Intelligent factory data management method and system based on digital twinning

Abstract

The invention discloses an intelligent factory data management method and system based on digital twinning. The method comprises the following steps: constructing a multi-level storage architecture; setting a data storage mapping relationship between equipment and the multi-level storage architecture; correspondingly storing the equipment data in the multi-level storage architecture according to the data storage mapping relation; dynamically migrating the device data in different storage hierarchies based on the access demand state of the device data; therefore, a two-way data synchronization mechanism between the physical equipment and the virtual model is established, hierarchical storage and dynamic caching of the digital twin model are supported, the contradiction between the data real-time performance and the storage efficiency is solved, and therefore it is ensured that the method can adapt to the dynamically-changing factory environment.

Description

Technical Field

[0001] The present invention relates to the cross - technical field of intelligent manufacturing and computer storage technology, and particularly relates to a method and system for intelligent factory data management based on digital twin. Background Art

[0002] Traditional intelligent factory management systems usually use a single database to store factory operation data. With the rapid development of intelligent manufacturing technology, digital twin technology, as one of its core technologies, is playing an increasingly important role in factory production management. Digital twin technology realizes the seamless integration of the physical space and the information space by constructing the mapping relationship between the virtual model and the physical factory, providing a new solution for factory production management.

[0003] Current digital twin technology needs to process a large amount of heterogeneous data, such as equipment status, production parameters, environmental data, etc. However, the existing memory architecture is difficult to support both high - concurrent writing and low - latency access at the same time, resulting in a lag in data update between the virtual model and the physical entity.

[0004] Therefore, in order to give full play to the role of digital twin technology in intelligent factory management, it is necessary to design a data storage and management method, establish a two - way data synchronization mechanism between physical devices and virtual models, support the hierarchical storage and dynamic caching of digital twin models, solve the contradiction between data real - time performance and storage efficiency, so as to ensure adaptation to the dynamically changing factory environment. Summary of the Invention

[0005] The present invention provides a method and system for intelligent factory data management based on digital twin, which establish a two - way data synchronization mechanism between physical devices and virtual models, support the hierarchical storage and dynamic caching of digital twin models, solve the contradiction between data real - time performance and storage efficiency, so as to ensure adaptation to the dynamically changing factory environment.

[0006] In the first aspect, a method for intelligent factory data management based on digital twin is provided, including the following steps:

[0007] Construct a multi - level storage architecture;

[0008] Set the data storage mapping relationship between the device and the multi - level storage architecture;

[0009] Correspondingly store the device data in the multi - level storage architecture according to the data storage mapping relationship;

[0010] Dynamically migrate the device data among different storage levels based on the access requirement status of the device data.

[0011] According to the first aspect, in the first possible implementation manner of the first aspect, "set the data storage mapping relationship between the device and the multi-level storage architecture;

[0012] According to the data storage mapping relationship, correspondingly store the device data in the multi-level storage architecture", the steps specifically include the following steps:

[0013] The multi-level storage architecture includes a hot data layer, a warm data layer, and a cold data layer;

[0014] The hot data layer correspondingly stores the real-time status data of the device, the warm data layer correspondingly stores the historical operation data of the device, and the cold data layer correspondingly stores the simulation parameters of the digital twin device;

[0015] When it is detected that device data is output, the device data is respectively and correspondingly stored in the hot data layer, the warm data layer, and the cold data layer.

[0016] According to the first possible implementation manner of the first aspect, in the second possible implementation manner of the first aspect, the step of "dynamically migrating device data among different storage levels based on the access demand status of the device data" specifically includes the following steps:

[0017] According to the access demand status of the device data, dynamically generate the migration path of the device data;

[0018] According to the migration path, dynamically migrate the device data among different storage levels;

[0019] Based on the performance differences of different storage levels, respectively generate redundancy check codes for each level, and check the migrated data in each storage level according to the redundancy check codes of each level;

[0020] If it is detected that the check inconsistency rate is greater than the preset threshold, start the error correction and repair mode.

[0021] According to the second possible implementation manner of the first aspect, in the third possible implementation manner of the first aspect, the steps of "according to the access demand status of the device data, dynamically generate the migration path of the device data; according to the migration path, dynamically migrate the device data among different storage levels" specifically include the following steps:

[0022] The warm data layer includes an encrypted warm data layer and an unencrypted warm data layer;

[0023] When it is detected that the access frequency of the first target data in the warm data layer or the cold data layer is greater than the preset frequency threshold, generate a storage level upgrade migration path, and migrate the first target data to the hot data layer;

[0024] When it is detected that the access frequency of the second target data in the encrypted warm data layer is greater than a preset frequency threshold and the storage time is greater than a preset time threshold, a storage level downgrade migration path is generated, and the second target data is migrated to the cold data layer for compressed storage;

[0025] When it is detected that the storage time of the third target data in the non-encrypted warm data layer is greater than a preset time threshold, a storage level upgrade migration path is generated, and the third target data is migrated to the cold data layer for compressed storage;

[0026] When it is detected that the storage time of the third target data in the cold data layer is greater than a preset time threshold, the third target data is deleted.

[0027] According to the second possible implementation manner of the first aspect, in the fourth possible implementation manner of the first aspect, the step of "generating redundant check codes for each layer respectively according to the performance differences of different storage layers" specifically includes the following steps:

[0028] Based on the IO performance differences of different storage layers, generate CRC check codes in the hot data layer, RS error correction check codes in the warm data layer, and hash chain check codes in the cold data layer.

[0029] According to the first aspect, in the fifth possible implementation manner of the first aspect, after the step of "dynamically migrating device data in different storage layers based on the access demand status of device data", the following steps are specifically included:

[0030] When it is detected that the target data is migrated to different storage layers, allocate the migration path bandwidth based on the priority weight calculation formula;

[0031] The calculation formula of the priority weight Priority is as follows:

[0032] Priority = α·(1 - e^(-β·SLA urgency)) + γ·log(1 + number of associated twins) + δ·(storage layer transition coefficient);

[0033] Among them, α is the basic real-time weight; β is the non-linear adjustment factor; γ is the topological association weight; δ is the cross-layer migration cost weight.

[0034] According to the third possible implementation manner of the first aspect, in the sixth possible implementation manner of the first aspect, after the step of "dynamically migrating device data in different storage layers based on the access demand status of device data", the following steps are specifically included:

[0035] Dynamically adjust the preset frequency threshold and the preset time threshold according to the real-time working mode of the device.

[0036] In a second aspect, there is provided an intelligent factory data management system based on digital twin, including:

[0037] An architecture construction module for constructing a multi-level storage architecture;

[0038] A mapping module, communicatively connected to the architecture construction module, for setting a data storage mapping relationship between devices and the multi-level storage architecture;

[0039] A corresponding storage module, communicatively connected to the mapping module, for correspondingly storing device data in the multi-level storage architecture according to the data storage mapping relationship; and,

[0040] A dynamic migration module, communicatively connected to the corresponding storage module, for dynamically migrating device data among different storage levels based on the access requirement status of device data.

[0041] In a third aspect, there is provided a computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method for managing intelligent factory data based on digital twin as described above.

[0042] In a fourth aspect, there is provided an electronic device, including a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, wherein the processor, when running the computer program, implements the method for managing intelligent factory data based on digital twin as described above.

[0043] Compared with the prior art, the advantages of the present invention are as follows: By constructing a multi-level storage architecture and dynamically allocating storage resources according to the real-time requirements of data, the problems of uneven distribution of storage resources and data fragmentation caused by traditional single-database storage can be effectively solved. Further, by setting a data storage mapping relationship between devices and the multi-level storage architecture; correspondingly storing device data in the multi-level storage architecture according to the data storage mapping relationship; thus, a two-way data synchronization mechanism between physical devices and virtual models is established, supporting hierarchical storage and dynamic caching of digital twin models, and ensuring real-time update of data between virtual models and physical entities; finally, based on the access requirement status of device data, dynamically migrating device data among different storage levels can meet the adaptability requirements of the dynamic factory environment for the storage structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a schematic flowchart of an embodiment of a method for managing intelligent factory data based on digital twin of the present invention;

[0045] Figure 2It is a schematic flowchart of another embodiment of a method for managing data in an intelligent factory based on digital twin according to the present invention;

[0046] Figure 3 It is a schematic flowchart of another embodiment of a method for managing data in an intelligent factory based on digital twin according to the present invention;

[0047] Figure 4 It is a schematic structural diagram of a data management system for an intelligent factory based on digital twin according to the present invention. Detailed implementation manners

[0048] Now, specific embodiments of the present invention will be described in detail. Examples of the present invention are illustrated in the accompanying drawings. Although the present invention will be described in conjunction with specific embodiments, it will be understood that it is not intended to limit the present invention to the described embodiments. On the contrary, it is intended to cover modifications, variations, and equivalents included within the spirit and scope of the present invention as defined by the appended claims. It should be noted that the method steps described herein can all be implemented by any functional block or functional arrangement, and any functional block or functional arrangement can be implemented as a physical entity or a logical entity, or a combination of both.

[0049] To enable those skilled in the art to better understand the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation manners.

[0050] Note: The examples to be introduced next are only specific examples and do not limit the embodiments of the present invention to the following specific steps, numerical values, conditions, data, sequences, etc. Those skilled in the art can use the concept of the present invention to construct more embodiments not mentioned in this specification by reading this specification.

[0051] See Figure 1 As shown, an embodiment of the present invention provides a method for managing data in an intelligent factory based on digital twin, including the following steps:

[0052] S100, constructing a multi-level storage architecture;

[0053] S200, setting a data storage mapping relationship between the device and the multi-level storage architecture;

[0054] S300, storing device data correspondingly in the multi-level storage architecture according to the data storage mapping relationship;

[0055] S400, dynamically migrating device data at different storage levels based on the access requirement status of the device data.

[0056] Specifically, in this embodiment, the present invention constructs a multi-level storage architecture, dynamically allocates storage resources according to the real-time requirements of data, and can effectively solve problems such as uneven distribution of storage resources and data fragmentation caused by traditional single-database storage. Then, by setting the data storage mapping relationship between the device and the multi-level storage architecture; storing the device data in the multi-level storage architecture according to the data storage mapping relationship; thus, a two-way data synchronization mechanism between the physical device and the virtual model is established, supporting hierarchical storage and dynamic caching of the digital twin model, and ensuring real-time update of data between the virtual model and the physical entity; finally, based on the access demand status of the device data, the device data is dynamically migrated among different storage levels, which can meet the adaptability requirements of the dynamic changing factory environment for the storage structure.

[0057] Preferably, in another embodiment of the present application, the steps of "S200, setting the data storage mapping relationship between the device and the multi-level storage architecture; S300, storing the device data in the multi-level storage architecture according to the data storage mapping relationship" specifically include the following steps:

[0058] The multi-level storage architecture includes a hot data layer, a warm data layer, and a cold data layer;

[0059] The hot data layer correspondingly stores the real-time status data of the device, the warm data layer correspondingly stores the historical operation data of the device, and the cold data layer correspondingly stores the simulation parameters of the digital twin device;

[0060] When it is detected that the device data is output, the device data is respectively stored in the hot data layer, the warm data layer, and the cold data layer correspondingly.

[0061] Specifically, in this embodiment, the multi-level storage architecture includes a hot data layer, a warm data layer, and a cold data layer.

[0062] For the selection of the memory of the hot data layer, there are the following several types:

[0063] a. The hot data layer uses a solid-state drive (SSD) as a cache to store real-time device status data, such as sensor readings, control instructions, etc., supporting low-latency access and update, and the data access latency can be reduced to the millisecond level.

[0064] b. The hot data layer uses a Redis in-memory database to cache real-time device temperature data, sets the data survival time (TTL) to 10 seconds, and automatically degrades to the warm data layer after timeout.

[0065] c. The hot data layer can use Memcached in-memory caching to store real-time device status data, supporting high-concurrency access.

[0066] For the selection of the memory of the warm data layer, there are the following several types:

[0067] a. The warm data layer uses a MongoDB distributed database cluster and manages and stores historical operation data such as production logs and energy consumption records by sharding according to timestamps.

[0068] b. The warm data layer uses an Apache Cassandra distributed columnar storage database and manages and stores historical operation data by sharding according to timestamps.

[0069] c. The warm data layer uses an InfluxDB time series database to store historical operation data and manages it according to timestamps.

[0070] For the selection of the memory of the warm data layer, there are the following several options:

[0071] a. The cold data layer is based on Alibaba Cloud Object Storage Service (OSS) to archive long-term simulation models and configuration parameters, and uses the LZW compression algorithm to reduce the storage space occupancy, and the compression ratio can reach 5:1.

[0072] b. The cold data layer is based on Baidu Cloud Object Storage Service (BOS) to archive long-term simulation models and configuration parameters, and uses the Deflate compression algorithm to reduce the storage space occupancy.

[0073] c. The cold data layer is based on Huawei Cloud Object Storage Service (OBS) to archive long-term simulation models and configuration parameters, and uses the Brotli compression algorithm to reduce the storage space occupancy.

[0074] Therefore, the data storage mapping relationship between the device and the multi-level storage architecture is as described above, that is, the hot data layer corresponds to storing the real-time state data of the storage device, the warm data layer corresponds to storing the historical operation data of the storage device, and the cold data layer corresponds to storing the simulation parameters of the digital twin device. When detecting the device data output, the device data is respectively stored in the hot data layer, the warm data layer and the cold data layer.

[0075] Preferably, in another embodiment of the present application, also refer to Figure 2 As shown, the step of "S400. Dynamically migrate device data in different storage levels based on the access requirement status of device data" specifically includes the following steps:

[0076] S410. Dynamically generate the migration path of the device data according to the access requirement status of the device data;

[0077] S420. Dynamically migrate the device data in different storage levels according to the migration path;

[0078] S430. Based on the performance differences of different storage levels, respectively generate redundancy check codes for each level, and check the migrated data in each storage level according to the redundancy check codes of each level;

[0079] S440: If it is detected that the verification inconsistency rate is greater than a preset threshold, the error correction and repair mode is started.

[0080] Specifically, in this embodiment, based on the real-time state change characteristics of the data twin, a multi-dimensional migration decision model is constructed that includes update frequency, associated twin model call weights, and timing access prediction factors; the multi-dimensional migration decision model is used to dynamically generate a migration path (hot→warm→cold layer) and a reverse migration path (cold→warm→hot layer) according to the access demand status of the device data, and then dynamically migrate the device data in different storage levels according to the dynamically generated migration path.

[0081] During data migration, redundant checksums are used to ensure cross-level data consistency. In view of the differences in I / O performance at different storage levels, adaptive hierarchical redundant checksums are generated; the hot data layer uses low-latency CRC+timestamp dual checksums, the warm data layer uses RS error correction code+version number checksums, and the cold data layer uses blockchain-based hash chain checksums. By using a redundant checksum mechanism to ensure cross-level data consistency, the data loss rate is less than 0.01%, ensuring the integrity and reliability of two-way data synchronization between physical devices and virtual models.

[0082] When data migration is completed, the redundancy check code check is triggered. If the check inconsistency rate is detected to be greater than the preset threshold, the error correction and repair mode is started.

[0083] The hot data layer data blocks are attached with a CRC-32 checksum and nanosecond timestamp, and when the check fails, the adjacent node replica comparison is triggered.

[0084] The warm data layer data blocks are divided into RS (28, 24) error correction code groups and the version number sequence is embedded to achieve incremental verification.

[0085] The cold data layer data blocks generate a Merkle tree hash chain, and each batch of migrated data builds a blockchain structure. During verification, the hash consistency is verified through smart contracts.

[0086] The specific error correction and repair modes are as follows:

[0087] Phase 1: Perform local repair in the target (to-be-corrected) storage layer. The hot data layer CRC uses adjacent data block XOR for fast recovery, and the warm data layer calls the RS error correction code decoder.

[0088] Phase 2: If local repair fails, start cross-layer repair and obtain verification reference data from upper-layer storage (warm / cold layer);

[0089] Phase 3: When cross-layer data is also damaged, the blockchain evidence query is triggered and the historical hash chain is verified through the smart contract.

[0090] Preferably, in another embodiment of the present application, also refer to Figure 3 As shown, the steps of "S410. Dynamically generate a migration path for device data according to the access demand status of the device data; S420. Dynamically migrate the device data in different storage levels according to the migration path" specifically include the following steps:

[0091] The warm data layer includes an encrypted warm data layer and an unencrypted warm data layer;

[0092] S421. When it is detected that the access frequency of the first target data in the warm data layer or the cold data layer is greater than a preset frequency threshold, generate a storage level upgrade migration path and migrate the first target data to the hot data layer;

[0093] S422. When it is detected that the access frequency of the second target data in the encrypted warm data layer is greater than a preset frequency threshold and the storage time is greater than a preset time threshold, generate a storage level downgrade migration path and migrate the second target data to the cold data layer for compressed storage;

[0094] S423. When it is detected that the storage time of the third target data in the unencrypted warm data layer is greater than a preset time threshold, generate a storage level upgrade migration path and migrate the third target data to the cold data layer for compressed storage;

[0095] S424. When it is detected that the storage time of the third target data in the cold data layer is greater than a preset time threshold, delete the third target data.

[0096] Specifically, in this embodiment, the warm data layer stores production logs, energy consumption records, and device operation records, which are managed by sharding according to timestamps; the warm data layer is divided into an encrypted warm data layer and an unencrypted warm data layer. Among them, the encrypted warm data layer stores production logs, and the unencrypted warm data layer stores energy consumption records and device operation records.

[0097] When the continuous access to the first target data in the warm data layer or the cold data layer exceeds the preset frequency threshold, the data is migrated from the warm data layer or the cold data layer to the hot data layer. When the encrypted warm data layer stores the production log calls exceeding the preset frequency threshold 5 times and the production log storage time exceeds the preset time threshold of 24 months, the encrypted warm data layer reduces the storage space occupancy through a compression algorithm and is converted to the encrypted cold data layer for storage. Because after the production log data is called 5 times and exceeds 2 years, the possibility of being called decreases. At this time, the data can be compressed to reduce the space occupancy. When the storage time of the non-encrypted warm data layer exceeds the preset time threshold of 12 months, the non-encrypted warm data layer reduces the storage space occupancy through a compression algorithm and is converted to the cold data layer for storage; because the calls of non-production logs are less, when the storage time exceeds 1 year, such data can be converted to the cold data layer for storage to reduce the space occupancy. When the storage time of the non-encrypted warm data layer converted to the cold data layer exceeds 5 years, this data is deleted.

[0098] Preferably, in another embodiment of the present application, after the step of "S400, dynamically migrating device data in different storage levels based on the access requirement status of device data", the following steps are specifically included:

[0099] When it is detected that the target data is migrated to different storage levels, allocate the migration path bandwidth based on the priority weight calculation formula;

[0100] The calculation formula of the priority weight Priority is as follows:

[0101] Priority = α·(1 - e^(-β·SLA urgency)) + γ·log(1 + number of associated twins) + δ·(storage layer transition coefficient);

[0102] Among them, α is the basic real-time weight; β is the non-linear adjustment factor; γ is the topological association weight; δ is the cross-layer migration cost weight.

[0103] Specifically, in this embodiment, for the common multi-twin collaboration scenarios in the industrial Internet (such as the production line digital twin and the quality inspection twin simultaneously requesting the same process data), differential services are realized through the priority formula.

[0104] In the formula, Priority = α·(1 - e^(-β·SLA urgency)) + γ·log(1 + number of associated twins) + δ·(storage layer transition coefficient);

[0105] α = 0.5 (basic real-time weight), β = 2.0 (non-linear adjustment factor), γ = 0.3 (topological association weight), δ = 0.2 (cross-layer migration cost weight), storage layer transition coefficient:

[0106]

[0107] The bandwidth of the migration path is allocated based on the priority weight calculation formula. This step can use a bandwidth quantization allocator: divide the total migration bandwidth into minimum quantum units (such as 10Mbps / unit) and allocate it according to the priority weight ratio:

[0108] The number of allocation units = ceil (Priority / ΣPriorities×the total number of sub-units).

[0109] And implement a dynamic preemption mechanism: when a high-priority task arrives, it can preempt the bandwidth of low-priority tasks that is no higher than 30% of its own Priority value.

[0110] The calculation of the SLA urgency includes:

[0111] Time-sensitive factor: (remaining migration time window - estimated transfer time) / remaining migration time window

[0112] Data freshness factor: 1-(current time-last update time) / data life cycle

[0113] The final calculation formula is as follows:

[0114] SLA urgency = max (time sensitivity factor, 0.1) × data freshness factor × device criticality level.

[0115] The bandwidth quantization allocator performs the following optimization steps:

[0116] Phase 1: Reserve basic bandwidth (minimum 1 quantum unit) for each migration task;

[0117] Phase 2: Allocate 70% of the remaining bandwidth according to the priority ratio;

[0118] Phase 3: The remaining 30% of bandwidth is used as an emergency pool and is dynamically allocated by the real-time generated digital twin network simulation model.

[0119] Preferably, in another embodiment of the present application, after the step of “S400, dynamically migrating device data in different storage tiers based on the access demand state of the device data”, the following steps are specifically included:

[0120] The preset frequency threshold and the preset time threshold are dynamically adjusted according to the real-time working mode of the device.

[0121] Specifically, in this embodiment, the preset frequency threshold is dynamically calculated according to the real-time working mode of the data twin (normal operation / fault diagnosis / simulation deduction); in the fault diagnosis mode, the threshold is automatically lowered by 30%-50% and preventive data migration is initiated.

[0122] Dynamically calculating the preset frequency threshold specifically includes the following steps:

[0123] Basic threshold T_base = α×(remaining capacity of thermal layer / total capacity)+β×(current system load rate);

[0124] Mode correction coefficient: normal operation mode K = 1.0, fault diagnosis mode K = 0.5, simulation mode K = 0.8;

[0125] The final threshold T_final=K×T_base×(1+ln(1+historical access volatility)).

[0126] Since the existing technology generally adopts a fixed frequency threshold (such as 5 accesses per minute), the present invention realizes intelligent threshold through triple dynamic adjustment (capacity / load / operating mode). For example, when the sudden vibration of wind power equipment exceeds the standard, the system automatically enters the fault diagnosis mode, and the access threshold of blade stress history data is reduced from 5 times / minute to 2 times / minute, ensuring the early migration of key data.

[0127] See also Figure 4 As shown, an embodiment of the present invention provides a smart factory data management system 100 based on digital twins, including:

[0128] An architecture building module 110 is used to build a multi-level storage architecture;

[0129] A mapping module 120, which is in communication with the architecture building module 110 and is used to set a data storage mapping relationship between a device and the multi-level storage architecture;

[0130] A corresponding storage module 130 is in communication with the mapping module 120 and is used to store the device data in the multi-level storage architecture according to the data storage mapping relationship; and

[0131] The dynamic migration module 140 is in communication with the corresponding storage module 130 and is used to dynamically migrate device data among different storage tiers based on the access demand status of the device data.

[0132] The mapping module 120 and the corresponding storage module 130, the multi-level storage architecture includes a hot data layer, a warm data layer and a cold data layer; the hot data layer corresponds to the real-time status data of the storage device, the warm data layer corresponds to the historical operation data of the storage device, and the cold data layer corresponds to the simulation parameters of the digital twin device; when the device data output is detected, the device data is stored in the hot data layer, the warm data layer and the cold data layer respectively.

[0133] The dynamic migration module 140 is used to dynamically generate a migration path for device data according to the access demand status of the device data; dynamically migrate the device data in different storage levels according to the migration path; respectively generate redundant check codes for each level based on the performance differences of different storage levels, and check the migrated data in each storage level according to the redundant check codes of each level; if it is detected that the check inconsistency rate is greater than a preset threshold, start the error correction and repair mode.

[0134] The system of the present invention further includes: a migration conflict optimization 150 communicatively connected to the dynamic migration module 140, and the migration conflict optimization 150 is used to allocate the migration path bandwidth based on a priority weight calculation formula when it is detected that the target data is migrated to different storage levels.

[0135] It further includes: a threshold adjustment module 160 communicatively connected to the dynamic migration module 140, and the threshold adjustment module 160 is used to dynamically adjust the preset frequency threshold and the preset time threshold according to the real-time working mode of the device.

[0136] In summary, the beneficial effects of the present invention are as follows:

[0137] 1. The storage resource utilization rate is significantly improved. By constructing a three-layer storage architecture of hot, warm, and cold, the storage resources are dynamically allocated according to the real-time data requirements. The hierarchical storage reduces the cache occupancy rate by more than 30%, and the cold data compression rate can reach 5:1, effectively solving the problems of uneven storage resource allocation and data fragmentation caused by traditional single database storage.

[0138] 2. The real-time performance is greatly enhanced. The high-frequency data access latency is reduced to the millisecond level, supporting the second-level synchronization of the digital twin model and the physical device, overcoming the defect that the existing memory architecture is difficult to support high-concurrency writing and low-latency access at the same time, and ensuring the real-time update of data between the virtual model and the physical entity.

[0139] 3. The data mapping is dynamically flexible. The data dynamic mapping between the physical device and the virtual model is realized through a bidirectional index table, and the storage level is automatically migrated based on the data update frequency, meeting the adaptability requirements of the dynamically changing factory environment for the storage structure.

[0140] 4. The data consistency and reliability are high. The redundant check code mechanism is adopted to ensure the cross-level data consistency, and the data loss rate is lower than 0.01%, ensuring the integrity and reliability of the two-way data synchronization between the physical device and the virtual model.

[0141] 5. The system has strong integration. The method and system of the present invention can be seamlessly integrated with existing industrial Internet of Things terminals, distributed databases, cloud storage and other technologies, and has good compatibility and scalability.

[0142] Specifically, this embodiment corresponds one by one to the above method embodiment, and the functions of each module have been described in detail in the corresponding method embodiment, so they will not be elaborated here one by one.

[0143] Based on the same inventive concept, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, all or part of the method steps of the above method are implemented.

[0144] The implementation of all or part of the processes in the above method of the present invention can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0145] Based on the same inventive concept, an embodiment of the present application further provides an electronic device, including a memory and a processor. A computer program is stored on the memory and runs on the processor. When the processor executes the computer program, all or part of the method steps of the above method are implemented.

[0146] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may also be any conventional processor, etc. The processor is the control center of the computer device, and uses various interfaces and lines to connect all parts of the entire computer device.

[0147] The memory can be used to store computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory, the processor can implement various functions of the computer device. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, video data, etc.). In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0148] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, a server, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0149] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), servers, and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or multiple flows and / or blocks

[0150] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one or more of the flows Figure 1 or multiple flows and / or blocks

[0151] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, causing a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one process or a plurality of processes and / or blocks Figure 1 one process or a plurality of processes and / or blocks Figure 1 steps for implementing the functions specified in one block or a plurality of blocks.

[0152] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent factory data management method based on digital twin, characterized in that It includes the following steps: Construct a multi-level storage architecture; Set the data storage mapping relationship between the device and the multi-level storage architecture; Correspondingly store the device data in the multi-level storage architecture according to the data storage mapping relationship; Dynamically migrate the device data among different storage levels based on the access requirement status of the device data.

2. The method for managing intelligent factory data based on digital twin according to claim 1, wherein The steps of "setting the data storage mapping relationship between the device and the multi-level storage architecture; correspondingly storing the device data in the multi-level storage architecture according to the data storage mapping relationship" specifically include the following steps: The multi-level storage architecture includes a hot data layer, a warm data layer, and a cold data layer; The hot data layer correspondingly stores the real-time status data of the device, the warm data layer correspondingly stores the historical operation data of the device, and the cold data layer correspondingly stores the simulation parameters of the digital twin device; When detecting the output of device data, the device data is respectively and correspondingly stored in the hot data layer, the warm data layer, and the cold data layer.

3. The method for managing intelligent factory data based on digital twin according to claim 2, wherein, The step of "dynamically migrating the device data among different storage levels based on the access requirement status of the device data" specifically includes the following steps: Dynamically generate the migration path of the device data according to the access requirement status of the device data; Dynamically migrate the device data among different storage levels according to the migration path; Based on the performance differences of different storage levels, respectively generate redundancy check codes for each level, and check the migrated data in each storage level according to the redundancy check codes of each level; If it is detected that the check inconsistency rate is greater than the preset threshold, start the error correction and repair mode.

4. The method for managing intelligent factory data based on digital twin according to claim 3, characterized in that, The step of "dynamically generating the migration path of the device data according to the access requirement status of the device data; Dynamically migrate the device data among different storage levels according to the migration path" specifically includes the following steps: The warm data layer includes an encrypted warm data layer and an unencrypted warm data layer; When detecting that the access frequency of the first target data in the warm data layer or the cold data layer is greater than the preset frequency threshold, generate a storage level upgrade migration path and migrate the first target data to the hot data layer; When detecting that the access frequency of the second target data in the encrypted warm data layer is greater than the preset frequency threshold and the storage time is greater than the preset time threshold, generate a storage level downgrade migration path and migrate the second target data to the cold data layer for compressed storage; When detecting that the storage time of the third target data in the unencrypted warm data layer is greater than the preset time threshold, generate a storage level upgrade migration path and migrate the third target data to the cold data layer for compressed storage; When detecting that the storage time of the third target data in the cold data layer is greater than the preset time threshold, delete the third target data.

5. The method for managing intelligent factory data based on digital twin according to claim 3, wherein The step of "based on the performance differences of different storage levels, respectively generate redundancy check codes for each level" specifically includes the following steps: Based on the IO performance differences of different storage levels, generate CRC check codes in the hot data layer, RS error correction check codes in the warm data layer, and hash chain check codes in the cold data layer.

6. The method for managing intelligent factory data based on digital twin according to claim 1, wherein After the step of "dynamically migrating device data based on the access requirement status of device data across different storage levels", the following steps are specifically included: When it is detected that the target data is migrated to different storage levels, allocate the bandwidth of the migration path based on the priority weight calculation formula. The calculation formula of the priority weight Priority is as follows: Priority = α·(1 - e^(-β·SLA urgency)) + γ·log(1 + number of associated twins) + δ·(storage layer transition coefficient); Among them, α is the basic real-time weight; β is the non-linear adjustment factor; γ is the topological association weight; δ is the cross-layer migration cost weight.

7. The method for managing intelligent factory data based on digital twin according to claim 4, wherein, After the step of "dynamically migrating device data based on the access requirement status of device data across different storage levels", the following steps are specifically included: Dynamically adjust the preset frequency threshold and the preset time threshold according to the real-time working mode of the device.

8. An intelligent factory data management system based on digital twin, characterized in that, Including: An architecture construction module for constructing a multi-level storage architecture; A mapping module, communicatively connected to the architecture construction module, for setting the data storage mapping relationship between the device and the multi-level storage architecture; A corresponding storage module, communicatively connected to the mapping module, for correspondingly storing the device data in the multi-level storage architecture according to the data storage mapping relationship; And, A dynamic migration module, communicatively connected to the corresponding storage module, for dynamically migrating the device data across different storage levels based on the access requirement status of the device data.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the digital twin-based intelligent factory data management method according to any one of claims 1 to 7.

10. An electronic device, comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that, When the processor runs the computer program, it implements the digital twin-based intelligent factory data management method according to any one of claims 1 to 7.

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