A data processing method, apparatus and device

Abstract

The embodiment of the specification discloses a data processing method, device and equipment, the method comprises: receiving the semantic acquisition request for the target data, the semantic acquisition request includes semantic encoding information and semantic parameter information; based on the semantic encoding information, the semantic configuration information corresponding to the target data is acquired, and based on the semantic parameter information, the semantic object logical attribute value corresponding to the target data is initialized; based on the semantic configuration information corresponding to the target data, the semantic calculation unit is called, and based on the semantic configuration information corresponding to the target data and the initialized semantic object logical attribute value corresponding to the target data, the semantic calculation unit is called to perform semantic calculation on the target data, and the corresponding semantic calculation result is obtained; based on the semantic configuration information corresponding to the target data and the semantic calculation result, the semantic information of the target data is constructed and output.

Description

TECHNICAL FIELD

[0001] The present document relates to the technical field of computer, and particularly relates to a data processing method, device and equipment. BACKGROUND

[0002] With people paying more and more attention to their own arbitrary data, privacy protection has become one of the most concerned problems of people at present. In the privacy protection business, the personal information ledger system of the privacy protection platform is positioned to close various types of data, and according to the business demand, the data objects are marked with characteristic tags. However, in the usual development mode, for different business semantics, technical personnel need to specially develop corresponding rule program codes and perform technical iteration, and at the same time, different data objects need to be queried respectively, the development workload is large, the development process is long, the iteration period is long, and flexible adjustment cannot be made according to the business demand. Therefore, it is necessary to provide a more optimal business semantic construction mechanism, especially for the construction of the business semantics of data operation, so as to reduce the development workload, shorten the development process and iteration period. SUMMARY

[0003] The purpose of the embodiments of the present specification is to provide a more optimal business semantic construction mechanism, especially for the construction of the business semantics of data operation, so as to provide fast, general and adjustable object data acquisition when the business semantics are calculated, and support the configuration calculation ability of the business semantics.

[0004] In order to achieve the above technical scheme, the embodiments of the present specification are implemented as follows:

[0005] The data processing method provided by the embodiments of the present specification comprises: receiving a semantic acquisition request for target data, wherein the semantic acquisition request comprises semantic encoding information and semantic parameter information. Based on the semantic encoding information, semantic configuration information corresponding to the target data is obtained, and based on the semantic parameter information, semantic object logical attribute values corresponding to the target data are initialized. Based on the semantic configuration information corresponding to the target data, a semantic calculation unit is called, and based on the semantic configuration information corresponding to the target data and the initialized semantic object logical attribute values corresponding to the target data, the target data is calculated by the called semantic calculation unit, and a corresponding semantic calculation result is obtained. Based on the semantic configuration information corresponding to the target data and the semantic calculation result, semantic information of the target data is constructed and output.

[0006] An embodiment of the present specification provides a data processing apparatus, the apparatus comprising a semantic computing interface, a semantic configuration module, a semantic logical context module, and a semantic computing engine, wherein: the semantic computing interface is configured to receive a semantic acquisition request for target data, the semantic acquisition request comprising semantic encoding information and semantic parameter information. The semantic configuration module is configured to manage configuration information describing logical semantic rules of the target data, and is configured to acquire semantic configuration information corresponding to the target data based on the semantic encoding information. The semantic logical context module is configured to save and acquire semantic object logical attribute values corresponding to the target data in the process of semantic computing, and is configured to initialize semantic object logical attribute values corresponding to the target data based on the semantic parameter information. The semantic configuration module is configured to call a semantic computing unit in the semantic computing engine based on the semantic configuration information corresponding to the target data. The semantic computing engine is configured to perform semantic computing on the target data by the called semantic computing unit based on the semantic configuration information corresponding to the target data and the initialized semantic object logical attribute values corresponding to the target data, to obtain a corresponding semantic computing result, and to construct and output semantic information of the target data based on the semantic configuration information corresponding to the target data and the semantic computing result.

[0007] An embodiment of the present specification provides a data processing device, the data processing device comprising: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to: receive a semantic acquisition request for target data, the semantic acquisition request comprising semantic encoding information and semantic parameter information. Acquire semantic configuration information corresponding to the target data based on the semantic encoding information, and initialize semantic object logical attribute values corresponding to the target data based on the semantic parameter information. Call a semantic computing unit based on the semantic configuration information corresponding to the target data, and perform semantic computing on the target data by the called semantic computing unit based on the semantic configuration information corresponding to the target data and the initialized semantic object logical attribute values corresponding to the target data, to obtain a corresponding semantic computing result. Construct and output semantic information of the target data based on the semantic configuration information corresponding to the target data and the semantic computing result.

[0008] The embodiment of the present specification further provides a storage medium for storing computer executable instructions, which, when executed by a processor, implement the following processes: receiving a semantic acquisition request for target data, the semantic acquisition request including semantic encoding information and semantic parameter information. Obtain semantic configuration information corresponding to the target data based on the semantic encoding information, and initialize semantic object logical attribute values corresponding to the target data based on the semantic parameter information. Call a semantic computing unit based on the semantic configuration information corresponding to the target data, and perform semantic computing on the target data through the called semantic computing unit based on the semantic configuration information corresponding to the target data and the initialized semantic object logical attribute values corresponding to the target data, to obtain a corresponding semantic computing result. Construct and output semantic information of the target data based on the semantic configuration information corresponding to the target data and the semantic computing result.

[0009] The embodiment of the present specification further provides a computer program product, comprising a computer program which, when executed by a processor, implements the following processes: receiving a semantic acquisition request for target data, the semantic acquisition request including semantic encoding information and semantic parameter information. Obtain semantic configuration information corresponding to the target data based on the semantic encoding information, and initialize semantic object logical attribute values corresponding to the target data based on the semantic parameter information. Call a semantic computing unit based on the semantic configuration information corresponding to the target data, and perform semantic computing on the target data through the called semantic computing unit based on the semantic configuration information corresponding to the target data and the initialized semantic object logical attribute values corresponding to the target data, to obtain a corresponding semantic computing result. Construct and output semantic information of the target data based on the semantic configuration information corresponding to the target data and the semantic computing result. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present specification or the prior art, brief introductions to the drawings needed in the embodiment or prior art description will be given below. Obviously, the drawings in the following description are only some embodiments described in the present specification, and those skilled in the art can also obtain other drawings according to these drawings without creative labor;

[0011] Figure 1 It is a structural schematic diagram of a data processing system of the present specification;

[0012] Figure 2 It is an embodiment of a data processing method of the present specification;

[0013] Figure 3 It is a page schematic diagram of semantic information acquisition of the present specification;

[0014] Figure 4 A schematic diagram of a data processing procedure of the present specification;

[0015] Figure 5 A schematic diagram of another data processing procedure of the present specification;

[0016] Figure 6 A schematic diagram of another data processing procedure of the present specification;

[0017] Figure 7 A schematic diagram of another data processing procedure of the present specification;

[0018] Figure 8 A schematic diagram of a semantic logic context module processing procedure of the present specification;

[0019] Figure 9 A schematic diagram of another data processing procedure of the present specification;

[0020] Figure 10 A schematic diagram of another data processing procedure of the present specification;

[0021] Figure 11 A schematic diagram of another data processing procedure of the present specification;

[0022] Figure 12 A schematic diagram of another data processing procedure of the present specification;

[0023] Figure 13 A schematic diagram of a semantic computing procedure of the present specification;

[0024] Figure 14 A schematic diagram of a semantic computing unit execution procedure of the present specification;

[0025] Figure 15 A schematic diagram of a relationship between an application, an interface, and a link of the present specification;

[0026] Figure 16 A schematic diagram of another data processing procedure of the present specification;

[0027] Figure 17 A schematic diagram of another data processing procedure of the present specification;

[0028] Figure 18 A schematic diagram of a data processing device embodiment of the present specification;

[0029] Figure 19 A schematic diagram of another data processing device embodiment of the present specification;

[0030] Figure 20 A schematic diagram of a data processing system structure of the present specification;

[0031] Figure 21 An embodiment of a data processing device is provided in the present specification. DETAILED DESCRIPTION

[0032] An embodiment of the present specification provides a data processing method, device and equipment.

[0033] In order for those skilled in the art to better understand the technical solutions in the present specification, the technical solutions in the present specification will be described clearly and completely in the present specification, combined with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present specification, not all. Based on the embodiments in the present specification, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present specification.

[0034] An embodiment of the present specification provides a business semantic construction mechanism for data operation. As people pay more and more attention to their own arbitrary data, privacy protection has become one of the most concerned problems of the current people. In the privacy protection business, the personal information ledger system of the privacy protection platform is positioned to close various data, and the data objects therein are marked with characteristic tags according to business needs. However, in the usual development mode, as shown in Figure 1 different business semantics need technical personnel to specially develop corresponding rule program codes and technical iterations, and different data objects also need to be queried respectively, which has large development workload, long development process, long iteration period, and on the other hand, the calculation logic of the business semantics is usually only reflected in the software program code, which is not clear and direct enough for the business logic, and cannot be flexibly adjusted according to the business needs. On the other hand, different semantic calculation programs repeatedly consume data of different data objects, and when the data objects increase or modify attribute information, different semantic calculation programs need to modify the data consumption logic respectively, so that the change has a large impact and a long change period. Therefore, a more optimal business semantic construction mechanism is needed, especially for the construction of business semantics for data operation. An implementable way is provided in the present specification, which realizes the structured logic modeling and rule description of business semantics by configuring the business semantic calculation rules, so that the business semantic configuration process is digitized, the research and development period of semantic customization development is shortened, and through one-stop management of different data object attribute information, quick, general and adjustable object data acquisition can be provided during business semantic calculation, supporting the configuration calculation ability of business semantics. Specific processing can be referred to the specific content in the following embodiments.

[0035] As Figure 2As shown, the embodiment of the present specification provides a data processing method, the execution subject of the method can be a terminal device or a server, etc., wherein the terminal device can be a mobile terminal device such as a mobile phone, a tablet computer, etc., can also be a computer device such as a notebook computer or a desktop computer, or can also be an IoT device (specifically, a smart watch, a vehicle-mounted device, etc.), etc., wherein the server can be an independent server, can also be a server cluster composed of multiple servers, etc., the server can be a background server of a financial service or a network shopping service, etc., can also be a background server of an application program, etc. In the embodiment, the execution subject is taken as the server for detailed description, for the case of the execution subject being the terminal device, the case of the server can be referred to, which will not be repeated here. The method can specifically include the following steps:

[0036] In step S202, a semantic acquisition request for target data is received, and the semantic acquisition request includes semantic encoding information and semantic parameter information.

[0037] Wherein, the target data can be any data, for example, the target data can be the private data of a certain user, specifically, the identity information of the user, the account information of the user, etc., the target data can also be the data of a certain application program, the target data can also be the data of a certain commodity, the target data can also be the transaction data between different users, the target data can also be the transaction data between different users and a certain merchant, etc., which can be set according to actual conditions. The semantic encoding information can be unique encoding information for identifying semantic information, which can be constructed in various ways, for example, the semantic encoding information can be composed of a digital string composed of multiple numbers, can also be composed of an alphabet string composed of multiple letters, can also be composed of a string composed of a combination of letters and numbers, specifically, SC00001, H5486ta, etc., in addition, it can also be composed of other special characters, multiple letters and numbers, which can be set according to actual conditions. The semantic parameter information can be the information of the parameter that needs to be transmitted for the semantic information, which can contain numerical values, characters, etc., which can be set according to actual conditions, which is not limited in the embodiment of the present specification.

[0038] In implementation, when the semantic information of a certain data (i.e. target data) needs to be determined, the corresponding page can be acquired through the terminal device, for example, Figure 3As shown, the page can include an input box of semantic coding information, an input box of semantic parameter information, a determination button, a cancel button, and the like. In addition to the input boxes and the button, the page can further include other information input boxes and / or buttons, which can be set according to actual conditions. The technical personnel can input the corresponding semantic coding information and semantic parameter information in the input boxes, and click the determination button after the input is completed. The terminal device can obtain the semantic coding information and the semantic parameter information input by the technical personnel, and further obtain other related information, such as the identification of the target data and the application scenario information of the target data, and generate a semantic obtaining request for the target data based on the obtained information. The semantic obtaining request can be sent to the server, and the server can receive the semantic obtaining request for the target data.

[0039] For example, a certain business system includes multiple platforms, such as a basic security platform, an intelligent interaction platform, a content security platform, and the like. Each platform includes corresponding applications, but does not include a privacy protection platform. The current applications do not include applications with an application label of a privacy protection platform. If the target data is an application A, and the application A actually belongs to an application of a privacy protection platform, the application A needs to be labeled with an application label. In order to label the application A with an application label, the semantic information of the application A needs to be determined first. Based on this, the terminal device can obtain the page, the technical personnel can input the corresponding semantic coding information and semantic parameter information in the page, and click the determination button. The terminal device can obtain the semantic coding information and the semantic parameter information, and generate a semantic obtaining request for the application A based on this. The semantic obtaining request can be sent to the server, and the server can receive the semantic obtaining request for the application A.

[0040] In step S204, the semantic configuration information corresponding to the target data is obtained based on the semantic coding information, and the semantic object logical attribute value corresponding to the target data is initialized based on the semantic parameter information.

[0041] The semantic configuration information can be semantic rules pre-configured for a specified service (such as a newly added service) or data, and the semantic configuration information is loaded in the process of semantic calculation to perform semantic calculation to obtain the corresponding result. The semantic configuration information can include a plurality of different information or semantic rules, for example, the semantic configuration information can include one or more of semantic coding information, semantic name, semantic type, input data configuration information, output data configuration information, and calling rule. The semantic name can be used to intuitively explain the literal description of the semantic service meaning, for example, privacy protection platform application identification, etc. The semantic type can include a plurality of types, for example, data verification type, semantic object labeling type, etc., which can be set according to actual conditions. The input data configuration information can be used to standardize or unify the display form of the input data, the output data configuration information can be used to standardize or unify the display form of the output data, and the calling rule can be a rule for scheduling a specified data or execution unit, etc., which can be as shown in Table 1.

[0042] Table 1

[0043]

[0044] In implementation, after the server receives the semantic obtaining request for the target data, the semantic obtaining request can be parsed, and the semantic coding information and the semantic parameter information contained in the semantic obtaining request can be extracted based on the parsing result. The corresponding semantic configuration information can be loaded according to the semantic coding information, and the loaded semantic configuration information can be used as the semantic configuration information corresponding to the target data. The semantic object logical attribute value corresponding to the target data can be initialized according to the semantic parameter information, and the initialized semantic object logical attribute value corresponding to the target data is obtained.

[0045] In step S206, the semantic calculation unit is called based on the semantic configuration information corresponding to the target data, and the semantic calculation unit is called based on the semantic configuration information corresponding to the target data and the initialized semantic object logical attribute value corresponding to the target data. The target data is subjected to semantic calculation by the called semantic calculation unit to obtain the corresponding semantic calculation result.

[0046] In implementation, since the semantic configuration information can be used to call the execution unit to execute the corresponding business, the specified semantic computing unit can be called through the semantic configuration information corresponding to the target data (or the specified information or the calling rule in the semantic configuration information corresponding to the target data, etc.), the called semantic computing unit can include one or more, if the called semantic computing unit includes multiple, the calling sequence of the multiple semantic computing units can also be set, in addition, if the multiple semantic computing units have a hierarchical structure, the calling sequence of the semantic computing unit of each layer can be set, the result obtained by the semantic computing unit of the lower layer can be returned to the corresponding semantic computing unit of the upper layer, the result obtained by the semantic computing unit of the upper layer is fed back upward until the top layer semantic computing unit is reached.

[0047] As shown in Figure 4 The initialized semantic object logical attribute value corresponding to the target data can be analyzed to determine the semantic object logical attribute information of the target data, including the type of the semantic object corresponding to the target data, the logical attribute name, the logical attribute type, etc. Based on the semantic name, the semantic type, the configuration information of the input data and other information in the semantic configuration information, and the semantic object logical attribute value, the semantic object logical attribute information, the semantic computing unit is called to perform semantic calculation on the target data, and the corresponding semantic calculation result is obtained.

[0048] In step S208, the semantic information of the target data is constructed and output based on the semantic configuration information corresponding to the target data and the semantic calculation result.

[0049] In implementation, the semantic calculation result can be adjusted based on the configuration information of the output data and other information in the semantic configuration information, the semantic information of the target data conforming to the configuration information of the output data is constructed, and the semantic information of the target data can be output. Subsequently, the target data can be data checked based on the semantic information of the target data, or the semantic object of the target data can be annotated based on the semantic information of the target data, for example, based on the above example, the semantic object of the application A can be annotated based on the semantic information of the target data, and finally the application A can be annotated as the application of the privacy protection platform.

[0050] The embodiment of the specification provides a data processing method. When a semantic acquisition request for target data is received, semantic configuration information corresponding to the target data is acquired based on semantic encoding information included in the semantic acquisition request, and a semantic object logical attribute value corresponding to the target data is initialized based on semantic parameter information included in the semantic acquisition request. Then, a semantic calculation unit can be called based on the semantic configuration information corresponding to the target data, and semantic calculation is performed on the target data by the called semantic calculation unit based on the semantic configuration information corresponding to the target data and the initialized semantic object logical attribute value corresponding to the target data, to obtain a corresponding semantic calculation result. Finally, semantic information of the target data can be constructed and output based on the semantic configuration information corresponding to the target data and the semantic calculation result. In this way, by constructing the semantic configuration information and the semantic object logical attribute information, the business semantics is digitally modeled, the semantic calculation unit is constructed, and the like, the online structured configuration of business semantics in a complex business data operation scenario and the data calculation based on the semantic configuration rule are realized, which can improve the data operation work efficiency, avoid repeated development work caused by business rule changes, shorten the work time consumption of data operation, and reduce the research and development time of program development.

[0051] In actual application, the specific processing mode of initializing the semantic object logical attribute value corresponding to the target data based on the semantic parameter information in the step S204 can be various, and the following provides an optional processing mode, for example, Figure 5 As shown in the following steps S2042 and S2044, the specific processing can be referred to.

[0052] In the step S2042, the semantic logical context corresponding to the target data is initialized based on the semantic parameter information.

[0053] The semantic logical context can be used to initialize the semantic object logical attribute value required in the semantic calculation process.

[0054] In implementation, as shown in the following step S2040, the semantic parameter information can be extracted from the semantic acquisition request, and the semantic logical context corresponding to the target data can be initialized based on the semantic parameter information to obtain the initialized semantic logical context. Figure 6

[0055] In the step S2044, the semantic logical context corresponding to the target data is created, and the semantic object logical attribute value corresponding to the target data is initialized according to the semantic parameter information.

[0056] In implementation, as shown in the following step S2042, the semantic parameter information can be extracted from the semantic acquisition request, and the semantic logical context corresponding to the target data can be initialized based on the semantic parameter information to obtain the initialized semantic logical context. Figure 6 ​As shown, based on the initialized semantic logical context, a semantic logical context corresponding to the target data can be created, and the semantic object logical attribute value corresponding to the target data can be initialized according to the semantic input parameter information, to obtain the initialized semantic object logical attribute value corresponding to the target data.

[0057] In actual application, the semantic configuration information can include reference semantic coding information, based on which, the specific processing manner of obtaining the semantic configuration information corresponding to the target data based on the semantic coding information in step S204 can be various, and the following provides an optional processing manner, which can include the following contents: based on the semantic coding information, the semantic configuration information corresponding to the reference semantic coding information matched with the semantic coding information is obtained from the pre-stored multiple semantic configuration information, and the obtained semantic configuration information is taken as the semantic configuration information corresponding to the target data.

[0058] In implementation, the semantic configuration information can include semantic coding information, which can be reference semantic coding information. After the semantic coding information is extracted from the semantic acquisition request, the semantic coding information can be compared with the reference semantic coding information in the semantic configuration information, if the semantic coding information is the same as a certain reference semantic coding information, the semantic configuration information corresponding to the reference semantic coding information can be obtained, and the obtained semantic configuration information can be taken as the semantic configuration information corresponding to the target data.

[0059] In actual application, the semantic configuration information can include a semantic calculation logical model, based on which, the specific processing manner of calling the semantic calculation unit based on the semantic configuration information corresponding to the target data in step S206 can be various, and the following provides an optional processing manner, which can include the following contents: the semantic calculation logical model is extracted from the semantic configuration information corresponding to the target data, and the semantic calculation unit is called based on the extracted semantic calculation logical model, the semantic calculation logical model is used to maintain the logical configuration of semantic calculation, and the logical configuration includes one or more of the type of the semantic calculation unit, the operator, the semantic object logical attribute information, the reference value, and the subsequent semantic calculation unit set.

[0060] The semantic calculation logical model is used to maintain the logical configuration of semantic calculation and to schedule each semantic calculation unit, and the logical configuration includes one or more of the type of the semantic calculation unit, the operator, the semantic object logical attribute information, the reference value, and the subsequent semantic calculation unit set, which can be shown in Table 2 as follows.

[0061] Table 2

[0062]

[0063] The semantic calculation logical model can be as follows:

[0064] {

[0065] type: and

[0066] next:[

[0067] {

[0068] / / Application data belongs to special offline project characteristics including ${A}

[0069] type: col

[0070] operator: feature-any_in-features

[0071] logicProp: bakApp.odsFeature

[0072] config: ${A}

[0073] },

[0074] {

[0075] / / Application features do not include ${B}

[0076] type: col

[0077] operator: !feature-any_in-features

[0078] logicProp: bakApp.features

[0079] config: ${B}

[0080] } ]

[0082] }

[0083] In implementation, a semantic computation logic model can be extracted from the semantic configuration information corresponding to the target data. Since the semantic computation logic model has the function of scheduling each semantic computation unit, the semantic computation unit can be called based on the extracted semantic computation logic model. In addition, the semantic computation logic model can also maintain information such as the type, operators, semantic object logical attribute information, reference values, and subsequent semantic computation unit sets of the semantic computation units in the semantic computation process.

[0084] In practical applications, the specific processing manner of the semantic calculation on the target data based on the semantic configuration information corresponding to the target data and the initialized semantic object logical attribute value corresponding to the target data in step S206 can be various, and the following provides an optional processing manner, as shown in the following steps S2062-S2066. Figure 7

[0085] In step S2062, whether the semantic object logical attribute value corresponding to the target data exists in the logical attribute cache is queried based on the initialized semantic object logical attribute value corresponding to the target data.

[0086] In implementation, as shown in the following steps S2062-S2066, the semantic object logical attribute information can be obtained by querying the logical attribute cache. Figure 8

[0087] Based on the above architecture, the logical attribute submodule needs to query the semantic object logical attribute information during the semantic calculation, at this time, the logical attribute submodule can query whether the cache data exists in the logical attribute cache, and whether the semantic object logical attribute value corresponding to the target data is contained in the cache data.

[0088] The logical attribute cache can be stored in a two-level key-value pair format, the first-level key-value pair key format is ${objectType}_${objectId}, which saves all logical attribute information of each semantic object, and the second-level key-value pair key is the logical attribute value of the semantic object, which saves each logical attribute information of a semantic object, which can be seen from the following example:

[0089] {

[0090] "bakApp_app1":{

[0091] ​​"belongFeature":"aa",

[0092] "ouCode":"bb

[0093] }

[0094] "api_apiCodeA":{

[0095] "controlStatus":"1"

[0096] }

[0097] }

[0098] In step S2064, if it exists, the semantic object logical attribute information corresponding to the target data is determined from the logical attribute cache based on the semantic object logical attribute value corresponding to the target data and according to the object data model. Based on the semantic configuration information and the semantic object logical attribute information corresponding to the target data, the semantic calculation unit is invoked to perform semantic calculation on the target data to obtain the corresponding semantic calculation result.

[0099] In implementation, such as Figure 8 As shown, if the logical attribute value of the semantic object corresponding to the target data exists in the logical attribute cache, it can be analyzed through the object data model based on the logical attribute value of the semantic object corresponding to the target data to determine the logical attribute information of the semantic object corresponding to the target data. Then, based on the semantic configuration information and the logical attribute information of the semantic object corresponding to the target data, semantic calculation can be performed on the target data by calling the semantic calculation unit to obtain the corresponding semantic calculation result. For the specific processing procedure, please refer to the aforementioned related content, which will not be repeated here.

[0100] In step S2066, if the target data does not exist, the semantic object logical attribute information corresponding to the target data is obtained by calling the external database according to the external data service configured in the object data model. The semantic object logical attribute value corresponding to the target data and the obtained semantic object logical attribute information corresponding to the target data are stored in the logical attribute cache. Based on the semantic configuration information and the semantic object logical attribute information corresponding to the target data, the semantic calculation unit is called to perform semantic calculation on the target data to obtain the corresponding semantic calculation result.

[0101] In implementation, if the semantic object logical attribute value corresponding to the target data does not exist in the logical attribute cache, an external data service configured in the object data model can be acquired, an external database of an external data submodule in the semantic logic context module is called through the external data service, and the semantic object logical attribute information corresponding to the target data can be acquired from the external database. The semantic object logical attribute value corresponding to the target data and the acquired semantic object logical attribute information corresponding to the target data can be stored in the logical attribute cache. Then, based on the semantic configuration information corresponding to the target data and the semantic object logical attribute information corresponding to the target data, the semantic calculation unit is called to perform semantic calculation on the target data, and a corresponding semantic calculation result is obtained. For specific processing process, reference can be made to the foregoing related content, and details are not described herein.

[0102] In actual application, the specific processing mode of acquiring the semantic object logical attribute information corresponding to the target data according to the external data service configured in the object data model to call the external database in the step S2066 can be various, and an optional processing mode is provided as follows. Figure 9 As shown in the figure, the specific processing can include the following steps S206602 and step S206604.

[0103] In the step S206602, the target semantic object data matched with the semantic object logical attribute value corresponding to the target data is acquired according to the external data service configured in the object data model to call the external database.

[0104] In the step S206604, the target semantic object data is converted into the semantic object logical attribute information corresponding to the target data based on the logical attribute conversion rule configured in the object data model.

[0105] In implementation, as shown in the figure, Figure 10As shown, considering that the data in the external database and the data locally can be different (such as different formats, different descriptions, etc.), a logical attribute conversion rule can be set in the object data model, which can be used to convert the data in the external database into data meeting the local requirements. In actual application, the logical attribute conversion rule can be a logical description (including a value path (the value path can start with “$”, that is, the semantic object logical attribute value is obtained from the query result of the external data service) and a conversion mode (the conversion mode can be wrapped by “@{}” to indicate a specific conversion mode or conversion rule) and the like) for obtaining a semantic object logical attribute value in the query result obtained through the external data service. If conversion is required, the semantic object logical attribute value obtained through the value path is subjected to format conversion processing. Based on this, the target semantic object data obtained through the value path can be subjected to format conversion processing using the logical attribute conversion rule configured in the object data model, so as to convert the target semantic object data into semantic object logical attribute information corresponding to the target data.

[0106] In actual application, the specific processing mode of the step S2066 of calling the external database according to the external data service configured in the object data model to obtain the semantic object logical attribute information corresponding to the target data can be various, and the following provides an optional processing mode. Figure 11 As shown, the specific processing can include the following steps S206606 to S206610.

[0107] In step S206606, the external database is called according to the external data service configured in the object data model, and it is queried whether the semantic object logical attribute value corresponding to the target data exists in the cache of the external database.

[0108] In implementation, as shown, Figure 12 According to the external data service configured in the object data model, the external database can be called, and then it can be firstly queried whether the semantic object logical attribute value corresponding to the target data exists in the cache of the external database.

[0109] The cache of the external database can be stored in a two-level key-value pair format, the first-level key-value pair key format is ${server}_${objectId}, which saves the data information of each semantic object obtained by calling different external data services, and the second-level key-value pair key is the data logical attribute information returned by the external data service, which saves the logical attribute information returned by calling the external data service. For reference can be made to the following example:

[0110] {

[0111] "bakAppQueryServerA_app1":{

[0112] "prop1":"cc",

[0113] "prop2":"dd"

[0114] }

[0115] "apiQueryServerB_apiCodeA":{

[0116] "prop3":"ee

[0117] }

[0118] }

[0119] In step S206608, if it exists, the semantic object logical attribute information corresponding to the target data is obtained from the cache of the external database based on the semantic object logical attribute value corresponding to the target data.

[0120] In step S206610, if the target data does not exist, the semantic object logical attribute information corresponding to the target data is obtained from the external database based on the semantic object logical attribute value corresponding to the target data.

[0121] In implementation, such as Figure 12 As shown, if the target data does not exist, the semantic object logical attribute information corresponding to the target data is obtained from the external database based on the semantic object logical attribute value corresponding to the target data. Then, the semantic object logical attribute value corresponding to the target data and the obtained semantic object logical attribute information corresponding to the target data can be stored in the cache of the external database. Alternatively, the semantic object logical attribute value corresponding to the target data and the obtained semantic object logical attribute information corresponding to the target data can also be stored in the logical attribute cache, etc.

[0122] In practical applications, the semantic computation logic model described above is used to maintain the logical configuration for semantic computation. This logical configuration includes operators and reference values. Based on this, the specific processing methods for obtaining the corresponding semantic computation results by calling the semantic computation unit to perform semantic computation on the target data based on the semantic configuration information corresponding to the target data and the logical attribute information of the semantic object corresponding to the target data can be varied. The following provides another optional processing method, such as... Figure 13 As shown, the specific process may include steps A2 to A6.

[0123] In step A2, the reference values ​​are obtained from the preset set of reference values ​​based on the semantic computing logic model in the semantic configuration information corresponding to the target data.

[0124] The reference value can be pre-stored in the reference value set when the semantic configuration information is defined, and the storage format can be, for example, key=reference value code, value=reference value value, and the like.

[0125] In step A4, operator information for performing semantic calculation is obtained based on the semantic configuration information corresponding to the target data and the semantic object logical attribute information corresponding to the target data.

[0126] The operator information can include various types, such as an addition operator, a subtraction operator, a multiplication operator, a division operator, a logical and operator, a logical or operator, and the like, which can be set according to actual conditions. The operator in this embodiment can be a function for performing semantic calculation, the input data of the function is the semantic object logical attribute information and the reference value, and the output data is the semantic calculation result. The operator format can be: semantic calculation result = (semantic object logical attribute information) operator (reference value), and the returned result is true or false. For example, as shown in Table 3 below

[0127] Table 3

[0128]

[0129] In step A6, the semantic calculation unit is called to perform semantic calculation using the obtained operator, taking the semantic object logical attribute information corresponding to the target data and the obtained reference value as input data, to determine the semantic calculation result for the target data.

[0130] In actual application, the semantic calculation unit can include a calculation type semantic calculation unit and a merging type semantic calculation unit. Based on this, if the semantic calculation unit is a calculation type semantic calculation unit, the specific processing mode of the above step A6 can be various, and an optional processing mode is provided as follows, which can include the following contents: the calculation type semantic calculation unit is called to perform semantic calculation using the obtained operator, taking the semantic object logical attribute information corresponding to the target data and the obtained reference value as input data, to obtain the semantic calculation result corresponding to the calculation type semantic calculation unit, and return the semantic calculation result corresponding to the calculation type semantic calculation unit to the upper level semantic calculation unit of the calculation type semantic calculation unit, to determine the semantic calculation result for the target data.

[0131] In implementation, as Figure 14As shown, after the invoked computing class semantic computing unit performs semantic computation to obtain the corresponding semantic computation result, the computing class semantic computing unit can return the semantic computation result corresponding to the computing class semantic computing unit to the upper-level semantic computing unit of the computing class semantic computing unit. If the upper-level semantic computing unit is still a computing class semantic computing unit, the above-mentioned processing can be performed, that is, the semantic object logical attribute information corresponding to the target data and the obtained reference value are used as input data, the obtained operator is used for semantic computation, the corresponding semantic computation result is obtained, and the obtained semantic computation result is returned to the upper-level semantic computing unit. If the upper-level semantic computing unit is a merging class semantic computing unit, the processing of steps A62 and A64 can be performed.

[0132] If the semantic computing unit is a merging class semantic computing unit, the specific processing mode of step A6 can be various, and an optional processing mode is provided below. Specifically, the processing of steps A62 and A64 can be performed.

[0133] In step A62, the output data of the next-level semantic computing unit of the invoked merging class semantic computing unit is obtained through the invoked merging class semantic computing unit.

[0134] In step A64, the obtained output data is merged to obtain the merging result of the merging class semantic computing unit, and the merging result of the merging class semantic computing unit is returned to the upper-level semantic computing unit of the merging class semantic computing unit to determine the semantic computation result for the target data.

[0135] In implementation, as shown in the following, Figure 14 After the merging class semantic computing unit obtains the merging result through the processing of steps A62 and A64, the merging result can be returned to the upper-level semantic computing unit of the merging class semantic computing unit. If the upper-level semantic computing unit is a computing class semantic computing unit, the semantic object logical attribute information corresponding to the target data and the merging result can be used as input data, the obtained operator can be used for semantic computation, the corresponding semantic computation result can be obtained, and the obtained semantic computation result can be returned to the upper-level semantic computing unit. If the upper-level semantic computing unit is still a merging class semantic computing unit, the processing of steps A62 and A64 can be performed until the highest level is reached, and finally the semantic computation result for the target data can be obtained.

[0136] In actual application, the object data model further includes one or more of object types, logical attribute identifiers, and attribute types. The object types include applications, interfaces, and links. The attribute types include value types and object types. The value types include texts and feature lists. The object types include applications and interfaces.

[0137] The object type is used to describe the type of the semantic object and can include a backend application bakApp, an interface api, and a link trFlow. The logical attribute identifier is a unique identifier of the logical attribute information of the semantic object. In actual applications, the format can be, for example, the object type and the attribute name concatenated with “.”. The attribute type is used to distinguish different types to which the logical attribute value of the semantic object belongs. The attribute type can include a value type and an object type. The value type can include text and a feature list. The object type can include a backend application and an interface. For details, refer to Table 4 shown below.

[0138] Table 4

[0139]

[0140] The relationship diagram of the data object model can be as shown in Figure 15 The application includes an application name (AppName), a subject, data ownership, and a feature label. The interface can include an interface name (InterfaceName), an application name (AppName), a management state, and a feature label. The link can include a request application name (ReqAppName), a response application name (RespAppName), an interface name (InterfaceName), and a feature label.

[0141] The attribute information of the application can be as shown in Table 5.

[0142] Table 5

[0143]

[0144] The attribute information of the interface can be as shown in Table 6.

[0145] Table 6

[0146]

[0147] The attribute information of the link can be as shown in Table 7.

[0148] Table 7

[0149]

[0150] In actual applications, the specific processing mode of the above step S208 can be various. An optional processing mode is provided as follows. Figure 16 As shown in FIG. 8, the specific processing can include the following steps S2082 and S2084.

[0151] In step S2082, the output option set and the semantic type information are extracted from the semantic configuration information corresponding to the target data, the output option set is used to output the output value specified by the semantic output of the output type, and the semantic type information is used to distinguish the type of the semantic calculation result.

[0152] In step S2084, the semantic information of the target data is constructed and output based on the output option set, the semantic type information, and the semantic calculation result.

[0153] In actual applications, the semantic information of the target data obtained by the above method can be further applied in data verification or annotation of semantic objects. For details, see the following content: based on the semantic information of the target data, data verification processing is performed on the target data or the semantic object corresponding to the target data, and a corresponding verification result is obtained; or, based on the semantic information of the target data, annotation processing is performed on the semantic object corresponding to the target data, and annotation information of the semantic object corresponding to the target data is obtained.

[0154] In actual applications, the target data can be data related to data operation under privacy protection services.

[0155] The data processing method provided by the embodiments of the present specification is described in detail below in combination with a specific application scenario, as shown in FIG. Figure 17 The semantic object corresponding to the target data is a backend application, and the purpose of determining the semantic information of the target data is to annotate the backend application. First, business semantic definition can be performed, i.e., the semantic object is determined to be a backend application, then business semantic structure analysis can be performed through the above processing process, so as to determine the logical attribute information (i.e., semantic object logical attribute information) involved in the semantic object, which can specifically include two types of logical attribute information, i.e., application data ownership label and application privacy feature label. Then, the semantic configuration information can be determined, which can include condition 1: the application data ownership label is within the range of “privacy protection data identifier”; condition 2: the application privacy feature label is not within the range of “privacy protection”. Based on the core information and additional information in the object data model as data support, the semantic information corresponding to the backend application can be determined through the semantic configuration information, and finally, the application label can be set for the backend application based on the determined semantic information, the information in the application label is a privacy protection platform, thereby completing the annotation of the backend application.

[0156] The embodiment of the present specification provides a data processing method. When a semantic acquisition request for target data is received, semantic configuration information corresponding to the target data is acquired based on semantic encoding information included in the semantic acquisition request, and a semantic object logical attribute value corresponding to the target data is initialized based on semantic parameter information included in the semantic acquisition request. Then, a semantic calculation unit can be called based on the semantic configuration information corresponding to the target data, and semantic calculation is performed on the target data by the called semantic calculation unit based on the semantic configuration information corresponding to the target data and the initialized semantic object logical attribute value corresponding to the target data, to obtain a corresponding semantic calculation result. Finally, semantic information of the target data can be constructed and output based on the semantic configuration information corresponding to the target data and the semantic calculation result. In this way, by constructing semantic configuration information and semantic object logical attribute information, the business semantics are digitally modeled, a semantic calculation unit is constructed, and the like, online structured configuration of business semantics in a complex business data operation scenario and data calculation based on semantic configuration rules are realized, which can improve the data operation efficiency, avoid repeated development work caused by changes in business rules, shorten the work time consumption of data operation, and reduce the research and development time of program development.

[0157] In addition, an object data model is constructed, and the object data model and the semantic object logical attribute information are uniformly managed. Semantic object consumption and changes do not need to be maintained in multiple places. Furthermore, the business semantics are logically modeled and rule described using structured semantic configuration information. The semantic configuration process is digitalized, and the rule logic is clear and intuitive. The business semantics calculate semantic results based on a semantic calculation unit. Based on the unified object data model, semantic results are calculated for different semantic configuration information. Further, the present scheme realizes configurable and calculable business semantic calculation rule management, and decouples object attribute acquisition and semantic calculation through semantic logical context management. In addition, through the configurable business semantic calculation rule management, structured logical modeling and rule description of business semantics are realized, so that the business semantic configuration process is digitalized, the research and development cycle of semantic customization development is shortened, and through the semantic logical context management and the constructed unified object data model, different semantic object logical attribute information and data query services are managed in one station. Fast, general, and adjustable information acquisition can be provided during business semantic calculation, and the configurable calculation capability of business semantics is supported.

[0158] The above is the data processing method provided by the embodiment of the present specification. Based on the same idea, the embodiment of the present specification also provides a data processing apparatus, as shown in Figure 18 .

[0159] The data processing apparatus includes a semantic calculation interface, a semantic configuration module, a semantic logical context module, and a semantic calculation engine, wherein:

[0160] The semantic computing interface is configured to receive a semantic acquisition request for target data, the semantic acquisition request including semantic encoding information and semantic parameter information;

[0161] The semantic configuration module is configured to manage configuration information describing logical semantic rules of the target data, and is configured to acquire semantic configuration information corresponding to the target data based on the semantic encoding information;

[0162] The semantic logical context module is configured to save and acquire semantic object logical attribute values corresponding to the target data in the process of semantic computing, and is configured to initialize the semantic object logical attribute values corresponding to the target data based on the semantic parameter information;

[0163] The semantic configuration module is configured to call a semantic computing unit in the semantic computing engine based on the semantic configuration information corresponding to the target data;

[0164] The semantic computing engine is configured to perform semantic computing on the target data by the called semantic computing unit based on the semantic configuration information corresponding to the target data and the initialized semantic object logical attribute values corresponding to the target data, to obtain a corresponding semantic computing result, and to construct and output semantic information of the target data based on the semantic configuration information corresponding to the target data and the semantic computing result.

[0165] In the embodiments of the present specification, as shown in Figure 19 The apparatus further includes an object data model configured to maintain and manage logical attribute information of a semantic object;

[0166] The semantic logical context module includes a logical attribute submodule and an external data submodule, wherein:

[0167] The logical attribute submodule is configured to query whether the semantic object logical attribute values corresponding to the target data exist in a logical attribute cache based on the initialized semantic object logical attribute values corresponding to the target data; if they exist, determine semantic object logical attribute information corresponding to the target data according to an object data model based on the semantic object logical attribute values corresponding to the target data from the logical attribute cache;

[0168] The external data submodule is configured to, if they do not exist, acquire the semantic object logical attribute information corresponding to the target data from an external database according to an external data service configured in the object data model, and store the semantic object logical attribute values corresponding to the target data and the acquired semantic object logical attribute information corresponding to the target data in the logical attribute cache;

[0169] The semantic computing engine is configured to perform semantic computing on the target data by invoking a semantic computing unit based on the semantic configuration information corresponding to the target data and the semantic object logical attribute information corresponding to the target data, to obtain a corresponding semantic computing result.

[0170] In implementation, as shown in the figure, Figure 20 The object data model can include links, interfaces and applications, wherein the links can include request application identifiers, response application identifiers, interface names, feature tags and other related information, the interfaces can include application identifiers, interface names, feature tags and other related information, the applications can include application identifiers, feature tags and other related information, and the object data model can provide data support for the semantic computing engine. In addition, a plurality of different semantic configuration information can be included, including semantic configuration information 1, semantic configuration information 2, semantic configuration information 3, and the like. In addition, a semantic configuration analysis processing mechanism can be included, and the plurality of different semantic configuration information can provide rule description for the semantic computing engine. Meanwhile, different business semantics (which can include business semantics 1, business semantics 2, business semantics 3, and the like) corresponding semantic acquisition requests can be received through the semantic computing interface.

[0171] In the embodiments of the present specification, the semantic logic context module is configured to initialize the semantic logic context corresponding to the target data based on the semantic input parameter information; create the semantic logic context corresponding to the target data, and initialize the semantic object logical attribute value corresponding to the target data according to the semantic input parameter information.

[0172] In the embodiments of the present specification, the semantic configuration module is configured to obtain, based on the semantic coding information, semantic configuration information corresponding to reference semantic coding information matched with the semantic coding information from a plurality of pre-stored semantic configuration information, and take the obtained semantic configuration information as the semantic configuration information corresponding to the target data.

[0173] The semantic configuration module is configured to extract a semantic computing logic model from the semantic configuration information corresponding to the target data, and invoke a semantic computing unit based on the extracted semantic computing logic model. The semantic computing logic model is used to maintain the logical configuration of semantic computing, and the logical configuration includes one or more of the type of the semantic computing unit, the operator, the semantic object logical attribute information, the reference value, and the subsequent semantic computing unit set.

[0174] In the embodiments of the present specification, the external data submodule is configured to call an external database according to the external data service configured in the object data model to obtain target semantic object data matched with the semantic object logical attribute value corresponding to the target data.

[0175] The logic attribute submodule is configured to convert the target semantic object data into semantic object logic attribute information corresponding to the target data based on a logic attribute conversion rule configured in the object data model.

[0176] In the embodiments of the present specification, the external data submodule is configured to call an external database according to an external data service configured in the object data model, and query whether the semantic object logic attribute value corresponding to the target data exists in the cache of the external database; if the semantic object logic attribute value corresponding to the target data exists, the semantic object logic attribute information corresponding to the target data is obtained from the cache of the external database based on the semantic object logic attribute value corresponding to the target data; if the semantic object logic attribute value corresponding to the target data does not exist, the semantic object logic attribute information corresponding to the target data is obtained from the external database based on the semantic object logic attribute value corresponding to the target data.

[0177] In the embodiments of the present specification, the semantic computing engine is configured to obtain the reference value from a preset reference value set based on a semantic computing logic model in the semantic configuration information corresponding to the target data; obtain operator information used for semantic computing based on the semantic configuration information corresponding to the target data and the semantic object logic attribute information corresponding to the target data; and use the obtained operator to perform semantic computing by calling the semantic computing unit, with the semantic object logic attribute information corresponding to the target data and the obtained reference value as input data, to determine a semantic computing result for the target data.

[0178] In the embodiments of the present specification, the semantic computing unit includes a computing type semantic computing unit and a merging type semantic computing unit,

[0179] If the semantic computing unit is the computing type semantic computing unit, the semantic computing engine is configured to use the obtained operator to perform semantic computing by calling the computing type semantic computing unit, with the semantic object logic attribute information corresponding to the target data and the obtained reference value as input data, to obtain a semantic computing result corresponding to the computing type semantic computing unit, and return the semantic computing result corresponding to the computing type semantic computing unit to an upper-level semantic computing unit of the computing type semantic computing unit, to determine a semantic computing result for the target data.

[0180] If the semantic computing unit is a merging type semantic computing unit, the semantic computing engine is configured to acquire output data of a next-level semantic computing unit of the invoked merging type semantic computing unit through the invoked merging type semantic computing unit; perform merging processing on the acquired output data to obtain a merging result of the merging type semantic computing unit, and return the merging result of the merging type semantic computing unit to a previous-level semantic computing unit of the merging type semantic computing unit, so as to determine a semantic computing result for the target data.

[0181] In an embodiment of the present specification, the object data model further includes one or more of an object type, a logical attribute identifier, and an attribute type, the object type includes an application, an interface, and a link, the attribute type includes a value type and an object type, the value type includes text and a feature list, and the object type includes an application and an interface.

[0182] In an embodiment of the present specification, the semantic computing engine is configured to extract an output option set and semantic type information from semantic configuration information corresponding to the target data, the output option set is used to output an output value specified by a semantic output of an output type, and the semantic type information is used to distinguish a type of the semantic computing result; and construct and output semantic information of the target data based on the output option set, the semantic type information, and the semantic computing result.

[0183] In an embodiment of the present specification, the apparatus further includes:

[0184] The verification module is configured to perform data verification processing on the target data or a semantic object corresponding to the target data based on the semantic information of the target data, to obtain a corresponding verification result.

[0185] The labeling module is configured to perform labeling processing on the semantic object corresponding to the target data based on the semantic information of the target data, to obtain labeling information of the semantic object corresponding to the target data.

[0186] In an embodiment of the present specification, the target data is data related to data operation under a privacy protection service.

[0187] The specific processing procedures involved in the above semantic computing interface, semantic configuration module, semantic logic context module, semantic computing engine, object data model, and the like can be referred to the foregoing related content, and will not be described here again.

[0188] The embodiment of the present specification provides a data processing apparatus. When a semantic acquisition request for target data is received, semantic configuration information corresponding to the target data is acquired based on semantic encoding information included in the semantic acquisition request, and a semantic object logical attribute value corresponding to the target data is initialized based on semantic parameter information included in the semantic acquisition request. Then, a semantic calculation unit can be called based on the semantic configuration information corresponding to the target data, and semantic calculation is performed on the target data by the called semantic calculation unit based on the semantic configuration information corresponding to the target data and the initialized semantic object logical attribute value corresponding to the target data, to obtain a corresponding semantic calculation result. Finally, semantic information of the target data can be constructed and output based on the semantic configuration information corresponding to the target data and the semantic calculation result. In this way, by constructing semantic configuration information and semantic object logical attribute information, the business semantics are digitally modeled, a semantic calculation unit is constructed, and the like, realizing online structured configuration of business semantics in a complex business data operation scenario and data calculation based on semantic configuration rules, which can improve the data operation efficiency, avoid repeated development work caused by changes in business rules, shorten the work time consumption of data operation, and reduce the research and development time of program development.

[0189] In addition, the object data model is constructed, the object data model and the semantic object logical attribute information are uniformly managed, semantic object consumption and changes do not need to be maintained in multiple places, and the like. Furthermore, the structured semantic configuration information is used to logically model and describe the business semantics, the semantic configuration process is digitalized, the rule logic is clear and intuitive, the business semantics are calculated based on the semantic calculation unit to obtain semantic results, and based on the unified object data model, the semantic results are calculated for different semantic configuration information. Further, the present scheme realizes configurable and calculable business semantic calculation rule management, and decouples object attribute acquisition and semantic calculation through semantic logical context management. In addition, through the configurable business semantic calculation rule management, structured logical modeling and rule description of the business semantics are realized, the business semantic configuration process is digitalized, the research and development period of semantic customization development is shortened, and the like. Furthermore, through the semantic logical context management and the unified object data model constructed, different semantic object logical attribute information and data query services are managed in one station, fast, general and adjustable information acquisition can be provided during business semantic calculation, and the configurable calculation capability of the business semantics is supported.

[0190] The above is the data processing apparatus provided by the embodiment of the present specification. Based on the same idea, the embodiment of the present specification also provides a data processing device, as shown in Figure 21 .

[0191] The data processing device can be a terminal device or a server, and the like provided by the above embodiment.

[0192] The data processing device can have a large difference due to different configurations or performances, and can include one or more processors 2101 and memories 2102, and the memories 2102 can store one or more stored applications or data. The memories 2102 can be temporary storage or persistent storage. The applications stored in the memories 2102 can include one or more modules (not shown in the figure), and each module can include a series of computer executable instructions in the data processing device. Further, the processor 2101 can be configured to communicate with the memory 2102 and execute a series of computer executable instructions in the memory 2102 on the data processing device. The data processing device can also include one or more power supplies 2103, one or more wired or wireless network interfaces 2104, one or more input / output interfaces 2105, and one or more keyboards 2106.

[0193] In particular, in the embodiment, the data processing device includes a memory and one or more programs, wherein one or more programs are stored in the memory, and the one or more programs can include one or more modules, and each module can include a series of computer executable instructions in the data processing device, and the one or more processors are configured to execute the one or more programs including the following computer executable instructions:

[0194] Receiving a semantic acquisition request for target data, wherein the semantic acquisition request includes semantic encoding information and semantic parameter information;

[0195] Obtaining semantic configuration information corresponding to the target data based on the semantic encoding information, and initializing semantic object logical attribute values corresponding to the target data based on the semantic parameter information;

[0196] Calling a semantic computing unit based on the semantic configuration information corresponding to the target data, and performing semantic computation on the target data through the called semantic computing unit based on the semantic configuration information corresponding to the target data and the initialized semantic object logical attribute values corresponding to the target data, to obtain a corresponding semantic computation result;

[0197] Based on the semantic configuration information corresponding to the target data and the semantic computation result, constructing and outputting semantic information of the target data.

[0198] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments. In particular, for the data processing device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.

[0199] The embodiment of the present specification provides a data processing device. When a semantic acquisition request for target data is received, semantic configuration information corresponding to the target data is acquired based on semantic encoding information included in the semantic acquisition request, and a semantic object logical attribute value corresponding to the target data is initialized based on semantic parameter information included in the semantic acquisition request. Then, a semantic calculation unit can be called based on the semantic configuration information corresponding to the target data, and semantic calculation is performed on the target data by the called semantic calculation unit based on the semantic configuration information corresponding to the target data and the initialized semantic object logical attribute value corresponding to the target data, to obtain a corresponding semantic calculation result. Finally, semantic information of the target data can be constructed and output based on the semantic configuration information corresponding to the target data and the semantic calculation result. In this way, by constructing semantic configuration information and semantic object logical attribute information, the business semantics are digitally modeled, a semantic calculation unit is constructed, and the like, online structured configuration of business semantics in a complex business data operation scenario and data calculation based on semantic configuration rules are realized, which can improve the data operation efficiency, avoid repeated development work caused by changes in business rules, shorten the work time consumption of data operation, and reduce the research and development time of program development.

[0200] In addition, an object data model is constructed, and the object data model and the semantic object logical attribute information are uniformly managed. Semantic object consumption and changes do not need to be maintained in multiple places. Furthermore, the business semantics are logically modeled and rule described using structured semantic configuration information. The semantic configuration process is digitalized, and the rule logic is clear and intuitive. The business semantics calculate semantic results based on a semantic calculation unit. Based on a unified object data model, semantic results are calculated for different semantic configuration information. Further, the present scheme realizes configurable and calculable business semantic calculation rule management, and decouples object attribute acquisition and semantic calculation through semantic logical context management. In addition, through configurable business semantic calculation rule management, structured logical modeling and rule description of business semantics are realized, so that the business semantic configuration process is digitalized, the research and development cycle of semantic customization development is shortened, and through semantic logical context management and the constructed unified object data model, different semantic object logical attribute information and data query services are managed in one station. Fast, general, and adjustable information acquisition can be provided during business semantic calculation, and the configurable calculation capability of business semantics is supported.

[0201] Further, based on the above Figures 2 to 16 The one or more embodiments of the present specification also provide a storage medium for storing computer executable instruction information. In a specific embodiment, the storage medium can be a U disk, an optical disk, a hard disk, or the like. The computer executable instruction information stored in the storage medium can implement the following processes when executed by a processor.

[0202] receiving a semantic obtaining request for target data, the semantic obtaining request including semantic coding information and semantic parameter information;

[0203] obtaining semantic configuration information corresponding to the target data based on the semantic coding information, and initializing semantic object logical attribute values corresponding to the target data based on the semantic parameter information;

[0204] calling a semantic computing unit based on the semantic configuration information corresponding to the target data, and performing semantic computation on the target data through the called semantic computing unit based on the semantic configuration information corresponding to the target data and the initialized semantic object logical attribute values corresponding to the target data, to obtain a corresponding semantic computing result;

[0205] constructing and outputting semantic information of the target data based on the semantic configuration information corresponding to the target data and the semantic computing result.

[0206] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts of each of the embodiments can be referred to each other. Each of the embodiments focuses on the difference from other embodiments. In particular, for the above-mentioned storage medium embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the related parts can be referred to the part of the method embodiment.

[0207] The storage medium provided by the embodiment of the specification can obtain semantic configuration information corresponding to target data based on semantic coding information included in a semantic obtaining request for the target data when receiving the semantic obtaining request, and initialize semantic object logical attribute values corresponding to the target data based on semantic parameter information included in the semantic obtaining request. Then, a semantic computing unit can be called based on the semantic configuration information corresponding to the target data, and semantic computation can be performed on the target data through the called semantic computing unit based on the semantic configuration information corresponding to the target data and the initialized semantic object logical attribute values corresponding to the target data, to obtain a corresponding semantic computing result. Finally, semantic information of the target data can be constructed and output based on the semantic configuration information corresponding to the target data and the semantic computing result. In this way, by constructing semantic configuration information and semantic object logical attribute information, the business semantics is digitally modeled, the semantic computing unit is constructed, and the like, the online structured configuration of business semantics in a complex business data operation scenario and the data computation based on the semantic configuration rule are realized, which can improve the data operation work efficiency, avoid repeated development work caused by business rule changes, shorten the work time consumption of data operation, and reduce the research and development time of program development.

[0208] Further, the object data model is constructed, the object data model and semantic object logical attribute information are uniformly managed, semantic object consumption and change do not need to be maintained in multiple places, structured semantic configuration information is used for logical modeling and rule description of business semantics, the semantic configuration process is digitized, the rule logic is clear and intuitive, the business semantics is calculated based on a semantic calculation unit to obtain a semantic result, and based on the unified object data model, the semantic result is calculated for different semantic configuration information. Further, the scheme realizes configuration of computable business semantic calculation rule management, and decouples object attribute acquisition and semantic calculation through semantic logical context management. In addition, through the configuration of the business semantic calculation rule management, structured logical modeling and rule description of business semantics are realized, the business semantic configuration process is digitized, the research and development cycle of semantic customization development is shortened, and through the semantic logical context management and the unified object data model, different semantic object logical attribute information and data query services are managed in one station, fast, universal and adjustable information acquisition can be provided during business semantic calculation, and configuration calculation capability of business semantics is supported.

[0209] Further, based on the above Figures 2 to 16 The method shown in the one or more embodiments of the present specification also provides a computer program product including a computer program, and the computer program in the computer program product can realize the following flow when executed by a processor.

[0210] Receiving a semantic acquisition request for target data, the semantic acquisition request including semantic encoding information and semantic parameter information;

[0211] Based on the semantic encoding information, the semantic configuration information corresponding to the target data is obtained, and based on the semantic parameter information, the semantic object logical attribute value corresponding to the target data is initialized;

[0212] Based on the semantic configuration information corresponding to the target data, a semantic calculation unit is called, and based on the semantic configuration information corresponding to the target data and the initialized semantic object logical attribute value corresponding to the target data, the target data is calculated through the called semantic calculation unit based on the semantic configuration information corresponding to the target data, to obtain a corresponding semantic calculation result;

[0213] Based on the semantic configuration information corresponding to the target data and the semantic calculation result, the semantic information of the target data is constructed and output.

[0214] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each of the embodiments focuses on the difference from other embodiments. In particular, for the above-mentioned computer program product embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.

[0215] The embodiment of the specification provides a computer program product. When a semantic acquisition request for target data is received, semantic configuration information corresponding to the target data is acquired based on semantic encoding information included in the semantic acquisition request, and a semantic object logical attribute value corresponding to the target data is initialized based on semantic parameter information included in the semantic acquisition request. Then, a semantic calculation unit can be called based on the semantic configuration information corresponding to the target data, and semantic calculation is performed on the target data by the called semantic calculation unit based on the semantic configuration information corresponding to the target data and the initialized semantic object logical attribute value corresponding to the target data, to obtain a corresponding semantic calculation result. Finally, semantic information of the target data can be constructed and output based on the semantic configuration information corresponding to the target data and the semantic calculation result. In this way, by constructing the semantic configuration information and the semantic object logical attribute information, the business semantics is digitally modeled, the semantic calculation unit is constructed, and the online structured configuration of the business semantics in the complex business data operation scenario and the data calculation based on the semantic configuration rule are realized. The work efficiency of data operation can be improved, repeated development work caused by business rule changes is avoided, the work time consumption of data operation is shortened, and the research and development time of program development is reduced.

[0216] In addition, the object data model is constructed, the object data model and the semantic object logical attribute information are uniformly managed, the semantic object consumption and change do not need to be maintained in multiple places, and the structured semantic configuration information is used for logical modeling and rule description of the business semantics. The semantic configuration process is digitalized, the rule logic is clear and intuitive, the business semantics is calculated based on the semantic calculation unit to obtain a semantic result, and based on the unified object data model, the semantic result is calculated for different semantic configuration information. Further, the scheme realizes the configurable and calculable business semantic calculation rule management, and decouples the object attribute acquisition and the semantic calculation through the semantic logical context management. In addition, through the configurable business semantic calculation rule management, the structured logical modeling and rule description of the business semantics are realized, the business semantic configuration process is digitalized, the research and development period of semantic customization development is shortened, and through the semantic logical context management and the unified object data model, different semantic object logical attribute information and data query services are managed in one station. Fast, general and adjustable information acquisition can be provided during business semantic calculation, and the configurable calculation capability of business semantics is supported.

[0217] The above described embodiments of the present description have been described. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order and still achieve desirable results. Additionally, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

[0218] In the 1990s, it was possible to distinguish whether an improvement in a technology was a hardware improvement (e.g., an improvement in the circuit structure of a diode, transistor, switch, etc.) or a software improvement (an improvement in a method flow). However, as technology has advanced, many improvements in method flows today can be considered as direct improvements in hardware circuit structures. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into a hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (e.g., a field programmable gate array (FPGA)) is an integrated circuit whose logic function is determined by user programming of the device. A digital system is "integrated" on a PLD by the designer programming it themselves, without having to ask a chip manufacturer to design and fabricate a custom integrated circuit chip. Moreover, instead of manually fabricating an integrated circuit chip, this programming is now mostly implemented using "logic compiler" software, which is similar to the software compiler used when developing a program, and the original code before compilation must also be written in a specific programming language, which is called a hardware description language (HDL), and there are many types of HDL, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc., and the most commonly used are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also be aware that it is very easy to obtain a hardware circuit that implements a logical method flow by simply logically programming the method flow in one of the above-mentioned hardware description languages and programming it into an integrated circuit.

[0219] The controller can be implemented in any suitable way, for example, the controller can take the form of a microprocessor or processor and a computer readable medium storing computer readable program code, such as software or firmware, executable by the microprocessor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller and an embedded microcontroller, examples of which include but are not limited to the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320, the memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also know that, in addition to implementing the controller in pure computer readable program code, it is possible to implement the same functionality in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers and embedded microcontrollers by logically programming the method steps. Such a controller can therefore be considered to be a hardware component, and the means included therein for implementing the various functions can also be considered to be structures within the hardware component. Alternatively, or even additionally, the means for implementing the various functions can be considered to be both a software module implementing the method and a structure within the hardware component.

[0220] The systems, apparatuses, modules or units illustrated by the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0221] For the sake of description, the above apparatuses are described in various units with functions respectively. Of course, the functions of each unit can be implemented in one or more software and / or hardware in implementing one or more embodiments of the present specification.

[0222] Those skilled in the art will understand that the embodiments of the present specification can be provided as a method, a system, or a computer program product. Therefore, one or more embodiments of the present specification can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, one or more embodiments of the present specification can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0223] The embodiments of the present specification are described with reference to flowcharts and / or block diagrams of the method, device (system) and computer program product according to the embodiments of the present specification. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor or other programmable electronic devices to produce a machine, so that the instructions executed by the computer or other programmable electronic devices generate a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 one or more flows and / or blocks

[0224] These computer program instructions can also be stored in a computer readable memory capable of directing the computer or other programmable electronic devices to work in a specific way, so that the instructions stored in the computer readable memory produce a product including instruction devices, which implement the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 one or more flows and / or blocks

[0225] These computer program instructions can also be loaded into a computer or other programmable electronic devices, so that a series of operation steps are performed on the computer or other programmable electronic devices to produce a computer implemented process, so that the instructions executed on the computer or other programmable electronic devices provide steps for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 one or more flows and / or blocks

[0226] In a typical configuration, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces and memories.

[0227] The memory can include non-persistent memory in the computer readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of the computer readable medium.

[0228] Computer-readable media includes permanent and non-permanent, movable and non-movable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carriers.

[0229] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or apparatus that comprises a list of elements does not only include those elements, but can also include other elements not expressly listed or inherent to such process, method, article or apparatus. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0230] Those skilled in the art will appreciate that embodiments of the present specification can be provided as methods, systems or computer program products. Therefore, one or more embodiments of the present specification can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, one or more embodiments of the present specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0231] One or more embodiments of the present specification can be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the present specification can also be practiced in a distributed computing environment, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in both local and remote computer storage media, including storage devices.

[0232] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each of the embodiments focuses on the difference from other embodiments. In particular, for the system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiments.

[0233] The above only describes the embodiments of the specification and is not used to limit the file. The specification can have various changes and changes for those skilled in the art. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the specification shall be included in the claim range of the specification.

Claims

1. A data processing method, the method comprising: receiving a semantic acquisition request for target data, the semantic acquisition request including semantic encoding information and semantic parameter information; acquiring semantic configuration information corresponding to the target data based on the semantic encoding information, and initializing semantic object logical attribute values corresponding to the target data based on the semantic parameter information; calling a semantic computing unit based on the semantic configuration information corresponding to the target data, and performing semantic computation on the target data by the called semantic computing unit based on the semantic configuration information corresponding to the target data and the initialized semantic object logical attribute values corresponding to the target data, to obtain a corresponding semantic computation result; constructing and outputting semantic information of the target data based on the semantic configuration information corresponding to the target data and the semantic computation result.

2. The method of claim 1, wherein the initializing the semantic object logical attribute values corresponding to the target data based on the semantic parameter information comprises: initializing a semantic logical context corresponding to the target data based on the semantic parameter information; creating the semantic logical context corresponding to the target data, and initializing the semantic object logical attribute values corresponding to the target data according to the semantic parameter information.

3. The method of claim 2, wherein the semantic configuration information includes reference semantic encoding information and a semantic computing logical model, and the acquiring the semantic configuration information corresponding to the target data based on the semantic encoding information comprises: based on the semantic encoding information, acquiring semantic configuration information corresponding to the reference semantic encoding information that matches the semantic encoding information from a plurality of pre-stored semantic configuration information, and taking the acquired semantic configuration information as the semantic configuration information corresponding to the target data; the calling the semantic computing unit based on the semantic configuration information corresponding to the target data comprises: extracting a semantic computing logical model from the semantic configuration information corresponding to the target data, and calling a semantic computing unit based on the extracted semantic computing logical model, the semantic computing logical model being used to maintain a logical configuration of semantic computation, the logical configuration including one or more of a type of the semantic computing unit, an operator, semantic object logical attribute information, a reference value, and a subsequent semantic computing unit set.

4. The method of claim 3, wherein the performing semantic computation on the target data by the called semantic computing unit based on the semantic configuration information corresponding to the target data and the initialized semantic object logical attribute values corresponding to the target data to obtain a corresponding semantic computation result comprises: based on the initialized semantic object logical attribute values corresponding to the target data, querying whether the semantic object logical attribute values corresponding to the target data exist in a logical attribute cache. If the semantic object logical attribute information corresponding to the target data exists in the logical attribute cache, the semantic computing unit is invoked to perform semantic computing on the target data based on the semantic configuration information corresponding to the target data and the semantic object logical attribute information corresponding to the target data, and a corresponding semantic computing result is obtained. If the semantic object logical attribute information corresponding to the target data does not exist in the logical attribute cache, the external database is invoked to obtain the semantic object logical attribute information corresponding to the target data according to the external data service configured in the object data model, the semantic object logical attribute value corresponding to the target data and the obtained semantic object logical attribute information corresponding to the target data are stored in the logical attribute cache, and the semantic computing unit is invoked to perform semantic computing on the target data based on the semantic configuration information corresponding to the target data and the semantic object logical attribute information corresponding to the target data, and a corresponding semantic computing result is obtained.

5. The method of claim 4, wherein the obtaining the semantic object logical attribute information corresponding to the target data by invoking the external database according to the external data service configured in the object data model comprises: invoking the external database to obtain target semantic object data matching the semantic object logical attribute value corresponding to the target data according to the external data service configured in the object data model; and converting the target semantic object data into the semantic object logical attribute information corresponding to the target data based on the logical attribute conversion rule configured in the object data model.

6. The method of claim 4 or 5, wherein the obtaining the semantic object logical attribute information corresponding to the target data by invoking the external database according to the external data service configured in the object data model comprises: invoking the external database according to the external data service configured in the object data model, and querying whether the semantic object logical attribute value corresponding to the target data exists in a cache of the external database; if the semantic object logical attribute value corresponding to the target data exists in the cache of the external database, obtaining the semantic object logical attribute information corresponding to the target data based on the semantic object logical attribute value corresponding to the target data; if the semantic object logical attribute value corresponding to the target data does not exist in the cache of the external database, obtaining the semantic object logical attribute information corresponding to the target data from the external database based on the semantic object logical attribute value corresponding to the target data.

7. The method of claim 6, wherein the performing semantic computing on the target data by invoking the semantic computing unit based on the semantic configuration information corresponding to the target data and the semantic object logical attribute information corresponding to the target data, and obtaining a corresponding semantic computing result comprises: obtaining a reference value from a preset reference value set based on a semantic computing logic model in the semantic configuration information corresponding to the target data; obtaining operator information used for semantic computing based on the semantic configuration information corresponding to the target data and the semantic object logical attribute information corresponding to the target data; and performing semantic computing on the target data based on the semantic configuration information corresponding to the target data, the semantic object logical attribute information corresponding to the target data, and the operator information. The semantic computing unit called by the semantic computing unit determines the semantic computing result of the target data by using the semantic object logical attribute information corresponding to the target data and the reference value obtained as input data, and performing semantic computing using the obtained operator.

8. The method of claim 7, wherein the semantic computing unit comprises a computing type semantic computing unit and a merging type semantic computing unit. If the semantic computing unit is the computing type semantic computing unit, the semantic computing unit called by the semantic computing unit determines the semantic computing result of the target data by using the semantic object logical attribute information corresponding to the target data and the reference value obtained as input data, and performing semantic computing using the obtained operator. The computing type semantic computing unit called by the semantic computing unit uses the semantic object logical attribute information corresponding to the target data and the reference value obtained as input data, and performs semantic computing using the obtained operator to obtain the semantic computing result corresponding to the computing type semantic computing unit, and returns the semantic computing result corresponding to the computing type semantic computing unit to the upper-level semantic computing unit of the computing type semantic computing unit to determine the semantic computing result of the target data. If the semantic computing unit is the merging type semantic computing unit, the semantic computing unit called by the semantic computing unit determines the semantic computing result of the target data by using the semantic object logical attribute information corresponding to the target data and the reference value obtained as input data, and performing semantic computing using the obtained operator. The merging type semantic computing unit called by the semantic computing unit obtains the output data of the lower-level semantic computing unit of the merging type semantic computing unit called. The merging type semantic computing unit merges the obtained output data to obtain the merging result of the merging type semantic computing unit, and returns the merging result of the merging type semantic computing unit to the upper-level semantic computing unit of the merging type semantic computing unit to determine the semantic computing result of the target data.

9. The method of claim 8, wherein the object data model further comprises one or more of an object type, a logical attribute identifier, and an attribute type, the object type comprises an application, an interface, and a link, the attribute type comprises a value type and an object type, the value type comprises a text and a feature list, and the object type comprises an application and an interface.

10. The method of claim 1, wherein the constructing and outputting the semantic information of the target data based on the semantic configuration information corresponding to the target data and the semantic computing result comprises: extracting an output option set and semantic type information from the semantic configuration information corresponding to the target data, the output option set being used to output an output value specified by a semantic output of an output type, and the semantic type information being used to distinguish a type of the semantic computing result; and constructing and outputting the semantic information of the target data based on the output option set, the semantic type information, and the semantic computing result.

11. The method of claim 1, further comprising: perform data verification processing on the target data or a semantic object corresponding to the target data based on semantic information of the target data, to obtain a corresponding verification result; or, perform annotation processing on a semantic object corresponding to the target data based on semantic information of the target data, to obtain annotation information of the semantic object corresponding to the target data.

12. The method of claim 1, wherein the target data is data related to data operation under a privacy protection service.

13. A data processing apparatus, comprising a semantic computing interface, a semantic configuration module, a semantic logical context module, and a semantic computing engine, wherein: the semantic computing interface is configured to receive a semantic acquisition request for target data, the semantic acquisition request including semantic encoding information and semantic parameter information; the semantic configuration module is configured to manage configuration information describing logical semantic rules of the target data, and is configured to obtain semantic configuration information corresponding to the target data based on the semantic encoding information; the semantic logical context module is configured to save and obtain semantic object logical attribute values corresponding to the target data in the process of semantic computing, and is configured to initialize the semantic object logical attribute values corresponding to the target data based on the semantic parameter information; the semantic configuration module is configured to call a semantic computing unit in the semantic computing engine based on the semantic configuration information corresponding to the target data; and the semantic computing engine is configured to perform semantic computing on the target data by the called semantic computing unit based on the semantic configuration information corresponding to the target data and the initialized semantic object logical attribute values corresponding to the target data, to obtain a corresponding semantic computing result, and to construct and output semantic information of the target data based on the semantic configuration information corresponding to the target data and the semantic computing result.

14. The apparatus of claim 13, further comprising an object data model configured to maintain and manage logical attribute information of a semantic object; and the semantic logical context module comprises a logical attribute submodule and an external data submodule, wherein: the logical attribute submodule is configured to query whether the semantic object logical attribute values corresponding to the target data exist in a logical attribute cache based on the initialized semantic object logical attribute values corresponding to the target data, and if so, to determine semantic object logical attribute information of the target data based on the semantic object logical attribute values corresponding to the target data and the object data model from the logical attribute cache; and the external data submodule is configured to obtain the semantic object logical attribute information of the target data from an external database according to an external data service configured in the object data model if the semantic object logical attribute values corresponding to the target data do not exist, and to store the semantic object logical attribute values corresponding to the target data and the obtained semantic object logical attribute information of the target data in the logical attribute cache. ​ ​ ​ ​ ​ ​ ​ ​ The semantic computing engine is configured to perform semantic computation on the target data by invoking a semantic computing unit based on the semantic configuration information corresponding to the target data and the semantic object logical attribute information corresponding to the target data, to obtain a corresponding semantic computing result.

15. A data processing device, the data processing device comprising: a processor; and a memory arranged to store computer executable instructions which, when executed, cause the processor to: receive a semantic acquisition request for target data, the semantic acquisition request including semantic encoding information and semantic parameter information; obtain semantic configuration information corresponding to the target data based on the semantic encoding information, and initialize semantic object logical attribute values corresponding to the target data based on the semantic parameter information; invoke a semantic computing unit based on the semantic configuration information corresponding to the target data, and perform semantic computation on the target data by the invoked semantic computing unit based on the semantic configuration information corresponding to the target data and the initialized semantic object logical attribute values corresponding to the target data, to obtain a corresponding semantic computing result; construct and output semantic information of the target data based on the semantic configuration information corresponding to the target data and the semantic computing result.

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