Resource data query method and device, equipment and storage medium

Abstract

The invention discloses a resource data query method and device, equipment and a storage medium. The method comprises the steps of obtaining query request information; disassembling the query request information to obtain target query conditions of multiple dimensions; and querying the deployed resource directory according to each target query condition to obtain target resource data corresponding to the query request information. By utilizing the method, the query request information is disassembled, any dimension combination query can be supported, and the efficiency, accuracy and reliability of resource data query are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data query, and in particular to a resource data query method, device, equipment and storage medium. BACKGROUND

[0002] With the acceleration of the digitalization and intelligentization of power grids, a large amount of multi-source heterogeneous data such as telemetry time series, GIS space, video monitoring, alarm logs and work order texts are generated in power systems.

[0003] However, the existing data query technology mainly depends on a single dimension label, and the retrieval dimension is limited, which reduces the flexibility of retrieval and the efficiency and accuracy of data query. SUMMARY

[0004] The present application provides a resource data query method, device, equipment and storage medium to support arbitrary dimension combination query and improve the efficiency and accuracy of resource data query.

[0005] In a first aspect, an embodiment of the present application provides a resource data query method, which comprises:

[0006] obtaining query request information;

[0007] dissolving the query request information to obtain target query conditions of multiple dimensions;

[0008] querying a deployed resource directory according to each target query condition to obtain target resource data corresponding to the query request information.

[0009] In a second aspect, an embodiment of the present application provides a resource data query device, which comprises:

[0010] an information obtaining module configured to obtain query request information;

[0011] a dissolving module configured to dissolve the query request information to obtain target query conditions of multiple dimensions;

[0012] a querying module configured to query a deployed resource directory according to each target query condition to obtain target resource data corresponding to the query request information.

[0013] In a third aspect, an embodiment of the present application provides an electronic device, which comprises:

[0014] at least one processor;

[0015] and a memory in communication connection with the at least one processor;

[0016] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the resource data query method according to any one of the embodiments of the application.

[0017] In a fourth aspect, the embodiments of the application further provide a computer readable storage medium, which stores computer instructions for enabling a processor to perform the resource data query method according to any one of the embodiments of the application when the processor executes the computer instructions.

[0018] The technical scheme of the embodiments of the application comprises the following steps: obtaining query request information; disassembling the query request information to obtain target query conditions of multiple dimensions; and querying a deployed resource directory according to each target query condition to obtain target resource data corresponding to the query request information. The query request information is disassembled, and any dimension combination query can be supported, so that the efficiency, accuracy and reliability of resource data query are improved.

[0019] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the application, nor is it used to limit the scope of the application. Other features of the application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative effort on the basis of these drawings.

[0021] Figure 1 A flowchart of a resource data query method provided by the embodiments of the application is shown.

[0022] Figure 2 A structural schematic diagram of a resource data query device provided by the embodiments of the application is shown.

[0023] Figure 3 A structural schematic diagram of an electronic device that can be used to implement the embodiments of the application is shown. DETAILED DESCRIPTION

[0024] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative effort should fall into the protection scope of the present application.

[0025] It should be noted that the terms "first", "second" and the like in the description and claims of the present application and the above drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in other than the order illustrated or described herein. In addition, the terms "comprise" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0026] The embodiment of the present application provides a resource data query method, Figure 1 The flowchart of the resource data query method provided by the embodiment of the present application can be applicable to the scene of querying heterogeneous resource data stored in a resource directory. The method can be executed by a resource data query device. The device can be implemented in the form of software and / or hardware, and can be implemented by an electronic device, preferably a mobile terminal, a desktop computer, a notebook computer, a server or the like.

[0027] As shown in Figure 1 The resource data query method provided by the embodiment of the present application can specifically include the following steps.

[0028] S101, acquiring query request information.

[0029] In the embodiment, the way of acquiring the query request information can be receiving the query request information input by a user in a provided client interface. For example, the input method can include input in an input box, voice input or form submission, and the query request information can be a question, a group of keywords or content filled in a form, etc.

[0030] S102, decomposing the query request information to obtain a plurality of dimensions of target query conditions.

[0031] The target query condition can be considered as an independent query condition containing a single dimension.

[0032] It can be understood that, in order to accurately query the desired data, the query request information can be a complex information including multiple dimensions of query conditions.

[0033] Therefore, in the embodiment, the query request information can be disassembled into multiple independent and operable target query conditions according to the preset disassembly dimensions.

[0034] For example, the preset disassembly dimensions can include time dimension, space dimension, structured data dimension, text data dimension, and vector data dimension. The target query condition obtained by disassembling according to the time dimension can be "query data on August 22, 2025"; the target query condition obtained by disassembling according to the space dimension can be "query data in XX area"; the target query condition obtained by disassembling according to the structured data dimension can be "query data of equipment type transformer"; the target query condition obtained by disassembling according to the text data dimension can be "query data containing the keyword 'fault'"; and the target query condition obtained by disassembling according to the vector data dimension can be "query image data similar to the given image".

[0035] S103, query the deployed resource directory according to each target query condition, and obtain the target resource data corresponding to the query request information.

[0036] The resource directory can be considered as a directory storing resource data and resource data related information, and the resource directory can include a unified and clear directory structure. The resource data related information can include metadata information, global identifiers, and multi-dimensional tags of the resource data. The resource directory can be deployed in a database, a file system, or a cloud storage, etc. The target resource data can be understood as resource data meeting each target query condition in the query request information, and the resource data can be heterogeneous data from a power grid.

[0037] In the embodiment, the resource directory that has been deployed can be queried according to each target query condition, and the resource data meeting all target query conditions is filtered out, which is returned to the user as the query result.

[0038] In an optional embodiment, each target query condition can be executed in parallel and accelerated by columnar memory and result cache. The resource directory deployed is queried according to each target query condition in parallel execution, and candidate resource data corresponding to each target query condition is obtained. The target resource data can be determined from the candidate resource data by set operation based on the global identifier, and a second-level response is realized.

[0039] The resource data query method provided by the embodiment of the present application comprises the following steps: obtaining query request information; disassembling the query request information to obtain target query conditions of multiple dimensions; and querying a deployed resource directory according to each target query condition to obtain target resource data corresponding to the query request information. The query request information is disassembled, and arbitrary dimension combination query can be supported, so that the efficiency, accuracy and reliability of resource data query are improved.

[0040] As a first optional embodiment of the embodiment of the present application, on the basis of the above embodiment, the step of querying the deployed resource directory according to each target query condition to obtain the target resource data corresponding to the query request information can be specifically implemented as the following steps:

[0041] a1) querying the resource directory in parallel according to each target query condition to obtain candidate resource data corresponding to each target query condition and a global identifier of the candidate resource data.

[0042] The global identifier can be considered as an identifier for ensuring that each piece of resource data can be uniquely identified in the entire resource directory.

[0043] In the embodiment, the resource directory can include resource data, a level of the resource data in the directory structure, metadata information of the resource data, a global identifier and multi-dimensional tags and the like, so that corresponding data or information in the resource directory can be queried in parallel according to the target query conditions of each dimension to quickly and accurately locate the candidate resource data.

[0044] Optionally, on the basis of parallel query, columnar memory technology and result caching mechanism can be combined for acceleration. The columnar memory technology stores data in memory by column, and only the column related to the query condition needs to be read during query, thereby reducing unnecessary data reading and improving query performance. The result caching mechanism stores frequently queried data results in memory, and when the same query is initiated again, the results can be directly returned from the cache without re-executing the query. This not only reduces the consumption of computing resources, but also significantly improves the query response speed.

[0045] b1) determining the target resource data corresponding to the query request information based on target set operations between the global identifiers; and the target set operations are determined according to the logical relationship between each target query condition.

[0046] In the embodiment, the target set operation can be considered as a set operation for processing the global identifier. The set operation can include AND operation, OR operation, NOT operation, etc. The AND operation is used to return data satisfying multiple target query conditions; the OR operation is used to return data satisfying any target query condition; and the NOT operation is used to return data not satisfying a certain target query condition.

[0047] The technical solution described above in the embodiment can quickly locate target resource data, ensure real-time and efficient response, accurately depict data attributes and internal relations from multiple dimensions, and significantly improve the hit rate and accuracy of the search.

[0048] As a second optional embodiment of the embodiment, after the target query conditions of multiple dimensions are obtained, the method further includes:

[0049] a2) determining recommended query dimensions and / or recommended query conditions according to historical query request information.

[0050] The historical query request information can be understood as query request information input by the user when the user has a data query demand.

[0051] Optionally, the query dimensions and / or query conditions that the user may have missed can be determined according to the historical query request information by using a machine learning algorithm or a pre-trained model, and the query dimensions and / or query conditions are determined as recommended query dimensions and / or recommended query conditions, so as to help the user more comprehensively and accurately query resource data and accurately obtain expected target resource data.

[0052] b2) rewriting the target query conditions based on the recommended query dimensions and / or recommended query conditions, and updating the target query conditions according to the rewriting result.

[0053] In the embodiment, the query request information and the target query conditions can be parsed and analyzed, such as semantic analysis, and then the syntax errors or ambiguous expressions in the target query conditions are rewritten according to the recommended query dimensions and / or recommended query conditions, so that the target query conditions are more consistent with the query rules of the resource directory, thereby improving the accuracy of the query.

[0054] In an optional implementation manner, the recommended query dimensions and / or recommended query conditions can also be displayed to the user, and the user manually updates the target query conditions according to the recommended query dimensions and / or recommended query conditions.

[0055] In an optional implementation, the historical similar target resource data matching the current query request information can also be determined according to the historical query request information to provide the user, so as to improve the decision accuracy and response speed.

[0056] As a third optional embodiment of the embodiment of the application, the step of constructing the resource directory can be specifically optimized as follows:

[0057] a3) obtaining power grid data from multiple data sources, and determining global identifiers and metadata information corresponding to the power grid data.

[0058] It can be understood that there are various types of data sources in the power grid system, and the data generated by these data sources has different formats and contents.

[0059] In this embodiment, the power grid data from multiple data sources can be obtained from a unified interface, and the power grid data is determined as resource data. Exemplarily, it can include voice data, text data, image data, telemetry data, GIS data, video data, log files, and work order data, etc.

[0060] After obtaining the power grid data, in order to ensure that each piece of power grid data can be accurately distinguished and located, a unique global identifier can be generated for each piece of power grid data. Exemplarily, the global identifier can be generated in combination with a timestamp and a random number of obtaining the power grid data, or the global identifier can be generated through a preset algorithm.

[0061] In this embodiment, metadata information of each piece of power grid data can also be obtained to provide a basis for subsequent data management and retrieval. The metadata information can include data type, source, timestamp, and related business line of the power grid data, etc.

[0062] b3) according to the data characteristics of the power grid data, mapping the power grid data and the metadata information and global identifiers corresponding to the power grid data to the constructed multi-level directory structure.

[0063] Among them, the data characteristics can include fields, modalities, time characteristics, and spatial characteristics of the data, etc. Exemplarily, for a device operation log data, the fields can include device type, device number, operation time, and fault code, etc. Exemplarily, the modalities of the data can include voice, text, and image, etc.; the time characteristics of the data can include generation time, recording time, etc.; and the spatial characteristics of the data can include geographical location of data generation, location of the device corresponding to the data, etc.

[0064] In this embodiment, the power grid data can be parsed to extract data features of the power grid data. According to the preset data classification rules and data features, the power grid data, as well as the metadata information and the global identifier corresponding to the power grid data, are mapped to the corresponding level in the multi-level directory structure.

[0065] For example, the multi-level directory structure can be divided into three levels. The first level directory represents the highest data classification of the power grid data, the second level directory represents the classification of the data classification under the first level directory, and the third level directory further refines the data classification under the second level directory.

[0066] In an optional implementation, only the power grid data can be mapped to the corresponding level in the multi-level directory structure, and the metadata information and the global identifier corresponding to the power grid data are associated with the power grid data and stored in the database of the resource directory.

[0067] As one of the implementation manners, the mapping of the power grid data and the metadata information and the global identifier corresponding to the power grid data to the constructed multi-level directory structure according to the data features of the power grid data can be further embodied as the following steps:

[0068] b31) According to the preset data classification rules and the data features of the power grid data, the initial mapping result of the power grid data under each data classification rule and the confidence of the initial mapping result are determined.

[0069] The initial mapping result can be considered as the mapping result of the power grid data based on a single data classification rule.

[0070] For example, the preset data classification rules can include field value classification rules, modal classification rules, and spatio-temporal condition classification rules.

[0071] b32) If there is only one highest priority among the priorities of the data classification rules, the initial mapping result under the data classification rule corresponding to the highest priority is determined as the target mapping result; otherwise, the initial mapping result with the highest confidence is determined as the target mapping result.

[0072] In this embodiment, the priority of each data classification rule can be set in advance. The data classification rule with higher priority has higher priority in conflict resolution. If the field classification rule is defined to be higher than the modal classification rule and the spatio-temporal condition classification rule, the initial mapping result under the field classification rule can be determined as the target mapping result.

[0073] It can be understood that if the priorities of the data classification rules are the same, the initial mapping result with the highest confidence can be determined as the target mapping result.

[0074] b33) mapping the grid data and the metadata information and the global identifier corresponding to the grid data to the built multi-level directory structure according to the target mapping result.

[0075] In the embodiment, the grid data and the metadata information and the global identifier corresponding to the grid data can be mapped to the built multi-level directory structure, and the grid data is determined as the resource data.

[0076] The technical solution of the embodiment ensures correct classification of the grid data by classifying the grid data under each preset data classification rule according to the data characteristics of the grid data, and resolving conflicts according to the priority of the data classification rule and the confidence of the initial mapping result.

[0077] c3) determining a target label corresponding to the grid data, and storing the target label and an evidence pointer corresponding to the target label to the resource directory.

[0078] The target label can be regarded as a label used to describe or define the grid data. The evidence pointer can be understood as evidence supporting the generation of the target label, and can include the grid data, metadata information related to the grid data, data characteristics, and the confidence of the target label.

[0079] In the embodiment, candidate labels can be generated in relation to the grid data in various ways. For example, the candidate labels can be generated based on rules, algorithms, manual intervention, and the like. Then, the target label corresponding to the grid data can be determined from the candidate labels based on preset rules or calculation methods. The target label and the evidence pointer corresponding to the target label can be associated and stored with the corresponding grid data.

[0080] The technical solution of the embodiment determines the target label corresponding to the grid data, and stores the target label and the evidence pointer corresponding to the target label to the resource directory, thereby achieving further accurate characterization of the grid data, providing strong support for subsequent rapid and accurate positioning of target resource data. At the same time, through the evidence pointer, the generation process and basis of the label can be traced back, facilitating problem troubleshooting and quality control, thereby ensuring the accuracy and reliability of the target label.

[0081] As one of the implementation manners, the determination of the target label corresponding to the grid data can be further optimized as follows:

[0082] c31) obtaining a preset candidate label corresponding to the grid data.

[0083] The preset candidate label can be considered as a candidate label generated by manual intervention. The candidate label can be understood as a label used to provide data support for selecting a target label. For example, the candidate label can be determined by an expert through manual judgment.

[0084] c32) generating a first candidate label corresponding to the power grid data based on a preset label classification rule according to the data characteristics of the power grid data.

[0085] The preset label classification rule can include field value conditions, space-time conditions, etc. The first candidate label can be understood as a candidate label generated based on the label classification rule.

[0086] c33) generating a second candidate label corresponding to the power grid data based on a preset label classification algorithm according to the data characteristics of the power grid data.

[0087] For example, the preset label classification algorithm can be a machine learning algorithm, such as a classification algorithm or a clustering algorithm. The second candidate label can be considered as a candidate label generated based on the label classification algorithm.

[0088] c34) determining a target label corresponding to the power grid data according to the preset candidate label, the first candidate label and the second candidate label; wherein the target label includes a basic label, a business label and a semantic label.

[0089] The basic label can be understood as a label used to describe the basic attributes of the power grid data, and the basic attributes of the power grid data can include data source, type and format, etc. The business label can be understood as a label used to describe the power grid data based on a specific business scenario. For example, the data is assigned a business label according to the specific business scenario of the power system, and the business label can include power grid equipment state, operation mode, etc. The semantic label can be understood as a label of the power grid data determined according to the semantic meaning, which is used to describe the content meaning and context relationship of the power data, etc. For example, the semantic label can include fault type, event severity.

[0090] Alternatively, the way to determine the target label corresponding to the power grid data according to the preset candidate label, the first candidate label and the second candidate label can be: if the candidate labels are not all the same, the preset candidate label is determined as the target label corresponding to the power grid data, or the confidence degrees corresponding to the preset candidate label, the first candidate label and the second candidate label are obtained, and the candidate label with the highest confidence degree is determined as the target label corresponding to the power grid data; if the candidate labels are all the same, any one of the preset candidate label, the first candidate label and the second candidate label is determined as the target label corresponding to the power grid data.

[0091] Exemplarily, the preset candidate labels include preset candidate labels corresponding to the basic label, the business label and the semantic label respectively; the first candidate labels include first candidate labels corresponding to the basic label, the business label and the semantic label respectively; and the second candidate labels include second candidate labels corresponding to the basic label, the business label and the semantic label respectively. If the preset candidate labels, the first candidate labels and the second candidate labels corresponding to the basic label are not all the same, the preset candidate label corresponding to the basic label can be determined as the basic label in the target label corresponding to the power grid data; if the preset candidate labels, the first candidate labels and the second candidate labels corresponding to the basic label are all the same, any one of the preset candidate labels, the first candidate labels and the second candidate labels corresponding to the basic label can be determined as the basic label in the target label corresponding to the power grid data. The determination manner of the business label and the semantic label in the target label can refer to the determination manner of the basic label in the target label, which will not be described herein again.

[0092] In an optional implementation, in the case that the candidate labels are all the same, the confidence of the target label can also be determined, and the specific manner can be: fusing the confidence corresponding to the preset candidate label, the first candidate label and the second candidate label to obtain the confidence of the target label.

[0093] In an optional implementation, the coverage rate, the mutual exclusion rate and the artificial intervention rate and the like of the target label can also be generated, so as to ensure the continuous optimization of the label system.

[0094] The technical solution of the embodiment described above determines the candidate labels corresponding to the power grid data in multiple manners, and determines the multi-dimensional target label corresponding to the power grid data according to the candidate labels, thereby improving the accuracy and reliability of the target label, and accurately describing the data properties and internal correlation from multiple dimensions, which significantly improves the efficiency, hit rate and accuracy of subsequent data query.

[0095] As a fourth optional embodiment of the embodiment of the application, the method further includes:

[0096] a4) determining a change scheme of the resource directory according to the evaluation index data of the resource directory in the deployment process.

[0097] The evaluation index data can include performance index data such as access frequency, hit rate and delay of data query.

[0098] In the embodiment, the evaluation index data of the resource directory in the deployment process can be continuously monitored, and the reinforcement learning and the rule engine are combined to dynamically adjust the directory and the rules and algorithms for generating candidate labels and target labels, so as to form the change scheme.

[0099] b4) updating the resource directory based on the change scheme.

[0100] In the embodiment, when the updating of the rules and algorithms for generating the candidate labels and the target labels is included in the change scheme, the target labels of the historical resource data (historical power grid data) can be updated in an incremental backfilling manner, so as to update the resource directory. Optionally, the effect of the target label generation can also be evaluated according to indexes such as the coverage rate, the mutual exclusion rate and the manual intervention rate of the target labels.

[0101] c4) After deploying the updated resource directory, obtaining the to-be-detected performance index values of the updated resource directory; if at least one to-be-detected performance index value is lower than the corresponding index threshold, replacing the deployed resource directory according to the resource directory before the updating.

[0102] The to-be-detected performance index values can include the coverage rate and the hit rate of the resource directory.

[0103] In the embodiment, the updated resource directory is deployed to provide the user with resource data query. In the deployment process, the updated resource directory can be gradually verified through the gray release manner according to the data source or the business line. The to-be-detected performance index values of the new and old resource directories can be compared through the visualization tool, or the to-be-detected performance index values can be compared with the corresponding index threshold. If at least one to-be-detected performance index value meets the rollback condition, such as being lower than the corresponding index threshold, the rollback strategy is used to roll back to the resource directory before the updating. Optionally, the original resource directory can also be rolled back to.

[0104] In an optional implementation manner, the whole process of the resource directory change deployment can be recorded to generate detailed audit logs. For example, the time, content and operator of the change deployment and the like can be recorded. The version management is performed on the resource directory, and a unique identifier and detailed description are given to each version of the resource directory. Through the trace audit and the version management, the traceability of the resource directory change is ensured, the problem troubleshooting and the responsibility identification are facilitated, and the data integrity and consistency in the resource directory change process are ensured, so that the data loss or error is avoided.

[0105] The technical scheme in the embodiment determines the change scheme of the resource directory according to the evaluation index data of the resource directory in the deployment process to update the resource directory, and realizes the continuous and automatic dynamic optimization of the directory and the target labels through the gray release and the rollback strategy, so that the resource directory can be timely and adaptively adjusted with the new business access and the resource data change, the long-term maintenance cost is significantly reduced, and the overall operation stability and the scalability are greatly enhanced.

[0106] Figure 2 A structure schematic diagram of a resource data query device provided by an embodiment of the present application is shown in FIG. 1. Figure 2As shown, the device comprises: an information acquisition module 21, a disassembly module 22 and a query module 23, wherein,

[0107] The information acquisition module 21 is used for acquiring query request information;

[0108] The disassembly module 22 is used for disassembling the query request information to obtain target query conditions of multiple dimensions;

[0109] The query module 23 is used for querying a deployed resource directory according to each target query condition to obtain target resource data corresponding to the query request information.

[0110] The resource data query device provided by the embodiment of the application acquires query request information, disassembles the query request information to obtain target query conditions of multiple dimensions, queries a deployed resource directory according to each target query condition, and obtains target resource data corresponding to the query request information. By disassembling the query request information, arbitrary dimension combination query can be supported, and the efficiency, accuracy and reliability of resource data query are improved.

[0111] Further, the query module 23 can be specifically used for:

[0112] querying the resource directory in parallel according to each target query condition to obtain candidate resource data corresponding to each target query condition and global identifiers of the candidate resource data;

[0113] determining target resource data corresponding to the query request information based on target set operations between the global identifiers;

[0114] The target set operations are determined according to logical relationships between each target query condition.

[0115] Further, the device further comprises a recommended rewriting module, and the recommended rewriting module can be specifically used for:

[0116] After the target query conditions of multiple dimensions are obtained, recommended query dimensions and / or recommended query conditions are determined according to historical query request information;

[0117] The target query conditions are rewritten based on the recommended query dimensions and / or recommended query conditions, and the target query conditions are updated according to the rewriting result.

[0118] Further, the device further comprises a resource directory construction module, and the resource directory construction module can specifically comprise:

[0119] A multi-source data acquisition unit is used for acquiring power grid data from multiple data sources and determining global identifiers and metadata information corresponding to the power grid data.

[0120] mapping the power grid data and the metadata information and the global identifier corresponding to the power grid data to the constructed multi-level directory structure according to a data feature of the power grid data;

[0121] a label determination unit configured to determine a target label corresponding to the power grid data, and store the target label and an evidence pointer corresponding to the target label to the resource directory.

[0122] Further, the mapping unit can be specifically configured to:

[0123] determine an initial mapping result of the power grid data under each data classification rule and a confidence degree of the initial mapping result according to preset data classification rules and a data feature of the power grid data;

[0124] if there is a unique highest priority among the priorities of the data classification rules, determine the initial mapping result under the data classification rule corresponding to the highest priority as a target mapping result;

[0125] if there is no unique highest priority among the priorities of the data classification rules, determine the initial mapping result with the highest confidence degree as the target mapping result;

[0126] map the power grid data and the metadata information and the global identifier corresponding to the power grid data to the constructed multi-level directory structure according to the target mapping result.

[0127] Further, the label determination unit can be specifically configured to:

[0128] obtain a preset candidate label corresponding to the power grid data;

[0129] generate a first candidate label corresponding to the power grid data based on preset label classification rules according to a data feature of the power grid data;

[0130] generate a second candidate label corresponding to the power grid data based on preset label classification algorithms according to a data feature of the power grid data;

[0131] determine a target label corresponding to the power grid data according to the preset candidate label, the first candidate label and the second candidate label;

[0132] wherein the target label includes a basic label, a business label and a semantic label.

[0133] Further, the apparatus further includes an updating module, which can be specifically configured to:

[0134] According to the evaluation index data of the resource directory in the deployment process, a change scheme of the resource directory is determined;

[0135] Based on the change scheme, the resource directory is updated;

[0136] After the updated resource directory is deployed, a to-be-detected performance index value of the updated resource directory is obtained;

[0137] If at least one to-be-detected performance index value is lower than a corresponding index threshold value, the deployed resource directory is replaced according to the resource directory before the update.

[0138] The resource data query device provided in the embodiments of the present application can execute the resource data query method provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0139] Figure 3 A structural schematic diagram of an electronic device 30 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0140] As shown in Figure 3 The electronic device 30 includes at least one processor 31, and a memory, such as a read-only memory (ROM) 32, a random access memory (RAM) 33, etc., which is communicatively connected to the at least one processor 31, wherein the memory stores a computer program that can be executed by the at least one processor, and the processor 31 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 32 or the computer program loaded from the storage unit 38 into the random access memory (RAM) 33. In the RAM 33, various programs and data required for the operation of the electronic device 30 can also be stored. The processor 31, the ROM 32, and the RAM 33 are connected to each other through a bus 34. An input / output (I / O) interface 35 is also connected to the bus 34.

[0141] A plurality of components in the electronic device 30 are connected to the I / O interface 35, including: an input unit 36, such as a keyboard, a mouse, etc.; an output unit 37, such as various types of displays, speakers, etc.; a storage unit 38, such as a magnetic disk, an optical disk, etc.; and a communication unit 39, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 39 allows the electronic device 30 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0142] The processor 31 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the processor 31 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 31 performs various methods and processes described above, such as the resource data query method.

[0143] In some embodiments, the resource data query method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 38. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 30 via the ROM 32 and / or the communication unit 39. When the computer program is loaded onto the RAM 33 and executed by the processor 31, one or more steps of the resource data query method described above can be performed. Alternatively, in other embodiments, the processor 31 can be configured to perform the resource data query method by any other appropriate means, such as by means of firmware.

[0144] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0145] Computer programs for implementing the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program, when executed, enables the functions / acts specified in the flowcharts and / or block diagrams to be implemented. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a standalone software package and partially on a remote machine or entirely on a remote machine or server.

[0146] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0147] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0148] The systems and techniques described herein can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described herein, or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0149] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0150] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be executed in parallel, executed in sequence, or executed in different orders, as long as the desired results of the technical solutions of the present disclosure can be achieved, and the present disclosure is not limited herein.

[0151] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for querying resource data, characterized in that, include: Retrieve query request information; The query request information is broken down to obtain target query conditions in multiple dimensions; The resource directory deployed is queried according to the target query conditions to obtain the target resource data corresponding to the query request information.

2. The method according to claim 1, characterized in that, The step of querying the deployed resource directory according to each of the target query conditions to obtain the target resource data corresponding to the query request information includes: The resource catalog is queried in parallel according to each of the target query conditions to obtain candidate resource data corresponding to each of the target query conditions and the global identifier of the candidate resource data. Based on the target set operations between the global identifiers, the target resource data corresponding to the query request information is determined. The target set operation is determined based on the logical relationship between each of the target query conditions.

3. The method according to claim 1, characterized in that, After obtaining the target query conditions across multiple dimensions, the process also includes: Based on historical query request information, determine the recommended query dimensions and / or recommended query conditions; Based on the recommended query dimensions and / or recommended query conditions, the target query conditions are rewritten, and the target query conditions are updated according to the rewriting results.

4. The method according to claim 1, characterized in that, The steps for constructing the resource catalog include: Power grid data is obtained from multiple data sources, and the global identifier and metadata information corresponding to the power grid data are determined. Based on the data characteristics of the power grid data, the power grid data, along with the corresponding metadata information and global identifiers, are mapped to a pre-constructed multi-level directory structure. Determine the target label corresponding to the power grid data, and store the target label and the evidence pointer corresponding to the target label in the resource directory.

5. The method according to claim 4, characterized in that, The step of mapping the power grid data, along with its corresponding metadata information and global identifiers, to a pre-constructed multi-level directory structure based on the data characteristics of the power grid data includes: Based on the preset data classification rules and the data characteristics of the power grid data, determine the initial mapping result of the power grid data under each data classification rule and the confidence level of the initial mapping result; If there is a unique highest priority among the priorities of each data classification rule, then the initial mapping result under the data classification rule corresponding to the highest priority is determined as the target mapping result; Otherwise, the initial mapping result with the highest confidence level is determined as the target mapping result; Based on the target mapping results, the power grid data, along with the corresponding metadata information and global identifiers, are mapped to the constructed multi-level directory structure.

6. The method according to claim 4, characterized in that, Determining the target label corresponding to the power grid data includes: Obtain the preset candidate labels corresponding to the power grid data; Based on the data characteristics of the power grid data, a first candidate label corresponding to the power grid data is generated according to a preset label classification rule; Based on the data characteristics of the power grid data, a second candidate label corresponding to the power grid data is generated based on a preset label classification algorithm; The target label corresponding to the power grid data is determined based on the preset candidate label, the first candidate label, and the second candidate label. The target tags include basic tags, business tags, and semantic tags.

7. The method according to claim 1, characterized in that, Also includes: Based on the evaluation metrics data of the resource catalog during the deployment process, determine the change plan for the resource catalog; Based on the aforementioned change plan, update the resource catalog; After deploying the updated resource catalog, obtain the performance metric values ​​to be tested for the updated resource catalog; If at least one performance metric value is lower than the corresponding threshold, the deployed resource directory will be replaced with the resource directory before the update.

8. A resource data query device, characterized in that, include: The information acquisition module is used to acquire query request information; The decomposition module is used to decompose the query request information to obtain target query conditions in multiple dimensions; The query module is used to query the deployed resource directory according to each of the target query conditions to obtain the target resource data corresponding to the query request information.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the resource data query method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the resource data query method according to any one of claims 1-7.

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