Data asset acquisition and fusion method, device and equipment based on identification analysis platform

Abstract

The invention relates to a data asset collection and fusion method, device and equipment based on an identification analysis platform, and the method comprises the steps: carrying out the registration of industrial equipment based on the identification analysis platform, configuring a plurality of asset collection templates for the registered industrial equipment, and configuring the asset use objects corresponding to the asset collection templates for the registered industrial equipment, each asset collection template is configured with multiple asset collection indexes, multiple collection tasks corresponding to the multiple configured asset collection templates and asset use objects are established according to registration information of the industrial equipment, and multi-level cutting optimization is performed on the multiple collection tasks based on cutting contract rules to obtain optimized collection tasks. And executing data acquisition according to the optimized acquisition tasks, fusing the acquired data by adopting a data block means to obtain fused data corresponding to each optimized acquisition task, storing the fused data in a data block format, and performing identity information authentication and asset ownership confirmation on the data blocks. By adopting the method, interconnection and intercommunication of data assets can be realized.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device and equipment for data asset collection and fusion based on an identity resolution platform. Background Art

[0002] With the rapid development of the Industrial Internet, the collection, processing, analysis and application of industrial data have become the key to improving the level of intelligent manufacturing. As an important part of the Industrial Internet, the Industrial Internet identification resolution system provides a globally unique identification code for each physical entity and digital object, and realizes accurate positioning and query of information through the resolution system, making it possible to share data assets.

[0003] However, due to the inconsistency of data formats and coding standards between different enterprises and systems, data silos are serious, data integration and sharing face huge challenges, data ownership is difficult to confirm, and data security is difficult to guarantee. Therefore, how to improve the integration of data assets in the industrial Internet identification resolution system has become a technical issue. Summary of the invention

[0004] Based on this, it is necessary to provide a data asset collection and fusion method, device and equipment based on an identity resolution platform that can achieve interconnection and interoperability of data assets in response to the above technical problems.

[0005] A data asset collection and fusion method based on an identity resolution platform, the method comprising:

[0006] Based on the identification resolution platform, industrial equipment is registered, and multiple asset collection templates and asset use objects corresponding to each asset collection template are configured for the registered industrial equipment, and each asset collection template is configured with multiple asset collection indicators;

[0007] Construct multiple corresponding collection tasks according to the registration information of industrial equipment, multiple configured asset collection templates and asset usage objects;

[0008] Based on the cutting contract rules, multiple collection tasks are optimized by multi-level cutting to obtain optimized collection tasks;

[0009] Data collection is performed according to the optimized collection task, and the collected data is fused by means of data blocks to obtain fused data corresponding to each optimized collection task, which is stored in a data block format, and identity information authentication and asset ownership confirmation are performed on the data blocks.

[0010] In one embodiment, the registration information of the industrial equipment includes an industrial equipment code;

[0011] The asset collection template includes multiple configured asset collection indicators and collection cycles, and the asset collection indicators include a unique identification code, a hash value, an indicator name, a collection address, a data protocol, an asset provider, and a collection priority of the asset collection indicator.

[0012] The asset usage object includes a unique identification code of the asset usage object, an object name, data permissions, and a validity period.

[0013] In one embodiment, when building corresponding multiple collection tasks according to the registration information of the industrial equipment, multiple configured asset collection templates, and asset usage objects:

[0014] Build a corresponding collection task based on an asset collection template;

[0015] Arrange all asset collection indicators in an asset collection template through collection priority and linked list data structure relationship to build a two-way indicator block chain;

[0016] Constructing a template hash primary key according to the industrial equipment code, the collection cycle of the asset collection template, and the identification code of the asset user;

[0017] The corresponding collection task is obtained according to the template hash primary key and the two-way indicator block chain.

[0018] In one embodiment, the contract tailoring rules are:

[0019] A first matrix is ​​constructed based on all bidirectional indicator block chains in the plurality of acquisition tasks;

[0020] A template acquisition cycle matrix is ​​constructed based on all acquisition cycles in the plurality of acquisition tasks;

[0021] Constructing an asset use object matrix according to the identification codes of all asset use objects in the plurality of acquisition tasks;

[0022] A template hash primary key matrix is ​​constructed according to the template collection period matrix and the asset usage object matrix, and is used as the second matrix;

[0023] The first matrix is ​​grouped according to the second matrix, and bidirectional indicator block chains with consistent template hash primary keys are grouped into one group to obtain a two-dimensional grouping matrix, in which each group of data includes multiple bidirectional indicator block chains;

[0024] Merging the asset acquisition indicators in each group of bidirectional indicator block chains in the two-dimensional grouping matrix according to the acquisition priority, and constructing a new bidirectional indicator block chain, and optimizing the two-dimensional grouping matrix into a third matrix;

[0025] An optimized collection task is generated according to the new bidirectional indicator block chain in the third matrix and the corresponding template hash primary key.

[0026] In one embodiment, a template hash key matrix is ​​constructed based on the template collection period matrix and the asset usage object matrix, using the following formula:

[0027]

[0028] In the above formula, u(v) represents the template acquisition period matrix, p(s) represents the asset usage object matrix, and T represents matrix inversion. represents a grouping operation, disct represents a matrix deduplication operation, v, s represents any variable in the matrix, cd represents a device code, and hash represents a hash algorithm.

[0029] In one embodiment, the clipping contract is integrated into the LSM tree data structure in the form of code to construct an LSM clipper;

[0030] The LSM cropper is used to implement cropping optimization for multiple acquisition tasks.

[0031] In one embodiment, when the fused data is stored in a data block format:

[0032] Arrange the data assets obtained from all the collection indicators in an optimized collection task through the indicator relationship and the linked list data structure relationship to build a corresponding bidirectional data block chain;

[0033] The corresponding asset fusion data block is obtained according to the template hash primary key and the two-way data block chain;

[0034] In the LSM storage structure, key-value is used to store asset fusion data blocks, where key is the template hash primary key and value is the bidirectional data block chain;

[0035] Based on the identification code of the asset user object in the template hash primary key, the identity information of the asset fusion data block is authenticated and the asset ownership is confirmed.

[0036] The present application also provides a data asset collection and fusion device based on an identity resolution platform, the device comprising:

[0037] The collection information acquisition module is used to register industrial equipment based on the identification resolution platform, and configure multiple asset collection templates and asset use objects corresponding to each asset collection template for the registered industrial equipment, and each asset collection template is configured with multiple asset collection indicators;

[0038] A collection task generation module is used to construct corresponding multiple collection tasks according to the registration information of industrial equipment, multiple configured asset collection templates and asset usage objects;

[0039] The collection task optimization module is used to perform multi-level cutting optimization on multiple collection tasks based on cutting contract rules to obtain optimized collection tasks;

[0040] The data collection fusion and storage module is used to perform data collection according to the optimized collection task, and fuse the collected data by means of data blocks to obtain the fused data corresponding to each optimized collection task, and store it in the data block format, and authenticate the identity information of the data block and confirm the asset ownership.

[0041] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0042] Based on the identification resolution platform, industrial equipment is registered, and multiple asset collection templates and asset use objects corresponding to each asset collection template are configured for the registered industrial equipment, and each asset collection template is configured with multiple asset collection indicators;

[0043] Construct multiple corresponding collection tasks according to the registration information of industrial equipment, multiple configured asset collection templates and asset usage objects;

[0044] Based on the cutting contract rules, multiple collection tasks are optimized by multi-level cutting to obtain optimized collection tasks;

[0045] Data collection is performed according to the optimized collection task, and the collected data is fused by means of data blocks to obtain fused data corresponding to each optimized collection task, which is stored in a data block format, and identity information authentication and asset ownership confirmation are performed on the data blocks.

[0046] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0047] Based on the identification resolution platform, industrial equipment is registered, and multiple asset collection templates and asset use objects corresponding to each asset collection template are configured for the registered industrial equipment, and each asset collection template is configured with multiple asset collection indicators;

[0048] Construct multiple corresponding collection tasks according to the registration information of industrial equipment, multiple configured asset collection templates and asset usage objects;

[0049] Based on the cutting contract rules, multiple collection tasks are optimized by multi-level cutting to obtain optimized collection tasks;

[0050] Data collection is performed according to the optimized collection task, and the collected data is fused by means of data blocks to obtain fused data corresponding to each optimized collection task, which is stored in a data block format, and identity information authentication and asset ownership confirmation are performed on the data blocks.

[0051] The above-mentioned data asset collection and fusion method, device and equipment based on the identification resolution platform registers industrial equipment based on the identification resolution platform, and configures multiple asset collection templates and asset user objects corresponding to each asset collection template for the registered industrial equipment. Each asset collection template is configured with multiple asset collection indicators. According to the registration information of the industrial equipment, the multiple configured asset collection templates and the asset user objects, multiple corresponding collection tasks are established. Based on the cutting contract rules, multiple collection tasks are multi-level tailored and optimized to obtain optimized collection tasks. Data collection is performed according to the optimized collection tasks, and the collected data is fused by means of data blocks to obtain fused data corresponding to each optimized collection task, and the data is stored in the data block format, and the identity information and asset ownership confirmation are performed on the data blocks. The use of this method can realize the interconnection and interoperability of data assets. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flowchart of a data asset collection and fusion method based on an identity resolution platform in one embodiment;

[0053] Figure 2 A schematic diagram of a process for constructing an asset collection task in one embodiment;

[0054] Figure 3 Schematic diagram of LSM indicator block chain grouping and merging in one embodiment

[0055] Figure 4 A schematic diagram of LSM multi-level local task cutting in one embodiment;

[0056] Figure 5 A schematic diagram of LSM tree structure multi-layer task cutting in another embodiment;

[0057] Figure 6 A schematic diagram of the SSTable data block chain storage structure in one embodiment;

[0058] Figure 7 It is a structural block diagram of a data asset collection and fusion device based on an identity resolution platform in one embodiment;

[0059] Figure 8 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0061] In the prior art, when processing data, there is a lack of unique identification of data assets, and asset data cannot be traced. There is a lack of a unified method for converting heterogeneous asset data formats, and data aggregation is disorganized, which increases the cost of data asset integration and cannot efficiently achieve information integration between different systems. There is a lack of identity authentication for data assets that require identification resolution, and a lack of control over the use rights of data assets. It is impossible to ensure the security of data assets and effectively ensure that data is not leaked or tamper-proof. In this application, if Figure 1 As shown, a data asset collection and fusion method based on an identity resolution platform is provided, which specifically includes the following steps:

[0062] Step S100, based on the identification resolution platform, register the industrial equipment, and configure multiple asset collection templates and asset usage objects corresponding to each asset collection template for the registered industrial equipment, and each asset collection template is configured with multiple asset collection indicators.

[0063] Step S110 , constructing corresponding multiple collection tasks according to the registration information of the industrial equipment, the configured multiple asset collection templates, and the asset usage objects.

[0064] Step S120 , performing multi-level tailoring optimization on multiple collection tasks based on the tailoring contract rules to obtain optimized collection tasks.

[0065] Step S130, data collection is performed according to the optimized collection task, and the collected data is fused by means of data blocks to obtain fused data corresponding to each optimized collection task, and the fused data is stored in a data block format, and identity information authentication and asset ownership confirmation are performed on the data blocks.

[0066] In this method, we are committed to creating a simple and efficient data asset fusion standard solution and system in the industrial Internet identification resolution system. Register multiple equipment asset information in the industrial identification resolution platform, construct asset collection tasks based on asset collection templates and asset usage objects, and formulate tailoring contract rules to achieve complete tailoring of asset collection tasks, remotely obtain asset data resources and standardized processing, and complete asset data fusion based on a two-way data block chain. Store asset fusion data based on the LSM database, and implement identity information authentication and asset ownership confirmation of asset fusion data in the identification resolution system. Finally, by formulating industrial data asset fusion standard solutions at multiple links, the problem of data asset islands is solved, the data asset security and immutability of the industrial identification resolution system are improved, the technical difficulty of data asset fusion and sharing is reduced, and the complexity of system development and project architecture is simplified.

[0067] In step S100, multiple asset collection templates are added to the industrial identification resolution platform, and multiple asset collection indicators are configured for each asset collection template. Multiple enterprise node systems and networked devices are connected to the industrial identification resolution platform, and multiple industrial equipment asset information can be registered, multiple asset collection templates are configured for each industrial equipment, and each asset collection template is configured with a unique asset use object.

[0068] In this embodiment, the registration information of industrial equipment includes a unique identification code assigned by an industrial identification resolution platform and used as the industrial equipment code. The asset collection template includes multiple configured asset collection indicators and collection cycles. The asset collection indicators include the unique identification code, hash value, indicator name, collection address, data protocol, asset provider and collection priority of the asset collection indicators. The asset usage object includes the unique identification code, object name, data permissions and validity period of the asset usage object.

[0069] Specifically, a set of multiple different asset collection indicators is first constructed. Suppose there are N asset collection indicators, and the priority of any asset collection indicator I is rs, then the asset collection indicator set is defined as: Among them I i Represents any asset collection indicator, rs i Indicates the priority of the current asset collection indicator. By using the industrial Internet identification resolution system, a unique identification code is assigned to each asset collection indicator, which can realize the unique identification of the collection indicator.

[0070] In one embodiment, the data structure of the asset collection indicator may be set as follows:

[0071] class Index{

[0072] String iid,

[0073] String ihash,

[0074] String iname,

[0075] String url,

[0076] String protocol,

[0077] String producer,

[0078] int sort

[0079] }

[0080] Among them, iid is the unique identification code of the collection indicator, ihash is the hash value of the collection indicator, iname is the name of the collection indicator, url is the data collection address, protocol is the data protocol, producer is the asset provider, and sort is the indicator priority.

[0081] Furthermore, a set of multiple different asset usage objects is constructed. Suppose there are L asset usage objects, and the unique identification code of any usage object S is sd, then the usage object set is defined as: Where S i Represents any asset user object. By using the industrial Internet identification resolution system, each asset user object is assigned a unique identification code, which can realize the unique identification of asset users.

[0082] In an embodiment, the data structure of the asset usage object may be set as follows:

[0083] class Subscriber {

[0084] String sd,

[0085] String sname,

[0086] String spmiss,

[0087] String validity

[0088] }

[0089] Among them, sd is the unique identification code of the object, sname is the name of the object, spmiss is the data permission, and svalidity is the validity period.

[0090] Furthermore, multiple asset collection templates containing different asset collection indicators are configured according to the asset collection indicator set. Suppose H asset collection templates are added, and the collection period of any asset collection template T is fq, and the number of collection period types is R (R∈[1,H]). Select n (n=1,2,...,N) asset collection indicators for any collection template from the collection indicator set Irc, and define the collection template unit as: in Indicates that elements are selected without duplication. Similarly, if H asset collection templates are configured with variable n asset collection indicators, the collection template set is constructed as: Among them, i Represents any collection template.

[0091] In one embodiment, the data structure of the asset collection template may be set as follows:

[0092] class Template{

[0093] String fq,

[0094] List <index>indexes

[0095] }

[0096] Among them, fq is the collection period, and indexes are multiple asset collection indicators configured in the asset collection template.

[0097] Furthermore, in the industrial identification resolution platform, assume that M industrial equipment asset information is registered, where the unique code of any industrial equipment E is cd, select h (h = 1, 2, ..., H) asset collection templates for any industrial equipment from the collection template set Tin, and configure an asset use object for any selected asset collection template from the asset use object set Iss, then define the equipment entity unit as: in Indicates that elements can be selected repeatedly. Similarly, if h asset collection templates are configured for each of M industrial equipment, the constructed equipment entity set is: where Te i Represents any equipment entity. By using the industrial Internet identification resolution system, each industrial equipment asset is assigned a unique identification code (equipment code), which can realize the unique identification of industrial equipment assets.

[0098] In this embodiment, the data structure of the industrial equipment that has been configured with the asset collection template and the asset usage object can be expressed as:

[0099] class Device{

[0100] String cd,

[0101] List <template>templates,

[0102] List <subscriber>subscribers,

[0103] Map<String,String> relationship

[0104] }

[0105] Among them, cd is the device code of the industrial equipment, templates are multiple configured asset collection templates, subscribers are multiple configured asset usage objects, and relationship is the correspondence between the collection template and the asset usage object.

[0106] In step S110, when multiple corresponding collection tasks are constructed according to the registration information of the industrial equipment and the configured multiple asset collection templates: the corresponding collection task is constructed based on an asset collection template, all asset collection indicators in an asset collection template are arranged through collection priority and linked list data structure relationship, a two-way indicator block chain is constructed, and a template hash primary key is constructed according to the industrial equipment code, the collection cycle of the asset collection template and the object identification code used by the asset, and then the corresponding collection task is constructed according to the template hash primary key and the two-way indicator block chain, such as Figure 2 shown.

[0107] Specifically, the collection index and priority are extracted from the asset collection template unit, the device code and template collection cycle are extracted, and the asset usage object configured by the current collection template is extracted. The relationship arrangement and connection of the bidirectional linked list structure are performed based on the collection index priority, and the bidirectional linked list relationship is defined as br, then the bidirectional index block chain unit is constructed as: Where n represents the number of collection indicators configured in the asset collection template, I i Indicates any asset collection indicator, br ij Indicates the current collection index I i A bidirectional linked list relationship.

[0108] At the same time, the hash value is calculated by splicing the device code, template collection cycle and asset usage object identification code, and defined as the template hash primary key, encapsulating the template hash primary key and the bidirectional indicator block chain unit. The collection task that can be constructed is: Task = hash (cd, fq, sd) + IC, where hash is the hash algorithm, cd represents the device code, fq represents the template collection cycle, and sd represents the asset usage object identification code.

[0109] In an embodiment, a data structure of a bidirectional relationship arrangement of collection indicators may be set as follows:

[0110] class IndexShip{

[0111] String ihash,

[0112] String prewihash,

[0113] String postihash,

[0114] Index index

[0115] }

[0116] Among them, index is the current collection index, ihash is the hash value of the current collection index, prewihash is the hash value of the previous collection index, and postihash is the hash value of the post-collection index.

[0117] In this embodiment, the data structure of the bidirectional indicator block chain unit can be set as follows:

[0118] class IndexChain{

[0119] String icd,

[0120] LinkedList <indexship>indexShips

[0121] }

[0122] Among them, icd is the unique identification code of the indicator block chain, indexShips is the bidirectional relationship arrangement of multiple collection indicators, and LinkedList is a bidirectional linked list data structure.

[0123] In this embodiment, the data structure of the acquisition task unit can be set as follows:

[0124] class Task{

[0125] String keyhash,

[0126] IndexChain indexChain

[0127] }

[0128] Among them, keyhash is the template hash primary key, and indexChain is the bidirectional index block chain.

[0129] Furthermore, when the industrial equipment Te is configured with h (h = 1, 2, ..., H) asset collection templates and asset usage objects, h template hash keys and h bidirectional indicator block chain units can be obtained, and then h asset collection tasks can be constructed as follows: Among them, Task i Represents any asset collection task.

[0130] In one embodiment, there is a device entity set Tte constructed by M industrial devices configured with multiple asset collection templates. Based on the template hash primary key and the bidirectional index block chain unit, the asset collection task set can be constructed as follows: Task ij Represents any asset collection task among M equipment entities.

[0131] Specifically, a one-dimensional array Tk of asset collection tasks corresponding to a single industrial device can be constructed as follows:

[0132] Tk=[t1,t2,t3,t4,...,t h ]

[0133] Among them, h is the number of columns, that is, the number of collection tasks, and t is any asset collection task.

[0134] In step S120, the contract rules for cutting are as follows: first, a first matrix is ​​constructed based on all bidirectional indicator blocks in multiple collection tasks, a template collection cycle matrix is ​​constructed based on all collection cycles in multiple collection tasks, an asset usage object matrix is ​​constructed based on the identification codes of all asset usage objects in multiple collection tasks, a template hash primary key matrix is ​​constructed based on the template collection cycle matrix and the asset usage object matrix, and the matrix is ​​used as a second matrix, the first matrix is ​​grouped according to the second matrix, and bidirectional indicator blocks with consistent template hash primary keys are grouped into a group to obtain a two-dimensional grouping matrix, in which each group of data includes multiple bidirectional indicator blocks, the asset collection indicators in each group of bidirectional indicator blocks in the two-dimensional grouping matrix are merged according to the collection priority, and a new bidirectional indicator block chain is constructed, the two-dimensional grouping matrix is ​​optimized into a third matrix, and an optimized collection task is generated according to the new bidirectional indicator block chain in the third matrix and the corresponding template hash primary key.

[0135] In this embodiment, assuming that there are h acquisition tasks, the first matrix represents h(x)={x|x∈(x1,x2,x3,...,x h )}, get the corresponding h template collection periods and set the number of collection period types to r (r∈[1,h]), and define the template collection period matrix as: u(v)={v|v∈(v1,v2,v3,...,v h )}, and at the same time, the corresponding h asset usage object identification codes are obtained and the matrix is ​​defined as: p(s) = {s|s∈(s1,s2,s3,...,s h )}.

[0136] Furthermore, the template hash key matrix is ​​constructed based on the template collection cycle matrix and the asset usage object matrix, using the following formula:

[0137]

[0138] In the above formula, u(v) represents the template acquisition period matrix, p(s) represents the asset usage object matrix, and T represents matrix inversion. represents the grouping operation, disct represents the matrix deduplication operation, v, s represents any variable in the matrix, cd represents the device code, and hash represents the hash algorithm. That is, based on the same asset, multiple template collection cycles are grouped into matrices using object identification codes and data is deduplicated. Suppose the number of template hash primary keys after grouping and deduplication is f(f∈[1,h]), then the final constructed template hash primary key matrix, that is, the second matrix, is expressed as: k(w)={w|w∈(w1,w2,w3,...,w f )}

[0139] Specifically, when the first matrix is ​​grouped according to the second matrix, the bidirectional index block chains with the same template hash primary key are grouped into one group. And the matrix grouping operation is used to express it as:

[0140]

[0141] In the above formula, It represents the grouping operation, that is, multiple indicator block chains are grouped into matrices based on the same template hash primary key. Let the number of indicator block chains in each group after grouping be the variable z (z∈[1,h]). It can be seen that f(x) is a two-dimensional array with a size of f rows × z columns.

[0142] Specifically, the constructed collection task indicator block chain grouping two-dimensional array f(x) is as follows:

[0143]

[0144] In the above formula, f is the number of groups, i.e. the number of template hash key types, x is any index block chain after grouping, z1,z2,..,z f is the number of indicator blocks per group.

[0145] Furthermore, the bidirectional indicator block chain IC of all collection tasks in any group is obtained, and it can be known that the indicator block chain set in the group is: Among them, IC i Represents any indicator chain before the group is merged. Let the total number of all collected indicators in the group be s, and let the merge function of the indicator chain in the group be merge, that is, the linked list relationship is rearranged and connected according to the priority of the collected indicators, and finally the complete merge of the indicator chain set in the group is realized. Let the complete merge operation of the indicator chain in the group be: That is, the independent two-way indicator block chain within the group is reconstructed as: Among them I i Indicates any collection indicator after the group is merged. ij Indicates the current collection index I i A bidirectional linked list relationship.

[0146] Similarly, the indicator block chain matrix merging operation can be set as: Where b(x) is the third matrix obtained by optimizing the two-dimensional grouping matrix, that is, the indicator block chain matrix is ​​rearranged and completely merged based on the indicator block chain merging function, and the bidirectional indicator block chain of the f-group asset acquisition task can be completely merged. It can be seen that b(x) is a one-dimensional array of size 1 row × f columns, and the bidirectional indicator block chain set after the complete merger is constructed as: Among them IC' i It represents any indicator block chain after the matrix is ​​merged. Finally, the indicator block chains of h collection tasks are completely merged into f, that is, the h asset collection tasks can also be cut into f. It can be understood that f is smaller than h.

[0147] In an embodiment, a one-dimensional array b(x) of an indicator block chain based on a tailored contract rule is constructed as follows:

[0148] b(x')=[x'1,x'2,x'3,x'4,...,x' f ]

[0149] In the above formula, f is the number of columns, that is, the number of types of template hash primary keys, and x' is any index block chain after complete merging.

[0150] In this embodiment, the LSM indicator block chain clipper is used to implement the clipping optimization of multiple collection tasks of industrial equipment Te. The above clipping contract is integrated into the LSM tree data structure in the form of code and defined as the LSM indicator block chain clipper, that is, when executing the code, multiple indicator block chains can be grouped and merged based on the same template hash primary key, such as Figure 3 At the same time, LSM local clipper and LSM global clipper are designed. LSM local clipper is used to locally merge and clip the indicator block chains of multiple collection tasks, and LSM global clipper is used to completely merge and clip the indicator block chains of multiple asset collection tasks.

[0151] Specifically, assume that there are h indicator block chains of collection tasks in the device entity unit Te, and assume that the LSM tree data structure has a total of K layers. The trimming contract rules are integrated into the LSM tree data structure in the form of code, and the trimming contract code is run when each layer of the LSM tree structure performs data compaction operations.

[0152] Furthermore, the LSM local pruner completes the local pruning of the indicator block chain through a multi-level compaction operation based on the LSM data structure, that is, the indicator block chains of h collection tasks are gradually imported into the LSM memory cache. When the cache overflows, the indicator block chain is locally pruned and flushed to the Level0 layer. When the number of indicator block chains at the Level0 layer exceeds the threshold, it is locally pruned again and stored in Level1, and so on to Level K-1 Assume that the number of index block chains at any level of LSM is variable u (u∈[1,h]), and the number of template hash primary key types is v (v∈[1,f]), then define the index block chain at any level x as: Among them, IC' i represents the index block chain of the xth level after pruning, u x Indicates the number of indicator block chains at the xth level. After the pruned contract operation based on local compaction, it can be seen that the index block chain matrix merge operation at the x+1th level after pruned is set as: It can be seen that the indicator block chain of the x+1th layer directly converges to v x That is, the number of types of template hash primary keys is consistent with the number of types of template hash primary keys at the xth layer, such as Figure 4 shown.

[0153] Furthermore, by partially merging and pruning the indicator block chains at each level, the indicator block chain set of all levels can be constructed as follows: Among them, IC' ij It represents the indicator block chain of any level after local pruning. It can be seen that f3 is a two-dimensional array of size K rows × u columns. The number of statistical indicator block chain sets is: sf = sum(f3), and it can be seen that the condition is satisfied: sf∈[f,h]. Through the local merging of multi-level indicator block chains, the indicator block chains of h collection tasks can be slowly converged to f, and finally the local merging and pruning of the bidirectional indicator block chain is completed based on local pruning, that is, the h asset collection tasks are slowly converged to f.

[0154] In this embodiment, a two-dimensional array f3(x) of all-level indicator block chains based on local clipping is constructed as follows:

[0155]

[0156] In the above formula, K is the number of rows, i.e., the number of LSM levels after local pruning, x is the indicator block chain of any level after local pruning, u1,u2,..,u K The number of indicator blocks per level.

[0157] In this embodiment, the LSM global pruner completes the global pruning of the indicator block chain through a global compaction operation based on the LSM data structure, that is, the LSM timed micro-batch data compaction strategy is adopted to perform global pruning of all local levels (Level0, Level1,..., Level K-1 ) are fully compacted and pruned, and the pruned index block chains are stored in the Level 1 of the LSM data structure. K-1 Layer, such as Figure 5 As shown. After the pruned contract based on global compaction is calculated, it can be known that the pruned Level K-1 The number of indicator block chains of the layer completely converges to f, which is consistent with the number of template hash primary key types. It can be seen that the indicator block chain matrix merging operation with global clipping is: At the same time, it can be seen that the constructed global clipping indicator block chain set also satisfies the expression: It can be seen that f2 is a one-dimensional array with only f columns, and the number of indicator block chains cut after global cutting is: diff1 = hf. Finally, the complete merging and cutting of the two-way indicator block chain is completed based on global cutting, that is, the h asset collection tasks are completely cut into f.

[0158] In an embodiment, a one-dimensional array f2 of the last level indicator block chain based on global clipping is constructed as follows:

[0159] f2(x)=[x1,x2,x3,x4,...,x f ]

[0160] In the above formula, f is the number of columns, that is, the number of indicator block chains after global pruning, and x is any indicator block chain after global pruning.

[0161] In this embodiment, when using LSM local clippers and LSM global clippers to clip collection tasks, it is assumed that an asset collection task set Ttk is constructed for M industrial equipment, and M groups of LSM clippers are constructed based on the clipping contract rules, and complete clipping of the asset collection task set is achieved based on the LSM clippers.

[0162] Specifically, based on the LSM clipper operation in the equipment entity unit Te, h asset collection tasks can be completely clipped into f, that is, the asset collection task unit is reconstructed as: Task'=hash(cd,fq,sd)+IC', where IC' represents the bidirectional indicator block chain after complete clipping. After the LSM clipper operation, the asset collection task is reconstructed: Among them, Task' i Represents any acquisition task after trimming. It can be seen that Tk' is a one-dimensional array with only f columns. It can be seen that the number of task trimmings of the device entity unit is also: diff1 = hf.

[0163] Furthermore, based on the LSM clipper operation in the M registered industrial equipment Tte, the asset collection task set can be reconstructed as: Task' ij Represents any asset collection task in M ​​industrial equipment. It can be seen that Ttk' is a two-dimensional array with a size of M rows × f columns. It can be seen that the total number of task clippings for M industrial equipment is: where h i ,f i represents the number of original tasks and the number of tasks after trimming for any device, and sum represents the number of statistical sets. Finally, the trimmed asset collection task Ttk' is stored based on the LSM database.

[0164] Specifically, construct a one-dimensional array Tk' of acquisition tasks based on the LSM clipper as follows:

[0165] Tk'=[t'1,t'2,t'3,t'4,...,t' f ]

[0166] In the above formula, f is the number of columns, that is, the number of acquisition tasks after LSM pruning, and t' is any acquisition task after pruning.

[0167] In the embodiment, a two-dimensional array Ttk' of acquisition tasks based on the LSM clipper is constructed as follows:

[0168]

[0169] In the above formula, M is the number of rows, i.e. the number of registered industrial devices, t' is any collection task after trimming, f1,f2,..,f M The number of tasks tailored for any device.

[0170] In step S130, after executing the optimized collection task, multiple collected data obtained from one collection task are fused. During this process, the collected data is first standardized, the standardized data assets are organized and connected, and hash coding is used to ensure that the asset data cannot be tampered with. Finally, the device asset data is fused into a two-way data block chain.

[0171] Specifically, after the task is cut by the LSM cutter, the asset collection task Task is submitted to the industrial identification resolution platform. i , and access the enterprise node system and networked devices. Obtain the indicator block chain IC' in any asset collection task, extract all asset collection indicators, remotely obtain the device data resources of the enterprise node through the data collection address url, and perform standardized processing of heterogeneous device asset data based on the device data protocol protocol.

[0172] In any collection task Task' i In the standardized data resources, let the total number of indicator data assets be g, let the data asset unit of a single collection indicator be d, hash the data asset d through the hash algorithm, and enhance the security of the data asset by making the hash data tamper-proof. Get the collection indicators and indicator relationships in the two-way indicator block chain IC', arrange and connect all the collection data assets according to the indicator relationship, and perform the sequence numbering of the data assets. Finally, complete the asset data fusion through the arrangement and connection of data assets. Let the data asset arrangement relationship be dr, then the two-way data block chain unit constructed based on data asset fusion is: d i Represents any indicator data asset, dr ij Represents the current data asset d i From the bidirectional linked list relationship, we can see that rc is a one-dimensional array with only g columns.

[0173] In this embodiment, a one-dimensional array rc of data block chain units based on data asset fusion is constructed as follows:

[0174] rc=[d1,d2,d3,d4,...,d g ]

[0175] In the above formula, g is the number of columns, i.e., the number of data assets in a certain collection task, and d is any data asset.

[0176] Furthermore, the data structure of a single data asset obtained by executing a certain collection task can be set as:

[0177] class Data{

[0178] String dhash,

[0179] String context,

[0180] int number,

[0181] Index index

[0182] }

[0183] Among them, dhash is the unique hash value of the data asset, context is the content of the data asset, number is the data asset number, and index is the asset collection index.

[0184] Furthermore, the data structure of the bidirectional relationship orchestration of data assets can be set as follows:

[0185] class DataShip{

[0186] String dhash,

[0187] String prewdhash,

[0188] String postdhash,

[0189] Data data

[0190] }

[0191] Among them, data is the current data asset, dhash is the current data asset hash value, prewdhash is the pre-data asset hash value, and postdhash is the post-data asset hash value.

[0192] Furthermore, the data structure of the bidirectional data block chain unit after the collected data is fused is set as:

[0193] class DataChain{

[0194] String dcd,

[0195] LinkedList <dataship>dataShips

[0196] }

[0197] Among them, dcd is the unique identification code of the data block chain, dataShips is the bidirectional relationship arrangement of multiple data assets, and LinkedList is a bidirectional linked list data structure.

[0198] When the task cutting operation is not performed, the asset collection task set Ttk of M industrial equipment, the data block chain set constructed based on data asset fusion is: where rc ij Represents a bidirectional data block chain of any uncut collection task. After the task is cut by the LSM cutter, the asset collection task set Ttk' of M industrial equipment, the data block chain set constructed based on data fusion is: where rc ij Representing the bidirectional data block chain of any cropped collection task, it can be seen that Rtk' is a two-dimensional array of size M rows × f columns. After the asset collection task is cropped by the LSM cropper, it can be seen that the total number of asset data block chains cropped for M industrial equipment is also: diff2, which is consistent with the total number of asset collection tasks.

[0199] Specifically, a two-dimensional array Rtk' of a data block chain set based on data asset fusion is constructed as follows:

[0200]

[0201] In the above formula, M is the number of rows, i.e. the number of registered industrial devices, rc is any data block chain, rc1, rc2, .., rc M The number of blockchains for each industrial device.

[0202] In this embodiment, when the fused data is stored in a data block format, i.e., a bidirectional data block chain: the data assets obtained from all the collection indicators in an optimized collection task are arranged through indicator relationships and linked list data structure relationships to construct a corresponding bidirectional data block chain, and the corresponding asset fusion data block is obtained according to the template hash primary key and the bidirectional data block chain. The key-value key value is used to store the asset fusion data block in the LSM storage structure, wherein the key is the template hash primary key, and the value is the bidirectional data block chain. Based on the identification code of the asset user object in the template hash primary key, the identity information of the asset fusion data block is authenticated and the asset ownership is confirmed.

[0203] Specifically, the corresponding asset fusion data block is obtained according to the template hash primary key and the two-way data block chain. After obtaining the asset data block chain set Rtk' constructed by M industrial equipment, the LSM database is used to build the identity resolution secondary node industry resource pool database, and the fused asset data block chain Rtk' is stored based on the LSM database, that is, the key-value key value is used to store the asset fusion data block in the SSTable storage table structure of the LSM database, where the key is the template hash primary key and the value is the two-way data block chain, such as Figure 6 shown.

[0204] In this embodiment, the data structure of the asset fusion data block may be set as follows:

[0205] class KeyValue{

[0206] String keyhash,

[0207] DataChain dataChain

[0208] }

[0209] Among them, keyhash is the template hash primary key, and dataChain is the bidirectional data block chain.

[0210] Furthermore, the identity information authentication and asset ownership confirmation of the asset fusion data are realized in the identification resolution system, that is, based on the identification code of the asset user object in the template hash primary key, the identity information authentication and asset ownership confirmation of the fused data block are performed, and the data access permission control is realized and the security of the asset data is increased through the unique identification code authentication method. Specifically, when accessing asset data, it is necessary to provide the unique identification code of the data query party object, and construct the data query party hash value according to the device code, template collection cycle and query party identification code, and compare the query party hash value with the template hash primary key in the asset fusion data block. If they are consistent, data access permission is granted, and if they are inconsistent, access is denied.

[0211] Furthermore, the bidirectional data block chain storage method after fusion is adopted in the identity resolution system, which can store asset data in an orderly manner, retrieve asset data blocks based on the template hash primary key, and accurately locate to improve the retrieval efficiency of asset data. By collecting and integrating heterogeneous assets between different enterprises and systems into data blocks through data sources, the data island problem can be effectively solved, and the efficiency of data asset sharing can be greatly improved. The template hash primary key also contains asset user information, and the security of identity resolution data assets is guaranteed by data permissions, effectively achieving data leakage and non-tampering. Finally, in the identity resolution system, by obtaining asset fusion data from the LSM database, such as material information, production information, and equipment attributes, a comprehensive data asset view can be formed.

[0212] In the above-mentioned data asset collection and fusion method based on the identification resolution platform, the data asset collection and fusion method, device and equipment based on the identification resolution platform register the industrial equipment based on the identification resolution platform, and configure multiple asset collection templates and asset usage objects for the registered industrial equipment. Each asset collection template is configured with multiple asset collection indicators. According to the registration information of the industrial equipment and the configured multiple asset collection templates, multiple corresponding collection tasks are constructed. Based on the cutting contract rules, multiple collection tasks are multi-level cut and optimized to obtain optimized collection tasks. Data collection is performed according to the optimized collection tasks, and the collected data is fused by means of data blocks to obtain fused data corresponding to each optimized collection task, and it is stored in the data block format, and the identity information of the data block is authenticated and the asset ownership is confirmed. This method can realize the interconnection and interoperability of data assets.

[0213] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0214] In one embodiment, Figure 7 As shown, a data asset collection and fusion device based on an identity resolution platform is provided, comprising: a collection information acquisition module 200, a collection task generation module 210, a collection task optimization module 220 and a collection data fusion and storage module 230, wherein:

[0215] The collection information acquisition module 200 is used to register the industrial equipment based on the identification resolution platform, and configure multiple asset collection templates and asset use objects corresponding to each asset collection template for the registered industrial equipment, and each asset collection template is configured with multiple asset collection indicators;

[0216] The collection task generation module 210 is used to construct corresponding multiple collection tasks according to the registration information of the industrial equipment, the configured multiple asset collection templates and the asset use objects;

[0217] A collection task optimization module 220 is used to perform multi-level tailoring optimization on multiple collection tasks based on tailoring contract rules to obtain optimized collection tasks;

[0218] The data collection fusion and storage module 230 is used to perform data collection according to the optimized collection task, and fuse the collected data by means of data blocks to obtain the fused data corresponding to each optimized collection task, and store it in the data block format, and perform identity information authentication and asset ownership confirmation on the data block.

[0219] For the specific limitations of the data asset collection and fusion device based on the identity resolution platform, please refer to the limitations of the data asset collection and fusion method based on the identity resolution platform above, which will not be repeated here. Each module in the above-mentioned data asset collection and fusion device based on the identity resolution platform can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0220] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a data asset collection and fusion method based on an identity resolution platform is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0221] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0222] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0223] Based on the identification resolution platform, industrial equipment is registered, and multiple asset collection templates and asset usage objects corresponding to each asset collection template are configured for the registered industrial equipment, and each asset collection template is configured with multiple asset collection indicators;

[0224] Construct multiple corresponding collection tasks according to the registration information of industrial equipment, multiple configured asset collection templates and asset usage objects;

[0225] Based on the cutting contract rules, multiple collection tasks are optimized by multi-level cutting to obtain optimized collection tasks;

[0226] Data collection is performed according to the optimized collection task, and the collected data is fused by means of data blocks to obtain fused data corresponding to each optimized collection task, which is stored in a data block format, and identity information authentication and asset ownership confirmation are performed on the data blocks.

[0227] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0228] Based on the identification resolution platform, industrial equipment is registered, and multiple asset collection templates and asset usage objects corresponding to each asset collection template are configured for the registered industrial equipment, and each asset collection template is configured with multiple asset collection indicators;

[0229] Construct multiple corresponding collection tasks according to the registration information of industrial equipment, multiple configured asset collection templates and asset usage objects;

[0230] Based on the cutting contract rules, multiple collection tasks are optimized by multi-level cutting to obtain optimized collection tasks;

[0231] Data collection is performed according to the optimized collection task, and the collected data is fused by means of data blocks to obtain fused data corresponding to each optimized collection task, which is stored in a data block format, and identity information authentication and asset ownership confirmation are performed on the data blocks.

[0232] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0233] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0234] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.< / dataship> < / indexship> < / subscriber> < / template> < / index>

Claims

1. A data asset collection and fusion method based on an identity resolution platform, characterized in that: The method comprises: Based on the identification resolution platform, industrial equipment is registered, and multiple asset collection templates and asset use objects corresponding to each asset collection template are configured for the registered industrial equipment, and each asset collection template is configured with multiple asset collection indicators; Construct multiple corresponding collection tasks according to the registration information of industrial equipment, multiple configured asset collection templates and asset usage objects; Based on the cutting contract rules, multiple collection tasks are optimized by multi-level cutting to obtain optimized collection tasks; Data collection is performed according to the optimized collection task, and the collected data is fused by means of data blocks to obtain fused data corresponding to each optimized collection task, which is stored in a data block format, and identity information authentication and asset ownership confirmation are performed on the data blocks.

2. The data asset collection and fusion method according to claim 1 is characterized in that: The registration information of the industrial equipment includes the industrial equipment code; The asset collection template includes multiple configured asset collection indicators and collection cycles, and the asset collection indicators include a unique identification code, hash value, indicator name, collection address, data protocol, asset provider, and collection priority of the asset collection indicator; The asset usage object includes a unique identification code of the asset usage object, an object name, data permissions, and a validity period.

3. The data asset collection and fusion method according to claim 2 is characterized in that: When building corresponding multiple collection tasks based on the registration information of industrial equipment, multiple configured asset collection templates, and asset usage objects: Build a corresponding collection task based on an asset collection template; Arrange all asset collection indicators in an asset collection template through collection priority and linked list data structure relationship to build a two-way indicator block chain; Constructing a template hash primary key according to the industrial equipment code, the collection cycle of the asset collection template, and the identification code of the asset user; The corresponding collection task is obtained according to the template hash primary key and the two-way indicator block chain.

4. The data asset collection and fusion method according to claim 3 is characterized in that: The tailoring contract rules are: A first matrix is ​​constructed based on all bidirectional indicator block chains in the plurality of acquisition tasks; A template acquisition cycle matrix is ​​constructed based on all acquisition cycles in the plurality of acquisition tasks; Constructing an asset use object matrix according to the identification codes of all asset use objects in the plurality of acquisition tasks; A template hash primary key matrix is ​​constructed according to the template collection period matrix and the asset usage object matrix, and is used as the second matrix; The first matrix is ​​grouped according to the second matrix, and bidirectional indicator block chains with consistent template hash primary keys are grouped into one group to obtain a two-dimensional grouping matrix, in which each group of data includes multiple bidirectional indicator block chains; Merging the asset acquisition indicators in each group of bidirectional indicator block chains in the two-dimensional grouping matrix according to the acquisition priority, and constructing a new bidirectional indicator block chain, and optimizing the two-dimensional grouping matrix into a third matrix; An optimized collection task is generated according to the new bidirectional indicator block chain in the third matrix and the corresponding template hash primary key.

5. The data asset collection and fusion method according to claim 4 is characterized in that: The template hash key matrix is ​​constructed based on the template collection cycle matrix and the asset usage object matrix, using the following formula: In the above formula, u(v) represents the template acquisition period matrix, p(s) represents the asset usage object matrix, and T represents matrix inversion. represents a grouping operation, disct represents a matrix deduplication operation, v, s represents any variable in the matrix, cd represents a device code, and hash represents a hash algorithm.

6. The data asset collection and fusion method according to claim 5 is characterized in that: Integrate the clipping contract into the LSM tree data structure in the form of code to construct the LSM clipper; The LSM cropper is used to implement cropping optimization for multiple acquisition tasks.

7. The data asset collection and fusion method according to claim 6 is characterized in that: When storing the fused data in a data block format: Arrange the data assets obtained from all the collection indicators in an optimized collection task through the indicator relationship and the linked list data structure relationship to build a corresponding bidirectional data block chain; The corresponding asset fusion data block is obtained according to the template hash primary key and the two-way data block chain; In the LSM storage structure, key-value is used to store asset fusion data blocks, where key is the template hash primary key and value is the bidirectional data block chain; Based on the identification code of the asset user object in the template hash primary key, the identity information of the asset fusion data block is authenticated and the asset ownership is confirmed.

8. A data asset collection and fusion device based on an identity resolution platform, characterized in that: The device comprises: The collection information acquisition module is used to register industrial equipment based on the identification resolution platform, and configure multiple asset collection templates and asset use objects corresponding to each asset collection template for the registered industrial equipment, and each asset collection template is configured with multiple asset collection indicators; A collection task generation module is used to construct corresponding multiple collection tasks according to the registration information of industrial equipment, multiple configured asset collection templates and asset usage objects; The collection task optimization module is used to perform multi-level cutting optimization on multiple collection tasks based on cutting contract rules to obtain optimized collection tasks; The data collection fusion and storage module is used to perform data collection according to the optimized collection task, and fuse the collected data by means of data blocks to obtain the fused data corresponding to each optimized collection task, and store it in the data block format, and authenticate the identity information of the data block and confirm the asset ownership.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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