Interface document construction method, key parameter processing model training method and computing equipment

Abstract

The present application discloses a method for constructing an interface document, a method for training a key parameter processing model, and a computing device. The method for constructing an interface document includes: obtaining a target data document of a data center, the target data document including multiple target data objects, the target data objects being used to describe configuration information of multiple target facilities in the data center; determining, based on key parameter processing rules, the association relationship between the key configuration information of the multiple target facilities from the target interface data document; constructing data of the configuration information of the multiple target facilities based on the association relationship between the key configuration information of the multiple target facilities; and generating an interface document of the target type based on the constructed data.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method for constructing an interface document, a method for training a key parameter processing model, and a computing device. Background Art

[0002] During the development of the upper-level application software of the data center management tool, developers cannot access the real data of the data center stored in the user data center management tool; therefore, it is necessary to simulate the interface document of the user data center management tool based on the target data document of the user data center management tool to support developers in the functional development and debugging of the upper-level application software of the data center management tool.

[0003] The interface document of the user data center management tool includes multiple data objects, and the configuration information of the facilities of the data center described by each of the multiple data objects may have a certain association relationship. In other words, in multiple data objects, there may be configuration information used to describe the same facility information. The value patterns of these configuration information used to describe the same facility information are the same, but the keys corresponding to the values are not the same; therefore, before constructing the interface document, it is necessary to first determine the association relationship between the configuration information of the multiple facilities based on the target data document, so that in multiple target data objects, the same values are constructed for these related configuration information, thereby saving the time required for simulation data construction.

[0004] However, when the amount of data information in the target data document or the simulation data document is large, it is relatively slow to manually identify the relationship between the key configuration information of multiple target facilities and construct the related data information. Summary of the Invention

[0005] This application discloses a method for constructing an interface document, a method for training a key parameter processing model, and a computing device. By obtaining the associations between key configuration information of multiple target facilities based on pre-configured key parameter processing rules, this method can solve the problem of slow manual identification of associations between key configuration information of multiple target facilities when the data volume is large.

[0006] In a first aspect, an embodiment of the present application provides a method for constructing an interface document, the method being executed by a computing device, and characterized in that the method includes:

[0007] Acquire a target data document of a data center, wherein the target data document includes a plurality of target data objects, and the target data objects are used to describe configuration information of a plurality of target facilities in the data center;

[0008] Determining, based on key parameter processing rules, association relationships between key configuration information of multiple target facilities from the target interface data document;

[0009] Based on the association relationship between the key configuration information of the multiple target facilities, data construction of the configuration information of the multiple target facilities is performed;

[0010] Based on the constructed data, an interface document of the target type is generated.

[0011] In this way, the association relationship between key configuration information of multiple target facilities can be obtained according to pre-configured key parameter processing rules, thereby solving the problem of slow manual identification of the association relationship between key configuration information of multiple target facilities when the data volume is large.

[0012] In a possible implementation, the key parameter processing rule includes a pre-trained key parameter processing model or a pre-configured rule set; the pre-configured rule set defines at least one regular expression corresponding to each of multiple category labels;

[0013] The determining of the association between key configuration information of multiple target facilities from the target interface data document based on key parameter processing rules includes:

[0014] matching the regular expression with the configuration information of the multiple target facilities, and determining the configuration information that successfully matches as the key configuration information of the multiple target facilities;

[0015] Based on the patterns of the key configuration information of the multiple target facilities, association relationships between the key configuration information of the multiple target facilities are determined.

[0016] In this embodiment, by defining a unified pattern through regular expressions, key configuration information that meets the specific regular expression can be quickly filtered out from a large amount of data, ensuring that the key configuration information has a similar pattern, saving the time required to determine the association relationship of the key configuration information, and improving the efficiency of interface document construction.

[0017] In one possible implementation, the method further includes:

[0018] Determining, based on the category labels corresponding to the regular expressions, category labels corresponding to the key configuration information of the multiple target facilities; wherein the multiple category labels have a hierarchical relationship;

[0019] For any target data object, determining the category label of the target data object according to the category label corresponding to the key configuration information of the target facility included in the target data object and the hierarchical relationship;

[0020] The category label of the target data object indicates the category of the target facility corresponding to the target data object.

[0021] In this embodiment, since the configuration information of facilities at a higher level includes not only the data information of the facilities at the higher level themselves, but may also include the data information of facilities at a lower level, the category label of a single target data object can be determined based on the different category labels corresponding to the configuration information of a single target facility and the hierarchy between the category labels, thereby obtaining the category of the target facility corresponding to the target data object.

[0022] In a possible implementation, determining the category label of the target data object according to the category label corresponding to the key configuration information of the target facility included in the target data object and the hierarchical relationship includes:

[0023] The highest-level category label among the category labels corresponding to all key configuration information included in the target data object is determined as the category label of the target data object.

[0024] In this embodiment, the category label at the highest level can be directly determined as the category label of the target data object, thereby saving the time required to determine the category label of the target data object and improving the construction efficiency of the interface document.

[0025] In a possible implementation, the configuration information includes a key-value pair;

[0026] The values corresponding to the key configuration information of a plurality of target facilities having an associated relationship have the same pattern.

[0027] In this embodiment, the values corresponding to the key configuration information of multiple target facilities having an associated relationship have the same pattern and are all used to describe the same or similar content, for example, they can all be used to describe the location information of a room.

[0028] In a possible implementation, constructing data of the configuration information of the multiple target facilities based on the association relationship between the key configuration information of the multiple target facilities includes:

[0029] Based on the association relationship between the key configuration information of the multiple target facilities, data construction of the key configuration information of the multiple target facilities is performed;

[0030] And, data construction of non-critical configuration information of the various target facilities is performed.

[0031] In this embodiment, since the patterns of the values of the key configuration information with associated relationships are the same, the same pattern can be used to construct these values with the same pattern when simulating data; and, for the key configuration information of multiple target facilities with corresponding relationships, the same values are constructed for the key configuration information with associated relationships, thereby saving the time required to simulate the configuration information of the management unit and improving the construction efficiency of the interface document.

[0032] In a possible implementation, it is characterized in that the key parameter processing model includes a BERT model.

[0033] Compared to other machine learning models, the BERT model features a bidirectional Transformer architecture that considers contextual information when processing each word, enabling BERT to better capture the true meaning of words. Furthermore, the BERT model can be fine-tuned for specific tasks based on user needs, achieving good matching results even with only a small amount of user sample data, thereby improving the efficiency of interface document construction.

[0034] In a possible implementation, matching the regular expression with the configuration information of the multiple target facilities includes:

[0035] The regular expression is matched with the configuration information of the multiple target facilities through BIO notation.

[0036] In this embodiment, because the BIO annotation method can clearly define entity boundaries during matching, is easily extended to multiple entity types, and supports efficient and accurate model training and performance evaluation, more accurate matching results can be obtained through the BIO annotation method, thereby improving the construction efficiency of the interface document.

[0037] In a second aspect, an embodiment of the present application provides a device for constructing an interface document, characterized in that the device includes:

[0038] a data information acquisition module configured to acquire a target data document of a data center, wherein the target data document includes a plurality of target data objects, and the target data objects are used to describe configuration information of a plurality of target facilities in the data center;

[0039] a key parameter processing module configured to determine, based on key parameter processing rules, association relationships between key configuration information of multiple target facilities from the target interface data document;

[0040] a data information construction module configured to construct data of the configuration information of the multiple target facilities based on associations between key configuration information of the multiple target facilities;

[0041] The interface document generation module is configured to generate an interface document of a target type based on the constructed data.

[0042] In this way, the association relationship between key configuration information of multiple target facilities can be obtained according to pre-configured key parameter processing rules, thereby solving the problem of slow manual identification of the association relationship between key configuration information of multiple target facilities when the data volume is large.

[0043] In a possible implementation, the key parameter processing rule includes a pre-trained key parameter processing model or a pre-configured rule set; the pre-configured rule set defines at least one regular expression corresponding to each of multiple category labels;

[0044] The key parameter module is specifically configured as follows:

[0045] matching the regular expression with the configuration information of the multiple target facilities, and determining the configuration information that successfully matches as the key configuration information of the multiple target facilities;

[0046] Based on the patterns of the key configuration information of the multiple target facilities, association relationships between the key configuration information of the multiple target facilities are determined.

[0047] In this embodiment, by defining a unified pattern through regular expressions, key configuration information that meets the specific regular expression can be quickly filtered out from a large amount of data, ensuring that the key configuration information has a similar pattern, saving the time required to determine the association relationship of the key configuration information, and improving the efficiency of interface document construction.

[0048] In a possible implementation, the key parameter processing module is further configured to determine, based on the category labels corresponding to the regular expressions, category labels corresponding to the key configuration information of the multiple target facilities; the multiple category labels have a hierarchical relationship;

[0049] The device also includes a facility category determination module, which is configured to determine the category label of the target data object for any target data object based on the category label corresponding to the key configuration information of the target facility included in the target data object and the hierarchical relationship; wherein the category label of the target data object indicates the category of the target facility corresponding to the target data object.

[0050] In this embodiment, since the configuration information of facilities at a higher level includes not only the data information of the facilities at the higher level themselves, but may also include the data information of facilities at a lower level, the category label of a single target data object can be determined based on the different category labels corresponding to the configuration information of a single target facility and the hierarchy between the category labels, thereby obtaining the category of the target facility corresponding to the target data object.

[0051] In a possible implementation, the facility category determination module is specifically configured to determine the highest-level category label among the category labels corresponding to all key configuration information included in the target data object as the category label of the target data object.

[0052] In this embodiment, the category label at the highest level can be directly determined as the category label of the target data object, thereby saving the time required to determine the category label of the target data object and improving the construction efficiency of the interface document.

[0053] In a possible implementation, the configuration information includes a key-value pair;

[0054] The values corresponding to the key configuration information of a plurality of target facilities having an associated relationship have the same pattern.

[0055] In this embodiment, the values corresponding to the key configuration information of multiple target facilities having an associated relationship have the same pattern and are all used to describe the same or similar content, for example, they can all be used to describe the location information of a room.

[0056] In a possible implementation, the data information construction module is specifically configured as follows:

[0057] Based on the association relationship between the key configuration information of the multiple target facilities, data construction of the key configuration information of the multiple target facilities is performed;

[0058] And, data construction of non-critical configuration information of the various target facilities is performed.

[0059] In this embodiment, since the patterns of the values of the key configuration information with associated relationships are the same, the same pattern can be used to construct these values with the same pattern when simulating data; and, for the key configuration information of multiple target facilities with corresponding relationships, the same values are constructed for the key configuration information with associated relationships, thereby saving the time required to simulate the configuration information of the management unit and improving the construction efficiency of the interface document.

[0060] In a possible implementation, it is characterized in that the key parameter processing model includes a BERT model.

[0061] Compared to other machine learning models, the BERT model features a bidirectional Transformer architecture that considers contextual information when processing each word, enabling BERT to better capture the true meaning of words. Furthermore, the BERT model can be fine-tuned for specific tasks based on user needs, achieving good matching results even with only a small amount of user sample data, thereby improving the efficiency of interface document construction.

[0062] In a possible implementation, the key parameter configuration module is specifically configured to match the regular expression with the configuration information of the multiple target facilities through a BIO notation method.

[0063] In this embodiment, because the BIO annotation method can clearly define entity boundaries during matching, is easily extended to multiple entity types, and supports efficient and accurate model training and performance evaluation, more accurate matching results can be obtained through the BIO annotation method, thereby improving the construction efficiency of the interface document.

[0064] In a third aspect, a method for training a key parameter processing model is provided. The method comprises:

[0065] Acquire multiple training samples, the training samples including: a sample data document of a data center, a pre-configured sample rule set, and associations between key configuration information of multiple target facilities; wherein the sample data document includes multiple sample data objects, and the sample data objects are used to describe configuration information of the multiple target facilities in the data center;

[0066] The model is trained based on the multiple training samples to obtain a key parameter processing model.

[0067] In this method, when receiving the user's data document, the trained key parameter processing model can be used to directly obtain the association relationship between the key configuration information of multiple target facilities from the target data document.

[0068] In a fourth aspect, an embodiment of the present application provides a computing device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and is characterized in that when the processor executes the computer program, it implements the method described in the above-mentioned first aspect or any possible implementation of the first aspect.

[0069] In a fifth aspect, an embodiment of the present application provides a computer storage medium, in which instructions are stored. When the instructions are executed on a computer, the computer executes the method described in the first aspect or any possible implementation of the first aspect.

[0070] In a sixth aspect, an embodiment of the present application provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the method described in the first aspect or any possible implementation of the first aspect.

[0071] It can be understood that the beneficial effects of the fourth to sixth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0073] Figure 1 A schematic diagram of the structure of a data center provided as an example in an embodiment of the present application;

[0074] Figure 2 A schematic diagram illustrating the corresponding relationship between various facilities provided as an example in an embodiment of the present application;

[0075] Figure 3 A schematic diagram of a JSON data structure corresponding to a target data document provided as an example in an embodiment of the present application;

[0076] Figure 4 One of the flow charts of a method for constructing an interface document provided in an embodiment of the present application;

[0077] Figure 5 A schematic diagram of a structure of a model provided as an example in an embodiment of the present application;

[0078] Figure 6 A flowchart of a method for training a key parameter processing model provided in an embodiment of the present application;

[0079] Figure 7 A second flow chart of a method for constructing an interface document provided in an embodiment of the present application;

[0080] Figure 8 A schematic diagram of the structure of an interface document provided in an embodiment of the present application;

[0081] Figure 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0082] The following will describe the technical solutions in the embodiments of the present application in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments.

[0083] In order to better understand the embodiments of the present application, some key terms are explained below.

[0084] Data Center: A data center is a physical facility designed to store, manage, and process large amounts of data or computing tasks. These facilities typically include numerous server cabinets, storage devices, network components, and other related infrastructure. Data centers are a core component of modern information technology infrastructure, supporting a variety of businesses, including internet services, information technology (IT) systems, and cloud computing services.

[0085] Data Center Management Tools: Data Center Management Tools are a collection of software and hardware solutions used to monitor, manage, and optimize a data center's physical infrastructure and IT resources. Examples include, but are not limited to, Configuration Management Databases (CMDBs) and Data Center Infrastructure Management (DCIMs). CMDBs are primarily used to store and manage detailed information on all configuration items in IT resources, including hardware, software, documents, services, and other IT service-related assets. DCIM, on the other hand, monitors, manages, and supports the data on the data center's physical infrastructure, including information on power, cooling, space, security, and other aspects.

[0086] Bidirectional Encoder Representations from Transformers (BERT) is a pre-trained language model based on the Transformer architecture that leverages contextual information to generate word representations when handling natural language processing tasks. Furthermore, because the BERT model can bidirectionally understand the meaning of words in text, it can also be used for other natural language processing tasks such as text classification, named entity recognition, question-answering systems, text summarization, and semantic parsing.

[0087] JS key-value data: JavaScript Object Notation (JSON) is a lightweight data format that is easy for humans to read and write, and easy for machines to parse and generate. It is widely used to transmit data in web applications and other environments. The basic structure of JSON data includes objects and arrays. Among them, objects are composed of single or multiple key-value pairs; the content of the key is a string; the content of the value can be data of various data types, such as strings, numbers, Boolean values, arrays, objects, NULL, etc. Arrays are composed of lists of values, and the content of the values can be of any type. In JSON data, objects are surrounded by curly braces "{}", and key-value pairs are separated by commas ","; arrays are surrounded by square brackets "[]", and values are separated by commas ",".

[0088] Pattern: A pattern is a regularity or characteristic in data that helps identify or classify different objects or content within the data. For example, when referring to strings, a pattern refers to the format, rules, or template that the string should follow.

[0089] Next, the system architecture or scenario of this application is briefly introduced.

[0090] With the widespread application of cloud computing and big data technologies, the data center industry is developing rapidly, and the scale of data centers is also getting larger and larger. For example, for a user's data center, the data center may include the following different types of facilities, such as rooms, cabinets, equipment interfaces, etc. Different types of facilities may have different levels; among them, a lower-level facility may be placed or installed with one or more higher-level facilities. For example, Figure 1 and Figure 2 As shown, the user's data center includes three levels of facilities: rooms, cabinets, and equipment interfaces. Among them, four cabinets are placed in a single room, and two equipment interfaces are installed on a single cabinet.

[0091] Each facility in the data center has its own configuration information; the configuration information can be represented by JSON data format or other possible data formats. This application mainly uses JSON data as an example for illustrative description. The configuration information of each facility can be regarded as a data object; the configuration information of multiple facilities can be stored as multiple data objects in one data document or in multiple data documents; the data format of the data document can adopt JSON data format; this application mainly uses the configuration information of a facility as a data object as an example for illustrative description. For the configuration information of a single facility, such as Figure 3As shown in , the configuration information of a single facility may include a single or multiple key-value pairs. For facilities at a lower level, the configuration information of the lower level facility may only include its own data information, such as Figure 3 As shown in the target data object "room" in the figure, the room configuration information only includes the room's own data information; for facilities at a higher level, the configuration information of the higher level facility will not only include the facility's own data information, but may also include the data information of other facilities at a lower level, such as Figure 3 As shown in the target data object "cabinet" in FIG, the configuration information of the cabinet includes not only the data information of the cabinet, but also the data information of the room where the cabinet is placed.

[0092] In addition, since there may be a correspondence between multiple facilities, the configuration information of multiple facilities may contain corresponding data information, and the corresponding data information may be expressed in the JSON data format as the same value but different keys. Figure 2 As shown, there are 4 cabinets in Room A, and each cabinet has 2 device interfaces. There is a correspondence between Room A, the 4 cabinets in Room A, and the 8 device interfaces. The configuration information of the room, cabinet, and device may all contain data information about the room. In the configuration information of the facility "Room A," the key corresponding to the value "Room A" indicating the data information of the room is "Description." In the configuration information of the facility "Cabinet Aa," the key corresponding to the value "Room A" indicating the data information of the room to which the cabinet belongs is "Room." In the configuration information of the facility "Device Interface Aa1," the key corresponding to the value "Rack" indicating the data information of the room to which the device interface belongs is "Rack." The aforementioned three pieces of data information all have the same value "Room A," but the keys corresponding to the same value "Room A" are different.

[0093] For data centers with large-scale facilities, users typically need to use dedicated data center management tools to perform daily operations and maintenance on the configuration information of these large-scale facilities. During the software development process for the upper-level applications of the data center management tools, facility configuration information is often required as data support. For example, when developing visualization functions for data in a CMDB system, facility configuration information is required to support development. During software development, developers cannot connect to the actual data in the customer's data center management tool, and thus cannot obtain the actual configuration information of the facilities. Therefore, it is necessary to simulate the user's facility configuration information based on the target data center document obtained from the user in advance, and simulate the interface service of the data center management tool by, for example, simulating the interface document of the user's data center management tool, thereby supporting the software development of the data center management tool. The interface document of the data center management tool refers to the document that describes how to access the facility configuration information stored in the data center management tool through the interface. In the interface document, the key to the facility configuration information may exist in the form of a request parameter, a request body, or a response format.

[0094] However, the target data objects included in the target data document obtained from the user in advance may only be part of the data objects in the user data center. In other words, there is no corresponding relationship between the various facilities corresponding to these target data objects, such as Figure 3 As shown, there are three target data objects in the target data document. The cabinet "XX Computer Room A1-7" installed in the target facility "Equipment Interface B6-6" corresponding to the target data object "Equipment Interface" is different from the target facility "XX Computer Room B6-6" corresponding to the target data object "Cabinet". Therefore, there is no corresponding relationship between the target facility "Equipment Interface B6-6" and the standard facility "XX Computer Room B6-6".

[0095] However, the configuration information of these facilities may still have data describing facilities of the same category, and the value pattern of the data information in the JSON data format is the same. For example Figure 3As shown, the value "XX Room 2-A-01" of the data information indicating a room in the target data object "Cabinet" has the same pattern as the value "XX Room A1-7" of the data information indicating a room in the target data object "Device Interface". Since the configuration information of facilities of the same category corresponds to multiple identical keys in the target data objects, the keys corresponding to the values of the configuration information with the same pattern can be determined based on the pattern of the values in the configuration information of multiple facilities that do not have a corresponding relationship, that is, the association relationship between the configuration information of multiple facilities can be determined; wherein, the configuration information of multiple facilities with an association relationship has the same pattern of configuration information values and is used to describe the same content. Furthermore, when constructing simulation data, the same value can be used for the configuration information of the multiple facilities with an association relationship, and the values of these associated key configurations can be constructed based on the association relationship between the multiple configuration information, thereby saving the time required to simulate the configuration information of the facilities.

[0096] like Figure 4 As shown, in the process of simulating the configuration information of a user's facility, the user's target data document is first obtained, wherein the target data document includes multiple target data objects. Secondly, based on the multiple key-value pairs included in the multiple target data objects in the target data document, the content of the data information indicated by the multiple key-value pairs is manually analyzed, and the keys corresponding to the values with the same data format are obtained from the multiple key-value pairs corresponding to each of the multiple target data objects. Next, each key in the target data document is entered into a table, and for keys corresponding to values with the same data format, the value corresponding to each key is manually generated in the table. Subsequently, the values corresponding to the other keys are manually generated. In this way, one or more data documents can be constructed based on the user's target document, thereby simulating the configuration information of the user's facility. In the case where the user's data document contains configuration information for a large number of facilities, identifying the keys corresponding to the values with the same data format is labor-intensive.

[0097] In order to achieve more efficient data relationship identification, an interface document construction method is provided in an embodiment of the present application, which can be executed by a computing device such as a server, etc. The server can be a rack server, a cabinet server or a blade server, etc., or the server can be a general-purpose server, a GPU server or a DPU server, etc., and a model for key parameter processing can be deployed on the server. The method includes: obtaining a target data document corresponding to the data center, the target data document includes multiple target data objects, and the target data objects are used to describe the configuration information of multiple target facilities in the data center; based on the key parameter processing rules, determining the association relationship between the key configuration information of multiple target facilities from the target interface data document; based on the association relationship between the key configuration information of multiple target facilities, performing data construction of the configuration information of multiple target facilities; based on the constructed data, generating an interface document of the target type.

[0098] In this way, the association relationship between key configuration information of multiple target facilities can be obtained according to pre-configured key parameter processing rules, thereby solving the problem of slow manual identification of the association relationship between key configuration information of multiple target facilities when the data volume is large.

[0099] Below, we first describe in detail the models provided in this application. A model is a function or set of rules learned from data through an algorithm, which can be used to predict outcomes or behaviors of unknown data. Simply put, a model is a computer program that improves its performance by training on a dataset, without requiring explicit programming to produce predictions.

[0100] Figure 5 A schematic diagram of the architecture of a model provided in an embodiment of the present application is shown as follows: Figure 5 As shown, the model provided by the embodiment of the present application may include: Transformer encoder, normalization layer, embedding layer, output layer. As mentioned above, the model may be a BERT model.

[0101] The embedding layer processes each word input to the model, adding all the processed information together to form the final input representation. The embedding layer processes each word through one or more of the following methods: using the word embedding layer to represent the meaning of the word itself; using the paragraph embedding layer to mark which paragraph a sentence belongs to; and using the position embedding layer to represent the word's position within the sentence.

[0102] The Transformer encoder is the core of the model. It consists of multiple identical layers stacked together, each of which consists of two sublayers: an attention layer and a feedforward layer. The attention layer captures the dependencies between different positions in the input representation, comparing each position with all other positions, calculating attention weights, and generating new data based on these weights. The feedforward layer is a feedforward neural network that applies a nonlinear transformation to the output of each position.

[0103] Normalization layers are placed after each sublayer of the Transformer encoder to speed up the training process, and residual connections are applied before each sublayer of the Transformer encoder to help alleviate the vanishing gradient problem.

[0104] The output layer is used to output specific output results for various downstream tasks. These downstream tasks may include but are not limited to text classification, question answering, and other similar language processing tasks.

[0105] When users want to train a model, they first obtain training samples for training. The training samples include: sample data documents corresponding to the data center, pre-configured sample rule sets, and the associations between key configuration information of various target facilities.

[0106] For example, the sample data document may contain the following six sample data objects: wherein a single sample data object includes part or all of the data information in the configuration information of a corresponding single facility.

[0107] First data A: {KeyA1: Dongguan computer room 1F-01, KeyA2: computer room, location: Dongguan};

[0108] First data B: {KeyB1: Dongguan computer room 2F-A-01, KeyB2: IT cabinet, KeyB3: A-01, UnitNum: 42};

[0109] First Data C: {KeyC1: Xi'an Laboratory 1st Floor 01 Computer Room, KeyC2: A1-01 Cabinet, BeginU: 1, EndU: 3, UnitNum: 30};

[0110] First data D: {Key D1: Beijiao computer room 1F01, Key D2: computer room, location: Zhengzhou};

[0111] First data E: {KeyE1: AI supercomputer room 2F-A-01, KeyE2: IT cabinet, KeyE3: A-01, UnitNum: 21};

[0112] First data F: {KeyF1: Room 01, 1st floor, Xi'an Laboratory, KeyF2: Cabinet A1-01, StartU: 4, EndU: 7, UnitNum: 30}.

[0113] The sample data object used to describe the configuration information of a facility may include one or more keys and values corresponding to the keys. For example, the first data C includes part of the configuration information of the facility "device interface C". There are 5 keys in the first data C: "KeyC1", "KeyC2", "BeginU", "EndU", and "UnitNum"; among them, the value corresponding to the key "KeyC1" is the data information of the computer room to which the facility belongs, indicating that the computer room to which the facility "Equipment Interface C" belongs is located in room numbered 01 on the first floor of Xi'an Laboratory; the value corresponding to the key "KeyC2" is the data information of the cabinet to which the facility belongs, indicating that the cabinet to which the facility "Equipment Interface C" belongs is numbered A1-01; the value corresponding to the key "BeginU" is the data information of the facility itself, indicating that the starting position number of the facility "Equipment Interface C" installed in the cabinet is 1; the value corresponding to the key "EndU" is the data information of the facility itself, indicating that the ending installation position number of the facility "Equipment Interface C" in the cabinet is 3; the value corresponding to the key "UnitNum" is the data information of the cabinet to which the facility "Equipment Interface C" belongs, indicating that the RU number of the cabinet to which the facility "Equipment Interface C" belongs is 30.

[0114] When users want to train a machine learning model, they also need to obtain a sample rule set corresponding to the sample data document. This sample rule set defines at least one regular expression corresponding to each of multiple category labels. Similar to the different hierarchies between multiple facilities in a data center, multiple sample category labels may also have different or identical hierarchies. For example, the following sample rule set contains sample category labels at three different hierarchies.

[0115] ROOM: {*computer room, *room, *laboratory*};

[0116] RACK: {UnitNum*, *-*, HeightU*, *-*-*, Unum, *-*-*-*, Cabinet*, *-*};

[0117] DEVICE (device): {BeginU*, EndU*, StartU*, EndU*, StartU*, *Height, StartU*, *Height}.

[0118] Each sample category label can correspond to a single or multiple regular expressions. In each data information of the configuration information, there is the same pattern; therefore, by judging whether each data information included in the configuration information contains a predetermined regular expression, and whether each data information has the same pattern, the key set corresponding to the category label can be determined from the data information; and then the category label corresponding to the configuration information can be determined according to the key set corresponding to the category label. Among them, the "*" in the regular expression corresponding to the category label is a wildcard, which means that the content corresponding to the "*" position can be any character except the 0 character; multiple regular expressions corresponding to the category label are separated by commas ",".

[0119] like Figure 6 As shown, Figure 6 A training method for a key parameter processing model provided in an embodiment of the present application.

[0120] Step S61, obtaining multiple training samples, the training samples include: a sample data document of the data center, a pre-configured sample rule set, and the association relationship between key configuration information of multiple target facilities; wherein, the sample data document includes multiple sample data objects, and the sample data objects are used to describe the configuration information of multiple target facilities in the data center.

[0121] Before training a machine learning model, it is first necessary to obtain sample data, sample rule sets, and the association relationships between key configuration information of multiple target facilities for training; the association relationships between key configuration information of multiple target facilities can be obtained in advance manually from sample data documents.

[0122] Step S63: training the model based on multiple training samples to obtain a key parameter processing model.

[0123] The sample data document and the sample rule set are input into the model, and key configuration information is obtained from the model according to the sample rule set.

[0124] Subsequently, based on the key configuration information and the correlation between the key configuration information of multiple target facilities, the forward propagation calculation model outputs the correlation relationship identification result according to the key configuration information; and according to the predetermined loss function, the correlation relationship identification result, and the correlation between the key configuration information of multiple target facilities, the corresponding gradient information is determined and the model parameters of the model are updated according to the gradient information.

[0125] For example, the association relationship between key configuration information of multiple target facilities can be represented by a key set; wherein, multiple keys belonging to the same key set have an association relationship. In other words, when the number of keys included in any key set is greater than 1, any two keys in the arbitrary key set belong to different sample data objects, and the values corresponding to any two core keys have the same pattern.

[0126] The model can identify multiple key-value pairs included in the sample data document according to the regular expression corresponding to the sample category label, and identify the identification key corresponding to the sample category label.

[0127] Then, based on the identified key, key set, and loss function, the corresponding gradient information is determined and the model parameters are updated accordingly, completing model training. Subsequently, upon receiving a user's target data document, the trained model can be used to directly retrieve the key set representing the associations between key configuration information for various target facilities from the target data document.

[0128] The following specific embodiments are used to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described below are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0129] Figure 7 A flowchart of a data relationship processing method provided in an embodiment of the present application.

[0130] See Figure 7 The method may include but is not limited to part or all of the following steps S71 to S79. The method is executed by a computing device in which a model for key parameter processing is deployed.

[0131] Step S71: Acquire a target data document corresponding to the data center. The target data document includes multiple target data objects. The target data objects are used to describe configuration information of multiple target facilities in the data center.

[0132] The target data document corresponding to the data center can be provided in advance by the user. The multiple target data objects included in the target data document can be multiple configuration information corresponding to multiple facilities in the user's data center; wherein, the multiple facilities corresponding to the multiple target data objects included in the target data document can be unrelated and of different categories.

[0133] Step S73: Based on the key parameter processing rules, the association relationship between the key configuration information of the multiple target facilities is determined from the target interface data document.

[0134] Among them, the key parameter processing rules include a pre-trained key parameter processing model or a pre-configured rule set; the pre-configured rule set defines at least one regular expression corresponding to each of multiple category labels.

[0135] For example, a regular expression can be matched against the configuration information of multiple target facilities based on key parameter processing rules, and the configuration information that successfully matches can be determined as the key configuration information of the multiple target facilities. Subsequently, based on the patterns of the key configuration information of the multiple target facilities, the associations between the key configuration information of the multiple target facilities can be determined. The values corresponding to the key configuration information of the multiple target facilities that have an association have the same pattern.

[0136] For example, a regular expression can be matched with a key-value pair in a target data document, and the key in the successfully matched key-value pair can be determined as a core key; then, each determined core key can be divided into multiple key sets. As mentioned above, the configuration information includes key-value pairs, and the key configuration information includes core key-value pairs. The association relationship between key configuration information can be represented by a key set; in other words, multiple keys in the same key set have an association relationship, and the values corresponding to multiple keys in the same key set have the same pattern.

[0137] Continuing with the above example, the target data document obtained from the user may include the six first data A, B, C, D, E, and F, each of which is a sample data object. In the target data document, there are six key-value pairs that match the regular expression "*Computer Room" corresponding to the category label "ROOM": key-value pair "KeyA1: Dongguan Computer Room 1F-01", key-value pair "KeyB1: Dongguan Computer Room 2F-A-01", key-value pair "KeyC1: Xi'an Laboratory 1F01 Computer Room", key-value pair "KeyD1: Beijiao Computer Room 1F01", key-value pair "KeyE1: AI Supercomputing Computer Room 2F-A-01", and key-value pair "KeyF1: Xi'an Laboratory 1F01 Computer Room". These six keys: key "KeyA1", key "KeyB1", key "KeyC1", key "KeyD1", key "KeyF1", and key "KeyE1" are determined as core keys, and the values corresponding to these six keys have the same pattern "*computer room". These six keys are divided into the key set corresponding to the category label "ROOM".

[0138] Similarly, in the target data document, there are four key-value pairs that match the regular expression "UnitNum*, *-*" corresponding to the category label "RACK": the key-value pair "KeyB3:A-01" and the key-value pair "UnitNum:42" in the first data B, the key-value pair "KeyC2:A1-01 cabinet" and the key-value pair "UnitNum:30" in the first data C; the key-value pair "KeyE3:A-01" and the key-value pair "Un itNum:21", the key-value pair "KeyF2:A1-01 cabinet" and the key-value pair "UnitNum:30" in the first data F; these four groups of keys: key "KeyB3" and key "UnitNum", key "KeyC2" and key "UnitNum", key "KeyE3" and key "UnitNum", key "KeyF2" and key "UnitNum" are determined as core keys, and these four groups of keys are divided into the key set corresponding to the category label "ROOM".

[0139] Similar to the category tag “RACK”, the matching between the regular expression corresponding to the category tag “DEVICE” and the key-value pairs in the target data document will not be described in detail.

[0140] It should be noted that, as shown in the above example, when determining the association relationship between the key configuration information of multiple target facilities, it is also possible to determine the category labels corresponding to the key configuration information of the multiple target facilities based on the category labels corresponding to the regular expressions; multiple category labels have a hierarchical relationship.

[0141] In a possible implementation, the regular expression may be matched with the configuration information of various target facilities through BIO notation.

[0142] BIO annotation is a commonly used natural language processing annotation method used to represent the part of speech and morphological information of a word in a sentence. It is often used in tasks such as named entity recognition. BIO annotation can determine whether a key-value pair contains fields that match a regular expression. Furthermore, BIO annotation can directly annotate one or more key-value pairs containing fields that match the regular expression as one of three tag types: B, I, or O. B indicates that the key-value pair is at the beginning of a tag, I indicates that the key-value pair is within a tag, and O indicates that the key-value pair is not at the beginning or within any tag.

[0143] For example, for the first data B: {KeyB1: Dongguan Computer Room 2F-A-01, KeyB2: IT Rack, KeyB3: A-01, UnitNum: 42}, "KeyB1: Dongguan Computer Room 2F-A-01" should be labeled "I-ROOM"; "KeyB2: IT Rack" should be labeled "O"; and "KeyB3: A-01, UnitNum: 42" should be labeled "I-RACK". The core key corresponding to the label can then be obtained based on the labeling results.

[0144] In addition, through the BIO notation method, the key-value pair to which the core key belongs and its position in the sample data object can also be obtained.

[0145] Then, as shown in the first data B in the above example, the key-value pair "KeyB1: Dongguan Computer Room 2F-A-01" is located at positions 1-14 in the first data B, the key-value pair "Key3: A-01" is located at positions 29-38 in the first data B; the key-value pair "Unum: 42" is located at positions 41-48 in the first data B.

[0146] Optionally, in step S75, for any target data object, a category label of the target data object is determined based on the category label corresponding to the key configuration information of the target facility included in the target data object and the hierarchical relationship, wherein the category label of the target data object indicates the category of the target facility corresponding to the target data object.

[0147] Exemplarily, the category label with the highest level among the category labels corresponding to all key configuration information included in the target data object is determined as the category label of the target data object.

[0148] Similar to the above content, since the configuration information of facilities at a higher level will not only include the data information of the facilities at a higher level themselves, but may also include the data information of facilities at a lower level; therefore, the category label of a single target data object can be determined based on the different category labels corresponding to the key set to which the single or multiple core keys included in the single target data object belong, as well as the hierarchy between the category labels.

[0149] Different category tags are used to indicate facilities of different categories. According to the category tag of a single target data object, the configuration information indicating that the single target data object is a facility of the same level as the category tag of the single target data object can be used.

[0150] Exemplarily, the category label with the highest level among the category labels corresponding to the key sets to which all the core keys included in the target data object belong is determined as the category label of the target data object.

[0151] Continuing with the previous example, a data center can include facilities at three levels: rooms, racks, and device interfaces. Rooms are at the lowest level, and device interfaces are at the highest. Similarly, category tags can include "ROOM," "RACK," and "DEVICE," with "ROOM" being the lowest and "DEVICE" being the highest. The hierarchy of category tags corresponds one-to-one with the hierarchy of facilities.

[0152] If a target data object only includes a core key corresponding to the category tag "ROOM", the category tag of the target data object is determined to be the category tag "ROOM".

[0153] For example, as shown in the first data A, the first data A only includes one core key "KeyA1", and the core key "KeyA1" corresponds to the category label "ROOM". The category label corresponding to the first data A is the category label "ROOM", indicating that the first data A is used to describe the configuration information of the facility "room".

[0154] If a target data object only includes the core key corresponding to the category tag "ROOM" and also includes the core key corresponding to the category tag "RACK", the category tag of the target data object is determined to be the category tag "RACK".

[0155] As shown in the first data B, there are three core keys in the first data B. The category label corresponding to the core key "KeyB1" is the category label "ROOM", and the category label corresponding to the core key "KeyB3" and the core key "UnitNum" is the category label "RACK". It is determined that the category label corresponding to the first data B is the category label "RACK", indicating that the first data B is used to describe the configuration information of the facility "cabinet".

[0156] If a target data object only includes the core key corresponding to the category label "ROOM", also includes the core key corresponding to the category label "RACK", and also includes the core key corresponding to the category label "DEVICE", then the category label of the target data object is determined to be the category label "DEVICE".

[0157] For example, as shown in the first data C, there are five core keys in the first data C, the category label corresponding to the core key "KeyC1" is the category label "ROOM", the category label corresponding to the core key "KeyC2" and the core key "UnitNum" is the category label "RACK", and the category label corresponding to the core key "BeginU" and the core key "EndU" is the category label "DEVICE", then it is determined that the category label corresponding to the first data C is the category label "DEVICE", indicating that the first data C is used to describe the configuration information of the facility "device interface".

[0158] Step S77 : constructing data of the configuration information of the various target facilities based on the association relationship between the key configuration information of the various target facilities.

[0159] The data of the configuration information of the constructed multiple target facilities can be described by simulation data objects. The category label of the simulation data object is the same as the category label of the sample data object corresponding to the simulation data object. The number of simulation data objects can be determined according to the pre-determined levels of multiple facilities, the number of facilities at different levels, and the corresponding relationship between multiple facilities. Figure 1 、 Figure 2 The data document corresponding to the data center shown can include 52 simulated data objects, that is, there are 52 facilities corresponding to the data center. Among them, the 52 facilities can include 4 facility "rooms"; each facility "room" can include 4 facility "cabinets", and each facility "cabinet" includes 2 facility "equipment interfaces". In the above data center, the 4 facility "cabinets" and 8 facility "equipment interfaces" included in the same facility "room" have a corresponding relationship. Among the data objects corresponding to these 13 facilities, the values corresponding to the 13 core keys of the key set corresponding to the category label "ROOM" are the same. Similarly, for Figure 2 As shown, a single facility "cabinet" and the two facilities "equipment interfaces" included in the facility "cabinet" have a corresponding relationship. Among the data objects corresponding to these three facilities, the values corresponding to the three core keys of the key set corresponding to the category label "RACK" are the same.

[0160] Exemplarily, for multiple core keys included in the configuration information of multiple target facilities, multiple simulation values corresponding to the multiple core keys are constructed according to the preset pattern corresponding to the key set to which the multiple core keys belong; wherein, when the facilities corresponding to the multiple simulation data objects to which the multiple core keys belong in any key set have a corresponding relationship, the same simulation value is constructed for the multiple core keys with an associated relationship included in the multiple simulation data objects with a corresponding relationship.

[0161] For the multiple non-core keys included in the configuration information of the multiple target facilities, simulation values corresponding to the multiple non-core keys are constructed according to the preset modes corresponding to the multiple non-core keys.

[0162] For example, the preset mode corresponding to the key set corresponding to the category label "ROOM" may be "XX computer room-X-XX". Figure 2As shown, the 13 facilities associated with "Room A" all have core keys in the key set corresponding to the category label "ROOM." For these 13 core keys, the "X"s in the preset data pattern are filled with identical data, for example, "Beijing Computer Room-A-21," resulting in 13 identical simulated values.

[0163] For a single or multiple non-core keys included in the configuration information of various target facilities, each non-core key may have its own corresponding preset mode. Therefore, it is necessary to construct a simulation value corresponding to the non-core key according to the preset mode corresponding to each non-core key.

[0164] This makes it possible to realize data structure of configuration information of various facilities in a user data center.

[0165] Step S79: Generate an interface document of the target type based on the constructed data.

[0166] The target type interface document refers to a document that describes how to access the configuration information of the facilities stored in the data center management tool through the interface. The key of the configuration information of the target facility may exist in the interface document in the form of request parameters, request body or response format. The type of interface document can be a Representational State Transfer Application Programming Interface (RESTful API) document or other types of interface documents, such as the Redfish Application Programming Interface (Redfish API) document, the Simple Object Access Protocol Application Programming Interface (SOAP API) document, etc.

[0167] After the interface document of the data center management tool is generated, the interface document can be used to support the development of the upper-layer software of the user's data center management tool.

[0168] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of method. In order to realize the above functions, the data processing device includes a hardware structure and / or software module corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software driven hardware manner depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0169] In the embodiment of the present application, the functional modules of the log analysis device can be divided according to the above method. For example, the data processing device can include functional modules corresponding to the functional divisions, or two or more functions can be integrated into one processing module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical functional division. There may be other division methods in actual implementation.

[0170] The embodiment of the present application also provides a data processing device 80. The data processing device includes: a data information acquisition module 81, a key parameter processing module data information construction module 83, and an interface document generation module 84.

[0171] The data information acquisition module 81 is configured to acquire a target data document corresponding to the data center. The target data document includes multiple target data objects. The target data objects are used to describe configuration information of multiple target facilities in the data center.

[0172] The key parameter processing module 82 is configured to determine the association relationship between key configuration information of multiple target facilities from the target interface data document based on the key parameter processing rules.

[0173] The data information construction module 83 is configured to construct data of the configuration information of the various target facilities based on the association relationship between the key configuration information of the various target facilities.

[0174] The interface document generation module 84 is configured to generate an interface document of the target type based on the constructed data.

[0175] Figure 9 This is a hardware structure diagram of an electronic device provided in an embodiment of the present application. Figure 9The electronic device 90 may include a processor 91 and a memory 92 . The processor 91 and the memory 92 may communicate with each other; illustratively, the processor 91 and the memory 92 communicate with each other via a communication bus 93 .

[0176] The memory 92 is used to store computer-executable instructions.

[0177] The processor 91 is used to execute the computer execution instructions stored in the memory 92, so that the processor 91 executes the technical solution shown in the above method embodiment.

[0178] Optionally, the electronic device 90 may further include a communication interface, which may include a transmitter and / or a receiver.

[0179] Optionally, the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), or application-specific integrated circuits (ASIC). A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of the present application may be directly implemented as being executed by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor.

[0180] The electronic device provided in the embodiments of the present application can be used to execute the technical solutions shown in any of the above method embodiments. Its implementation principles and technical effects are similar and will not be described in detail here.

[0181] The embodiment of the present application also provides a chip, which is used to execute the version upgrade method executed in any of the above method embodiments. Its implementation principle and technical effects are similar and will not be described in detail here.

[0182] An embodiment of the present application also provides a computer-readable storage medium, in which computer instructions are stored. When the computer instructions are executed by a computer, a version upgrade method as performed in any of the above method embodiments is implemented. The implementation principle and technical effects are similar and will not be elaborated here.

[0183] An embodiment of the present application also provides a computer program product, including a computer program. When the computer program is executed by a computer, it implements the version upgrade method performed by any of the above method embodiments. Its implementation principles and technical effects are similar and will not be repeated here.

[0184] All or part of the steps of the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a readable memory. When the program is executed, it performs the steps of the above-mentioned method embodiments; and the aforementioned memory (storage medium) includes: read-only memory (ROM), RAM, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disc, and any combination thereof.

[0185] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable terminal device to generate a machine, so that the instructions executed by the processing unit of the computer or other programmable terminal device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0186] These computer program instructions can also be loaded onto a computer or other programmable terminal device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0187] Obviously, those skilled in the art may make various changes and modifications to the embodiments of the present application without departing from the spirit and scope of the embodiments of the present application. Thus, if these modifications and variations of the embodiments of the present application fall within the scope of the claims of the present application and their equivalents, the embodiments of the present application are intended to include such modifications and variations.

[0188] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0189] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0190] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this application are merely illustrative and not restrictive, and it should not be assumed that these advantages, strengths, and effects are required of each embodiment of the present disclosure. In addition, the specific details disclosed above are merely illustrative and for ease of understanding, and are not restrictive. The above details do not limit the present disclosure to necessarily being implemented using the above specific details.

[0191] The block diagrams of the devices, apparatuses, equipment, and systems involved in this disclosure are intended to be illustrative examples only and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise.

[0192] It should also be noted that in the apparatus, device, and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0193] The above description has been provided for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for constructing an interface document, the method being executed by a computing device, characterized in that: The method comprises: Obtaining a target data document of a data center, wherein the target data document includes a plurality of target data objects, and the target data objects are used to describe configuration information of a plurality of target facilities in the data center; wherein the configuration information includes key-value pairs; Based on a key parameter processing rule, an association relationship between key configuration information of multiple target facilities is determined from the target data document, and the values corresponding to the key configuration information of the multiple target facilities having the association relationship have the same pattern; wherein the key parameter processing rule includes a pre-trained key parameter processing model or a pre-configured rule set; the pre-configured rule set defines multiple category labels and at least one regular expression corresponding to each of the category labels; the key configuration information indicates a key-value pair of the target facility that successfully matches the regular expression; Based on the association relationship between the key configuration information of the multiple target facilities, data construction of the configuration information of the multiple target facilities is performed; wherein the constructed data includes values of a unified pattern generated for multiple key configuration information with an association relationship; based on the constructed data, an interface document of the target type is generated.

2. The method according to claim 1, characterized in that The determining of associations between key configuration information of multiple target facilities from the target interface data document based on key parameter processing rules includes: matching the regular expression with the configuration information of the multiple target facilities, and determining the configuration information that successfully matches as the key configuration information of the multiple target facilities; Based on the patterns of the key configuration information of the multiple target facilities, association relationships between the key configuration information of the multiple target facilities are determined.

3. The method according to claim 2, characterized in that The method further comprises: Determining, based on the category labels corresponding to the regular expressions, category labels corresponding to the key configuration information of the multiple target facilities; wherein the multiple category labels have a hierarchical relationship; For any target data object, determining the category label of the target data object according to the category label corresponding to the key configuration information of the target facility included in the target data object and the hierarchical relationship; The category label of the target data object indicates the category of the target facility corresponding to the target data object.

4. The method according to claim 3, characterized in that Determining the category label of the target data object according to the category label corresponding to the key configuration information of the target facility included in the target data object and the hierarchical relationship includes: The highest-level category label among the category labels corresponding to all key configuration information included in the target data object is determined as the category label of the target data object.

5. The method according to claim 1, wherein The data construction of the configuration information of the multiple target facilities based on the association relationship between the key configuration information of the multiple target facilities includes: Based on the association relationship between the key configuration information of the multiple target facilities, data construction of the key configuration information of the multiple target facilities is performed; And, data construction of non-critical configuration information of the various target facilities is performed.

6. The method according to claim 2, characterized in that The key parameter processing model includes a BERT model.

7. The method according to claim 2, characterized in that The matching of the regular expression with the configuration information of the multiple target facilities includes: The regular expression is matched with the configuration information of the multiple target facilities through BIO notation.

8. A training method for a key parameter processing model, characterized in that: include: Acquire multiple training samples, the training samples including: a sample data document of a data center, a pre-configured sample rule set, and an association relationship between key configuration information of multiple target facilities; wherein the sample data document includes multiple sample data objects, the sample data objects are used to describe the configuration information of the multiple target facilities in the data center, and the configuration information includes key-value pairs; the pre-configured rule set defines multiple category labels and at least one regular expression corresponding to each of the category labels; the key configuration information indicates the key-value pairs of the target facilities that successfully match the regular expression; and the association relationship indicates the correspondence between the key-value pairs of the multiple target facilities having the same pattern value; The model is trained based on the multiple training samples to obtain a key parameter processing model, which is used to quickly determine the configuration association relationship between multiple target facilities in the target data document, so as to facilitate data construction of the configuration information of the multiple target facilities based on the association relationship between the key configuration information of the multiple target facilities.

9. A computing device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

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