Multi-platform integration methods, devices, computer equipment, and storage media

Abstract

This application relates to a method, apparatus, computer device, and storage medium for multi-platform interoperability. The method includes: receiving request information sent by a first platform; determining a target platform and obtaining its data structure information based on the request information; parsing the data structure information to be processed to obtain multiple data filling objects; constructing a target data structure framework based on the target data structure; traversing the target data structure framework using a deep recursive parsing algorithm; converting the multiple data filling objects into first key-value pairs; obtaining supplementary data objects; assigning values ​​to each supplementary data object; converting the supplementary data objects into second key-value pairs; constructing structured data in the target data format based on the first key-value pairs, the second key-value pairs, and the target data structure framework; and sending the structured data in the target data format to the target platform. This method can improve the versatility of platform interoperability processing.

Description

Technical Field

[0001] This application relates to the field of Internet technology, and in particular to a method, apparatus, computer device, and storage medium for multi-platform interoperability. Background Technology

[0002] Existing storage management software is generally built on a B / S architecture. B / S (Browser / Server) is a network architecture model that emerged with the rise of the web, where the web browser is the primary client application. This model centralizes the core functionality of the system on the server, simplifying system development, maintenance, and use. It enables graphical management of the storage system. However, storage management software can only manage the current storage cluster. In many scenarios, different customers on different platforms have storage management needs, requiring storage management software to adapt to and be compatible with the data specifications of different platforms. Currently, when adapting to different platforms, the storage management software must develop interface specifications, data reporting, and platform integration for each platform, followed by comprehensive testing—a time-consuming and labor-intensive process. Summary of the Invention

[0003] Therefore, it is necessary to provide a highly compatible multi-platform interoperability method, apparatus, computer device, and storage medium to address the aforementioned technical problems. To solve these technical problems, firstly, a multi-platform interoperability method is provided, which includes:

[0004] Receive request information sent by the first platform, wherein the request information carries data structure information to be processed;

[0005] The target platform is determined based on the requested information, and the target platform's data structure information is obtained, including the target data format.

[0006] Based on the target platform's data structure information, the data structure information to be processed is converted into structured data in the target data format, and the structured data in the target data format is sent to the target platform;

[0007] The process of converting the data structure information to be processed into structured data in the target data format includes:

[0008] The data structure information to be processed is parsed to obtain multiple data filling objects;

[0009] Based on the target data structure, a target data structure framework is constructed. A deep recursive parsing algorithm is used to traverse the target data structure frame and convert multiple data filling objects into the first key-value pair.

[0010] Obtain the data supplement object, assign values ​​to each data supplement object, and convert the data supplement object into a second key-value pair;

[0011] Structured data in the target data format is constructed based on the first key-value pair, the second key-value pair, and the target data structure framework.

[0012] In one embodiment, the target data structure framework includes a root node and several child nodes. Constructing structured data in the target data format based on the first key-value pair, the second key-value pair, and the target data structure framework includes:

[0013] Based on the attribute information of the data-filled object, the first key-value pair is located to several child nodes in the target data structure framework;

[0014] Traverse the target data structure framework to obtain the path between the root node and several child nodes, and concatenate the path between the root node and several child nodes into a chain.

[0015] Determine the level and type of each node in the string chain, merge nodes with the same level and type into one node, and obtain the target connection relationship between the root node and its child nodes.

[0016] Based on the target connection relationship between the root node and its child nodes, the first key-value pair is filled into the corresponding node in the target structural framework;

[0017] When all the first key-value pairs are filled into the target structural frame, obtain the nodes in the target structural frame that have not been filled and generate a set of nodes that have not been filled.

[0018] Obtain the node context information of each node in the unfilled node set, match the node context information of each node with the second key-value pair, and obtain the degree of matching between the node context information of each node and the second key-value pair.

[0019] The second key-value pair is filled into the unfilled nodes by sorting based on the degree of matching, forming structured data in the target data format.

[0020] In one embodiment, after determining the target platform based on the request information and obtaining the target platform's data structure information, the process includes:

[0021] A full data structure table is constructed based on the data structure information to be processed. The full data structure table includes field names, field types, and field value information.

[0022] Fuzzy matching is performed between the field types in the full data table and the target data structure information to obtain the target field type for each field of the target data structure information. Based on the target field type of each field of the target data structure information, a field mapping relationship between the data structure to be processed and the target data structure is generated. The field mapping relationship is written into the data mapping table and persisted to the data storage structure. Based on the field mapping table, the data structure information to be processed is converted into structured data in the target data format.

[0023] In one embodiment, after persisting the field mapping table to the data storage structure, the following steps are included:

[0024] Send an authentication request to the target platform. The authentication request carries the first identifier and the first token of the first platform. The first token includes the first token identifier and the first version data.

[0025] If a second token identifier identical to the first token identifier is detected in the token storage space of the target platform, the target platform obtains the second version data included in the second token of the second token identifier and the second identifier of the docking management platform. The token storage space is used to store the token authorized for use by the target platform.

[0026] If the first version data is the same as the second version data shown, and the first identifier is the same as the second identifier, then the target platform returns authentication data to the docking management platform.

[0027] In one implementation, fuzzy matching is performed between the field types in the full data table and the target data structure information to obtain the target field type for each field of the target data structure information, including:

[0028] When any field type in the target data structure information does not match the field type in the full data table, scan the data mapping tables of other platforms in the data storage structure;

[0029] Match the field types of target data structure information that do not match the field types in the full data table with the field types stored in the data mapping tables of other platforms;

[0030] If a match is found, a field mapping relationship between the data structure to be processed and the target data structure is generated based on the target field type of each field in the target data structure information, and the field mapping relationship is written into the data mapping table.

[0031] If there is a mismatch, data reporting information is generated based on the field type and field description information of the target data structure information that does not match the field type in the full data table, and data reporting operation is performed.

[0032] In one embodiment, the method further includes:

[0033] In response to receiving data reporting information, the data storage structure identifies field description information from the reported data and redetermines the data structure type corresponding to the data reporting information based on the field description information.

[0034] The data structure type corresponding to the data reporting information is locally encoded, and the locally modified code is sent to the storage cluster.

[0035] In response to the storage cluster receiving locally modified code, a task to add the corresponding data structure type is executed, adding the data structure type corresponding to the data reporting information to the data mapping table.

[0036] In one embodiment, re-determining the data structure type corresponding to the data reporting information based on the field description information includes:

[0037] Feature vectors are extracted from the field description information in the reported data. Based on the extracted feature vectors, structural vectors similar to the feature vectors are identified in the model library. Specifically, the header information in the field description information is used as the parameter of the mapping function. By calculating discrete values ​​for different header information, the field description information is mapped to a structural vector of N-dimensional discrete vectors. Based on the structural vectors, chart configuration information with a matching degree greater than a threshold is retrieved. The data structure type corresponding to the data reported information is determined based on the chart configuration information.

[0038] To address the aforementioned technical issues, a second aspect provides a multi-platform interoperability device, which includes:

[0039] The receiving module is used to receive request information sent by the first platform, wherein the request information carries data structure information to be processed; determine the target platform based on the request information and obtain the target platform data structure information, wherein the target platform data structure information includes the target data format;

[0040] The conversion module is used to convert the data structure information to be processed into structured data in the target data format based on the target platform's data structure information, and then send the structured data in the target data format to the target platform. The conversion of the data structure information to be processed into structured data in the target data format includes: parsing the data structure information to be processed to obtain multiple data fill objects; constructing a target data structure framework based on the target data structure, traversing the target data structure frame using a deep recursive parsing algorithm, and converting the multiple data fill objects into first key-value pairs; obtaining data supplement objects, assigning values ​​to each data supplement object, and converting the data supplement objects into second key-value pairs; and constructing structured data in the target data format based on the first key-value pairs, the second key-value pairs, and the target data structure framework.

[0041] To address the aforementioned technical problems, a third aspect provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: the processor executes the steps of the method described in the first aspect.

[0042] In order to solve the above-mentioned technical problems, in a fourth aspect, this application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described in the first aspect.

[0043] Unlike existing technologies, this application parses the data structure information to be processed to obtain multiple data filling objects. Based on the target data structure, a target data structure framework is constructed. A deep recursive parsing algorithm is used to traverse the target data structure framework, converting the multiple data filling objects into first key-value pairs, obtaining data supplementary objects, assigning values ​​to each data supplementary object, and converting the data supplementary objects into second key-value pairs. Based on the first key-value pairs, the second key-value pairs, and the target data structure framework, structured data in the target data format is constructed and sent to the target platform. In this way, a data structure from one platform can automatically undergo data structure conversion according to the requirements of different target platforms' target data structures to generate structured data with the target data format and push it to at least one target platform, thus solving the complex problem of inter-system integration between large-scale internet platforms. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating a multi-platform integration method in one embodiment;

[0045] Figure 2 This is a flowchart illustrating a multi-platform integration method in another embodiment;

[0046] Figure 3 This is a structural block diagram of a multi-platform docking device in one embodiment;

[0047] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0049] To address the aforementioned technical problems, in one embodiment, such as Figure 1 As shown, a multi-platform integration method is provided, which includes the following steps:

[0050] Step 101: Receive request information sent by the first platform, wherein the request information carries data structure information to be processed.

[0051] Step 102: Determine the target platform based on the request information and obtain the target platform data structure information, wherein the target platform data structure information includes the target data format.

[0052] When the docking management platform receives a docking request from the first platform to dock with the target platform, it retrieves the target data structure information of the target platform based on the request. After receiving the target data structure returned by the target platform, it compares the target platform's target data structure with the data structure to be processed from the first platform. There can be multiple target platforms. When the target data structure matches the data structure to be processed, the target platform and the first platform can directly dock their data. When the target data structure does not match the data structure to be processed, the target platform and the first platform cannot dock their data. In this case, data transformation can be performed on the first platform to ensure that the data structure to be processed is consistent with the data structure of the target platform.

[0053] Step 103: Based on the target platform's data structure information, convert the data structure information to be processed into structured data in the target data format, and send the structured data in the target data format to the target platform.

[0054] The process of converting the data structure information to be processed into structured data in the target data format includes:

[0055] Step 201: Parse the data structure information to be processed to obtain multiple data filling objects.

[0056] In one implementation, the data processing structure information can be parsed using a parser corresponding to the data structure to be processed, resulting in multiple data filling objects. These data filling objects can be parsed objects or arrays. A framework for a data structure identical to the target data structure is created. For example, if the target platform requires JSON data, and the JSON data structure is known to be a tree structure, a tree-like data structure framework is created as the target data structure framework.

[0057] Step 202: Construct a target data structure framework based on the target data structure, and use a deep recursive parsing algorithm to traverse the target data structure frame to convert multiple data filling objects into the first key-value pair.

[0058] Step 203: Obtain the data supplement object, assign values ​​to each data supplement object, and convert the data supplement object into a second key-value pair.

[0059] A deep recursive parsing algorithm can be used to traverse the target data structure framework, finding the correspondence between each node of the target framework and multiple data filler objects, i.e., determining the node position of each data filler object. In practical applications, some nodes of the target data framework may be missing data objects. In this case, the data filler objects can be obtained, and the data filler objects and their corresponding values ​​can be converted into key-value pairs. These multiple key-value pairs can then be embedded into the target data structure framework.

[0060] The process of obtaining data supplementation objects and assigning values ​​to multiple data supplementation objects includes: selecting at least one attribute information that conforms to the target data structure rules for assignment; the attribute information conforming to the target data structure rules includes one or more of the following: attribute information of the data to be processed, commonly used fixed attribute information, and pre-defined business attributes. Optionally, the pre-defined filling attributes can be obtained from the data to be processed, commonly used fixed attributes (system time, constants, etc.), or additional business attributes obtained by calling the business platform's interface. By filling the target data structure framework with data filling objects and data supplementation objects, missing data can be avoided, and the accuracy and reliability of the structured data under the target data format can be improved.

[0061] Step 204: Construct structured data in the target data format based on the first key-value pair, the second key-value pair, and the target data structure framework.

[0062] The target data structure framework can be a tree-like data structure framework, which includes a root node and several child nodes. After obtaining multiple data fill objects and data supplement objects through the above method, values ​​are assigned to these objects. After obtaining the assigned values, the child nodes of the target data framework can be traversed based on the root node. The assigned values ​​of the multiple data fill objects and data supplement objects are then filled into the child nodes of the target data framework. The assigned values ​​of the multiple data fill objects and data supplement objects are then converted into first key-value pairs and second key-value pairs, respectively, and these first key-value pairs and second key-value pairs are filled into the sub-nodes of the target data framework, serving as the node data of each node in the target data, thus forming the target data.

[0063] In one specific implementation, step 204, constructing structured data under the target data format based on the first key-value pair, the second key-value pair, and the target data structure framework, may specifically include:

[0064] S1: Based on the attribute information of the data-filled object, locate the first key-value pair to several child nodes in the target data structure framework.

[0065] The target data structure may include several child nodes, as well as leaf nodes. Each node includes a node ID, a node field name, and node information (data population object). First, define a node, the hierarchy between the data population object and the root node information, and then locate the first key-value pair on several child nodes in the target data structure framework.

[0066] S2: Traverse the target data structure framework, obtain the path between the root node and several child nodes, and concatenate the path between the root node and several child nodes into a chain.

[0067] S3: Determine the level and type of each node in the string, merge nodes with the same level and type into one node, and obtain the target connection relationship between the root node and its child nodes.

[0068] Traverse the target data structure framework to obtain the path information of the root node and several child nodes of the first key-value pair currently positioned to several child nodes, and convert the path information into a string. For example, string 1, A (root node)-B (child node)-C (leaf node), string 2, A (root node)-C (child node). Determine the level and type of each node in the string. At this time, the root node information in both string 1 and string 2 is A. These are nodes with the same node level and the same node type, and can be merged into one node, forming a tree structure of root node A, child nodes B and C; and leaf node C of child node B. In this way, the target connection relationship between the root node and child nodes is obtained. Then, according to the target connection relationship between the root node and child nodes, the first key-value pair is filled into the corresponding node in the target structure framework. In this way, the interference of identical nodes on the subsequent generation of structured data in the target format can be eliminated.

[0069] S4: Based on the target connection relationship between the root node and child nodes, fill the first key-value pair into the corresponding node in the target structural framework.

[0070] S5: When all the first key-value pairs are filled into the target structural frame, obtain the nodes in the target structural frame that have not been filled and generate a set of nodes that have not been filled.

[0071] Once all the first key-value pairs have been filled into the target structural frame, the nodes in the target frame structure that have not been filled can be grouped into a set of nodes that have not been filled, so that the nodes that have not been filled can be quickly located later.

[0072] S6: Obtain the node context information of each node in the unfilled node set, match the node context information of each node with the second key-value pair, and obtain the matching degree between the node context information of each node and the second key-value pair.

[0073] S7: Based on the degree of matching, the second key-value pairs are filled into the unfilled nodes to form structured data in the target data format.

[0074] In one embodiment, features of the superior and subordinate nodes of each node in the set of unfilled nodes can be extracted, and the similarity between the extracted features and the second key-value pair can be calculated to obtain the matching degree between the node context information of each node and the second key-value pair. The matching degree can be a numerical value, and the nodes are sorted according to the size of the numerical value. Priority is given to filling the unfilled nodes corresponding to the second key-value pairs with high matching degree.

[0075] In one implementation, after obtaining the data structure information to be processed based on the request information, the process includes constructing a full data structure table based on the data structure information to be processed. The full data structure table includes field names, field types, and field value information. Field value information may include: field value type, field value range, etc. Value types include numeric types, string types, boolean values, time types, etc. For numeric types, the maximum and minimum values ​​are defined; for strings, the maximum length is defined; for time types, the time format is defined; and for boolean values, it is true or false.

[0076] Specifically, the edit distance algorithm can be used to calculate the similarity between the target string of the field type in the full data table and the string of the field corresponding to the target data structure information. The string with the highest similarity is taken as the fuzzy matching result with the target string to obtain the field type corresponding to the target data structure information. Then, based on the target field type of each field in the target data structure information, a field mapping relationship between the data structure to be processed and the target data structure is generated. The field mapping relationship is written into the data mapping table and persisted to the data storage structure. Based on the field mapping table, the data structure information to be processed is converted into structured data in the target data format.

[0077] In practical applications, situations may arise where the fields in the target data structure information do not match the field types in the full data table, making it impossible to find the fields in the target data structure information from the full data table. This can be addressed by scanning the data mapping tables of multiple platforms within the data storage structure; matching the field types of the target data structure information that do not match the field types in the full data table with the field types stored in the data mapping tables of multiple platforms; if a match is found, the matching field types are updated in the full data table; if no match is found, data reporting information is generated based on the field types and descriptions of the target data structure information that do not match the field types in the full data table, and the data reporting operation is performed. This avoids the need for developers to manually code for adaptation to various platforms, reduces on-site upgrade issues, greatly saves manpower, facilitates code maintenance, enhances compatibility with other platforms, and improves user experience.

[0078] Specifically, in response to the data storage structure receiving data reporting information, the data storage structure identifies field description information from the reported data and redetermines the data structure type corresponding to the data reporting information based on the field description information; it performs local encoding on the data structure type corresponding to the data reporting information and sends the local modification code to the storage cluster; in response to the storage cluster receiving the local modification code, it executes the data structure type addition task corresponding to the local modification code and adds the data structure type corresponding to the data reporting information to the data mapping table.

[0079] For some unrecognizable target platform data structure types, the integration management platform allows users to configure settings on the data reporting interface. Based on the user's input of data type and description, clicking "Request New Data" will send the user-input information to the corresponding R&D personnel via email. Once the R&D personnel receive the data type and description, they can identify the data type and send the locally modified code to the integration management platform using local encoding. The platform will then execute this code (after backup, allowing for rollback) to store the newly added field data structure in the database. The interface will refresh, and a fuzzy match will be performed again between the field types in the full data table and the target data structure information to ensure that the data type of each field in the target data structure can be found in the data mapping table, thereby improving the efficiency of data structure identification.

[0080] In one embodiment, generating field description information according to data structure description rules and redetermining the data structure type corresponding to the data reporting information based on the field description information may include: extracting feature vectors from the field description information in the reported data, identifying structure vectors similar to the feature vectors in the model library based on the extracted feature vectors; wherein, using the header information in the field description information as the parameter of the mapping function, calculating discrete values ​​for different header information, and mapping the field description information to a structure vector of N-dimensional discrete vectors; retrieving chart configuration information with a matching degree greater than a threshold based on the structure vectors; and determining the data structure type corresponding to the data reporting information based on the chart configuration information.

[0081] For example, standardized field description information can be extracted according to predefined data structure description rules to facilitate data structure feature extraction operations in subsequent processes. For example, data description rules can be generated based on the logical relationships between data elements. Generally, data logical structures include sets, linear structures, tree structures, graph structures, etc. These logical structures are specifically implemented through methods such as sequence, linking, indexing, and hashing. A general template can be pre-set based on the data logical structure. The general template includes representative features of each structure. By matching with representative features, the data structure information corresponding to the reported information with a matching degree greater than a threshold is written into the general template. The general template includes multiple headers, which can be implemented using methods such as sequence, linking, indexing, and hashing. Each header is used as a basic dimension to analyze the fields, and the fields are filled under the corresponding header until all fields of the reported information are filled into the general template.

[0082] In this application, commonly used data type structures are pre-filled into a general template based on the header information and stored in the data storage structure. A text box is used to select the general template corresponding to the reported data and the filled portions of the general templates for each data type pre-stored in the data storage structure. For ease of explanation, the text box corresponding to the general template of the reported data is referred to as the first text box, and the text box pre-stored in the data storage structure is referred to as the second text box. The position coordinates of the first and second text boxes can be marked using a text region detection algorithm, outputting the coordinates of the four corner points of each text box. The position coordinate difference 'a', center point angle difference 'b', and center point Euclidean distance 'c' of each corner point of the text boxes between the first and second text boxes are calculated, obtaining the relationship information set N(a,b,c) between the first and second text boxes. This relationship information set is input into a pre-trained inference model. The inference model, based on a deep learning algorithm, outputs the type of each text box and the probability of a structural relationship with each text box. Optionally, a data merging layer in a feature extraction network can be used to merge the relationship information sets of two different text boxes to obtain a merged relationship feature set of the two different text boxes. The merged relationship feature set is input into the prediction network. The perceptual opportunities in the prediction network perform multi-layer perceptualization on the merged relationship feature set of different text boxes to obtain the current perceptual probability. Generally, the higher the perceptual probability value, the greater the likelihood of a structural relationship between the two text boxes. Candidate text box pairs with structural relationships can be determined based on the perceptual probability values ​​between different text boxes; based on the candidate text box pairs and the text box types, the first and second text boxes with structural relationships are determined. The first text box with a structural relationship is retained in the general template, while the content entered in the first text box without a structural relationship is deleted to avoid irrelevant information interfering with subsequent feature extraction.

[0083] The header of the general template also includes a drop-down list containing a description template for that header. For example, if it is a tree-structured information, the header can be in the form of sequence (level) or link (branch, direction). By analyzing the logical structure of the reported data based on methods such as sequence, link, index, and hash, the data structure information corresponding to the reported data is written into the general template. This allows for quick location of the data structure and selection of the corresponding field description information from the general template based on the structure.

[0084] Further feature extraction is performed on the field description information. Based on the extracted feature vectors, structural vectors similar to the feature vectors are identified in the model library. Specifically, the header information in the field description information is used as a parameter of the mapping function. By calculating discrete values ​​for different header information, the field description information is mapped into an N-dimensional discrete structural vector. The structural vector refers to data structure identification information, which converts data features into multi-dimensional feature vectors according to the mapping function. The structural vector is also the basis for subsequent chart configuration matching. Various charts that match common structural vectors can be pre-stored in the model library. Chart configuration information with a matching degree greater than a threshold can be retrieved based on the structural vector. The data structure type corresponding to the data reporting information is determined based on the chart configuration information. For example, the chart configuration can include the chart type and a mapping matrix using certain information compression, enabling efficient storage and retrieval of related information. All structural vectors in the model library can be traversed, and a sparse matrix graph can be constructed based on the multi-dimensional structural vectors and chart configurations. The sparse matrix graph represents the correlation index, i.e., the matching degree, between the structural feature vectors and the chart configurations. By retrieving chart configuration information with a matching degree greater than a threshold that matches the structure vector, the data structure type corresponding to the data reporting information can be determined, thereby improving the accuracy of data structure type identification.

[0085] In one embodiment, after persisting the field mapping table to the data storage structure, the process further includes the interface management platform initiating an information request to the target platform. This information request carries data such as the username and password generated by the interface management platform. The third-party data platform returns authentication information, including the target platform's username and password. This information is used to connect to the target platform. If the username and password are correct, a message is returned to the interface management platform. The returned message includes a response code (which can be 0 or 200 indicating success) and a token (or other information). The interface management platform then uses this token to report data to the target platform.

[0086] For example, an authentication request is sent to the target platform. The authentication request carries the first representation of the first platform and the first token. The first token includes a first token identifier and first version data. If a second token identifier identical to the first token identifier is detected in the target platform's token storage space, the target platform obtains the second version data included in the second token of the second token identifier and the second identifier of the docking management platform. The token storage space is used to store the token authorized for use by the target platform. If the first version data is the same as the second version data shown, and the first identifier is the same as the second identifier, the target platform returns authentication data to the docking management platform.

[0087] In one embodiment, the docking management platform can send an authentication request to the target platform to request authentication data, where the authentication data refers to the target platform's authentication token. This docking request can carry a first identifier and a first token of the first platform. The first token includes a first token identifier and first version data. The first identifier can be used to uniquely identify the first platform, and the first version data can be the version number of the first token, for example, the first version data version number can be 1.02. Here, 1 represents the first token, and 02 represents the version number of the first token updated in the second update. If a second token identifier identical to the first token identifier is detected in the target platform's token storage space, it indicates that the first token is an authorized and legitimate token. The second token corresponding to the second token identifier extracted from the token storage space is then obtained. The second identifier is used to uniquely identify the target platform. The token storage space can be a Redis cache, a specific cache space, or a relational database. This embodiment of the application determines whether the first platform is stealing other clients' tokens to obtain business data by detecting whether the first token carried in the request sent by the first client exists in the server, thereby preventing data leakage caused by different platforms using the same token and improving data security. Optionally, if no second token identifier identical to the first token identifier is detected in the token storage space, it indicates that the first token is not a legitimate token authorized by the server, and a request failure response can be returned for the above request. This request failure response can be used to reject the business request.

[0088] After obtaining the second version data and the second identifier, it is possible to check whether the first version data and the second version data are the same. If the first version data and the second version data are found to be the same, it indicates that the first token is the latest version of the token, and that the first token has not expired. Further checks can then be performed to check whether the first identifier and the second identifier are the same. If the first identifier and the second identifier are found to be the same, it indicates that the first platform and the interface management platform are the same client, that the first client sending the above business request is a legitimate client authorized by the server, and that the first token was not stolen by the interface management platform. Therefore, the authentication data requested in the authentication request can be returned to the interface management platform. Through multiple checks, it can be ensured that each token must be used on the device that generated the token, preventing the use of tokens across devices and improving the security of token authentication.

[0089] To better illustrate the multi-platform integration method of this application, in a specific embodiment, the steps of the multi-platform integration method are as follows: Figure 2 As shown:

[0090] Step 301: The management software constructs and maintains the data reporting management module (interfacing with the management platform).

[0091] Step 302: The user accesses the storage front-end page (connecting to the management platform front-end), enters the information of the target platform (including address, user, password, and target platform authentication information), the data type to be reported (alarm information, capacity information, node information, performance information, log collection information, etc.), and customizes the reporting time for each data type.

[0092] Step 303: The user clicks to query the reported data type (data structure information to be processed).

[0093] Step 304: The backend returns the data structure (data structure information to be processed) of the current data type of the current storage system based on the information queried in step (303), including field name, field meaning, value type, value range, whether it is required, and other options.

[0094] The multi-platform integration method in this application is applied to a multi-platform integration system, which includes a first platform, an integration management platform, and target platforms. The integration management platform includes a front-end and a back-end. The front-end is the platform integration module, where users can customize the target platform information (including address, user, password, and target platform authentication information), the reported data types (alarm information, capacity information, node information, performance information, log collection information, etc.), and the reporting time for each data type in the platform integration module interface within the storage interface. When a user performs a data type-based query operation, the back-end returns a data structure to be processed. This data structure is a table structure storing the current full data, including field names, field meanings, value types, value ranges, methods for obtaining each field attribute, and whether each field is required. Then, based on the field meanings and the target platform's field meanings, a fuzzy match is performed, data types are validated, and after automatic identification, the target platform's integration data attributes are written into each data attribute mapping table. If all data is sent from the front-end to the storage back-end platform integration module, the back-end persists the data.

[0095] Step 305: (Interconnecting with the management platform) The stored data returned in step (304) will be automatically filled into the data mapping table according to the target platform data. If the mapping table is completed, step (308) will be executed; otherwise, step (306) will be executed.

[0096] Based on the information request data from the target platform sent by the front-end of the management platform, the data storage structure carries the user and password sent by the front-end and initiates a connection request to the target platform's URL address. It obtains the authentication information returned by the target platform and compares it with the authentication information sent by the front-end, and saves the authentication token returned by the target platform. Then, the back-end storage starts a scheduled task based on the reporting data definition information sent by the front-end, converts the unprocessed data structure stored in the data storage structure into the target data structure required by the target platform, carries the authentication token, and reports the data to the target platform's URL address.

[0097] In step 306, the user will click "Add Attribute Request" and send the required detailed information.

[0098] In practical applications, situations may arise where the fields in the target data structure information do not match the field types in the full data table, making it impossible to find the fields in the target data structure information from the full data table. This can be addressed by scanning the data mapping tables of multiple platforms within the data storage structure; matching the field types of the target data structure information that do not match the field types in the full data table with the field types stored in the data mapping tables of multiple platforms; automatically scanning the data attribute mapping tables of other platforms that have already been entered into the database; and matching the meanings of data fields from other platforms to match the locally connected target platform data with the stored data. Finally, manual identification and adjustment of the data are required. If this cannot be confirmed, the user needs to confirm whether the newly added data attributes need to be stored and reported.

[0099] Step 307: The storage development team sends the instruction to obtain the requirement information (including field type and field description information) from step (306) to the data reporting module, and updates the data mapping table after running the instruction.

[0100] The storage backend will send this requirement information to the relevant R&D personnel via email. The management platform will identify the field descriptions in the requirement information and provide the required data structure type to the user's storage cluster via command or hard-coded method. The storage cluster will automatically retrieve the data structure type by executing the received code query and complete the data attribute mapping table. The backend will automatically add the new data to the corresponding data structure table in the storage. After the data mapping table is built, the user can modify and confirm it before issuing a request, establishing a connection with the target platform, and reporting the data. After the process is complete, the storage will update all data tables.

[0101] The table below shows the correspondence between the alarm information data retrieved from the storage side and the third-party data:

[0102]

[0103] In the above correspondence table, the data reporting module (connecting to the management platform) uses field description information to match the target data structure information of the third party with the data structure information to be processed stored in the data storage structure. If the target platform requires an alarm sequence number that the storage cannot provide, and other compatible platforms do not have this attribute information, the user clicks to add an attribute requirement (key: "deviceSequence", description: "alarm sequence number", explanation: "serial number of the device that generated the alarm"). The storage development team will send a command to the data reporting module to query the device serial number using the device ID, and persistently save the required alarmSequence information, acquisition method, type, etc., to the reporting module. Repair suggestions do not need to be reported. If the target platform requires an alarm source that the storage does not provide and cannot match, the alarm source description will be added to the storage's cluster ID attribute so that other platforms can directly match the alarm source in the future.

[0104] Among them, the alarm sequence number is the alarm number. The alarm level and handling suggestions are generated by the management platform through alarm templates. For example: (1) Hard disk status alarm - minor alarm, handling suggestion: find the alarm device according to the node name and hard disk slot number in the alarm source. If the disk is being replaced, observe whether the alarm is restored after the disk replacement is completed. If it is not restored, contact the technical engineer for handling; (2) Abnormal and important system disk space usage, handling suggestion: use the "du-sh" command to find large files and delete redundant files; use the "lsof" command to check the delete file. If the program corresponding to the delete file can be restarted, restart the corresponding process. If the alarm is still not restored after the above steps, contact the technical engineer for handling.

[0105] Step 308: Send the transformed data to the storage backend for saving.

[0106] Step 309: The storage backend receives data in step (308) and initiates a connection request to the target platform.

[0107] Step 310: If step (309) is executed successfully, that is, the information returned by the third party is stored correctly, then step (312) is executed; otherwise, step (311) is executed.

[0108] After persisting the field mapping table to the data storage structure, the process also includes the integration management platform initiating an information request to the target platform. This request carries data such as the username and password generated by the integration management platform. The third-party data platform returns authentication information, including the target platform's username and password. This information is used to connect to the target platform. If the username and password are correct, a message is returned to the integration management platform. The returned message includes a response code (which can be 0 or 200 indicating success) and a token (or other information). The integration management platform then uses the token to report data to the target platform.

[0109] Step 311: The backend returns a failure to establish a connection, prompting the user to confirm the disk platform information.

[0110] Step 312: The storage backend data reporting module starts a scheduled task based on the reporting data definition information sent by the frontend, and converts the stored information into the data format required by the target platform according to the generated data mapping relationship, and reports it to the target platform.

[0111] This application constructs and maintains an interface management platform using management software. The platform comprises a front-end and a back-end. Users input platform information, including authentication details, data types, and reporting intervals, into the front-end interface. The back-end connects based on the platform authentication information from the front-end, automatically identifying the data conversion between existing stored data and platform data and reporting the data. This method avoids the development tasks and time-sensitive issues associated with adapting to various platforms, and allows direct connection to new target platforms even at the customer's site, saving manpower and time costs and improving user experience.

[0112] It should be understood that, although Figures 1-2 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figures 1-2 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0113] In one embodiment, such as Figure 3 As shown, a multi-platform interoperability device is provided, including: a partitioning module 30 and a storage module 31, wherein:

[0114] The receiving module 30 is used to receive request information sent by the first platform, wherein the request information carries data structure information to be processed; determine the target platform based on the request information and obtain the target platform data structure information, wherein the target platform data structure information includes the target data format;

[0115] The conversion module 31 is used to convert the data structure information to be processed into structured data in the target data format based on the target platform's data structure information, and send the structured data in the target data format to the target platform. The conversion of the data structure information to be processed into structured data in the target data format includes: parsing the data structure information to be processed to obtain multiple data filling objects; constructing a target data structure framework based on the target data structure, traversing the target data structure frame using a deep recursive parsing algorithm, and converting the multiple data filling objects into first key-value pairs; obtaining data supplementary objects, assigning values ​​to each data supplementary object, and converting the data supplementary objects into second key-value pairs; and constructing structured data in the target data format based on the first key-value pairs, the second key-value pairs, and the target data structure framework.

[0116] In one embodiment, the above-described apparatus can implement another implementation of the multi-platform interoperability method, with the following specific steps: The target data structure framework includes a root node and several child nodes. Constructing structured data in the target data format based on the first key-value pair, the second key-value pair, and the target data structure framework includes:

[0117] Based on the attribute information of the data-filled object, the first key-value pair is located to several child nodes in the target data structure framework;

[0118] Traverse the target data structure framework to obtain the path between the root node and several child nodes, and concatenate the path between the root node and several child nodes into a chain.

[0119] Determine the level and type of each node in the string chain, merge nodes with the same level and type into one node, and obtain the target connection relationship between the root node and its child nodes.

[0120] Based on the target connection relationship between the root node and its child nodes, the first key-value pair is filled into the corresponding node in the target structural framework;

[0121] When all the first key-value pairs are filled into the target structural frame, obtain the nodes in the target structural frame that have not been filled and generate a set of nodes that have not been filled.

[0122] Obtain the node context information of each node in the unfilled node set, match the node context information of each node with the second key-value pair, and obtain the degree of matching between the node context information of each node and the second key-value pair.

[0123] The second key-value pair is filled into the unfilled nodes by sorting based on the degree of matching, forming structured data in the target data format.

[0124] In one embodiment, the above-described apparatus can implement another implementation of the multi-platform interoperability method, with the following specific steps: After determining the target platform based on the request information and obtaining the target platform's data structure information, the following steps are included:

[0125] A full data structure table is constructed based on the data structure information to be processed. The full data structure table includes field names, field types, and field value information.

[0126] Fuzzy matching is performed between the field types in the full data table and the target data structure information to obtain the target field type for each field of the target data structure information. Based on the target field type of each field of the target data structure information, a field mapping relationship between the data structure to be processed and the target data structure is generated. The field mapping relationship is written into the data mapping table and persisted to the data storage structure. Based on the field mapping table, the data structure information to be processed is converted into structured data in the target data format.

[0127] In one embodiment, the above-described apparatus can implement another implementation of the multi-platform integration method, with the following specific steps: After persisting the field mapping table to the data storage structure, the process includes:

[0128] Send an authentication request to the target platform. The authentication request carries the first identifier and the first token of the first platform. The first token includes the first token identifier and the first version data.

[0129] If a second token identifier identical to the first token identifier is detected in the token storage space of the target platform, the target platform obtains the second version data included in the second token of the second token identifier and the second identifier of the docking management platform. The token storage space is used to store the token authorized for use by the target platform.

[0130] If the first version data is the same as the second version data shown, and the first identifier is the same as the second identifier, then the target platform returns authentication data to the docking management platform.

[0131] In one embodiment, the above-described device can implement another implementation of the multi-platform interface method, with the following specific steps: Fuzzy matching is performed between the field types in the full data table and the target data structure information to obtain the target field type for each field of the target data structure information, including:

[0132] When any field type in the target data structure information does not match the field type in the full data table, scan the data mapping tables of other platforms in the data storage structure;

[0133] Match the field types of target data structure information that do not match the field types in the full data table with the field types stored in the data mapping tables of other platforms;

[0134] If a match is found, a field mapping relationship between the data structure to be processed and the target data structure is generated based on the target field type of each field in the target data structure information, and the field mapping relationship is written into the data mapping table.

[0135] If there is a mismatch, data reporting information is generated based on the field type and field description information of the target data structure information that does not match the field type in the full data table, and data reporting operation is performed.

[0136] In one embodiment, the above-described apparatus can implement another implementation of the multi-platform interoperability method, with the following specific steps: The method further includes:

[0137] In response to receiving data reporting information, the data storage structure identifies field description information from the reported data and redetermines the data structure type corresponding to the data reporting information based on the field description information.

[0138] The data structure type corresponding to the data reporting information is locally encoded, and the locally modified code is sent to the storage cluster.

[0139] In response to the storage cluster receiving locally modified code, a task to add the corresponding data structure type is executed, adding the data structure type corresponding to the data reporting information to the data mapping table.

[0140] In one embodiment, the above-described device can implement another implementation of the multi-platform interface method, with the following specific steps: Re-determining the data structure type corresponding to the data reporting information based on the field description information includes:

[0141] Feature vectors are extracted from the field description information in the reported data. Based on the extracted feature vectors, structural vectors similar to the feature vectors are identified in the model library. Specifically, the header information in the field description information is used as the parameter of the mapping function. By calculating discrete values ​​for different header information, the field description information is mapped to a structural vector of N-dimensional discrete vectors. Based on the structural vectors, chart configuration information with a matching degree greater than a threshold is retrieved. The data structure type corresponding to the data reported information is determined based on the chart configuration information.

[0142] Specific limitations regarding the multi-platform interface device can be found in the limitations of the multi-platform interface method described above, and will not be repeated here. Each module in the aforementioned multi-platform interface device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0143] In one embodiment, this application also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer is able to execute the multi-platform interoperability methods provided by the above methods.

[0144] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a multi-threaded data processing method. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0145] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0146] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0147] Step 101: Receive request information sent by the first platform, wherein the request information carries data structure information to be processed.

[0148] Step 102: Determine the target platform based on the request information and obtain the target platform data structure information, wherein the target platform data structure information includes the target data format.

[0149] Step 103: Based on the target platform's data structure information, convert the data structure information to be processed into structured data in the target data format, and send the structured data in the target data format to the target platform.

[0150] The process of converting the data structure information to be processed into structured data in the target data format includes:

[0151] Step 201: Parse the data structure information to be processed to obtain multiple data filling objects.

[0152] Step 202: Construct a target data structure framework based on the target data structure, and use a deep recursive parsing algorithm to traverse the target data structure frame to convert multiple data filling objects into the first key-value pair.

[0153] Step 203: Obtain the data supplement object, assign values ​​to each data supplement object, and convert the data supplement object into a second key-value pair.

[0154] Step 204: Construct structured data in the target data format based on the first key-value pair, the second key-value pair, and the target data structure framework.

[0155] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0156] The target data structure framework includes a root node and several child nodes. Based on the first key-value pair, the second key-value pair, and the target data structure framework, structured data in the target data format is constructed, including:

[0157] Based on the attribute information of the data-filled object, the first key-value pair is located to several child nodes in the target data structure framework;

[0158] Traverse the target data structure framework to obtain the path between the root node and several child nodes, and concatenate the path between the root node and several child nodes into a chain.

[0159] Determine the level and type of each node in the string chain, merge nodes with the same level and type into one node, and obtain the target connection relationship between the root node and its child nodes.

[0160] Based on the target connection relationship between the root node and its child nodes, the first key-value pair is filled into the corresponding node in the target structural framework;

[0161] When all the first key-value pairs are filled into the target structural frame, obtain the nodes in the target structural frame that have not been filled and generate a set of nodes that have not been filled.

[0162] Obtain the node context information of each node in the unfilled node set, match the node context information of each node with the second key-value pair, and obtain the degree of matching between the node context information of each node and the second key-value pair.

[0163] The second key-value pair is filled into the unfilled nodes by sorting based on the degree of matching, forming structured data in the target data format.

[0164] In one embodiment, when the processor executes the computer program, it further performs the following steps: after determining the target platform based on the request information and obtaining the target platform data structure information, it includes:

[0165] A full data structure table is constructed based on the data structure information to be processed. The full data structure table includes field names, field types, and field value information.

[0166] Fuzzy matching is performed between the field types in the full data table and the target data structure information to obtain the target field type for each field of the target data structure information. Based on the target field type of each field of the target data structure information, a field mapping relationship between the data structure to be processed and the target data structure is generated. The field mapping relationship is written into the data mapping table and persisted to the data storage structure. Based on the field mapping table, the data structure information to be processed is converted into structured data in the target data format.

[0167] In one embodiment, the processor, while executing the computer program, further performs the following steps: after persisting the field mapping table to the data storage structure, including:

[0168] Send an authentication request to the target platform. The authentication request carries the first identifier and the first token of the first platform. The first token includes the first token identifier and the first version data.

[0169] If a second token identifier identical to the first token identifier is detected in the token storage space of the target platform, the target platform obtains the second version data included in the second token of the second token identifier and the second identifier of the docking management platform. The token storage space is used to store the token authorized for use by the target platform.

[0170] If the first version data is the same as the second version data shown, and the first identifier is the same as the second identifier, then the target platform returns authentication data to the docking management platform.

[0171] In one embodiment, when the processor executes the computer program, it further performs the following steps: fuzzy matching based on the field types in the full data table and the target data structure information to obtain the target field type for each field of the target data structure information, including:

[0172] When any field type in the target data structure information does not match the field type in the full data table, scan the data mapping tables of other platforms in the data storage structure;

[0173] Match the field types of target data structure information that do not match the field types in the full data table with the field types stored in the data mapping tables of other platforms;

[0174] If a match is found, a field mapping relationship between the data structure to be processed and the target data structure is generated based on the target field type of each field in the target data structure information, and the field mapping relationship is written into the data mapping table.

[0175] If there is a mismatch, data reporting information is generated based on the field type and field description information of the target data structure information that does not match the field type in the full data table, and data reporting operation is performed.

[0176] In one embodiment, the processor, while executing a computer program, further performs the following steps: the method further includes:

[0177] In response to receiving data reporting information, the data storage structure identifies field description information from the reported data and redetermines the data structure type corresponding to the data reporting information based on the field description information.

[0178] The data structure type corresponding to the data reporting information is locally encoded, and the locally modified code is sent to the storage cluster.

[0179] In response to the storage cluster receiving locally modified code, a task to add the corresponding data structure type is executed, adding the data structure type corresponding to the data reporting information to the data mapping table.

[0180] In one embodiment, when the processor executes the computer program, it further performs the following steps: re-determining the data structure type corresponding to the data reporting information based on the field description information, including:

[0181] Feature vectors are extracted from the field description information in the reported data. Based on the extracted feature vectors, structural vectors similar to the feature vectors are identified in the model library. Specifically, the header information in the field description information is used as the parameter of the mapping function. By calculating discrete values ​​for different header information, the field description information is mapped to a structural vector of N-dimensional discrete vectors. Based on the structural vectors, chart configuration information with a matching degree greater than a threshold is retrieved. The data structure type corresponding to the data reported information is determined based on the chart configuration information.

[0182] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0183] Step 101: Receive request information sent by the first platform, wherein the request information carries data structure information to be processed.

[0184] Step 102: Determine the target platform based on the request information and obtain the target platform data structure information, wherein the target platform data structure information includes the target data format.

[0185] Step 103: Based on the target platform's data structure information, convert the data structure information to be processed into structured data in the target data format, and send the structured data in the target data format to the target platform.

[0186] The process of converting the data structure information to be processed into structured data in the target data format includes:

[0187] Step 201: Parse the data structure information to be processed to obtain multiple data filling objects.

[0188] Step 202: Construct a target data structure framework based on the target data structure, and use a deep recursive parsing algorithm to traverse the target data structure frame to convert multiple data filling objects into the first key-value pair.

[0189] Step 203: Obtain the data supplement object, assign values ​​to each data supplement object, and convert the data supplement object into a second key-value pair.

[0190] Step 204: Construct structured data in the target data format based on the first key-value pair, the second key-value pair, and the target data structure framework.

[0191] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0192] The target data structure framework includes a root node and several child nodes. Based on the first key-value pair, the second key-value pair, and the target data structure framework, structured data in the target data format is constructed, including:

[0193] Based on the attribute information of the data-filled object, the first key-value pair is located to several child nodes in the target data structure framework;

[0194] Traverse the target data structure framework to obtain the path between the root node and several child nodes, and concatenate the path between the root node and several child nodes into a chain.

[0195] Determine the level and type of each node in the string chain, merge nodes with the same level and type into one node, and obtain the target connection relationship between the root node and its child nodes.

[0196] Based on the target connection relationship between the root node and its child nodes, the first key-value pair is filled into the corresponding node in the target structural framework;

[0197] When all the first key-value pairs are filled into the target structural frame, obtain the nodes in the target structural frame that have not been filled and generate a set of nodes that have not been filled.

[0198] Obtain the node context information of each node in the unfilled node set, match the node context information of each node with the second key-value pair, and obtain the degree of matching between the node context information of each node and the second key-value pair.

[0199] The second key-value pair is filled into the unfilled nodes by sorting based on the degree of matching, forming structured data in the target data format.

[0200] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0201] After determining the target platform based on the request information and obtaining the target platform's data structure information, the following is included:

[0202] A full data structure table is constructed based on the data structure information to be processed. The full data structure table includes field names, field types, and field value information.

[0203] Fuzzy matching is performed between the field types in the full data table and the target data structure information to obtain the target field type for each field of the target data structure information. Based on the target field type of each field of the target data structure information, a field mapping relationship between the data structure to be processed and the target data structure is generated. The field mapping relationship is written into the data mapping table and persisted to the data storage structure. Based on the field mapping table, the data structure information to be processed is converted into structured data in the target data format.

[0204] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0205] After persisting the field mapping table to the data storage structure, the following steps are included:

[0206] Send an authentication request to the target platform. The authentication request carries the first identifier and the first token of the first platform. The first token includes the first token identifier and the first version data.

[0207] If a second token identifier identical to the first token identifier is detected in the token storage space of the target platform, the target platform obtains the second version data included in the second token of the second token identifier and the second identifier of the docking management platform. The token storage space is used to store the token authorized for use by the target platform.

[0208] If the first version data is the same as the second version data shown, and the first identifier is the same as the second identifier, then the target platform returns authentication data to the docking management platform.

[0209] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0210] Based on a fuzzy match between the field types in the full data table and the target data structure information, the target field types for each field in the target data structure information are as follows:

[0211] When any field type in the target data structure information does not match the field type in the full data table, scan the data mapping tables of other platforms in the data storage structure;

[0212] Match the field types of target data structure information that do not match the field types in the full data table with the field types stored in the data mapping tables of other platforms;

[0213] If a match is found, a field mapping relationship between the data structure to be processed and the target data structure is generated based on the target field type of each field in the target data structure information, and the field mapping relationship is written into the data mapping table.

[0214] If there is a mismatch, data reporting information is generated based on the field type and field description information of the target data structure information that does not match the field type in the full data table, and data reporting operation is performed.

[0215] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0216] The method also includes:

[0217] In response to receiving data reporting information, the data storage structure identifies field description information from the reported data and redetermines the data structure type corresponding to the data reporting information based on the field description information.

[0218] The data structure type corresponding to the data reporting information is locally encoded, and the locally modified code is sent to the storage cluster.

[0219] In response to the storage cluster receiving locally modified code, a task to add the corresponding data structure type is executed, adding the data structure type corresponding to the data reporting information to the data mapping table.

[0220] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0221] Based on the field description information, the data structure type corresponding to the data reporting information is re-determined, including:

[0222] Feature vectors are extracted from the field description information in the reported data. Based on the extracted feature vectors, structural vectors similar to the feature vectors are identified in the model library. Specifically, the header information in the field description information is used as the parameter of the mapping function. By calculating discrete values ​​for different header information, the field description information is mapped to a structural vector of N-dimensional discrete vectors. Based on the structural vectors, chart configuration information with a matching degree greater than a threshold is retrieved. The data structure type corresponding to the data reported information is determined based on the chart configuration information.

[0223] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0224] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0225] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for multi-platform integration, characterized in that, include: Receive request information sent by the first platform, wherein the request information carries data structure information to be processed; The target platform is determined based on the request information, and the target platform data structure information is obtained, wherein the target platform data structure information includes the target data format; Based on the target platform's data structure information, the data structure information to be processed is converted into structured data in the target data format, and the structured data in the target data format is sent to the target platform. The process of converting the data structure information to be processed into structured data in the target data format includes: The data structure information to be processed is parsed to obtain multiple data filling objects; A target data structure framework is constructed based on the target platform's data structure information. A deep recursive parsing algorithm is used to traverse the target data structure framework, converting multiple data filling objects into first key-value pairs. The target data structure framework includes a root node and several child nodes. Obtain the data supplement object, assign values ​​to the data supplement object one by one, and convert the data supplement object into a second key-value pair; Based on the attribute information of the data-filled object, the first key-value pair is located to several child nodes in the target data structure framework; Traverse the target data structure framework to obtain the path between the root node and several child nodes, and concatenate the path between the root node and several child nodes into a chain. Determine the level and type of each node in the string chain, merge nodes with the same level and type into one node, and obtain the target connection relationship between the root node and its child nodes. Based on the target connection relationship between the root node and child nodes, populate the first key-value pair into the corresponding node in the target data structure framework; When all the first key-value pairs are filled into the target data structure frame, obtain the nodes in the target data structure frame that have not been filled and generate a set of nodes that have not been filled. Obtain the node context information of each node in the unfilled node set, match the node context information of each node with the second key-value pair, and obtain the degree of matching between the node context information of each node and the second key-value pair. The second key-value pair is filled into the unfilled nodes based on the degree of matching, forming structured data in the target data format; The process of obtaining supplementary data objects and assigning values ​​to each of the supplementary data objects includes: selecting at least one attribute information that conforms to the target data structure rules and assigning it a value.

2. A multi-platform docking device, characterized in that, The device includes: A receiving module is configured to receive request information sent by a first platform, wherein the request information carries data structure information to be processed; determine a target platform based on the request information and obtain the target platform's data structure information, wherein the target platform's data structure information includes a target data format; The conversion module is used to convert the data structure information to be processed into structured data in the target data format based on the target platform's data structure information, and send the structured data in the target data format to the target platform. The conversion of the data structure information to be processed into structured data in the target data format includes: parsing the data structure information to be processed to obtain multiple data filling objects; constructing a target data structure framework based on the target platform's data structure information; traversing the target data structure framework using a deep recursive parsing algorithm; converting the multiple data filling objects into first key-value pairs; the target data structure framework includes a root node and several child nodes; obtaining data supplementary objects; assigning values ​​to each data supplementary object; converting the data supplementary objects into second key-value pairs; locating the first key-value pairs to several child nodes in the target data structure framework according to the attribute information of the data filling objects; traversing the target data structure framework to obtain the paths between the root node and several child nodes, and connecting the root node and several child nodes... The paths of nodes are linked into a chain; the level and type of each node in the chain are determined, and nodes with the same level and type are merged into one node to obtain the target connection relationship between the root node and its child nodes; according to the target connection relationship between the root node and its child nodes, the first key-value pairs are filled into the corresponding nodes in the target data structure framework; when all the first key-value pairs are filled into the target data structure framework, the nodes that have not been filled in the target data structure framework are obtained, and a set of nodes that have not been filled is generated; the node context information of each node in the set of nodes that have not been filled is obtained, and the node context information of each node is matched with the second key-value pair to obtain the degree of matching between the node context information of each node and the second key-value pair; based on the sorting of the degree of matching, the second key-value pairs are filled into the nodes that have not been filled to form structured data under the target data format, wherein obtaining data supplementary objects and assigning values ​​to the data supplementary objects one by one includes: selecting at least one attribute information that conforms to the rules of the target data structure for assignment.

3. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method of claim 1.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method of claim 1.

Images