PLM-driven equipment full-life-cycle ledger dynamic synchronization method and PLM-driven equipment full-life-cycle ledger dynamic synchronization system

Abstract

The invention discloses a PLM-driven equipment full-life-cycle ledger dynamic synchronization method and system, and the method comprises the steps: receiving design original data of equipment from a PLM system, carrying out the semantic reconstruction operation of the design original data, constructing a design semantic density matrix and a hierarchical distribution tensor, and achieving the multi-dimensional compression mapping of the design original data, generating design reference data with a unified semantic coordinate system; according to the equipment unique identification matrix and the semantic coordinates in the design reference data, exponential normalization coupling coding is carried out on the operation data collected in the equipment operation site, operation behavior tensors are constructed, and the operation behavior tensors correspond to the semantic coordinates in the design reference data one by one; and calculating a comprehensive deviation according to a structure deviation index and a parameter deviation index between the design reference data and the operation behavior tensor, and adaptively updating the standing book field through a deviation indication function.

Description

Technical Field

[0001] This invention belongs to the field of industrial internet software and equipment management technology, and more specifically, relates to a PLM-driven method and system for dynamic synchronization of equipment lifecycle ledgers. Background Technology

[0002] In the equipment lifecycle management process of industrial enterprises, Product Lifecycle Management (PLM) systems typically serve as the core platform during the design phase, storing equipment structural information, component parameters, and complete Bill of Materials (BOM) design data. However, after equipment is put into operation, its operating status, maintenance records, and fault information are usually recorded by a separate equipment management system or manually maintained by maintenance personnel. Because the systems used in the design and maintenance phases are independent and lack a data linkage mechanism, the disconnect between design data and maintenance data has become a common problem in existing technologies.

[0003] In existing technologies, because PLM systems and operation and maintenance systems are independent of each other, there is a lack of linkage mechanism between equipment design data and operational data. As a result, equipment ledgers need to be updated manually after design changes. Not only is the update cycle usually as long as 2 to 3 days, but it is also prone to data entry errors, making it difficult for the ledger data to accurately reflect the true status of the equipment. At the same time, design structure data, operational status information, and maintenance records are stored in a scattered manner, lacking a unified data chain that runs through the entire design-operation and maintenance process. This makes it difficult to trace the source of equipment performance abnormalities or quality problems in a timely manner. Furthermore, because it is impossible to compare design benchmarks with operational behavior in real time, the operation and maintenance end lacks reliable data support, making it impossible to form effective decisions based on equipment lifecycle information, which seriously restricts the improvement of the level of digitalization of equipment management.

[0004] In summary, current technologies lack a solution for automatically linking and dynamically synchronizing equipment design data with data from the equipment operation phase within a PLM system. They also lack a software system capable of tracing the entire process from design to manufacturing, operation, and maintenance when equipment malfunctions. Therefore, it is necessary to develop a new technical solution to achieve dynamic updates to equipment ledgers and process-level data traceability, thereby overcoming the aforementioned technical limitations of existing technologies. Summary of the Invention

[0005] To address the above technical problems, this invention proposes a PLM-driven method for dynamic synchronization of equipment lifecycle ledgers, comprising: Receive the original design data of the device from the PLM system, perform semantic reconstruction operation on the original design data, construct the design semantic density matrix and hierarchical distribution tensor, thereby realizing the multidimensional compression mapping of the original design data and generating design reference data with a unified semantic coordinate system; Based on the unique identification matrix and semantic coordinates of the equipment in the design reference data, the operation data collected at the equipment operation site is subjected to exponential normalized coupling encoding to construct the operation behavior tensor, wherein the operation behavior tensor corresponds one-to-one with the semantic coordinates in the design reference data. The comprehensive deviation is calculated based on the structural deviation index and parameter deviation index between the design baseline data and the operational behavior tensor, and the ledger fields are adaptively updated using the deviation indicator function.

[0006] Furthermore, constructing the design semantic density matrix and hierarchical distribution tensor includes: normalizing the original design data, generating semantic scalars, and combining all semantic scalars into the design semantic density matrix, so that different types of design parameters in the original design data are compressed into a unified semantic range; All semantic scalars in the designed semantic density matrix are weighted and aggregated according to the preset semantic basis vectors to form a fixed-dimensional semantic vector. Based on the hierarchical depth of the BOM hierarchy of the PLM system, the semantic vector is transformed in multiple levels. Each level compresses the semantic vector through a set of feature transformation matrices and weight vectors to generate a set of stacked feature vectors as a hierarchical distribution tensor. Read the historical design versions of the device from the PLM system and record the version identifier after each design change sequentially into the version chain vector; The hierarchical distribution tensor, semantic vector, and version chain vector are used to form the design baseline data.

[0007] Furthermore, following the equipment unique identifier matrix and semantic coordinates in the design baseline data, the process also includes: establishing a data acquisition index at the equipment operation site based on the version chain vector in the design baseline data; and collecting operation time sequence, load sequence, and vibration spectrum data corresponding to the equipment operation process from the equipment operation site based on the data acquisition index.

[0008] Furthermore, the exponential normalization coupling encoding of the operating data collected at the equipment operation site includes: scaling the sampled value of a certain sample in the operating time sequence with the maximum sampled value in the operating time sequence to obtain the operating time sequence value, so that the operating time sequence value is kept within a preset range in value; The sampled value of a certain time in the load time sequence is proportionalized with the maximum load value of the load sequence to obtain the load sequence value, so that the load level of the equipment at different operating stages can be compared on a uniform scale. The vibration energy at each frequency point in the vibration spectrum data is proportionalized to the total vibration energy across the entire frequency band to obtain the vibration energy ratio, so that the relative distribution of vibration energy in different frequency dimensions can be expressed in a unified form.

[0009] Furthermore, constructing the operational behavior tensor involves combining the runtime sequence value, load sequence, and vibration energy ratio to form the operational behavior tensor.

[0010] Furthermore, the calculation of the structural deviation index and parameter deviation index includes: mapping the fields one-to-one between the operational behavior tensor and the hierarchical distribution tensor based on the semantic coordinates of the design baseline data to obtain the operational side structural tensor and the operational side parameter vector. The calculation of the structural deviation index includes: , in, This is the structural deviation index. For hierarchical distribution tensors, For the running side structure tensor, It is the Frobenius norm; The calculation of the parameter deviation index includes: , in, For parameter deviation index, For semantic vectors, This is the runtime parameter vector.

[0011] Furthermore, the calculation of the overall deviation includes: , in, To account for the overall deviation, This is an index representing the change in the operating behavior of the equipment on the operating side; Index of changes in the operating behavior of computing devices on the operating side for: , in, To account for the overall deviation, Weights for runtime sequence values. For the first Runtime value of the second sample variance The weights of the load sequence, For the first The load sequence of the next sample variance As the weight of the vibration energy ratio, For the first The ratio of vibration energy at each frequency point The variance.

[0012] Furthermore, adaptive updates to the ledger fields using the deviation indicator function include: The deviation indication function is: , in, To update the index for the ledger, For trigger strength, To trigger the judgment threshold, For indicator functions; when When =1, the ledger field is updated adaptively; Calculate trigger strength for: , in, This is the mean of the overall deviation. For threshold adjustment factor, The standard deviation of the overall deviation This is to trigger the sensitivity factor.

[0013] This invention also proposes a PLM-driven dynamic synchronization system for equipment lifecycle ledgers, comprising: The baseline data generation module receives the original design data of the device from the PLM system, performs semantic reconstruction operation on the original design data, constructs the design semantic density matrix and hierarchical distribution tensor, thereby realizing the multidimensional compression mapping of the original design data and generating design baseline data with a unified semantic coordinate system. The module for constructing operational behavior tensors is used to perform exponentially normalized coupled encoding on the operational data collected at the equipment operation site based on the equipment unique identifier matrix and semantic coordinates in the design reference data, and construct operational behavior tensors. The operational behavior tensors correspond one-to-one with the semantic coordinates in the design reference data. The ledger update module is used to calculate the comprehensive deviation based on the structural deviation index and parameter deviation index between the design baseline data and the operational behavior tensor, and to adaptively update the ledger fields through the deviation indicator function.

[0014] Furthermore, constructing the design semantic density matrix and hierarchical distribution tensor includes: normalizing the original design data, generating semantic scalars, and combining all semantic scalars into the design semantic density matrix, so that different types of design parameters in the original design data are compressed into a unified semantic range; All semantic scalars in the designed semantic density matrix are weighted and aggregated according to the preset semantic basis vectors to form a fixed-dimensional semantic vector. Based on the hierarchical depth of the BOM hierarchy of the PLM system, the semantic vector is transformed in multiple levels. Each level compresses the semantic vector through a set of feature transformation matrices and weight vectors to generate a set of stacked feature vectors as a hierarchical distribution tensor. Read the historical design versions of the device from the PLM system and record the version identifier after each design change sequentially into the version chain vector; The hierarchical distribution tensor, semantic vector, and version chain vector are used to form the design baseline data.

[0015] In summary, the technical solutions conceived by this invention have the following beneficial effects compared with the prior art: (1) This invention realizes automatic comparison, deviation identification and version solidification of design data and operation data through PLM-based dynamic synchronization mechanism, so that the equipment ledger can be updated in minutes. Compared with the traditional method that relies on manual input and the update cycle is usually 2 to 3 days, it significantly improves the efficiency of ledger update. At the same time, since the update process is executed automatically, the update accuracy can be increased to more than 99%, effectively avoiding problems such as field omission, input error or delay caused by manual input.

[0016] (2) The present invention adopts an interface architecture compatible with the existing PLM system of the enterprise. Without modifying the PLM itself, data transmission and model generation can be realized through the interface, thereby enhancing the system's adaptability and deployment convenience. Moreover, the system interface is simple to interact with, and maintenance personnel do not need to have professional IT skills to complete the operation, which is conducive to the rapid implementation and application in enterprises and improves the system's feasibility in different business scenarios. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method in Embodiment 1 of the present invention; Figure 2 This is a system structure diagram of Embodiment 2 of the present invention. Detailed Implementation

[0018] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0019] The method provided by this invention can be implemented in a terminal environment that may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, which is loaded and executed by the processor to implement the method described in the following embodiments.

[0020] A processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts of the terminal, and performs various functions and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and by calling data stored in the storage medium.

[0021] Storage media can include random access memory (RAM) or read-only memory (ROM). Storage media can be used to store instructions, programs, code, code sets, or instructions.

[0022] The display screen is used to show the user interface of each application.

[0023] In addition, those skilled in the art will understand that the structure of the terminal described above does not constitute a limitation on the terminal. The terminal may include more or fewer components, or combine certain components, or have different component arrangements. For example, the terminal may also include radio frequency circuits, input units, sensors, audio circuits, power supplies, and other components, which will not be described in detail here.

[0024] Glossary: ​​PLM stands for Product Lifecycle Management, a software system used by enterprises to manage information about the entire process of a product from its inception to its retirement.

[0025] Example 1 like Figure 1 As shown, this embodiment proposes a PLM-driven method for dynamic synchronization of equipment lifecycle ledgers, including: Step 101: Receive the original design data of the device from the PLM system, perform semantic reconstruction operation on the original design data, construct the design semantic density matrix and hierarchical distribution tensor, thereby realizing the multidimensional compression mapping of the original design data and generating design reference data with a unified semantic coordinate system. This embodiment is compatible with mainstream PLM system interfaces, requires no modification to the enterprise's existing PLM architecture, and can quickly extract core data from the design phase as the basis for initializing the ledger, solving the problem of "design data being difficult to reuse".

[0026] Specifically, constructing the semantic density matrix and hierarchical distribution tensor includes: 1. Normalize the original design data to generate semantic scalars, and combine all semantic scalars into a design semantic density matrix, so that different types of design parameters in the original design data are compressed into a unified semantic range; Preferably, the semantic density matrix is ​​designed as follows: , in, To design the semantic density matrix, To design the first in the original data Design parameters semantic scalar, This is the matrix transpose.

[0027] 2. All semantic scalars in the semantic density matrix are weighted and aggregated according to the preset semantic basis vectors to form a fixed-dimensional semantic vector. Based on the hierarchical depth of the BOM hierarchy of the PLM system, the semantic vector is transformed in multiple levels. Each level compresses the semantic vector through a set of feature transformation matrices and weight vectors to generate a set of stacked feature vectors as a hierarchical distribution tensor. Preferably, the semantic vector is calculated as follows: , in, For semantic vectors, , To design the first in the original data Design parameters semantic scalar, The number of design parameters, For the first Design parameters basis vectors in semantic space For dimensions (such as semantic categories as shown below).

[0028] Regarding obtaining The method is as follows: First, predefine the semantic categories of the design parameters, for example: Geometric semantics: length, width, thickness, radius, etc.; Material semantics: elastic modulus, yield value, density; Functional semantics: rated power, torque, damping; Safety semantics: ultimate load, redundancy factor; Environmental semantics: temperature rating, protection rating; Each category corresponds to a set of semantic coordinate axes, and the first... Design parameters The semantic category to which a word belongs is mapped to the semantic axis corresponding to that semantic category, forming a one-hot or sparse vector. For example, if "thickness" belongs to geometric semantics, then activation is performed on the geometric dimension, resulting in... If the value is [1,0,0,0,0], and "yield strength" belongs to the material semantics, then activation is performed in the material dimension, resulting in... [0,1,0,0,0].

[0029] Preferably, the hierarchical distribution tensor is calculated as follows:

[0030] , in, For hierarchy depth, For the first The output feature vector of the layer, For the first The weight vector of a layer is non-negative and its sum is 1 (it can be set manually according to the importance or attention of the layer). For the first The feature transformation matrix of each layer This is an element-wise product.

[0031] Regarding the first Feature transformation matrix of each layer This embodiment provides the following example of how to obtain the data: Extracting the first from the original design data The layer design parameter matrix was obtained using PCA (Principal Component Analysis): the principal component matrix was constructed by sorting the principal components by importance and taking the first A columns. .

[0032] 3. Retrieve the historical design versions of the equipment from the PLM system, and sequentially record the version identifier after each design change into the version chain vector (i.e., the equipment unique identifier matrix). ,in, For version chain vectors, For the first Version identifier after the design change. Version number; 4. The hierarchical distribution tensor, semantic vector, and version chain vector are combined to form the design baseline data, i.e. ,in, This serves as the design baseline data.

[0033] Step 102: Based on the unique equipment identifier matrix and semantic coordinates in the design reference data, perform exponential normalization coupling encoding on the operation data collected at the equipment operation site to construct the operation behavior tensor, wherein the operation behavior tensor corresponds one-to-one with the semantic coordinates in the design reference data. Specifically, after using the unique equipment identifier matrix and semantic coordinates in the design baseline data, the process also includes: establishing a data acquisition index at the equipment operation site based on the version chain vector in the design baseline data; and collecting operation time sequence, load sequence, and vibration spectrum data corresponding to the equipment operation process from the equipment operation site based on the data acquisition index.

[0034] Specifically, the exponential normalization coupling encoding of the operating data collected at the equipment operation site includes: scaling the sampled value of a certain sample in the operating time sequence with the maximum sampled value in the operating time sequence to obtain the operating time sequence value, so that the operating time sequence value is kept within a preset range in value; Preferably, the calculation of runtime timing values ​​specifically involves: , in, For the first time in the running time sequence The sampled value of the next sample. To prevent the division of decimals by zero.

[0035] about For example, when monitoring equipment operating status, samples are taken every minute, and the equipment's rated operating cycle (e.g., 100 minutes) is used as the normalization benchmark, as shown in the table below: Sampling sequence number Scene Normalized value t(1) Cold start, run for 5% of the rated cycle. 0.05 t(2) Steady-state operation reaches 80% of the cycle 0.8 t(3) Light load operation up to 40% cycle 0.4 t(4) Heavy load operation to 90% cycle 0.9 t(5) Run for 20% briefly before shutdown 0.2 The sampled value of a certain time in the load time sequence is proportionalized with the maximum load value of the load sequence to obtain the load sequence value, so that the load level of the equipment at different operating stages can be compared on a uniform scale. Preferably, the calculation of the load sequence value is specifically as follows: , in, The first time in the load time series The sampled value of the next sample.

[0036] The vibration energy at each frequency point in the vibration spectrum data is proportionalized to the total vibration energy across the entire frequency band to obtain the vibration energy ratio, so that the relative distribution of vibration energy in different frequency dimensions can be expressed in a unified form.

[0037] Preferably, the vibration energy ratio is calculated as follows: , in, For the first Vibrational energy at a specific frequency point The number of frequency points, For the first Vibrational energy at a specific frequency point.

[0038] Specifically, constructing the operational behavior tensor involves combining runtime sequence values, load sequences, and vibration energy ratios to form the operational behavior tensor, i.e. ,in, For the behavior tensor.

[0039] Step 103: Calculate the comprehensive deviation based on the structural deviation index and parameter deviation index between the design baseline data and the operational behavior tensor, and adaptively update the ledger fields using the deviation indicator function.

[0040] This embodiment achieves automatic updates of ledger data through a synchronization mechanism involving the "design-operation-IoT" three parties, avoiding errors from manual data entry, ensuring consistency between the ledger and the actual status of the equipment and PLM design data, and solving the problem of "delayed ledger updates".

[0041] Specifically, the calculation of the structural deviation index and the parameter deviation index includes: 1. Based on the semantic coordinates of the design baseline data, perform a one-to-one field mapping between the running behavior tensor and the hierarchical distribution tensor to obtain the running side structure tensor and the running side parameter vector; Preferably, the semantic coordinates are (the first...) Design parameters basis vectors in semantic space , No. Design parameters (hierarchy in BOM hierarchy). Preferably, based on the semantic coordinates of the design baseline data, a one-to-one field mapping is performed between the operational behavior tensor and the hierarchical distribution tensor to obtain the operational side structure tensor and the operational side parameter vector. Specifically, this includes: processing the operational behavior tensor... When running each field, the relationship between each running field and each design parameter is calculated using field name matching rules, field description similarity determination methods, field type verification methods, and hierarchical inference methods (these four rules or methods are existing technologies, so they will not be described in detail in this embodiment). The semantic similarity between the semantic coordinates of the design fields is determined, and then, based on the semantic similarity between the running fields and the design fields, as well as the hierarchical correspondence, the hierarchical distribution tensor to which the running field should be mapped is determined. The specific hierarchical position; Once the semantic coordinates of a certain running field and a certain design field are successfully matched, the running field is written into the running-side structure tensor. The corresponding hierarchical position, and at the same time, according to the design parameter vector (multiple design parameters) The order of the design parameter vector (composed of all parameters) is used to rearrange all matched running fields to form the running parameter vector. .

[0042] 2. The calculation of the structural deviation index includes: , in, This is the structural deviation index. For hierarchical distribution tensors, For the running side structure tensor, It is the Frobenius norm; The calculation of the parameter deviation index includes: , in, For parameter deviation index, For semantic vectors, This is the runtime parameter vector.

[0043] Specifically, the calculation of the overall deviation includes: , in, To account for the overall deviation, This is an index representing the change in the operating behavior of the equipment on the operating side; Index of changes in the operating behavior of computing devices on the operating side for: , in, To account for the overall deviation, Weights for runtime sequence values. For the first Runtime value of the second sample variance The weights of the load sequence, For the first The load sequence of the next sample variance As the weight of the vibration energy ratio, For the first The ratio of vibration energy at each frequency point The variance.

[0044] Specifically, adaptive updates of ledger fields using the deviation indicator function include: The deviation indication function is: , in, To update the index for the ledger, For trigger strength, To trigger the judgment threshold, For indicator functions; when When =1, the ledger field is updated adaptively; Calculate trigger strength for: , in, This is the mean of the overall deviation. For threshold adjustment factor, The standard deviation of the overall deviation This is to trigger the sensitivity factor.

[0045] Example 2 like Figure 2 As shown, this embodiment proposes a PLM-driven dynamic synchronization system for the entire lifecycle ledger of equipment, including: The baseline data generation module receives the original design data of the device from the PLM system, performs semantic reconstruction operation on the original design data, constructs the design semantic density matrix and hierarchical distribution tensor, thereby realizing the multidimensional compression mapping of the original design data and generating design baseline data with a unified semantic coordinate system. The module for constructing operational behavior tensors is used to perform exponentially normalized coupled encoding on the operational data collected at the equipment operation site based on the equipment unique identifier matrix and semantic coordinates in the design reference data, and construct operational behavior tensors. The operational behavior tensors correspond one-to-one with the semantic coordinates in the design reference data. The ledger update module is used to calculate the comprehensive deviation based on the structural deviation index and parameter deviation index between the design baseline data and the operational behavior tensor, and to adaptively update the ledger fields through the deviation indicator function.

[0046] Other solutions in Example 2 correspond to those in Example 1, so they will not be described again in this example.

[0047] Example 3 This invention also proposes a storage medium storing multiple instructions, which are used to implement the PLM-driven method for dynamic synchronization of device lifecycle ledgers.

[0048] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0049] Optionally, in this embodiment, the storage medium is configured to store program code for performing the method steps of Embodiment 1.

[0050] Example 4 This invention also proposes an electronic device, including a processor and a storage medium connected to the processor. The storage medium stores multiple instructions, which can be loaded and executed by the processor to enable the processor to execute the PLM-driven dynamic synchronization method for the entire lifecycle ledger of a device.

[0051] Specifically, the electronic device in this embodiment can be a computer terminal, which may include one or more processors and a storage medium.

[0052] The storage medium can be used to store software programs and modules, such as the PLM-driven dynamic synchronization method for the entire lifecycle of equipment ledgers in this embodiment of the invention. The corresponding program instructions / modules are executed by the processor through the software programs and modules stored in the storage medium, thereby performing various functional applications and data processing, thus realizing the aforementioned PLM-driven dynamic synchronization method for the entire lifecycle of equipment ledgers. The storage medium may include high-speed random access storage media, and may also include non-volatile storage media, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include storage media remotely configured relative to the processor, which can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0053] The processor can execute the method steps of Embodiment 1 by calling the information and application stored in the storage medium through the transmission system.

[0054] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0055] In the several embodiments provided by this invention, it should be understood that the disclosed technical content can be implemented in other ways. The system embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between units or modules, and may be electrical or other forms.

[0056] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0057] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0058] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, optical disks, and other media capable of storing program code.

[0059] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A PLM-driven method for dynamic synchronization of equipment lifecycle ledgers, characterized in that, include: Receive the original design data of the device from the PLM system, perform semantic reconstruction operation on the original design data, construct the design semantic density matrix and hierarchical distribution tensor, thereby realizing the multidimensional compression mapping of the original design data and generating design reference data with a unified semantic coordinate system; Based on the unique identification matrix and semantic coordinates of the equipment in the design reference data, the operation data collected at the equipment operation site is subjected to exponential normalized coupling encoding to construct the operation behavior tensor, wherein the operation behavior tensor corresponds one-to-one with the semantic coordinates in the design reference data. The comprehensive deviation is calculated based on the structural deviation index and parameter deviation index between the design baseline data and the operational behavior tensor, and the ledger fields are adaptively updated using the deviation indicator function.

2. The method for dynamic synchronization of equipment lifecycle ledgers driven by PLM as described in claim 1, characterized in that, Constructing the design semantic density matrix and hierarchical distribution tensor includes: normalizing the original design data, generating semantic scalars, and combining all semantic scalars into the design semantic density matrix, so that different types of design parameters in the original design data are compressed into a unified semantic range; All semantic scalars in the designed semantic density matrix are weighted and aggregated according to the preset semantic basis vectors to form a fixed-dimensional semantic vector. Based on the hierarchical depth of the BOM hierarchy of the PLM system, the semantic vector is transformed in multiple levels. Each level compresses the semantic vector through a set of feature transformation matrices and weight vectors to generate a set of stacked feature vectors as a hierarchical distribution tensor. Read the historical design versions of the device from the PLM system and record the version identifier after each design change sequentially into the version chain vector; The hierarchical distribution tensor, semantic vector, and version chain vector are used to form the design baseline data.

3. The method for dynamic synchronization of equipment lifecycle ledgers driven by PLM as described in claim 2, characterized in that, Following the equipment unique identifier matrix and semantic coordinates in the design baseline data, the process also includes: establishing a data acquisition index at the equipment operation site based on the version chain vector in the design baseline data; and collecting operation time sequence, load sequence, and vibration spectrum data corresponding to the equipment operation process from the equipment operation site based on the data acquisition index.

4. The method for dynamic synchronization of equipment lifecycle ledgers driven by PLM as described in claim 3, characterized in that, The exponential normalization coupling encoding of the operating data collected at the equipment operation site includes: scaling the sampled value of a certain sample in the operating time sequence with the maximum sampled value in the operating time sequence to obtain the operating time sequence value, so that the operating time sequence value is kept within a preset range in value; The sampled value of a certain time in the load time sequence is proportionalized with the maximum load value of the load sequence to obtain the load sequence value, so that the load level of the equipment at different operating stages can be compared on a uniform scale. The vibration energy at each frequency point in the vibration spectrum data is proportionalized to the total vibration energy across the entire frequency band to obtain the vibration energy ratio, so that the relative distribution of vibration energy in different frequency dimensions can be expressed in a unified form.

5. The method for dynamic synchronization of equipment lifecycle ledgers driven by PLM as described in claim 4, characterized in that, Constructing the operational behavior tensor involves combining runtime sequence values, load sequences, and vibration energy ratios to form the operational behavior tensor.

6. The method for dynamic synchronization of equipment lifecycle ledgers driven by PLM as described in claim 1, characterized in that, Calculating the structural deviation index and parameter deviation index includes: mapping fields one-to-one between the operational behavior tensor and the hierarchical distribution tensor based on the semantic coordinates of the design baseline data to obtain the operational side structural tensor and the operational side parameter vector. The calculation of the structural deviation index includes: ， in, This is the structural deviation index. For hierarchical distribution tensors, For the running side structure tensor, It is the Frobenius norm; The calculation of the parameter deviation index includes: ， in, For parameter deviation index, For semantic vectors, This is the runtime parameter vector.

7. The method for dynamic synchronization of equipment lifecycle ledgers driven by PLM as described in claim 6, characterized in that, The calculation of the overall deviation includes: ， in, To account for the overall deviation, This is an index representing the change in the operating behavior of the equipment on the operating side; Index of changes in the operating behavior of computing devices on the operating side for: ， in, To account for the overall deviation, Weights for runtime sequence values. For the first Runtime value of the second sample variance The weights of the load sequence, For the first The load sequence of the next sample variance As the weight of the vibration energy ratio, For the first The ratio of vibration energy at each frequency point The variance.

8. The method for dynamic synchronization of equipment lifecycle ledgers driven by PLM as described in claim 7, characterized in that, Adaptive updating of ledger fields using deviation indication functions includes: The deviation indication function is: ， in, To update the index for the ledger, For trigger strength, To trigger the judgment threshold, For indicator functions; when When =1, the ledger field is updated adaptively; Calculate trigger strength for: ， in, This is the mean of the overall deviation. As a threshold adjustment factor, The standard deviation of the overall deviation. This is to trigger the sensitivity factor.

9. A PLM-driven dynamic synchronization system for equipment lifecycle ledgers, characterized in that, include: The baseline data generation module receives the original design data of the device from the PLM system, performs semantic reconstruction operation on the original design data, constructs the design semantic density matrix and hierarchical distribution tensor, thereby realizing the multidimensional compression mapping of the original design data and generating design baseline data with a unified semantic coordinate system. The module for constructing operational behavior tensors is used to perform exponentially normalized coupled encoding on the operational data collected at the equipment operation site based on the equipment unique identifier matrix and semantic coordinates in the design reference data, and construct operational behavior tensors. The operational behavior tensors correspond one-to-one with the semantic coordinates in the design reference data. The ledger update module is used to calculate the comprehensive deviation based on the structural deviation index and parameter deviation index between the design baseline data and the operational behavior tensor, and to adaptively update the ledger fields through the deviation indicator function.

10. A PLM-driven dynamic synchronization system for equipment lifecycle ledgers as described in claim 9, characterized in that, Constructing the design semantic density matrix and hierarchical distribution tensor includes: normalizing the original design data, generating semantic scalars, and combining all semantic scalars into the design semantic density matrix, so that different types of design parameters in the original design data are compressed into a unified semantic range; All semantic scalars in the designed semantic density matrix are weighted and aggregated according to the preset semantic basis vectors to form a fixed-dimensional semantic vector. Based on the hierarchical depth of the BOM hierarchy of the PLM system, the semantic vector is transformed in multiple levels. Each level compresses the semantic vector through a set of feature transformation matrices and weight vectors to generate a set of stacked feature vectors as a hierarchical distribution tensor. Read the historical design versions of the device from the PLM system and record the version identifier after each design change sequentially into the version chain vector; The hierarchical distribution tensor, semantic vector, and version chain vector are used to form the design baseline data.

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