Data classification and level-to-level management method and device for data networking scene

Abstract

The embodiment of the invention provides a data classification and level-to-level management method and a data classification and level-to-level management device for a data networking scene. Relates to the technical field of networking. The method comprises the steps of obtaining a first data model sent by a first service system; the first data model carries a first classification identifier, a first grading identifier and target data; respectively determining a second classification identifier corresponding to the first classification identifier and a second classification identifier corresponding to the first classification identifier in a classification identifier set and a classification identifier set in a second service system; and based on the second classification identifier, the second grading identifier and the target data, constructing a second data model in a second service system to complete classification and grading management of the target data in the second service system. According to the invention, the efficiency of data classification and level-to-level management for the data networking scene can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of digital networking, and specifically relates to a data classification and grading management method and device for digital networking scenarios. Background Art

[0002] Business systems are systems developed to implement specific businesses, such as scientific research systems and financial systems. Different business systems have different classifications and grading for the same or similar data. In addition, data flows frequently between business systems.

[0003] Currently, when data flows from the original system to the new system, the new system needs to manage the received data according to its own classification and grading, that is, to redefine the classification and grading of the data.

[0004] However, with the current solution, every time the system receives data, it needs to redefine the classification and grading of the data, which is a cumbersome process. Summary of the invention

[0005] The present invention aims to provide a data classification and grading management method, device, electronic device and readable storage medium for digital networking scenarios, which at least solves the problem that the system in the prior art needs to redefine the classification and grading of data, which is a cumbersome process.

[0006] In order to solve the above-mentioned technical problems, the present invention is achieved as follows: In a first aspect, an embodiment of the present invention provides a data classification and grading management method for a data networking scenario, the method comprising: Acquire a first data model sent by a first business system; the first data model carries a first classification identifier, a first grading identifier and target data; the first classification identifier and the first grading identifier are used to indicate type information and level information of the target data in the first business system; In the classification identifier set and the level identifier set in the second business system, respectively determine the second classification identifier corresponding to the first classification identifier and the second level identifier corresponding to the first level identifier; the second classification identifier and the second level identifier are used to indicate the type information and level information of the target data in the second business system; Based on the second classification identifier, the second grading identifier and the target data, a second data model is constructed in the second business system to complete the classification and grading management of the target data in the second business system.

[0007] Optionally, the step of respectively determining the second classification identifier corresponding to the first classification identifier and the second classification identifier corresponding to the first classification identifier in the classification identifier set and the classification identifier set in the second business system includes: Determine, in the classification identifier set, a second classification identifier corresponding to the first classification identifier of the first data model according to a pre-constructed first mapping relationship between the first classification identifier and the second classification identifier; According to a pre-constructed second mapping relationship between the first hierarchical identifier and the second hierarchical identifier, a second hierarchical identifier corresponding to the first hierarchical identifier of the first data model is determined in the hierarchical identifier set.

[0008] Optionally, the first data model carries a digital signature; after the step of obtaining the first data model sent by the first business system, the method further includes: Obtain the digital signature and the private key; the digital signature is obtained by the first business system encrypting the first summary information of the target data based on the public key corresponding to the private key; the first summary information is obtained by the first business system calculating the target data based on a preset hash algorithm; Decrypting the digital signature based on the private key to obtain the first summary information, and performing a calculation on the target data based on the preset hash algorithm to obtain the second summary information; When the first summary information matches the second summary information, it is determined that the target data acquired by the second business system is complete.

[0009] Optionally, before the step of determining, in the classification identifier set, the second classification identifier corresponding to the first classification identifier of the first data model, according to the pre-constructed first mapping relationship between the first classification identifier and the second classification identifier, the method further includes: Acquire at least one first classification identifier of the first business system and first type information corresponding to the at least one first classification identifier, and acquire at least one second classification identifier of the second business system and second type information corresponding to the at least one second classification identifier; When the first type information matches the second type information, the first mapping relationship between the first classification identifier and the second classification identifier is constructed.

[0010] Optionally, before the step of determining, in the set of hierarchical identifiers, the second hierarchical identifier corresponding to the first hierarchical identifier of the first data model according to the pre-constructed second mapping relationship between the first hierarchical identifier and the second hierarchical identifier, the method further includes: Acquire at least one first classification identifier of the first business system and first level information corresponding to the at least one first classification identifier, and acquire at least one second classification identifier of the second business system and second level information corresponding to the at least one second classification identifier; When the first level information matches the second level information, the second mapping relationship between the first hierarchical identifier and the second hierarchical identifier is constructed.

[0011] Optionally, before the step of acquiring the first data model sent by the first business system, the method further includes: Sending a data acquisition request carrying characteristic information to the first business system; The first business system is used to determine the corresponding target data according to the feature information in the data acquisition request, and send the first data model carrying the target data to the second business system.

[0012] Optionally, the method further includes: When there is no second classification identifier corresponding to the first classification identifier in the first mapping relationship and / or there is no second hierarchical identifier corresponding to the first hierarchical identifier in the second mapping relationship, generating a new second classification identifier and / or a new second hierarchical identifier; Constructing a second data model based on the new second classification identifier and / or the new second classification identifier, and the target data; A first mapping relationship between the first classification identifier and the new second classification identifier is constructed, and a second mapping relationship between the first hierarchical identifier and the new second hierarchical identifier is constructed.

[0013] In a second aspect, an embodiment of the present invention further provides a data classification and grading management device for a data networking scenario, the device comprising: A model acquisition module, used to acquire a first data model sent by a first business system; the first data model carries a first classification identifier, a first grading identifier and target data; the first classification identifier and the first grading identifier are used to indicate type information and level information of the target data in the first business system; an identification conversion module, used to determine, from a classification identification set and a classification identification set in a second business system, a second classification identification corresponding to the first classification identification, and a second classification identification corresponding to the first classification identification; the second classification identification and the second classification identification are used to indicate type information and level information of the target data in the second business system; A model building module is used to build a second data model in the second business system based on the second classification identifier, the second grading identifier and the target data to complete the classification and grading management of the target data in the second business system.

[0014] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method described in the first aspect.

[0015] In a fourth aspect, an embodiment of the present invention further provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0016] In an embodiment of the present invention, a first data model sent by a first business system is obtained; the first data model carries a first classification identifier, a first grading identifier and target data; the first classification identifier and the first grading identifier represent the type information and level information of the target data in the first business system; in the classification identifier set and the grading identifier set in the second business system, the second classification identifier corresponding to the first classification identifier and the second grading identifier corresponding to the first grading identifier are determined; the second classification identifier and the second grading identifier are used to represent the type information and level information of the target data in the second business system; based on the second classification identifier, the second grading identifier and the target data, a second data model is constructed in the second business system. Data and its classification and grading information can be transmitted between systems as a data model as a whole, and the system receiving the data model can determine the corresponding classification identifier and grading identifier, conveniently determine the classification and grading information of the data in the current system, and construct the data model of the current system, thereby improving the efficiency of data classification and grading management for the Internet of Things scenario. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flowchart of a data classification and grading management method for a data networking scenario provided by an embodiment of the present invention; Figure 2 is a schematic diagram of a first data model provided by an embodiment of the present invention; Figure 3 It is a flowchart of another method for data classification and grading management in a data networking scenario provided by an embodiment of the present invention; Figure 4 is a schematic diagram of a first mapping relationship provided by an embodiment of the present invention; Figure 5 is a schematic diagram of a second mapping relationship provided by an embodiment of the present invention; Figure 6 is a schematic diagram of a first data model and a second data model provided by an embodiment of the present invention; Figure 7 It is a block diagram of a data classification and grading management device for a data networking scenario provided by an embodiment of the present invention; Figure 8 It is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] The terms "first", "second", etc. in the specification and claims of the present invention are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of the same type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0020] With the continuous development of digital economy and intelligent applications, the demand for data interconnection and interoperability has become more urgent, and digital networking technology represented by digital object architecture has emerged. Digital networking technology draws on the design concept of the Internet and adopts the idea of ​​software definition. Through a data-centric open software architecture and standardized interoperability protocols, it connects various heterogeneous data platforms and systems, thus forming a "virtual / data" network on the "physical / machine" Internet, namely the data Internet, referred to as "digital networking".

[0021] The implementation of the Internet of Things can be based on the Digital Object Architecture (DOA). The Digital Object Architecture includes a basic model, two basic protocols and three core systems. A basic model refers to digital objects, which are used to abstract data in the Internet so that heterogeneous data resources can be modeled and described in a unified form. From a technical point of view, a digital object is a bit sequence or a collection of bit sequences that contains some valuable information for a person or organization. Each digital object must be assigned a globally unique identifier. As the core attribute of a digital object, the identifier does not change with the change of the owner, storage location or access method of the digital object. The two basic protocols are the Digital Object Interface Protocol (DOIP) and the Digital Object Identifier Resolution Protocol (DO-IRP). DOIP is used to interact with digital objects or information systems that manage digital objects, and DO-IRP is used to create, update, delete and resolve globally managed and allocated digital object identifiers. The three core systems are the digital object repository system, the registry system and the identifier / resolution system. The digital object warehouse system is mainly responsible for the physical encapsulation, storage, access and destruction of digital objects. The digital object registration system is mainly responsible for the metadata registration, publication, modification and deletion of digital objects. The digital object identification system is mainly responsible for the identification creation, analysis, modification and destruction of digital objects. DOA standardizes Internet data resources through digital objects, uses DOIP and DO-IRP protocols to standardize data interaction behaviors, and realizes the interconnection and interoperability of heterogeneous and remote data based on the open software architecture formed by the three core systems.

[0022] In the application scenario of data network interconnection, data will frequently flow in different business systems between different entities. When the classification and grading information of data is determined in the business system of a certain entity, corresponding classification and grading management measures can be applied to the data in the system. However, when the data flows to another business system, the classification and grading information of the data may be lost. Even if it is not lost, the new business system cannot recognize the original classification and grading information of the data.

[0023] Figure 1 The following is a flowchart of a data classification and grading management method for a data networking scenario provided by an embodiment of the present invention. The method may include: Step 101, obtaining a first data model sent by a first business system; the first data model carries a first classification identifier, a first level identifier and target data; the first classification identifier and the first level identifier are used to indicate type information and level information of the target data in the first business system; In the embodiment of the present disclosure, since the data transmission is carried out between two entities, there may be at least two business systems, a first business system and a second business system, and the first business system and the second business system may respectively run in two different terminals. The first business system is the data owner and can send data to the second business system. The second business system is the data acquirer and can acquire data from the first business system. The data transmission between the first business system and the second business system may be generated based on data transactions.

[0024] The second business system obtains the first data model sent by the first business system, and the first data model carries the first classification identifier, the first grading identifier and the target data. It can be understood that different classification identifiers and grading identifiers represent different type information and level information, and the first classification identifier and the first grading identifier represent the type information and level information of the current target data in the first business system. The type information can be scientific research data, personnel data, and financial data. The level information can refer to the confidentiality level, which can be level one or level two; the level information can also refer to the data size level.

[0025] For example, the first classification mark is 1.2.3 / 45b, which means the current target data is scientific research data; the first classification mark is 1.2.3 / 45e, which means the current target data is personnel data. The first level mark is 1.2.3 / 45c, which means the current target data is level 2.

[0026] Figure 2 It is a schematic diagram of the first data model provided by the embodiment of the present disclosure; the first data model 20 may record the target data identifier 1.2.3 / 45a, the first classification identifier 1.2.3 / 45b and the first grading identifier 1.2.3 / 45c, and also includes the target data.

[0027] Step 102, determining the second classification identifier corresponding to the first classification identifier and the second classification identifier corresponding to the first classification identifier in the classification identifier set and the classification identifier set in the second business system, respectively; the second classification identifier and the second classification identifier are used to indicate the type information and the level information of the target data in the second business system; In the embodiment of the present disclosure, the second business system may have a corresponding classification identification set and a grading identification set according to the classification and grading set by itself, each set includes at least one identification, each classification identification has corresponding type information, and each grading identification has corresponding level information. It is understandable that the first business system may also have a corresponding classification identification set and a grading identification set, which will not be repeated here.

[0028] It can be to determine the classification information represented by the current first classification identifier, and to determine the classification information corresponding to each second classification identifier in the classification identifier set of the second business system. The second business system can identify the classification information represented by the first classification identifier and the classification information corresponding to each second classification identifier in the second classification identifier set, and match them according to the content of the classification information, and then select the corresponding second classification identifier. For example, the classification information represented by the first classification identifier A is: food data, the classification information represented by the second classification identifier B is: vehicle data, and the classification information represented by the second classification identifier C is: catering data, then the first classification identifier A corresponds to the second classification identifier C.

[0029] Based on a similar method, the second hierarchical identifier corresponding to the first hierarchical identifier may be determined in the hierarchical identifier set of the second business system, which will not be described in detail herein.

[0030] Step 103: construct a second data model in the second business system based on the second classification identifier, the second grading identifier and the target data to complete the classification and grading management of the target data in the second business system.

[0031] In the embodiment of the present disclosure, similar to the first data model constructed by the first business system, the second business system can construct a second data model in the second business system based on the second classification identifier, the second classification identifier and the target data to complete the classification and classification management of the target data in the second business system. It can be understood that the data model can be a data storage structure, which can be based on Figure 2 The data model shown stores multiple data such as classification identifiers, grading identifiers, target data, time information, etc. in an associated manner.

[0032] In summary, in an embodiment of the present invention, by acquiring a first data model sent by a first business system; the first data model carries a first classification identifier, a first grading identifier and target data; the first classification identifier and the first grading identifier represent the type information and level information of the target data in the first business system; in the classification identifier set and the grading identifier set in the second business system, the second classification identifier corresponding to the first classification identifier and the second grading identifier corresponding to the first grading identifier are determined; the second classification identifier and the second grading identifier are used to represent the type information and level information of the target data in the second business system; based on the second classification identifier, the second grading identifier and the target data, a second data model is constructed in the second business system. Data and its classification and grading information can be transmitted between systems as a data model as a whole, and the system receiving the data model can determine the corresponding classification identifier and grading identifier, conveniently determine the classification and grading information of the data in the current system, and construct the data model of the current system, thereby improving the efficiency of data classification and grading management for Internet of Things scenarios.

[0033] Figure 3 It is a flowchart of the steps of another data classification and grading management method for a data networking scenario provided by an embodiment of the present invention. Figure 3 include: Step 301, obtaining a first data model sent by a first business system; the first data model carries a first classification identifier, a first level identifier and target data; the first classification identifier and the first level identifier are used to indicate type information and level information of the target data in the first business system; Step 302, determining the second classification identifier corresponding to the first classification identifier and the second classification identifier corresponding to the first classification identifier in the classification identifier set and the classification identifier set in the second business system, respectively; the second classification identifier and the second classification identifier are used to indicate the type information and the level information of the target data in the second business system; Step 303: construct a second data model in the second business system based on the second classification identifier, the second grading identifier and the target data to complete the classification and grading management of the target data in the second business system.

[0034] For details of the above steps 301-303, please refer to Figure 1 The contents of the embodiments will not be repeated here.

[0035] Optionally, step 302: determining the second classification identifier corresponding to the first classification identifier and the second classification identifier corresponding to the first classification identifier in the classification identifier set and the classification identifier set in the second business system, respectively, may include: Sub-step 3021, determining, in the classification identifier set, a second classification identifier corresponding to the first classification identifier of the first data model according to a pre-constructed first mapping relationship between the first classification identifier and the second classification identifier; Sub-step 3022: determining, in the set of hierarchical identifiers, a second hierarchical identifier corresponding to the first hierarchical identifier of the first data model according to a pre-constructed second mapping relationship between the first hierarchical identifier and the second hierarchical identifier.

[0036] In the disclosed embodiment, a first mapping relationship between a first classification identifier of a first business system and a second classification identifier of a second business system can be pre-constructed, and each first classification identifier and each second classification identifier can be matched. The first mapping relationship can be constructed based on the user's selection judgment. According to the first classification identifier of the first data model, the corresponding second classification identifier can be determined in the first mapping relationship. The second classification identifier represents the type information of the target data in the second business system. The construction process of the second mapping relationship can be similar and will not be repeated here.

[0037] Figure 4 It is a schematic diagram of the first mapping relationship provided by an embodiment of the present disclosure; the first classification identifier 401 "1.2.3 / 45b" corresponds to the second classification identifier 402 "1.2.3 / 45d". Figure 3 The mapping relationship between the classification identifiers is merely illustrated. More first mapping relationships between the classification identifiers may be constructed according to actual needs.

[0038] Figure 5 It is a schematic diagram of the second mapping relationship provided by the embodiment of the present disclosure; the first hierarchical identifier 501 "1.2.3 / 45c" corresponds to the second hierarchical identifier 502 "1.2.3 / 45e". Figure 4 The mapping relationship between the hierarchical identifiers is merely illustrated. More second mapping relationships between hierarchical identifiers may be constructed according to actual needs.

[0039] In the implementation of the embodiment of the present disclosure, the second classification identifier corresponding to the first classification identifier is determined in the classification identifier set through the first mapping relationship between the first classification identifier and the second classification identifier that is pre-constructed; the second hierarchical identifier corresponding to the first hierarchical identifier is determined in the hierarchical identifier set according to the second mapping relationship between the first hierarchical identifier and the second hierarchical identifier that is pre-constructed. The corresponding classification identifier and hierarchical identifier can be directly determined in the pre-created mapping relationship, thereby improving the efficiency of identifier conversion, and ultimately improving the efficiency of data classification and hierarchical management for data networking scenarios.

[0040] Optionally, the first data model carries a digital signature; after the step of obtaining the first data model sent by the first business system, the method further includes: Obtain the digital signature and the private key; the digital signature is obtained by the first business system encrypting the first summary information of the target data based on the public key corresponding to the private key; the first summary information is obtained by the first business system calculating the target data based on a preset hash algorithm; Decrypting the digital signature based on the private key to obtain the first summary information, and performing a calculation on the target data based on the preset hash algorithm to obtain the second summary information; When the first summary information matches the second summary information, it is determined that the target data acquired by the second business system is complete.

[0041] In the disclosed embodiment, the first business system can generate a public key and a private key based on an asymmetric encryption algorithm such as an RSA algorithm, or an elliptic curve cryptography algorithm (ECC) or other encryption algorithms. The first summary information of the target data can be encrypted with the public key to obtain a digital signature, which is also stored in the first data model (continue to refer to Figure 2 As shown). The first summary information is obtained by the first business system calculating the target data based on a preset hash algorithm.

[0042] The public key has a corresponding private key. The second business system can read the digital signature in the first data model and obtain the private key corresponding to the public key from the first business system. The digital signature is decrypted based on the private key to obtain the first summary information.

[0043] Similarly, the second business system also uses a hash algorithm to calculate the target data to obtain the second summary information. The first summary information and the second summary information are compared. If the two are the same, it is considered that the first summary information and the second summary information match, which means that the target data obtained by the second business system is the same as the target data sent by the first business system at the beginning, and the data is complete.

[0044] It should be understood that the digital signature is used to verify the integrity of the data. Therefore, if the data is unexpectedly damaged, the second summary information obtained by the second business system based on the damaged target data may be different from the first summary information in the digital signature encrypted by the first business system.

[0045] When the second summary information is different from the first summary information, an alarm or reminder message can be triggered so that the user knows that the data is damaged. When the second summary information is the same as the first summary information, a subsequent second data model can be constructed.

[0046] In implementing the embodiments of the present disclosure, the first summary information of the target data is encrypted by the first business system to obtain a digital signature, and the second business system decrypts the digital signature to obtain the first summary information, and the second business system itself also generates second summary information. When the first summary information and the second summary information match, it can be determined that the target data obtained by the second business system is complete, thereby improving the data accuracy during the data transmission process.

[0047] Optionally, before the step of determining, in step 3021, the second classification identifier corresponding to the first classification identifier of the first data model in the classification identifier set according to the pre-constructed first mapping relationship between the first classification identifier and the second classification identifier, the method further includes: Step A1, obtaining at least one first classification identifier of the first business system and first type information corresponding to the at least one first classification identifier, and obtaining at least one second classification identifier of the second business system and second type information corresponding to the at least one second classification identifier; Step A2: when the first type information matches the second type information, construct the first mapping relationship between the first classification identifier and the second classification identifier.

[0048] In the disclosed embodiment, the type information of the first classification identifier and the type information of the second classification identifier can be matched, for example, by calculating the similarity between the type information of the first classification identifier and the type information of the second classification identifier. When the similarity is greater than a preset threshold, it is determined that the first type information and the second type information match, and then a first mapping relationship between the first classification identifier and the second classification identifier can be established. For example, the classification information represented by the first classification identifier A is: food data, the classification information represented by the second classification identifier B is: vehicle data, and the classification information represented by the second classification identifier C is: catering data. Then the similarity between the first classification identifier A and the second classification identifier C is higher than the similarity between the first classification identifier A and the second classification identifier B, and therefore a first mapping relationship between the first classification identifier A and the second classification identifier C can be established.

[0049] Optionally, before the step of determining, in the set of hierarchical identifiers, the second hierarchical identifier corresponding to the first hierarchical identifier of the first data model according to the pre-constructed second mapping relationship between the first hierarchical identifier and the second hierarchical identifier in step 3022, the method further includes: Step B1, obtaining at least one first classification identifier of the first business system and first level information corresponding to the at least one first classification identifier, and obtaining at least one second classification identifier of the second business system and second level information corresponding to the at least one second classification identifier; Step B2: when the first level information matches the second level information, construct the second mapping relationship between the first hierarchical identifier and the second hierarchical identifier.

[0050] The specific contents of steps B1-B2 are similar to those of the above steps A1-A2 and will not be repeated here.

[0051] Optionally, before the step of acquiring the first data model sent by the first business system, the method further includes: Sending a data acquisition request carrying characteristic information to the first business system; The first business system is used to determine the corresponding target data according to the feature information in the data acquisition request, and send the first data model carrying the target data to the second business system.

[0052] In the embodiment of the present disclosure, the second business system may, based on the characteristic information of the required data, for example, the identifier of the data (see Figure 2 The data identifier 1.2.3 / 45a) or the data type, keyword, etc. in the data acquisition request is generated and sent to the first business system. The first business system can determine the corresponding target data according to the feature information in the data acquisition request, and send the first data model carrying the target data to the second business system.

[0053] Optionally, the method further includes: When there is no second classification identifier corresponding to the first classification identifier in the first mapping relationship and / or there is no second hierarchical identifier corresponding to the first hierarchical identifier in the second mapping relationship, generating a new second classification identifier and / or a new second hierarchical identifier; Constructing a second data model based on the new second classification identifier and / or the new second classification identifier, and the target data; A first mapping relationship between the first classification identifier and the new second classification identifier is constructed, and a second mapping relationship between the first hierarchical identifier and the new second hierarchical identifier is constructed.

[0054] In the disclosed embodiment, it is understandable that the classification identification set and / or classification identification set of the second business system may not contain the second classification identification and the second classification identification corresponding to the first classification identification and the first classification identification of the second business system. Then, the second classification identification and the second classification identification cannot be found in the first mapping relationship and the second mapping relationship.

[0055] Therefore, a corresponding new second classification identifier and / or a new second grading identifier may be generated. The specific situation where the second business system lacks an identifier may be the following three: If there is no corresponding second classification identifier and there is an existing corresponding second hierarchical identifier, a corresponding new second classification identifier is generated, and a second data model is constructed based on the new second classification identifier, the existing second hierarchical identifier and the target data; If there is no corresponding second classification identifier and there is an existing corresponding second classification identifier, a corresponding new second classification identifier is generated, and a second data model is constructed based on the new second classification identifier, the existing second classification identifier and the target data; If there is no corresponding second hierarchical identifier and no corresponding second classification identifier, a corresponding new second hierarchical identifier and a new second classification identifier are generated, and a second data model is constructed based on the new second hierarchical identifier, the new second classification identifier and the target data.

[0056] In addition, the first mapping relationship and the second mapping relationship need to be supplemented and constructed based on the new second classification identifier and the new second grading identifier.

[0057] The embodiments of the present disclosure are implemented by generating a new second classification identifier and / or a new second classification identifier when there is no corresponding second classification identifier in the first mapping relationship and / or there is no corresponding second classification identifier in the second mapping relationship; building a second data model based on the new second classification identifier and / or the new second classification identifier and the target data; and building a first mapping relationship between the first classification identifier and the new second classification identifier, and building a second mapping relationship between the first classification identifier and the new second classification identifier, which can be used directly later. This can further improve the efficiency of data classification and classification management for Internet of Things scenarios.

[0058] Figure 6 6 is a schematic diagram of the first data model and the second data model provided by the embodiment of the present disclosure; the first data model 610 includes the data identification, classification identification, classification identification, digital signature of the target data, and other more information such as modification time information (not shown); the classification identification corresponds to the classification information, and the classification identification corresponds to the classification information. The second data model 620 includes the data identification, classification identification, classification identification, digital signature of the target data, the classification identification corresponds to the classification information, and the classification identification corresponds to the classification information.

[0059] Figure 7 700 is a block diagram of a data classification and grading management device for a data networking scenario provided by an embodiment of the present invention. The device 700 includes: The model acquisition module 701 is used to acquire a first data model sent by a first business system; the first data model carries a first classification identifier, a first grading identifier and target data; the first classification identifier and the first grading identifier are used to indicate type information and level information of the target data in the first business system; The identifier conversion module 702 is used to determine the second classification identifier corresponding to the first classification identifier and the second classification identifier corresponding to the first classification identifier in the classification identifier set and the classification identifier set in the second business system; the second classification identifier and the second classification identifier are used to indicate the type information and the level information of the target data in the second business system; The model building module 703 is used to build a second data model in the second business system based on the second classification identifier, the second grading identifier and the target data to complete the classification and grading management of the target data in the second business system.

[0060] Optional, identity conversion module, including: A first mapping submodule, configured to determine, in the classification identifier set, a second classification identifier corresponding to the first classification identifier of the first data model according to a pre-constructed first mapping relationship between the first classification identifier and the second classification identifier; The second mapping submodule is used to determine the second hierarchical identifier corresponding to the first hierarchical identifier of the first data model in the hierarchical identifier set according to a pre-constructed second mapping relationship between the first hierarchical identifier and the second hierarchical identifier.

[0061] Optionally, the first data model carries a digital signature; after the step of obtaining the first data model sent by the first business system, the method further includes: Obtain the digital signature and the private key; the digital signature is obtained by the first business system encrypting the first summary information of the target data based on the public key corresponding to the private key; the first summary information is obtained by the first business system calculating the target data based on a preset hash algorithm; Decrypting the digital signature based on the private key to obtain the first summary information, and performing a calculation on the target data based on the preset hash algorithm to obtain the second summary information; When the first summary information matches the second summary information, it is determined that the target data acquired by the second business system is complete.

[0062] Optionally, the device further comprises: A first information module is used to obtain at least one first classification identifier of the first business system and first type information corresponding to the at least one first classification identifier, and to obtain at least one second classification identifier of the second business system and second type information corresponding to the at least one second classification identifier; The first mapping construction module is used to construct the first mapping relationship between the first classification identifier and the second classification identifier when the first type information matches the second type information.

[0063] Optionally, the device further comprises: A second information module is used to obtain at least one first classification identifier of the first business system and the first level information corresponding to the at least one first classification identifier, and to obtain at least one second classification identifier of the second business system and the second level information corresponding to the at least one second classification identifier; The second mapping construction module is used to construct the second mapping relationship between the first classification identifier and the second classification identifier when the first level information matches the second level information.

[0064] Optionally, the device further comprises: A data acquisition module, used to send a data acquisition request carrying feature information to the first business system; The first business system is used to determine the corresponding target data according to the feature information in the data acquisition request, and send the first data model carrying the target data to the second business system.

[0065] Optionally, the device further comprises: an identifier generation module, used to generate a new second classification identifier and / or a new second classification identifier when there is no second classification identifier corresponding to the first classification identifier in the first mapping relationship and / or there is no second classification identifier corresponding to the first classification identifier in the second mapping relationship; A model building module, used for building a second data model based on the new second classification identifier and / or the new second classification identifier, and the target data; The mapping supplement module is used to construct a first mapping relationship between the first classification identifier and the new second classification identifier, and to construct a second mapping relationship between the first grading identifier and the new second grading identifier.

[0066] In summary, in an embodiment of the present invention, by acquiring a first data model sent by a first business system; the first data model carries a first classification identifier, a first grading identifier and target data; the first classification identifier and the first grading identifier represent the type information and level information of the target data in the first business system; in the classification identifier set and the grading identifier set in the second business system, the second classification identifier corresponding to the first classification identifier and the second grading identifier corresponding to the first grading identifier are determined; the second classification identifier and the second grading identifier are used to represent the type information and level information of the target data in the second business system; based on the second classification identifier, the second grading identifier and the target data, a second data model is constructed in the second business system. Data and its classification and grading information can be transmitted between systems as a data model as a whole, and the system receiving the data model can determine the corresponding classification identifier and grading identifier, conveniently determine the classification and grading information of the data in the current system, and construct the data model of the current system, thereby improving the efficiency of data classification and grading management for Internet of Things scenarios.

[0067] The data classification and grading management device for the data networking scenario in the embodiment of the present invention can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. Exemplarily, the mobile electronic device can be a mobile phone, a tablet computer, a laptop computer, a PDA, an in-vehicle electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA), etc. The non-mobile electronic device can be a server, a network attached storage (NAS), a personal computer (PC), a television (TV), a teller machine or a self-service machine, etc., which is not specifically limited in the embodiment of the present invention.

[0068] The data classification and grading management device for the Internet of Things scenario in the embodiment of the present invention may be a device with an operating system. The operating system may be an Android operating system, an iOS operating system, or other possible operating systems, which are not specifically limited in the embodiment of the present invention.

[0069] The data classification and grading management device for the data networking scenario provided by the embodiment of the present invention can achieve Figure 1 In order to avoid repetition, the various processes implemented by the data classification and grading management device for the digital network scenario in the method embodiment will not be described here.

[0070] Optionally, an embodiment of the present invention further provides an electronic device, including a processor, a memory, and a program or instruction stored in the memory and executable on the processor. When the program or instruction is executed by the processor, the various processes of the above-mentioned data classification and grading management method embodiment for the Internet of Things scenario are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0071] It should be noted that the electronic devices in the embodiments of the present invention include the mobile electronic devices and non-mobile electronic devices mentioned above.

[0072] Figure 8 A schematic diagram of the hardware structure of an electronic device 1000 for implementing an embodiment of the present invention.

[0073] The electronic device 1000 includes but is not limited to: a radio frequency unit 1001, a network module 1002, an audio output unit 1003, an input unit 1004, a sensor 1005, a display unit 1006, an initiating user input unit 1007, an interface unit 1008, a memory 1009, and a processor 1010 and other components.

[0074] Those skilled in the art will appreciate that the electronic device 1000 may also include a power source (such as a battery) for supplying power to various components, and the power source may be logically connected to the processor 1010 through a power management system, thereby implementing functions such as managing charging, discharging, and power consumption management through the power management system. Figure 7 The electronic device structure shown in the figure does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently, which will not be described in detail here.

[0075] The processor 1010 is used to obtain a first data model sent by a first business system; the first data model carries a first classification identifier, a first classification identifier and target data; the first classification identifier and the first classification identifier are used to indicate type information and level information of the target data in the first business system; In the classification identifier set and the level identifier set in the second business system, respectively determine the second classification identifier corresponding to the first classification identifier and the second level identifier corresponding to the first level identifier; the second classification identifier and the second level identifier are used to indicate the type information and level information of the target data in the second business system; Based on the second classification identifier, the second grading identifier and the target data, a second data model is constructed in the second business system to complete the classification and grading management of the target data in the second business system.

[0076] Optionally, the processor 1010 is used to Determine, in the classification identifier set, a second classification identifier corresponding to the first classification identifier of the first data model according to a pre-constructed first mapping relationship between the first classification identifier and the second classification identifier; According to a pre-constructed second mapping relationship between the first hierarchical identifier and the second hierarchical identifier, a second hierarchical identifier corresponding to the first hierarchical identifier of the first data model is determined in the hierarchical identifier set.

[0077] Optionally, the processor 1010 is further configured to: Obtain the digital signature and the private key; the digital signature is obtained by the first business system encrypting the first summary information of the target data based on the public key corresponding to the private key; the first summary information is obtained by the first business system calculating the target data based on a preset hash algorithm; Decrypting the digital signature based on the private key to obtain the first summary information, and performing a calculation on the target data based on the preset hash algorithm to obtain the second summary information; When the first summary information matches the second summary information, it is determined that the target data acquired by the second business system is complete.

[0078] Optionally, the processor 1010 is further configured to obtain at least one first classification identifier of the first business system and first type information respectively corresponding to the at least one first classification identifier, and obtain at least one second classification identifier of the second business system and second type information respectively corresponding to the at least one second classification identifier; When the first type information matches the second type information, the first mapping relationship between the first classification identifier and the second classification identifier is constructed.

[0079] Optionally, the processor 1010 is further configured to: Acquire at least one first classification identifier of the first business system and first level information corresponding to the at least one first classification identifier, and acquire at least one second classification identifier of the second business system and second level information corresponding to the at least one second classification identifier; When the first level information matches the second level information, the second mapping relationship between the first hierarchical identifier and the second hierarchical identifier is constructed.

[0080] Optionally, the processor 1010 is further configured to: Sending a data acquisition request carrying characteristic information to the first business system; The first business system is used to determine the corresponding target data according to the feature information in the data acquisition request, and send the first data model carrying the target data to the second business system.

[0081] Optionally, the processor 1010 is further configured to: When there is no second classification identifier corresponding to the first classification identifier in the first mapping relationship and / or there is no second hierarchical identifier corresponding to the first hierarchical identifier in the second mapping relationship, generating a new second classification identifier and / or a new second hierarchical identifier; Constructing a second data model based on the new second classification identifier and / or the new second classification identifier, and the target data; A first mapping relationship between the first classification identifier and the new second classification identifier is constructed, and a second mapping relationship between the first hierarchical identifier and the new second hierarchical identifier is constructed.

[0082] It should be understood that in the embodiment of the present invention, the input unit 1004 may include a graphics processor (GPU) 10041 and a microphone 10042, and the graphics processor 10041 processes the image data of the static picture or video obtained by the image capture device (such as a camera) in the video capture mode or the image capture mode. The display unit 1006 may include a display panel 10061, and the display panel 10061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The initiating user input unit 1007 includes a touch panel 10071 and at least one of other input devices 10072. The touch panel 10071 is also called a touch screen. The touch panel 10071 may include two parts: a touch detection device and a touch controller. Other input devices 10072 may include, but are not limited to, a physical keyboard, function keys (such as a volume control key, a switch key, etc.), a trackball, a mouse, and a joystick, which will not be repeated here.

[0083] The memory 1009 can be used to store software programs and various data. The memory 1009 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required for at least one function (such as a sound playback function, an image playback function, etc.), etc. In addition, the memory 1009 may include a volatile memory or a non-volatile memory, or the memory 1009 may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM) and a direct memory bus random access memory (DRRAM). The memory 1009 in the embodiment of the present invention includes but is not limited to these and any other suitable types of memory.

[0084] The processor 1010 may include one or more processing units; optionally, the processor 1010 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to the operating system, initiating user interfaces and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor 1010.

[0085] An embodiment of the present invention also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the various processes of the above-mentioned data classification and grading management method embodiment for the Internet of Things scenario are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0086] The processor is a processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0087] An embodiment of the present invention further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned data classification and grading management method embodiment for the Internet of Things scenario, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0088] It should be understood that the chip mentioned in the embodiment of the present invention may also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0089] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0090] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A data classification and grading management method for a data networking scenario, characterized in that: The method comprises: Acquire a first data model sent by a first business system; the first data model carries a first classification identifier, a first grading identifier and target data; the first classification identifier and the first grading identifier are used to indicate type information and level information of the target data in the first business system; In the classification identifier set and the level identifier set in the second business system, respectively determine the second classification identifier corresponding to the first classification identifier and the second level identifier corresponding to the first level identifier; the second classification identifier and the second level identifier are used to indicate the type information and level information of the target data in the second business system; Based on the second classification identifier, the second grading identifier and the target data, a second data model is constructed in the second business system to complete the classification and grading management of the target data in the second business system.

2. The method according to claim 1, characterized in that The step of respectively determining the second classification identifier corresponding to the first classification identifier and the second classification identifier corresponding to the first classification identifier in the classification identifier set and the classification identifier set in the second business system includes: Determine, in the classification identifier set, a second classification identifier corresponding to the first classification identifier of the first data model according to a pre-constructed first mapping relationship between the first classification identifier and the second classification identifier; According to a pre-constructed second mapping relationship between the first hierarchical identifier and the second hierarchical identifier, a second hierarchical identifier corresponding to the first hierarchical identifier of the first data model is determined in the hierarchical identifier set.

3. The method according to claim 1, characterized in that The first data model carries a digital signature; after the step of obtaining the first data model sent by the first business system, the method further includes: Obtain the digital signature and the private key; the digital signature is obtained by the first business system encrypting the first summary information of the target data based on the public key corresponding to the private key; the first summary information is obtained by the first business system calculating the target data based on a preset hash algorithm; Decrypting the digital signature based on the private key to obtain the first summary information, and performing a calculation on the target data based on the preset hash algorithm to obtain the second summary information; When the first summary information matches the second summary information, it is determined that the target data acquired by the second business system is complete.

4. The method according to claim 2, characterized in that: Before the step of determining, in the classification identifier set, the second classification identifier corresponding to the first classification identifier of the first data model, according to the pre-constructed first mapping relationship between the first classification identifier and the second classification identifier, the method further includes: Acquire at least one first classification identifier of the first business system and first type information corresponding to the at least one first classification identifier, and acquire at least one second classification identifier of the second business system and second type information corresponding to the at least one second classification identifier; When the first type information matches the second type information, the first mapping relationship between the first classification identifier and the second classification identifier is constructed.

5. The method according to claim 2, characterized in that: Before the step of determining, in the set of hierarchical identifiers, the second hierarchical identifier corresponding to the first hierarchical identifier of the first data model according to the pre-constructed second mapping relationship between the first hierarchical identifier and the second hierarchical identifier, the method further includes: Acquire at least one first classification identifier of the first business system and first level information corresponding to the at least one first classification identifier, and acquire at least one second classification identifier of the second business system and second level information corresponding to the at least one second classification identifier; When the first level information matches the second level information, the second mapping relationship between the first hierarchical identifier and the second hierarchical identifier is constructed.

6. The method according to claim 1, characterized in that Before the step of acquiring the first data model sent by the first business system, the method further includes: Sending a data acquisition request carrying characteristic information to the first business system; The first business system is used to determine the corresponding target data according to the feature information in the data acquisition request, and send the first data model carrying the target data to the second business system.

7. The method according to claim 2, characterized in that The method further comprises: When there is no second classification identifier corresponding to the first classification identifier in the first mapping relationship and / or there is no second hierarchical identifier corresponding to the first hierarchical identifier in the second mapping relationship, generating a new second classification identifier and / or a new second hierarchical identifier; Constructing a second data model based on the new second classification identifier and / or the new second classification identifier, and the target data; A first mapping relationship between the first classification identifier and the new second classification identifier is constructed, and a second mapping relationship between the first hierarchical identifier and the new second hierarchical identifier is constructed.

8. A data classification and grading management device for a data networking scenario, characterized in that: The device comprises: A model acquisition module, used to acquire a first data model sent by a first business system; the first data model carries a first classification identifier, a first grading identifier and target data; the first classification identifier and the first grading identifier are used to indicate type information and level information of the target data in the first business system; an identification conversion module, used to determine, from a classification identification set and a classification identification set in a second business system, a second classification identification corresponding to the first classification identification, and a second classification identification corresponding to the first classification identification; the second classification identification and the second classification identification are used to indicate type information and level information of the target data in the second business system; A model building module is used to build a second data model in the second business system based on the second classification identifier, the second grading identifier and the target data to complete the classification and grading management of the target data in the second business system.

9. An electronic device, characterized in that: It includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor. When the program or instruction is executed by the processor, the steps of the data classification and grading management method for the Internet of Things scenario as described in any one of claims 1 to 7 are implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores programs or instructions, and when the programs or instructions are executed by the processor, the steps of the data classification and grading management method for the Internet of Things scenario as described in any one of claims 1 to 7 are implemented.

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