Customer data query method and system by utilizing bitmap model and storage medium
By using a bitmap model and concurrent thread pool technology, the performance bottleneck caused by the single interface and serial calls in bank customer information query was solved, realizing efficient and flexible customer data query, improving system performance and reducing maintenance costs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-31
- Publication Date
- 2026-04-17
AI Technical Summary
Traditional bank customer information query solutions suffer from long query times, low concurrency performance, and high business access and maintenance costs due to their single interface, serial calls, and lack of flexible combination capabilities.
A bitmap model is used for customer data querying. The query dimensions are accurately specified by combining bitmap parameters. The query path is intelligently planned based on predefined dimension association rules, and the association query tasks are executed efficiently using a concurrent thread pool.
It has enabled the transformation from multiple inefficient serial queries to a single accurate concurrent efficient query, improving system throughput and response speed, providing highly flexible query combination capabilities, and reducing the complexity of business system access and maintenance costs.
Smart Images
Figure CN121880387A_ABST
Abstract
Description
Technical Field
[0001] This specification relates to the field of data processing technology, and in particular to a method, system, and storage medium for querying customer data using a bitmap model. Background Technology
[0002] In the daily operations and complex financial transactions of banks, comprehensive and efficient access to customer information is fundamental to supporting all core processes such as risk control, marketing, and service. Customer information, as one of the most important data assets of a bank, typically includes multiple interrelated dimensions in its model, such as basic customer information, account details, asset and liability status, signed agreements, and transaction records.
[0003] Existing technical solutions suffer from several shortcomings: First, traditional customer information query services typically provide data using a single-dimensional interface. When the same business scenario requires retrieving customer information across multiple dimensions, the business system must make multiple serial calls to each independent query interface. This model easily leads to excessively long inter-system call chains, huge network round-trip overhead, and, under concurrent requests, the request pressure on the query service increases exponentially, severely restricting business processing efficiency and system throughput, affecting the continuity of critical business and user experience. Second, existing query solutions lack flexible and efficient data requirement expression capabilities. Businesses either call an interface that returns all fixed fields, containing a large amount of redundant data not needed for the current business, wasting computing and transmission resources; or they pre-develop a custom interface for each possible combination of query fields, resulting in an bloated number of interfaces and high maintenance costs. This model cannot support businesses in dynamically and accurately specifying the required combination of information dimensions to be queried based on real-time scenarios.
[0004] Therefore, there is an urgent need to provide a method, system, and storage medium for querying customer data using a bitmap model to solve the problems of query performance bottlenecks and query flexibility requirements of traditional technologies. Summary of the Invention
[0005] In view of this, the present invention aims to propose a method, system and storage medium for querying customer data using a bitmap model, in order to solve the problems of high query chain time, low concurrency performance and high business access and maintenance costs caused by the existing bank customer information query schemes due to the single interface, serial calls and lack of flexible combination capabilities.
[0006] To achieve the above objectives, the present invention adopts the following technical solution: A method for querying customer data using a bitmap model, the method being based on a predefined customer model, the method comprising: S1. Receive a panoramic query request sent by the client; the panoramic query request includes at least a customer identifier and a bitmap combination parameter, the bitmap combination parameter being used to specify the customer information dimension to be queried.
[0007] S2. Based on the bitmap combination parameters, determine a corresponding main bitmap component and at least one associated bitmap component through predefined dimension association rules; wherein, the main bitmap component is used to query the main dimension information containing the main association key value; the associated bitmap component is used to query the subordinate multi-dimensional information associated with the main dimension information.
[0008] S3. Execute the main bitmap component by querying the MySQL database; obtain the main dimension information according to the database query operation encapsulated by the main bitmap component.
[0009] S4. Use the main association key value as the input parameter when each associated bitmap component executes the query task, and concurrently call the thread pool to execute the query task corresponding to the associated bitmap component according to the preset query parameter conversion rules.
[0010] S5. Wait for all concurrent query tasks to complete, summarize the query results of the main bitmap component and each associated bitmap component according to the preset result assembly rules, form the final query result and return it to the client.
[0011] The beneficial effects of this solution are as follows: In existing technologies, bank customer information queries generally suffer from performance bottlenecks, insufficient flexibility, and high maintenance costs due to single interfaces, serial calls, and fixed query dimensions, which restrict business response speed and system scalability. This method achieves precise specification of query dimensions through bitmap combination parameters, intelligent planning of master-slave query paths based on predefined dimension association rules, and efficient execution of association query tasks using a concurrent thread pool, realizing a fundamental transformation from multiple inefficient serial queries to a single accurate concurrent efficient query. This method effectively solves the query performance bottleneck in high-concurrency scenarios, significantly improving system throughput and response speed; at the same time, it provides highly flexible query combination capabilities, realizing "one-time access, on-demand combination," significantly reducing the access complexity and long-term maintenance costs of business systems, and providing stable, efficient, and agile customer data service support for various banking financial businesses.
[0012] Furthermore, step S2 also includes the following steps: S210. Predefine and store foreign key relationships between various customer information dimensions.
[0013] S220. For each associated bitmap component determined by the bitmap combination parameters, query the foreign key association relationship to determine its associated primary dimension information and the corresponding foreign key field name.
[0014] S230. Based on the query results of step S220, generate corresponding query parameter conversion rules for each associated bitmap component; the query parameter conversion rules are used to convert the main associated key value into the input parameters required when the associated bitmap component performs the query task.
[0015] Beneficial effects: By predefining foreign key relationships and dynamically generating query parameter transformation rules, the system achieves automatic derivation of query paths and precise parameter passing. This mechanism ensures that in multi-dimensional concurrent queries, core identifiers can be automatically and accurately converted into specific conditions for each related query based on the inherent relationships between data. This not only guarantees data consistency but also completely eliminates the burden of manually maintaining complex query relationship logic, significantly improving the system's adaptability and maintainability.
[0016] Furthermore, the bitmap combination parameter is a 32-bit integer value, and each binary bit of the value is uniquely mapped to a predefined customer information dimension; wherein, the value 1 indicates that the dimension is included, and the value 0 indicates that the dimension is not included.
[0017] Beneficial Effects: Employing 32-bit integer bitmap encoding for query dimensions achieves a compact and precise expression of arbitrary dimension query combinations with extremely high information density and minimal transmission and parsing overhead. This encoding method not only standardizes the query request format, facilitating interface transmission and processing, but also enables rapid parsing and matching of dimension requirements through simple bitwise operations. This lays a lightweight and agile input foundation for subsequent intelligent query planning and efficient concurrent execution, greatly improving the system's efficiency and scalability in handling massive and diverse query requests.
[0018] Furthermore, the step of determining the bitmap component in step S2 further includes: By performing a bitwise AND operation between the integer value and the mask constants representing each dimension, if the result is not zero, it is determined that the customer information dimension corresponding to the binary bit needs to be activated and queried.
[0019] Beneficial effects: By using a bitwise AND operation to match mask constants, the parsing process of bitmap parameters is transformed into efficient binary computation. This method achieves instantaneous determination of complex dimensional combinations with extremely low CPU overhead, eliminating the performance loss caused by traditional string matching or list traversal, and ensuring that even in high-concurrency, multi-dimensional query scenarios, the system can quickly and accurately determine the query components to be executed.
[0020] Furthermore, the bitmap combination parameters are formed in one of the following ways: Multiple mask constants can be combined using bitwise OR operators to query multiple dimensions; The bitwise XOR operator is used to perform a calculation between a default set of dimensions and a specific mask constant to exclude that particular dimension.
[0021] Beneficial effects: By using bitwise OR and bitwise XOR operations, it supports intuitive and flexible combination or exclusion of query dimensions, enabling clients to accurately construct complex query intents in a declarative and efficient manner, greatly enhancing the applicability and ease of use of the query interface.
[0022] Furthermore, in step S4, a uniform timeout is set for the concurrent call to the thread pool process.
[0023] Beneficial effects: By setting a uniform timeout for the concurrent call thread pool, reliability control and resource protection for concurrent query tasks are achieved. This mechanism ensures that the system will not fall into infinite waiting when individual related query services are delayed or abnormal, thereby effectively avoiding resource exhaustion and cascading failures, and guaranteeing the deterministic response of core query services and the overall system stability.
[0024] Furthermore, the thread pool is created based on the Spring framework, and its task queue capacity is set to zero to avoid task queuing by adopting a direct submission strategy.
[0025] Beneficial effects: By configuring a thread pool based on the Spring framework and setting the task queue capacity to zero, a direct submission strategy is enforced, fundamentally eliminating waiting delays caused by task backlog. This optimization ensures that each related query task can be attempted to execute immediately after submission, maximizing the utilization of concurrent execution capabilities. It is particularly suitable for high-concurrency bank query scenarios that are extremely sensitive to response time, thereby improving the overall throughput and real-time performance of the system to the theoretical best practice level.
[0026] Furthermore, a customer data query system utilizing a bitmap model, the system comprising: The request receiving module is configured to receive panoramic query requests sent by clients, the requests containing at least client identifiers and bitmap combination parameters.
[0027] The query planning engine is configured to determine a primary bitmap component and at least one associated bitmap component based on the bitmap combination parameters and predefined dimensional association rules.
[0028] The bitmap component module stores multiple bitmap components, including the main bitmap component and associated bitmap components. Each component encapsulates the query logic for a specific customer information dimension.
[0029] A concurrent execution engine is configured to execute the main bitmap component to obtain the main associated key value, and based on the key value and query parameter conversion rules, concurrently call the thread pool to execute the query tasks of each associated bitmap component.
[0030] The result assembly module is configured to summarize the query results of each component according to the result assembly rules after all concurrent query tasks are completed, form the final query result, and return it.
[0031] Beneficial effects: This system achieves process decoupling and efficient collaboration for comprehensive customer queries through a modular architecture. Each module is responsible for parameter parsing, path planning, concurrent execution, and result assembly, integrating traditional multi-interface serial queries into intelligent concurrent processing driven by a single request. While ensuring data consistency, it significantly improves query throughput and response speed. Furthermore, with its flexible bitmap parameter mechanism, it meets diverse business query needs with a unified interface, greatly reducing system access and maintenance costs.
[0032] Furthermore, a computer storage medium storing a computer program that is executed by a processor to implement a method for querying customer data using a bitmap model.
[0033] Beneficial effects: By embedding the above method into a computer-readable storage medium, this efficient and flexible bank customer data query solution can be deployed and reused widely and conveniently without being limited to a specific hardware environment, providing stable, reliable, and high-performance standardized query capabilities for various financial information systems. Attached Figure Description
[0034] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein: Figure 1 This is an exemplary flowchart of a method for querying customer data using a bitmap model; Figure 2 This is a model structure diagram of the bitmap model in this application. Detailed Implementation
[0035] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.
[0036] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.
[0037] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.
[0038] The following detailed explanation illustrates the specific implementation methods: Example 1: Figure 1 This is an exemplary flowchart of a method for querying customer data using a bitmap model; Figure 2 This is a model structure diagram of the bitmap model of this application; such as Figure 1 and Figure 2 As shown, a method for querying customer data using a bitmap model is described, the method being based on a predefined customer model.
[0039] A customer model refers to a collection of interrelated information dimensions and their data structures that a bank abstracts and defines from a business perspective to fully describe a customer entity.
[0040] In this embodiment, the customer model divides customer information into seven dimensions: membership information, customer information, account information, asset and liability information, customer rights and interests information, contract information, and transaction information.
[0041] Each dimension corresponds to a specific data table or service interface in the database. The dimensions are linked through predefined key fields to form a panoramic view of the customer.
[0042] like Figure 2 As shown in the figure, this diagram visually illustrates these dimensions and the specific sub-sets of information they contain.
[0043] The member information dimension includes member information and their associated member-bound card information.
[0044] The customer information dimension includes customer information and the customer card information under their name.
[0045] The account information dimension covers specific account information for current accounts, general ledger accounts, virtual sub-accounts, fixed deposit accounts, and margin accounts, as well as account restriction information and account signing and card binding information.
[0046] The asset and liability information dimension is used to characterize a customer's asset and liability status, including time deposits, demand deposits, wealth management products sold through third parties, precious metals, fund shares, and various loan amounts.
[0047] The customer benefits information dimension is used to manage the non-asset benefits enjoyed by customers, including points, coupons, red envelopes, and information on marketing activities they participate in.
[0048] The contract information dimension records various agreements entered into between customers and banks, such as payment agreements, deposit agreements, account restriction agreements, wealth management agreements, and other agreements.
[0049] The transaction information dimension is used to trace customers' financial activities, including detailed transaction and fund details.
[0050] The query logic for each dimension and its sub-information set is independently encapsulated as a bitmap component and stored in the bitmap component library, so that each dimension corresponds to a query execution unit that can be independently configured, invoked, and managed. The specific encapsulation method is described in steps S1-S5 below.
[0051] A method for querying customer data using a bitmap model, the method comprising: S1. Receive the panoramic query request sent by the client.
[0052] A panoramic query request refers to a structured call initiated by a business system to this query service in order to obtain one or more dimensions of information about a specific customer.
[0053] In this embodiment, panoramic query requests can be received through an API interface deployed on the application server, with the front controller serving as the unified entry point. These panoramic query requests are typically carried using the HTTP / HTTPS protocol, and the data format is JSON.
[0054] In this embodiment, the panoramic query request includes at least customer identifier and customer ID, and the bitmap combination parameter is used to specify the customer information dimension to be queried.
[0055] A customer identifier is a key business code used to uniquely identify a customer in a banking system; it can also be called a customer number. For example, it can be a 10-digit numeric string "0102030405" uniformly assigned by the banking system. Alternatively, a customer identifier can be an identification number, such as a resident ID card number or passport number. Based on pre-configured rules, the system supports uniquely identifying the customer to be queried using either of these customer identifiers.
[0056] The bitmap combination parameter is used to specify the dimensions of customer information to be queried.
[0057] For example, a bitmap combination parameter can explicitly specify that this query only needs to retrieve data from the "customer basic information" and "account information" dimensions, without retrieving information from other dimensions. This bitmap combination parameter allows the client to freely combine any dimensions in a single request, thereby achieving flexible customization of the query scope and precise utilization of resources.
[0058] Furthermore, in this embodiment, the bitmap combination parameter can be a 32-bit integer value, where each binary bit of the value uniquely maps to a predefined customer information dimension; wherein, a value of 1 indicates that the dimension is included, and a value of 0 indicates that the dimension is not included.
[0059] As an example only, if the lowest bit (bit 0) is mapped to the "customer information" dimension and the second lowest bit (bit 1) is mapped to the "account information" dimension, then the bitmap combination parameter value 3, which is "...00011" in binary, indicates that this query needs to retrieve data from both the "customer information" and "account information" dimensions simultaneously.
[0060] S2. Based on the bitmap combination parameters, determine a corresponding main bitmap component and at least one associated bitmap component through predefined dimensional association rules.
[0061] The primary bitmap component is the core query unit that needs to be executed first, and its query results provide key input parameters for subsequent concurrent queries. These key input parameters are the primary join key values.
[0062] In this embodiment, the main bitmap component can be a customer information component, as it can directly provide customer numbers for linking accounts, memberships, benefits, transactions, and other dimensions. The specific deployment of the main bitmap component is set according to actual needs and is not limited here.
[0063] In this embodiment, the main bitmap component is used to query the main dimension information containing the main associated key value.
[0064] The primary related key value is a key field value that can uniquely identify a customer or a core business entity and can be used to directly or indirectly query other related dimension data; the primary dimension information is a complete set of dimension data returned by the primary bitmap component. As an example, when the primary bitmap component is a customer information component, the primary dimension information it returns is a complete customer profile containing fields such as customer number, name, document type, and document number.
[0065] The associated bitmap component is a subordinate component that relies on the primary associated key value provided by the primary bitmap component to execute a query. In this embodiment, the associated bitmap component includes... Figure 2 All components except the main bitmap component.
[0066] In this embodiment, the associated bitmap component is used to query subordinate multi-dimensional information associated with the main dimension information. The subordinate multi-dimensional information is a collection of other dimension data belonging to the same customer entity.
[0067] Predefined dimension association rules are rules used to determine bitmap components.
[0068] Furthermore, step S2 also includes the following steps, which further illustrate how to determine bitmap components based on predefined dimensional association rules.
[0069] S210. Predefine and store foreign key relationships between various customer information dimensions.
[0070] Foreign key relationships are structured constraints and descriptions that establish references and connections between data tables of different customer information dimensions. They indicate that a field in one dimension (sub-table) depends on a primary key field in another dimension (main table).
[0071] As an example, the "Customer Number" field in the "Account Information" table references the "Customer Number" primary key field in the "Customer Information" table, thus forming a foreign key relationship from the "Account Information" dimension to the "Customer Information" dimension.
[0072] In this embodiment, the predefinition of this relationship is achieved by establishing and maintaining a dimension association mapping table in the system configuration library. This table records at least the following fields: the name of the subordinate multi-dimensional information, the name of the primary dimension information associated with the subordinate multi-dimensional information, and the name of the foreign key field used for association in the data table storing the subordinate multi-dimensional information.
[0073] S220. For each associated bitmap component determined by the bitmap combination parameters, query the foreign key relationship to determine its associated primary dimension information and the corresponding foreign key field name.
[0074] S230. Based on the query results of step S220, generate corresponding query parameter conversion rules for each associated bitmap component; the query parameter conversion rules are used to convert the main associated key value into the input parameters required when the associated bitmap component performs the query task.
[0075] In this embodiment, the conversion process of the query parameter conversion rule is a parameter mapping and binding process. Based on the foreign key field name determined in S220, the system instantiates a parameter template or a parameter binder for each associated bitmap component. Once the main thread obtains the actual primary associated key value (such as customer number "C123456"), the conversion rule guides the system to fill this key value into the predefined parameter placeholder in the query logic of that associated component.
[0076] For example, for the "account information" related component, its query logic might be a parameterized SQL statement: SELECT * FROM account WHERE cust_id =?; At this point, the generated query parameter conversion rule specifies that the primary join key value should be bound to the first parameter of the SQL statement, namely the cust_id field, thereby generating a complete executable query: "SELECT * FROM account WHERE cust_id='C123456'." This process ensures that, during concurrent execution, each associated component can accurately query the data of its respective dimension based on a unified primary key value.
[0077] Furthermore, in this embodiment, since the bitmap combination parameter can be a 32-bit integer value, each binary bit of the value uniquely maps to a predefined customer information dimension.
[0078] Therefore, the step of determining the bitmap component in step S2 further includes: By performing a bitwise AND operation between the integer value and the mask constants representing each dimension, if the result is not zero, it is determined that the customer information dimension corresponding to the binary bit needs to be activated and queried.
[0079] As an example only, taking the lower 6 bits as an example, assume the system predefines the following mask constants: The mask for the customer information dimension is 1, which is 000001 in binary; the mask for the account information dimension is 2, which is 000010 in binary; and the mask for the asset and liability information dimension is 4, which is 000100 in binary.
[0080] If the received bitmap combination parameter value is 3, which is 000011 in binary; for the customer information dimension, 3&1=1, and the result is not 0, so a query is required; for the account information dimension, 3&2=2, and the result is not 0, so a query is required; for the asset and liability information dimension, 3&4=0, and the result is 0, so no query is required. Through the above bitwise AND operation, the system can accurately parse out the two bitmap components that need to be activated for this query: customer information and account information. Here, & is the bitwise AND operator.
[0081] Furthermore, the bitmap combination parameters are formed in one of the following ways: Multiple mask constants can be combined using the bitwise OR operator to query multiple dimensions.
[0082] The bitwise XOR operator is used to perform a calculation between a default set of dimensions and a specific mask constant to exclude that particular dimension.
[0083] As an example only, if the system needs to query both customer information and account information simultaneously, the parameters can be generated using bitwise OR operations: Bitmap combination parameter = 1 | 2 = 3.
[0084] To query all dimensions except account information, assuming the default total set parameter is 7 (binary 000111), the parameter can be generated using a bitwise XOR operation: bitmap combination parameter = 7^2 = 5 (binary 000101). The result indicates a query for customer information and asset and liability information, excluding account information.
[0085] In this embodiment, the parsing process of bitmap parameters is transformed into efficient binary computation by using a bitwise AND operation to match mask constants. This method achieves instantaneous determination of complex dimensional combinations with extremely low CPU overhead, eliminating the performance loss caused by traditional string matching or list traversal. It ensures that even in high-concurrency, multi-dimensional query scenarios, the system can quickly and accurately determine the query components to be executed. Through bitwise OR and bitwise XOR operations, it supports intuitive and flexible combination or exclusion of query dimensions, enabling clients to accurately construct complex query intents in a declarative and efficient manner, greatly enhancing the applicability and ease of use of the query interface.
[0086] S3. Execute the main bitmap component by querying the MySQL database; obtain the main dimension information based on the database query operation encapsulated by the main bitmap component.
[0087] S4. Use the main association key value as the input parameter when each associated bitmap component executes the query task, and concurrently call the thread pool to execute the query task corresponding to the associated bitmap component according to the preset query parameter conversion rules.
[0088] The query parameter conversion rule is a mapping specification that defines how to adapt and pass a unified primary association key value to the query logic of different association bitmap components.
[0089] In this embodiment, based on preset query parameter conversion rules, the system constructs a specific query task instance that can be executed immediately for each related bitmap component to be executed. In this instance, the main related key value has been parameter-bound according to the rules, and transformed into a complete instruction that can directly call the underlying database or service. Subsequently, the system submits this series of prepared task instances in batches to the task queue of the thread pool. The thread pool dynamically allocates idle worker threads according to its configured cores and maximum number of threads, and simultaneously grabs and executes multiple tasks in the queue, thereby realizing true concurrency of multiple related dimension queries. This process decouples parameter preparation from task scheduling, enabling the thread pool to process ready query tasks with the highest efficiency and maximize the throughput of parallel queries.
[0090] Furthermore, in this embodiment, a uniform timeout is set for the concurrent call to the thread pool process.
[0091] In this embodiment, the timeout period can be dynamically configured according to the business tolerance and system load, and is usually set to 5 to 10 seconds.
[0092] When the execution time of a query task for any associated bitmap component exceeds this threshold, the task will be forcibly interrupted. The system will no longer wait for the result and will instead continue to process other completed tasks and assemble partial responses, thereby ensuring the deterministic response of the core query service and the overall availability of the system.
[0093] In this embodiment, the maximum number of threads is set to 500, and the number of core threads is 50. This configuration ensures that under normal load, 50 core threads efficiently handle requests to maintain low resource overhead, while in instantaneous high-concurrency scenarios, it can be quickly expanded to 500 threads to cope with traffic peaks. Thus, while ensuring system resource utilization, it significantly improves the overall throughput and concurrency support capability of the panoramic query service.
[0094] Furthermore, the thread pool is created based on the Spring framework, and its task queue capacity is set to zero to avoid task queuing by adopting a direct submission strategy.
[0095] Specifically, by configuring the thread pool's task queue as a SynchronousQueue (with a capacity of 0) and setting the idle reclamation time for non-core threads to 30 seconds, instant task response and dynamic resource reclamation are achieved.
[0096] S5. Wait for all concurrent query tasks to complete, summarize the query results of the main bitmap component and each associated bitmap component according to the preset result assembly rules, form the final query result and return it to the client.
[0097] The result assembly rule is a mapping instruction that integrates scattered, multi-dimensional query results into a unified panoramic data response. This rule generates a target response template based on the bitmap combination of the current query. The system first fills the core fields with the results of the main component, then maps the results of each related component in parallel to the corresponding nested fields, such as account lists and asset lists, according to the rule, finally assembling them into a structured, complete response and returning it.
[0098] Example 2: A customer data query system using a bitmap model, the system comprising: The request receiving module is configured to receive panoramic query requests sent by clients, the requests containing at least client identifiers and bitmap combination parameters.
[0099] The query planning engine is configured to determine a primary bitmap component and at least one associated bitmap component based on the bitmap combination parameters and predefined dimensional association rules.
[0100] The bitmap component module stores multiple bitmap components, including the main bitmap component and associated bitmap components. Each component encapsulates the query logic for a specific customer information dimension.
[0101] A concurrent execution engine is configured to execute the main bitmap component to obtain the main associated key value, and based on the key value and query parameter conversion rules, concurrently call the thread pool to execute the query tasks of each associated bitmap component.
[0102] The result assembly module is configured to summarize the query results of each component according to the result assembly rules after all concurrent query tasks are completed, form the final query result, and return it.
[0103] In this embodiment, the system achieves process decoupling and efficient collaboration for comprehensive customer queries through a modular architecture. Each module is responsible for parameter parsing, path planning, concurrent execution, and result assembly, integrating traditional multi-interface serial queries into intelligent concurrent processing driven by a single request. While ensuring data consistency, this significantly improves query throughput and response speed. Furthermore, with its flexible bitmap parameter mechanism, it meets diverse business query needs with a unified interface, greatly reducing system access and maintenance costs.
[0104] A computer storage medium storing a computer program that, when executed by a processor, implements a method for querying customer data using a bitmap model.
[0105] In this embodiment, precise specification of query dimensions is achieved through bitmap combination parameters, intelligent planning of master-slave query paths based on predefined dimension association rules, and efficient execution of association query tasks using a concurrent thread pool. This fundamentally transforms query performance from multiple inefficient serial queries to a single, precise, concurrent, and efficient query. This method effectively solves the query performance bottleneck in high-concurrency scenarios, significantly improving system throughput and response speed. Simultaneously, it provides highly flexible query combination capabilities, enabling "one-time access, on-demand combination," significantly reducing the access complexity and long-term maintenance costs of business systems, and providing stable, efficient, and agile customer data service support for various banking financial businesses.
[0106] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.
[0107] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.
[0108] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.
[0109] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values are set as precisely as feasible.
[0110] For each patent, patent application, patent application publication, and other material such as articles, books, specifications, publications, and documents referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.
[0111] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.
Claims
1. A method for customer data query using a bitmap model, the method is based on a predefined customer model, characterized in that, The method includes: S1. Receive a panoramic query request sent by the client; the panoramic query request includes at least a customer identifier and a bitmap combination parameter, the bitmap combination parameter being used to specify the customer information dimension to be queried; S2. Based on the bitmap combination parameters, determine a corresponding main bitmap component and at least one associated bitmap component through predefined dimension association rules; wherein, the main bitmap component is used to query the main dimension information containing the main association key value; the associated bitmap component is used to query the subordinate multi-dimensional information associated with the main dimension information; S3. Execute the main bitmap component by querying the MySQL database; obtain the main dimension information according to the database query operation encapsulated by the main bitmap component; S4. Use the main association key value as the input parameter when each association bitmap component executes the query task, and concurrently call the thread pool to execute the query task corresponding to the association bitmap component according to the preset query parameter conversion rules. S5. Wait for all concurrent query tasks to complete, summarize the query results of the main bitmap component and each associated bitmap component according to the preset result assembly rules, form the final query result and return it to the client.
2. The method of claim 1, wherein, Step S2 further includes the following steps: S210. Predefine and store foreign key relationships between various customer information dimensions; S220. For each associated bitmap component determined by the bitmap combination parameters, query the foreign key association relationship to determine its associated primary dimension information and the corresponding foreign key field name. S230. Based on the query results of step S220, generate corresponding query parameter conversion rules for each associated bitmap component; the query parameter conversion rules are used to convert the main associated key value into the input parameters required when the associated bitmap component performs the query task.
3. The method according to claim 1, characterized in that, The bitmap combination parameter is a 32-bit integer value, and each binary bit of the value uniquely maps to a predefined customer information dimension; where a value of 1 indicates that the dimension is included, and a value of 0 indicates that the dimension is not included.
4. The method according to claim 3, characterized in that, The step of determining the bitmap component in step S2 further includes: By performing a bitwise AND operation between the integer value and the mask constants representing each dimension, if the result is not zero, it is determined that the customer information dimension corresponding to the binary bit needs to be activated and queried.
5. The method according to claim 4, characterized in that, The bitmap combination parameters are formed in one of the following ways: Multiple mask constants can be combined using bitwise OR operators to query multiple dimensions; The bitwise XOR operator is used to perform a calculation between a default set of dimensions and a specific mask constant to exclude that particular dimension.
6. The method according to claim 1, characterized in that, In step S4, a uniform timeout is set for the concurrent call to the thread pool process.
7. The method according to claim 6, characterized in that, The thread pool is created based on the Spring framework, and its task queue capacity is set to zero to avoid task queuing by adopting a direct submission strategy.
8. A customer data query system using a bitmap model, characterized in that, The system is used to implement the method of claim 1, the system comprising: The request receiving module is configured to receive panoramic query requests sent by clients, the requests including at least client identifier and bitmap combination parameters; The query planning engine is configured to determine a primary bitmap component and at least one associated bitmap component based on the bitmap combination parameters and predefined dimension association rules. The bitmap component module stores multiple bitmap components, including the main bitmap component and associated bitmap components. Each component encapsulates the query logic for a specific customer information dimension. A concurrent execution engine is configured to execute the main bitmap component to obtain the main associated key value, and based on the key value and the query parameter transformation rules, concurrently call the thread pool to execute the query tasks of each associated bitmap component; The result assembly module is configured to summarize the query results of each component according to the result assembly rules after all concurrent query tasks are completed, form the final query result, and return it.
9. A computer storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements a method for querying customer data using a bitmap model as described in any one of claims 1-7.