Target object acquisition method and device, equipment, computer medium and product

Abstract

The invention discloses a target object obtaining method and device, equipment, a computer medium and a product, and is applied to the field of object processing. Based on the initial object set, determining an object relation database corresponding to the initial object set; determining a query level of the initial object set based on the business type to which the initial object set belongs; obtaining a pre-constructed structured query statement template, wherein the structured query statement template comprises a plurality of variable placeholders; respectively filling the initial object set, the business type and the query level into corresponding variable placeholders, and constructing a structured query statement for querying an association relationship between the objects; and on the basis of the structured query statement, performing association relationship query on the object relationship database to obtain a target object having an association relationship with the initial object set. According to the method, the query efficiency and accuracy of the target object are improved, and computing resources are reduced.

Description

Technical Field

[0001] This application belongs to the field of object processing technology, and in particular relates to a method, apparatus, device, computer storage medium and computer program product for acquiring target objects. Background Technology

[0002] With the development of society, it is crucial to accurately identify the substantive relationships between objects (such as enterprises). In particular, it is of great significance to efficiently and accurately locate target objects that are related to the preset objects from massive amounts of data.

[0003] Currently, mainstream methods for acquiring target objects primarily rely on constructing object relationship graphs and performing queries using graph traversal algorithms. Common implementations include: fixed-level relationship-based penetration queries and community discovery or influence propagation models based on complex graph algorithms. However, the aforementioned fixed-level relationship-based penetration queries can only query a fixed level, resulting in low query efficiency, lack of intelligence, and poor accuracy of query results; model-based queries consume significant computational resources.

[0004] Therefore, how to provide a method for obtaining target objects to improve the efficiency and accuracy of target object queries while reducing computing resources is an urgent problem to be solved. Summary of the Invention

[0005] This application provides a method, apparatus, device, computer storage medium, and computer program product for obtaining target objects, which can improve the efficiency and accuracy of target object queries while reducing computing resources.

[0006] In a first aspect, embodiments of this application provide a method for obtaining a target object, the method comprising: Get the initial collection of objects; Based on the initial set of objects, determine the object relationship database corresponding to the initial set of objects; Based on the business type to which the initial object set belongs, determine the query level of the initial object set; Obtain a pre-built structured query statement template, which contains multiple variable placeholders; the variable placeholders include an initial object set identifier, a business type identifier, and a query level code; Based on the initial object set identifier, business type identifier, and query level code, the initial object set, the business type, and the query level are respectively filled into the corresponding variable placeholders to construct a structured query statement for querying the relationship between objects; Based on the structured query statement, a relational query is performed on the object relational database to obtain target objects that are related to the initial object set.

[0007] In some possible implementations, determining the query level of the initial object set based on the business type to which the initial object set belongs includes: Obtain a pre-built mapping table representing the correspondence between business types and query levels, wherein the mapping table stores key-value pairs of business types and query levels; Based on the business type to which the initial object set belongs and the mapping table, the query level of the initial object set is determined.

[0008] In some possible implementations, the key-value pair includes: a key corresponding to the business type identifier and a value corresponding to the query level configuration parameter; The step of determining the query level of the initial object set based on the business type to which the initial object set belongs and the mapping table includes: Obtain the identifier corresponding to the business type; Match the identifier with the key in the mapping table; If the identifier matches a key in the mapping table, the value corresponding to the key is determined as the query level.

[0009] In some possible implementations, determining the query level of the initial object set based on the business type to which the initial object set belongs includes: Obtain the feature data of the initial object set; The business type and the feature data are input into a pre-trained query level recommendation model to obtain the query level of the initial object set.

[0010] Among the possible implementations are: Obtain the attribute parameters of each initial object in the initial object set; From the initial object set, objects whose attribute parameters meet preset conditions are selected to form a subset of candidate objects; The process involves filling the initial object set identifier, the business type identifier, and the query level code into corresponding variable placeholders to construct a structured query statement for querying the relationships between objects, including: Based on the initial object set identifier, business type identifier, and query level code, the candidate object subset, the business type, and the query level are respectively filled into the corresponding variable placeholders to construct a structured query statement for querying the relationship between objects.

[0011] In some possible implementations, the step of performing a relational query on the object relational database based on the structured query statement to obtain target objects that are associated with the initial object set includes: Based on the structured query statement, the initial set of objects is used as the input set for the first query level; The object relational database is searched for all objects that have a direct relationship with the objects in the first query level input set, forming the result set of the second query level. The result set of the second query level is used as the input for the next round of query. The operation of finding objects is repeated until the query level of the initial object set is reached, and the target object that is associated with the initial object set is obtained.

[0012] In some possible implementations, after obtaining the target objects associated with the initial object set, the method further includes: Get the target object collection; Based on a preset period, acquire the updated feature data of each target object in the target object set; If the updated feature data does not meet the preset conditions, the corresponding target object is deleted from the target object set, and a warning message is output.

[0013] Secondly, embodiments of this application provide a target object acquisition device, the device comprising: The retrieval module is used to retrieve the initial collection of objects; The determination module is used to determine the object relationship database corresponding to the initial object set based on the initial object set; The determination module is also used to determine the query level of the initial object set based on the business type to which the initial object set belongs; The acquisition module is also used to acquire a pre-built structured query statement template, which contains multiple variable placeholders; the variable placeholders include an initial object set identifier, a business type identifier, and a query level code; The construction module is used to construct a structured query statement for querying the relationship between objects by filling the initial object set identifier, the business type identifier, and the query level into the corresponding variable placeholders based on the initial object set identifier, the business type identifier, and the query level code. The acquisition module is also used to perform a relational query on the object relational database based on the structured query statement, and to acquire target objects that have a relational relationship with the initial object set.

[0014] Thirdly, embodiments of this application provide a target object acquisition device, the device comprising: A processor and a memory storing computer program instructions; a method for obtaining a target object that implements any of the above when the processor executes the computer program instructions.

[0015] Fourthly, embodiments of this application provide a computer storage medium on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the target object acquisition method described above is implemented.

[0016] Fifthly, embodiments of this application provide a computer program product in which instructions, when executed by a processor of an electronic device, enable the electronic device to perform any of the target object acquisition methods described above.

[0017] The target object acquisition method, apparatus, device, computer storage medium, and computer program product of this application embodiment, after obtaining an initial object set, determine an object relationship database corresponding to the initial object set; then, determine the query level of the initial object set based on the business type to which the initial object set belongs; then, construct a structured query statement for querying the relationships between objects based on a pre-built structured query statement template, the initial object set, the business type, and the query level; finally, perform a relationship query on the object relationship database based on the structured query statement to obtain target objects that are related to the initial object set. On the one hand, this method can match a specific query level based on the business type, making the query rules accurately adapt to business needs, avoiding results that are too broad or too narrow due to a uniform query standard, and improving the accuracy of the query. On the other hand, structured query statements are strictly constructed based on the initial object set, business type, and query level, which can accurately define the query subject, conditions, and scope, ensuring that the database retrieval process is highly targeted and effectively improving the accuracy of relational identification. At the same time, using structured query statements not only reduces computing resources but also simplifies the query rule matching and instruction construction process. It eliminates the need for professional technicians to manually write complex query logic, reducing operational complexity and significantly improving the efficiency of the query preparation stage. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a method for obtaining a target object according to an embodiment of this application; Figure 2 This is a schematic flowchart of S130 provided in one embodiment of this application; Figure 3 This is a flowchart illustrating S160 provided in one embodiment of this application; Figure 4 This is a schematic diagram of the structure of a target object acquisition device provided in another embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in another embodiment of this application. Detailed Implementation

[0020] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0021] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0022] It should be noted that the acquisition, storage, use, and processing of data in this application embodiment all comply with the relevant provisions of national laws and regulations.

[0023] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0024] Currently, mainstream methods for acquiring target objects primarily rely on constructing object relationship graphs and performing queries using graph traversal algorithms. Common implementations include: fixed-level relationship-based penetration queries and community discovery or influence propagation models based on complex graph algorithms. However, the aforementioned fixed-level relationship-based penetration queries can only query a fixed level, resulting in low query efficiency, lack of intelligence, and poor accuracy of query results; model-based queries consume significant computational resources.

[0025] To address the problems in the prior art, embodiments of the present invention provide a method, apparatus, device, computer storage medium, and computer program product for acquiring a target object.

[0026] The target object acquisition method provided in the embodiments of the present invention will be introduced first below.

[0027] Figure 1 A flowchart illustrating a target object acquisition method according to an embodiment of the present invention is shown. Figure 1 As shown, the method may include the following steps: S110. Obtain the initial object collection.

[0028] In S110, the initial object set can be the starting subject set for the association query, consisting of one or more objects with clear business attributes and retrieval value. The objects can include entities such as enterprises and organizations that need to be identified through association. The set contains the unique identification information of the objects.

[0029] In practice, the first step is to determine the requirements of the business scenario and then filter out objects that meet these requirements and have passed the credibility verification. Collect the identification information for each object, including its name, unified social credit code, and other key data. Perform preliminary organization of the collected object information, removing duplicates and invalid data to form a structured initial object set, providing a precise basis for subsequent database matching and query operations.

[0030] S120. Based on the initial object set, determine the object relation database corresponding to the initial object set.

[0031] In S120, the object relational database is a structured data storage system that stores basic information about objects and relational data between objects. The relational data can include key information such as relation type, relation strength, and relation time, providing centralized data support for relational queries.

[0032] It's important to note that the initial object's related data is scattered across various data sources. Direct retrieval can lead to inefficiency, data disorganization, and potential query omissions. Object-relational databases, by integrating relevant data, achieve centralized management and standardized storage of related information, avoiding the retrieval problems caused by data fragmentation and laying a data foundation for subsequent efficient and accurate queries.

[0033] In practice, suitable data sources, including publicly registered data and business transaction data, can be selected based on the business attributes and associated data types of the initial object set. The selected data sources are then cleaned, deduplicated, and standardized to unify data formats and field naming, and to fill in any missing key related information. The processed basic object information and associated data are then entered into the database according to a preset structure, establishing a mapping relationship between core object identifiers and associated information, forming a dedicated object relationship database that matches the initial object set.

[0034] S130. Determine the query level of the initial object set based on the business type to which the initial object set belongs.

[0035] In S130, the query level is a standard for relational queries that adapts to business needs. It includes key parameters such as the number of query penetration layers and relational filtering conditions, which are used to define the depth and scope of relational queries.

[0036] It's important to note that different business types have significantly different query requirements. For example, some businesses need to comprehensively investigate deeply related objects, requiring a higher query level, while others only need to retrieve directly related objects, requiring a lower query level. Using a uniform query standard would result in results that are either too broad or too narrow, failing to meet actual business needs. Determining the query level ensures precise matching of query rules to business types, improving the relevance and usability of query results.

[0037] In practice, corresponding query level standards can be pre-defined for various business types, specifying parameters such as the maximum number of penetration layers and association filtering thresholds for each level. After obtaining the initial object set, accurately determine its business type and match the appropriate query level according to the pre-defined standards. If there are special business requirements, parameters can be fine-tuned based on the pre-defined levels to ensure that the query levels perfectly match the actual business scenario.

[0038] S140. Obtain a pre-built structured query template.

[0039] The structured query statement template is a general query framework built on the syntax of the structured query language. It includes query logic, fixed syntax structure and multiple variable placeholders. Variable placeholders are reserved and replaceable positions used to fill in exclusive parameters for different query scenarios, adapting to diverse related query needs.

[0040] For example, the structured query template includes multiple variable placeholders. Variable placeholders are set at locations where dynamic adjustments are needed based on different scenarios, and the parameter type corresponding to each placeholder is clearly defined. In this embodiment, the variable placeholders may include an initial object set identifier, a business type identifier, and a query level code.

[0041] S150. Based on the initial object set identifier, business type identifier, and query level code, the initial object set, business type, and query level are filled into the corresponding variable placeholders to construct a structured query statement for querying the relationship between objects.

[0042] It should be noted that the initial object set identifier is the unique identification information of the initial object set, used to accurately locate the starting subject of the query; the business type identifier is the standardized identifier of the business type, used to clarify the business scenario orientation of the query; and the query level code is the simplified identifier of the query level, corresponding to core parameters such as the number of query penetration layers and filtering conditions. The three together constitute the exclusive dynamic parameters of the query statement.

[0043] In practice, the process begins by extracting the initial object set identifier for the current query scenario, ensuring that the identifier is unique and consistent with the stored information in the object relational database. Then, the standardized identifier corresponding to the business type and the encoding information corresponding to the query level are obtained, with the encoding information corresponding one-to-one with the core parameters of the query level. Next, a pre-built structured query statement template is retrieved, and the placeholders for each variable in the template are identified. Based on the initial object set identifier, business type identifier, and query level encoding, the corresponding initial object set, business type, and query level are filled into the corresponding placeholder positions in the template. After filling, the syntax of the statement is automatically verified, ultimately forming a directly executable structured query statement.

[0044] It's important to note that object-relational databases store massive amounts of data, requiring precise commands to extract target association information. Structured query statements transform the initial object set, business type, and query level into database-recognizable execution commands, avoiding invalid data retrieval, ensuring efficient querying and accurate results, while also lowering the barrier to entry for manually writing query statements.

[0045] In practice, the core identifier information of the initial object set can be extracted, and the relevant data types to be retrieved can be determined based on the business type, thus clarifying the key query fields. Query logic and data filtering rules are set according to parameters such as the penetration level corresponding to the query level and filtering conditions. Following the syntax of a structured query language, the query body, query fields, query logic, and filtering rules are integrated to construct a logically complete and syntactically correct structured query statement, ensuring that the statement accurately maps to the query requirements.

[0046] S160. Based on structured query statements, perform relational queries on the object relational database to obtain target objects that are related to the initial object set.

[0047] In S160, the target object is identified through a relational query that confirms objects that are directly or indirectly related to the initial object set. The relational relationship can include equity relationship, business cooperation relationship, and other relational types that meet business needs.

[0048] In practice, the constructed structured query statement can be input into the query execution module of the object-relational database to initiate the query operation. Based on the query statement's instructions, the database retrieves matching related data layer by layer, clarifying the relationships between objects. The query results are then validated, duplicate and invalid records are removed, core information of valid target objects is extracted, and a structured list of target objects is compiled, providing direct support for subsequent business applications.

[0049] The target object acquisition method, apparatus, device, computer storage medium, and computer program product of this application embodiment, after obtaining an initial object set, determine an object relationship database corresponding to the initial object set; then, determine the query level of the initial object set based on the business type to which the initial object set belongs; then, construct a structured query statement for querying the relationships between objects based on a pre-built structured query statement template, the initial object set, the business type, and the query level; finally, perform a relationship query on the object relationship database based on the structured query statement to obtain target objects that are related to the initial object set. On the one hand, this method can match a specific query level based on the business type, making the query rules accurately adapt to business needs, avoiding results that are too broad or too narrow due to a uniform query standard, and improving the accuracy of the query. On the other hand, structured query statements are strictly constructed based on the initial object set, business type, and query level, which can accurately define the query subject, conditions, and scope, ensuring that the database retrieval process is highly targeted and effectively improving the accuracy of relational identification. At the same time, using structured query statements not only reduces computing resources but also simplifies the query rule matching and instruction construction process. It eliminates the need for professional technicians to manually write complex query logic, reducing operational complexity and significantly improving the efficiency of the query preparation stage.

[0050] In some embodiments, such as Figure 2 As shown, S130 may include: S1301. Obtain a pre-built mapping table that represents the correspondence between business types and query levels. The mapping table stores key-value pairs between business types and query levels. A mapping table is a structured data table that stores the correspondence between business types and query levels, enabling rapid matching between the two. For example, a key-value pair may include a key corresponding to a business type identifier and a value corresponding to a query level configuration parameter. Key-value pairs are the core data format of the mapping table, where the key is the business type identifier, a unique identifier assigned to each business type; the value is the query level configuration parameter, containing specific parameters that define the depth and scope of the query, such as the number of penetration layers and filtering thresholds.

[0051] In practice, we can first identify various common business types and assign a unique business type identifier to each. Then, based on the query requirements of each business type, we can set corresponding query level configuration parameters, specifying details such as the upper limit of penetration layers and the threshold for association filtering. Using the business type identifier as the key and the corresponding query level configuration parameters as the value, we can organize these into a structured mapping table and store it in the system database to ensure fast retrieval during queries.

[0052] S1302. Determine the query level of the initial object set based on the business type and mapping table to which the initial object set belongs.

[0053] The business type to which the initial object set belongs refers to the core business scenario category corresponding to the initial object set, which determines the core demand orientation of relational queries. The query level is a query rule adapted to the business type, obtained by matching through a mapping table, which directly defines the execution standard of relational queries, such as the number of penetration layers.

[0054] Since the mapping table stores the correspondence between business types and query levels, after determining the business type to which the initial object set belongs, the query level of the initial object set can be determined based on the business type and the mapping table.

[0055] By pre-building a mapping table between storage business types and query levels (key-value pairs), and using business type identifiers as the matching basis, query levels can be quickly determined. This not only significantly improves the matching efficiency of query levels and avoids the tedious operation of repeatedly analyzing requirements and manually configuring parameters for each query, but also ensures a high degree of compatibility between query levels and business types through standardized configuration parameters and accurate identifier matching. This reduces deviations and errors caused by manual configuration and lowers the operational threshold. Ordinary business personnel can determine query levels without professional technical knowledge. Furthermore, the mapping table supports flexible expansion and centralized management, which improves the consistency and maintainability of query rules and can quickly adapt to new business scenarios, providing reliable rule support for the accurate and efficient execution of subsequent relational queries.

[0056] As an example, when determining the query level of the initial object set based on the business type and mapping table to which the initial object set belongs, we can first obtain the identifier corresponding to the business type, match the identifier with the key in the mapping table, and if the identifier matches the key in the mapping table, determine the value corresponding to the key as the query level.

[0057] In practice, the identifier corresponding to the business type is first extracted. Then, the extracted business type identifier is compared with the keys in the mapping table one by one. When the identifier is completely consistent with a key in the mapping table, the query level configuration parameter corresponding to that key is obtained and determined as the query level of the initial object set, providing a clear basis for the construction of subsequent structured query statements.

[0058] By first obtaining the identifier corresponding to the business type, then accurately matching the identifier with the key in the mapping table, and finally determining the query level based on the value corresponding to the successfully matched key, the uniqueness of the identifier is used to avoid matching errors caused by differences in business type descriptions, greatly improving the accuracy of matching between the query level and the business type. Furthermore, the standardized "identifier-key-value" matching logic enables automatic and rapid locking of the query level, significantly improving the efficiency of query level determination.

[0059] In some embodiments, when determining the query level of the initial object set based on the business type to which the initial object set belongs, feature data of the initial object set can also be obtained. The business type and feature data are then input into a pre-trained query level recommendation model to obtain the query level of the initial object set.

[0060] For example, feature data is key information that reflects the core attributes and state of the initial object set. It covers dimensions related to the query requirements, such as object size, structural complexity, association stability, and business scope. It is the core basis for distinguishing the differences in querying different objects under the same business type.

[0061] It's important to note that different initial object sets within the same business type may have individual differences in their query requirements. Relying solely on business type cannot accurately match query levels, potentially leading to queries that are too deep or too shallow. Feature data can quantify the individual characteristics of the initial object set, providing data support for personalized query level recommendations, making the query level more aligned with the actual situation of the objects, and improving query accuracy.

[0062] In practice, the characteristic dimensions related to the query requirements can be clearly defined, and the specific data items to be collected can be determined based on the business scenario. Using data collection tools, characteristic data of the initial object set can be extracted from data sources such as object relational databases, public registration systems, and business management systems. This includes, but is not limited to, the number of directly related objects, equity structure levels, frequency of related changes in the past year, and business coverage areas. The collected characteristic data is then cleaned and standardized to remove invalid data, unify the data format, and form a structured characteristic dataset.

[0063] For example, the query level recommendation model is an intelligent prediction model built on machine learning algorithms. By learning a large number of samples corresponding to business types, feature data and query levels, it has the ability to output an appropriate query level based on the input data.

[0064] During training, the query-level recommendation model first collects a large amount of sample data, including feature data of different business types and corresponding objects, along with appropriate query levels. Then, a suitable machine learning algorithm, such as decision trees, random forests, or logistic regression, is selected to construct the query-level recommendation model. The sample data is proportionally divided into training and test sets. The model is trained using the training set, and its prediction performance is optimized by adjusting model parameters. The model's accuracy is then validated using the test set until it reaches a preset standard. In practical applications, the processed business type information and feature dataset are input into the trained model. The model uses its internal algorithm to calculate and output a query level that matches the initial object set, providing a basis for constructing subsequent structured query statements.

[0065] By acquiring feature data from the initial object set and inputting both the business type and feature data into a pre-trained query level recommendation model to determine the query level, this approach overcomes the limitations of matching query levels solely based on business type. It fully considers the individual attribute differences of different initial objects within the same business type and leverages the model's ability to mine and analyze multi-dimensional data to achieve personalized and precise recommendations for query levels. This avoids the problem of query levels not matching the actual needs of objects under a fixed matching model, improves the targeting and accuracy of relational queries, and reduces errors caused by human intervention by automatically calculating through the model, significantly improving the efficiency of query level determination.

[0066] To ensure the accuracy of obtaining target objects, in some embodiments, the attribute parameters of each initial object in the initial object set can also be obtained, and objects whose attribute parameters meet preset conditions can be selected from the initial object set to form a subset of candidate objects.

[0067] For example, the attribute parameter could be the percentage of equity held by the initial object.

[0068] It should be noted that the initial object set may contain objects with a low equity ratio. Such objects have low associated value. Therefore, objects that do not meet the conditions can be removed by checking whether the attribute parameters of each initial object meet the preset conditions.

[0069] When constructing a structured query statement for querying the relationships between objects by filling the initial object set identifier, business type identifier, and query level into the corresponding variable placeholders based on the initial object set identifier, business type identifier, and query level code, specifically, the candidate object subset, business type, and query level can be filled into the corresponding variable placeholders to construct a structured query statement for querying the relationships between objects.

[0070] By acquiring attribute parameters such as the equity ratio of initial objects, a subset of candidate objects is formed by filtering out objects that meet preset conditions. Then, based on this subset, a structured query statement is constructed in combination with business type and query level. Invalid objects with low relevance value, such as low equity ratio, are accurately eliminated from the query source, effectively reducing invalid data retrieval and computational interference in subsequent queries and significantly improving the accuracy of target object acquisition. At the same time, the query focuses on candidate objects with high relevance value, reducing the database retrieval load and improving the overall efficiency of relational query. Furthermore, the pre-processing of attribute parameter filtering makes the subsequently constructed query statement more targeted, further ensuring the validity and reliability of relational query results and providing high-quality data support for business decisions based on query results.

[0071] In some embodiments, such as Figure 3 As shown, S160 may include: S1601. Based on the structured query statement, use the initial object set as the input set for the first query level.

[0072] For example, the first query level is the starting level of relation retrieval, representing the first execution stage of directly related object retrieval for the initial object.

[0073] In practice, the constructed structured query statement can be parsed to extract the query level starting rules and initial retrieval subject information defined in the statement. The initial object set is then completely extracted and standardized according to the requirements of the structured query statement to conform to the retrieval data specifications of object relational databases. Finally, the processed initial object set is determined as the input set for the first query level retrieval.

[0074] S1602. Search the object relational database for all objects that have a direct relationship with the objects in the first query level input set, and form the result set of the second query level.

[0075] For example, a direct association is an association between objects without any intermediary entity connecting them, and it is the first identifiable association type in hierarchical retrieval. The result set of the second query level is a set of all directly related objects filtered out after the first query level retrieval is completed, and it also serves as the basic data for the next level of retrieval.

[0076] In practice, the retrieval execution module of the object relational database can be invoked. The input set for the first query level is input, and the search criteria are set to match direct relationships between objects. The retrieval module retrieves all object data directly related to objects in the input set from the database. After deduplication and validity checks, the data is organized and aggregated according to a preset data structure, ultimately forming the result set for the second query level.

[0077] S1603. Use the result set of the second query level as the input for the next round of query, and repeat the operation of finding objects until the query level of the initial object set is reached.

[0078] For example, the query level is a standard that defines the maximum depth of relational retrieval, representing the highest level at which relational searches can be progressively performed. Repeatedly executing the search operation refers to the cyclical process of continuously using the result set of the previous level as the input set of the next level to perform relational object retrieval before reaching the preset query level.

[0079] In practice, the result set of the second query level can be used as the input set for the next round of retrieval. Following the search rules for directly related objects, the direct related objects corresponding to the objects in this set are retrieved from the object relational database, forming a new result set. This process is repeated, using the result set of the previous level as the input set for the next level, and the association search operation is executed iteratively. After each round of retrieval, it is verified whether the current retrieval level has reached the query level required to match the initial object set. If not, the loop continues until the level requirement is met, at which point the retrieval terminates.

[0080] S1604. Determine the target objects that are associated with the initial object set.

[0081] For example, the target object is all objects that have been confirmed to have a direct or indirect relationship with the initial object set after the full-level search is completed, and it is the final valid result of this relationship query.

[0082] In practice, all result sets generated at each query level during the retrieval process can be collected. A full validation of the object data within each set is performed to remove duplicate or invalid object information. Following preset filtering rules, it is confirmed that all validated objects have a relationship with the initial object set that meets the query level requirements. Finally, these objects are integrated and organized into a structured set, which is then identified as the target object obtained in this query.

[0083] Through a hierarchical and progressive retrieval logic, the system first uses an initial set of objects as the first query level input set to retrieve directly related objects and form a result set. This result set is then used as the input for the next round of retrieval, iteratively searching until a preset query level is reached, ultimately identifying the target object. This approach strictly adheres to the retrieval depth defined by the query level, ensuring that the related retrieval neither misses deeply related objects that meet the requirements nor exceeds the scope to cause redundancy, thus guaranteeing the completeness and accuracy of the target object acquisition. Simultaneously, the hierarchical retrieval makes the relationship links clear and traceable, facilitating subsequent verification of the rationality of the relationships. Furthermore, the iterative retrieval method efficiently integrates related data at each level, improving the retrieval efficiency of the object relation database and providing comprehensive and reliable related object data support for business decisions.

[0084] To further improve the accuracy of target object acquisition results, in some implementations, after acquiring target objects that are related to the initial object set, a target object set can also be acquired. Based on a preset period, updated feature data of each target object in the target object set can be acquired. If the updated feature data does not meet the preset conditions, the corresponding target object is deleted from the target object set, and a warning message is output.

[0085] For example, the target object set is a complete set consisting of all objects that have a relationship with the initial object set after the relationship query is completed.

[0086] It should be noted that the characteristic data of the target object will change dynamically as business operations progress. Data is collected and updated at fixed intervals to keep track of the latest status of the object in real time. This avoids judging the validity of the object based on historical static data and ensures that the verification results of the target object are consistent with the actual situation, thus guaranteeing the accuracy of the target object set from the perspective of data timeliness.

[0087] In practice, a suitable preset cycle can be set according to the timeliness requirements of the business scenario, and the time interval and execution node of data collection can be clearly defined. At each cycle node, the data collection process is started to extract the latest feature data of each object in the target object set from data sources such as object relational databases and external data platforms, and data cleaning and standardization processing is completed to ensure that the updated data is complete, accurate and in a uniform format.

[0088] For example, the preset conditions can be target object validity judgment criteria formulated based on business needs, used to determine whether an object is qualified to continue to remain in the target object set. The warning information is prompt data generated for objects whose feature data is unqualified and have been removed, including core content such as object identification and reasons for non-compliance.

[0089] It should be noted that target objects whose feature data does not meet the preset conditions have lost their corresponding relevance value. Continuing to retain them will reduce the overall accuracy of the target object set and affect business decisions. Timely removal of unqualified objects and outputting warnings can ensure the validity of the data within the set, while also reminding relevant personnel to handle abnormal objects and reducing business risks.

[0090] In practice, clear preset conditions can be formulated based on business needs, and the criteria for judging the qualification of feature data can be defined. The updated feature data of each target object is compared and verified one by one with the preset conditions, and target objects whose feature data does not meet the conditions are screened out. All data of this type of object is deleted from the target object set, and an early warning message is generated simultaneously, clearly marking the unique identifier of the removed object and the unqualified feature items. The early warning message is output and pushed out according to the preset method.

[0091] By acquiring target objects and collecting updated feature data of them at preset intervals, and deleting target objects that do not meet preset conditions and issuing warnings, dynamic maintenance and real-time optimization of the target object set are achieved. This effectively eliminates objects that lose their association value or do not meet requirements due to feature changes, ensuring the continuous accuracy of the target object set from the entire lifecycle perspective. At the same time, the timely warning mechanism can remind relevant personnel to pay attention to abnormal objects, reduce business decision-making risks, and avoid decision-making biases caused by relying on outdated or invalid association object data, providing long-term, stable, and reliable association object data support for business applications.

[0092] The target object acquisition method, apparatus, device, computer storage medium, and computer program product of this application embodiment, after obtaining an initial object set, determine an object relationship database corresponding to the initial object set; then, determine the query level of the initial object set based on the business type to which the initial object set belongs; then, construct a structured query statement for querying the relationships between objects based on a pre-built structured query statement template, the initial object set, the business type, and the query level; finally, perform a relationship query on the object relationship database based on the structured query statement to obtain target objects that are related to the initial object set. On the one hand, this method can match a specific query level based on the business type, making the query rules accurately adapt to business needs, avoiding results that are too broad or too narrow due to a uniform query standard, and improving the accuracy of the query. On the other hand, structured query statements are strictly constructed based on the initial object set, business type, and query level, which can accurately define the query subject, conditions, and scope, ensuring that the database retrieval process is highly targeted and effectively improving the accuracy of relational identification. At the same time, using structured query statements not only reduces computing resources but also simplifies the query rule matching and instruction construction process. It eliminates the need for professional technicians to manually write complex query logic, reducing operational complexity and significantly improving the efficiency of the query preparation stage.

[0093] Based on the target object acquisition method provided in the above embodiments, this application also provides specific implementations of the target object acquisition device. Please refer to the following embodiments.

[0094] First see Figure 4 The target object acquisition device 400 provided in this application embodiment includes: Module 410 is used to obtain the initial object collection; The determination module 420 is used to determine the object relation database corresponding to the initial object set based on the initial object set; The determination module 420 is also used to determine the query level of the initial object set based on the business type to which the initial object set belongs; The acquisition module 410 is also used to acquire a pre-built structured query statement template, which contains multiple variable placeholders; the variable placeholders include an initial object set identifier, a business type identifier, and a query level code; Module 430 is used to construct a structured query statement for querying the relationship between objects by filling the initial object set identifier, business type identifier, and query level into the corresponding variable placeholders based on the initial object set identifier, business type identifier, and query level code. The acquisition module 410 is also used to perform relational queries on the object relational database based on structured query statements to obtain target objects that are related to the initial object set.

[0095] In some possible implementations, module 420 is also used for: Obtain a pre-built mapping table that represents the correspondence between business types and query levels. The mapping table stores key-value pairs of business types and query levels. The query level of the initial object set is determined based on the business type and mapping table to which the initial object set belongs.

[0096] In some possible implementations, key-value pairs include: a key corresponding to the business type identifier and a value corresponding to the query level configuration parameter; Module 420 is also used for: Retrieve the identifier corresponding to the business type; Match the identifier with the key in the mapping table; If the identifier matches a key in the mapping table, the value corresponding to the key is determined as the query level.

[0097] In some possible implementations, module 420 is also used for: Obtain the characteristic data of the initial object collection; The business type and feature data are input into a pre-trained query level recommendation model to obtain the query level of the initial object set.

[0098] In some possible implementations, module 410 is also used for: Retrieve the property parameters of each initial object in the initial object collection; Select objects from the initial object set whose attribute parameters meet the preset conditions to form a subset of candidate objects; Module 430 is also used for: Based on the initial object set identifier, business type identifier, and query level code, the candidate object subset, business type, and query level are filled into the corresponding variable placeholders to construct a structured query statement for querying the relationships between objects.

[0099] In some possible implementations, module 410 is also used for: Based on the structured query statement, the initial set of objects is used as the input set for the first query level; The second query level results set is formed by searching the object relational database for all objects that have a direct relationship with the objects in the first query level input set. The result set of the second query level is used as the input for the next round of query. The operation of finding objects is repeated until the query level of the initial object set is reached, and the target object that is related to the initial object set is obtained.

[0100] In some possible implementations, module 410 is also used for: Get the target object collection; Based on a preset period, acquire the updated feature data of each target object in the target object set; If the updated feature data does not meet the preset conditions, the corresponding target object is deleted from the target object set, and a warning message is output.

[0101] The various modules of the target object acquisition device provided in this application embodiment can achieve Figure 1 It provides functionality for each step of the method to obtain the target object and achieves the corresponding technical effect. For the sake of brevity, it will not be elaborated here.

[0102] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0103] The electronic device 500 may include a processor 501 and a memory 602 storing computer program instructions.

[0104] Specifically, the processor 501 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0105] Memory 502 may include mass storage for data or instructions. For example, and not limitingly, memory 502 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 502 may include removable or non-removable (or fixed) media. Where appropriate, memory 502 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 502 is non-volatile solid-state memory.

[0106] In certain embodiments, the memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Thus, generally, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of this application.

[0107] The processor 501 reads and executes computer program instructions stored in the memory 502 to implement any of the target object acquisition methods in the above embodiments.

[0108] In some examples, the electronic device 500 may also include a communication interface 503 and a bus 504. For example, Figure 5 As shown, the processor 501, memory 502, and communication interface 503 are connected through bus 504 and complete communication with each other.

[0109] The communication interface 503 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0110] Bus 504 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not as a limitation, bus 504 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 504 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0111] For example, the electronic device 500 can be a mobile phone, tablet computer, laptop computer, handheld computer, in-vehicle electronic device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc.

[0112] The electronic device 500 can execute the target object acquisition method in the embodiments of this application, thereby achieving the combination Figure 1 The method described for obtaining the target object.

[0113] In addition, in conjunction with the target object acquisition method in the above embodiments, this application also provides a computer-readable storage medium for implementation. The computer-readable storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the target object acquisition methods in the above embodiments. Examples of computer-readable storage media include non-transitory computer-readable storage media, such as portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, etc.

[0114] This application also provides a computer program product, which includes a computer program or instructions. When the computer program or instructions are executed by a processor, they implement any of the target object acquisition methods in the above embodiments.

[0115] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0116] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0117] The above are merely specific embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for obtaining a target object, characterized in that, include: Get the initial collection of objects; Based on the initial set of objects, determine the object relationship database corresponding to the initial set of objects; Based on the business type to which the initial object set belongs, determine the query level of the initial object set; Obtain a pre-built structured query statement template, which contains multiple variable placeholders; the variable placeholders include an initial object set identifier, a business type identifier, and a query level code; Based on the initial object set identifier, business type identifier, and query level code, the initial object set, the business type, and the query level are respectively filled into the corresponding variable placeholders to construct a structured query statement for querying the relationship between objects; Based on the structured query statement, a relational query is performed on the object relational database to obtain target objects that are related to the initial object set.

2. The target object acquisition method according to claim 1, characterized in that, Determining the query level of the initial object set based on its business type includes: Obtain a pre-built mapping table representing the correspondence between business types and query levels, wherein the mapping table stores key-value pairs of business types and query levels; Based on the business type to which the initial object set belongs and the mapping table, the query level of the initial object set is determined.

3. The target object acquisition method according to claim 2, characterized in that, The key-value pair includes: the key corresponding to the business type identifier and the value corresponding to the query level configuration parameter; The step of determining the query level of the initial object set based on the business type to which the initial object set belongs and the mapping table includes: Obtain the identifier corresponding to the business type; Match the identifier with the key in the mapping table; If the identifier matches a key in the mapping table, the value corresponding to the key is determined as the query level.

4. The target object acquisition method according to claim 1, characterized in that, Determining the query level of the initial object set based on its business type includes: Obtain the feature data of the initial object set; The business type and the feature data are input into a pre-trained query level recommendation model to obtain the query level of the initial object set.

5. The target object acquisition method according to claim 1, characterized in that, Also includes: Obtain the attribute parameters of each initial object in the initial object set; From the initial object set, objects whose attribute parameters meet preset conditions are selected to form a subset of candidate objects; The process involves filling the initial object set identifier, the business type identifier, and the query level code into corresponding variable placeholders to construct a structured query statement for querying the relationships between objects, including: Based on the initial object set identifier, business type identifier, and query level code, the candidate object subset, the business type, and the query level are respectively filled into the corresponding variable placeholders to construct a structured query statement for querying the relationship between objects.

6. The target object acquisition method according to claim 1, characterized in that, The step of performing a relational query on the object relational database based on the structured query statement to obtain target objects that are related to the initial object set includes: Based on the structured query statement, the initial set of objects is used as the input set for the first query level; The object relational database is searched for all objects that have a direct relationship with the objects in the first query level input set, forming the result set of the second query level. The result set of the second query level is used as the input for the next round of query. The operation of finding objects is repeated until the query level of the initial object set is reached, and the target object that is associated with the initial object set is obtained.

7. The target object acquisition method according to claim 1, characterized in that, After obtaining the target objects that are associated with the initial object set, the method further includes: Get the target object collection; Based on a preset period, acquire the updated feature data of each target object in the target object set; If the updated feature data does not meet the preset conditions, the corresponding target object is deleted from the target object set, and a warning message is output.

8. A target object acquisition device, characterized in that, The device includes: The retrieval module is used to retrieve the initial collection of objects; The determination module is used to determine the object relationship database corresponding to the initial object set based on the initial object set; The determination module is also used to determine the query level of the initial object set based on the business type to which the initial object set belongs; The acquisition module is also used to acquire a pre-built structured query statement template, which contains multiple variable placeholders; the variable placeholders include an initial object set identifier, a business type identifier, and a query level code; The construction module is used to fill the initial object set, the business type, and the query level into the corresponding variable placeholders based on the initial object set identifier, the business type identifier, and the query level code, and construct a structured query statement for querying the relationship between objects; The acquisition module is also used to perform a relational query on the object relational database based on the structured query statement, and to acquire target objects that have a relational relationship with the initial object set.

9. A target object acquisition device, characterized in that, The device includes: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, it implements the target object acquisition method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the target object acquisition method as described in any one of claims 1-7.

11. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device is able to perform the target object acquisition method as described in any one of claims 1-7.

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