

9. The method for full-process traceability management of large-scale mechanical equipment according to claim 7, characterized in that: The anti-counterfeiting strength of the laser engraved anti-counterfeiting layer meets S≥8.

2.

10. The method for full-process traceability management of large-scale mechanical equipment according to claim 3, characterized in that: If the information in step S23 indicates an abnormal situation, the NFC tag is replaced; if the device is scrapped, the NFC tag is destroyed.

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