Scene-based multi-source concurrent data acquisition method, device, equipment and medium

Abstract

The application relates to the technical field of data retrieval, and discloses a scene-based multi-source concurrent data acquisition method, device, equipment and medium, which comprises the following steps: acquiring account information and a supplier identifier of a supplier; acquiring a business test case under a target business scenario; acquiring business test data returned by each supplier according to the business test case; performing data desensitization processing and standardization processing on the business test data corresponding to each candidate account; acquiring a test score of desensitized test data of each supplier; converting the account information into a plurality of numerical parameters of each procurement account; calculating an account characteristic value of each procurement account according to the numerical parameters and the test score; and selecting an account with the largest account characteristic value as a query result account corresponding to a procurement data query request. The application can improve the accuracy of existing procurement data query.

Description

Technical Field

[0001] This invention relates to the field of data retrieval technology, and in particular to a method, apparatus, device and medium for multi-source concurrent data acquisition based on a scenario. Background Technology

[0002] Procurement data query is an essential operation for enterprises or groups with procurement business when carrying out procurement tasks. Through procurement data query, it is possible to use multi-dimensional feature conditions such as supplier identification, business purpose category tags, account status parameters and scenario requirements to match target procurement objects in different business scenarios from the database, such as querying procurement accounts that meet preset conditions.

[0003] Because procurement data queries, such as those involving procurement account searches, require consideration of numerous data dimensions, existing methods often employ fragmented field filtering and single-dimensional sequential query logic. These methods utilize common criteria such as independent group identifier matching, account status filtering, usage classification verification, and account type for data selection. However, the filtering conditions used by these existing methods only provide superficial account data, resulting in poor data reliability and real-time performance, thus reducing the accuracy of procurement data queries. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and medium for multi-source concurrent data acquisition based on scenarios, with the main purpose of addressing the low accuracy of existing procurement data queries.

[0005] Firstly, to achieve the above objectives, the present invention provides a scenario-based multi-source concurrent data acquisition method, comprising: When a procurement data query request for a target business scenario is received from a target user, the supplier identifiers of multiple suppliers are extracted from a preset supplier database according to the procurement data query request. Obtain the procurement account corresponding to each supplier identifier, and collect the account information of each procurement account using a preset multi-source data engine; Obtain business test cases under the target business scenario, send the business test cases to the supplier backend system corresponding to each candidate account, and obtain the business test data returned by each supplier backend system based on the business test cases; Data anonymization processing is performed on the business test data corresponding to each candidate account to obtain anonymized test data; The anonymized test data for each candidate account is standardized according to a preset data format. The standardized anonymized test data is then misaligned and distributed to the supplier backend system for each candidate account. The test score returned by each supplier backend system based on the received anonymized test data is then obtained. The account information is converted into multiple numerical parameters for each procurement account. Based on the numerical parameters and the test score, the account feature value of each procurement account is calculated, and the account with the largest account feature value is selected as the query result account corresponding to the procurement data query request.

[0006] Secondly, the present invention also provides a scenario-based multi-source concurrent data acquisition device, comprising: Data acquisition module: When a target user requests procurement data for a target business scenario, the module extracts supplier identifiers of multiple suppliers from a preset supplier database based on the procurement data query request, obtains the procurement account corresponding to each supplier identifier, and collects the account information of each procurement account using a preset multi-source data engine. Data Analysis Module: Obtain business test cases under the target business scenario, send the business test cases to the supplier backend system corresponding to each candidate account, obtain the business test data returned by each supplier backend system according to the business test cases, and perform data desensitization processing on the business test data corresponding to each candidate account to obtain desensitized test data; Scoring Calculation Module: Standardizes the anonymized test data corresponding to each candidate account according to a preset data format, distributes the standardized anonymized test data to the supplier backend system corresponding to each candidate account in a staggered manner, and obtains the test score returned by each supplier backend system based on the received anonymized test data; Account filtering module: Converts the account information into multiple numerical parameters for each procurement account, calculates the account feature value for each procurement account based on the numerical parameters and the test score, and selects the account with the largest account feature value as the query result account corresponding to the procurement data query request.

[0007] Thirdly, the present invention also provides an electronic device, the electronic device comprising: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the scenario-based multi-source concurrent data acquisition method described above.

[0008] Fourthly, the present invention also provides a computer-readable storage medium storing at least one computer program, which is executed by a processor in an electronic device to implement the scenario-based multi-source concurrent data acquisition method described above.

[0009] In this embodiment of the invention, when procurement data queries are required, on the one hand, business test data from each supplier in the target business scenario is obtained through business test cases. This allows for subsequent analysis of the test data to assess the true business capabilities of each supplier, improving the accuracy of the final data retrieval. On the other hand, the business test data is anonymized and standardized, and then sent to each supplier's backend system using a staggered allocation method. This obtains a professional and objective evaluation of each supplier's test results, ultimately analyzing the account characteristic values ​​of each supplier and filtering the query results. By utilizing anonymization, standardization, and staggered allocation, accurate and objective analysis of different test results is achieved, further improving the accuracy of the final query results. In summary, this method comprehensively improves the accuracy of existing order characteristic value analysis. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic diagram of an application environment for a scenario-based multi-source concurrent data acquisition method according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating a scenario-based multi-source concurrent data acquisition method according to an embodiment of the present invention. Figure 3 This is a functional block diagram of a scenario-based multi-source concurrent data acquisition device provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device that implements a scenario-based multi-source concurrent data acquisition method according to an embodiment of the present invention; Figure 5 This is another structural schematic diagram of an electronic device that implements a scenario-based multi-source concurrent data acquisition method according to an embodiment of the present invention.

[0012] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0013] It should be noted that in the technical solutions disclosed in this invention, the acquisition of user information (personal image data (e.g., facial videos or pictures, facial feature videos or pictures, etc.) and personal privacy information (e.g., name, ID number, occupation, address, etc.)) is all completed with the user's knowledge and consent, and the acquisition of the relevant user information is legal and compliant.

[0014] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0015] It should be noted that the terms "first," "second," etc., used in this disclosure and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0016] This application provides a scenario-based multi-source concurrent data acquisition method. The execution entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the device provided in this application: a server, a terminal, etc. In other words, the scenario-based multi-source concurrent data acquisition method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0017] This invention provides a scenario-based method for acquiring multi-source concurrent data, which can be applied in applications such as... Figure 1In this application environment, the client can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server can be implemented using a standalone server or a server cluster consisting of multiple servers. The invention will now be described in detail through specific embodiments.

[0018] Reference Figure 2 The diagram shown is a flowchart illustrating a scenario-based multi-source concurrent data acquisition method according to an embodiment of the present invention. In this embodiment, the scenario-based multi-source concurrent data acquisition method includes: S1. When a procurement data query request for a target business scenario is received from a target user, the supplier identifiers of multiple suppliers are extracted from a preset supplier database according to the procurement data query request.

[0019] In this embodiment of the invention, the target user can be any enterprise or group with external procurement business, such as bidding personnel or procurement personnel.

[0020] In detail, a procurement data query request for a specific business scenario is a data query request sent by the target personnel to a specific electronic system when the purchasing party (an enterprise or group) needs to procure products or services in a certain business scenario.

[0021] For example, when Company A needs to purchase a batch of fire-fighting equipment for high-temperature working conditions, the target user responsible for the purchase within Company A can send a query request to the data system corresponding to Company A's supplier database to query supplier data in the supplier database that meets the qualifications, product types, and other factors set in the procurement data query request for high-temperature working conditions (i.e., the target business scenario).

[0022] Specifically, the supplier database is pre-built and stores information such as the service types and production qualifications of multiple suppliers, as well as a supplier identifier, such as a supplier ID, for each supplier to uniquely identify the supplier.

[0023] In this embodiment of the invention, the step of retrieving supplier identifiers from a preset supplier database based on the procurement data query request includes: Extract the request header of the procurement data query request, parse the fields of the request header to obtain multiple request message fields contained in the request header; A decision tree model is constructed using multiple request message fields. Collect supplier data and supplier identifiers from a pre-defined supplier database; The decision tree model is used to filter the supplier data of each supplier, and the supplier identifiers corresponding to all the filtered supplier data are collected.

[0024] In detail, the request header contains various conditional data preset by the target user for the target business scenario. For example, when purchasing fire-fighting equipment in high-temperature working conditions, the supplier needs to have fire-fighting qualifications and proof that the product conforms to national standards, etc.

[0025] Specifically, since the format of the data request is relatively fixed, the procurement data query request can be split according to a preset fixed format to extract the request header of the procurement data query request, and the fields of the request header can be parsed using JSON, SQL and other parsers to generate the request message fields contained in the request header.

[0026] Furthermore, multiple request message fields generated can be used as decision tree nodes to generate a decision tree model. For example, if the request message includes the field "fire protection qualification", then a decision tree node is constructed based on this field to determine whether the user has fire protection qualification. If the user has fire protection qualification, the data can pass through the node; if the user does not have fire protection qualification, the data cannot pass through the node.

[0027] In detail, since the request header of the procurement data query request contains all the conditions that the target user expects the supplier to meet (i.e., request message fields), the decision tree node constructed by each request message field in the decision tree model can be used to filter the supplier data and select the supplier identifiers corresponding to all the filtered supplier data. The selected supplier identifiers are the supplier identifiers corresponding to the procurement data query request.

[0028] S2. Obtain the procurement account corresponding to each supplier identifier, and collect the account information of each procurement account using the preset multi-source data engine.

[0029] In this embodiment of the invention, the procurement account is an account used by each supplier to connect with procurement business, such as the payment account of each supplier.

[0030] Specifically, the procurement account corresponding to each supplier's identifier can be obtained through the supplier's authorization.

[0031] Specifically, the multi-source data engine is a pre-acquired data engine that can collect preset data through multi-source data channels, such as a browser that has access to an AI deep thinking model.

[0032] To achieve accurate analysis and screening of each supplier, a preset multi-source data engine can be used to collect account information for each purchasing account. The account information includes data related to the payment ability of each supplier account, such as cash flow stability, expected payment probability, and whether there are guarantees.

[0033] In this embodiment of the invention, the step of collecting account information for each purchasing account using a preset multi-source data engine includes: Select any procurement account one by one, and use the preset multi-source data engine to collect the basic information of the selected procurement account from multiple preset channels; Determine whether the data in the preset location field within the basic information corresponding to each preset channel is consistent; If they match, then the preset position field is deduplicated. If there is a discrepancy, the preset position field is corrected for outliers, and then the preset position field is deduplicated after the outlier correction. The process continues until all basic information corresponding to each preset channel has been removed, resulting in the account information of the selected procurement account.

[0034] In detail, the multiple preset channels may include supplier authorization channels (data content actively authorized by the supplier for display), online query channels (data content searchable on the Internet), back-end database channels (data content in a database built by the purchaser), etc.

[0035] In a practical application scenario of this invention, since data from any single channel is susceptible to issues such as data falsification and delayed updates, a multi-source data engine can be used to collect basic information of each supplier's purchasing account from different channels through multiple preset channels, thereby improving the authenticity and effectiveness of the collected data.

[0036] Furthermore, among the data collected from multiple channels, the data from different channels may be the same or different. If the data in the preset position field of the basic information corresponding to each preset channel is consistent, it means that the data obtained from different channels are consistent. In this case, it can be confirmed that the content of the preset position field is true, and simple deduplication can be performed.

[0037] However, if the data in the preset location field within the basic information corresponding to the preset channel is inconsistent, it is necessary to correct the outlier in the preset location field. This can be done by sending the preset location field to the responding supplier to confirm or modify the data content of the preset location field.

[0038] Finally, once the basic information corresponding to each preset channel has been removed or outliers corrected, the account number information of the selected procurement account can be obtained, until the account information of each procurement account is generated.

[0039] S3. Obtain business test cases under the target business scenario, send the business test cases to the supplier backend system corresponding to each candidate account, and obtain the business test data returned by each supplier backend system based on the business test cases.

[0040] In this embodiment of the invention, the business test cases are pre-acquired test data sets that can be used to test relevant data of the services or products provided by the supplier.

[0041] For example, when the supplier is a product supplier, the test cases can be test models of the product. The supplier can use the test model to test its own products and obtain various test data of the product, such as the number of times the product can be used, the product's sealing performance, fire resistance, corrosion resistance, coupling performance, etc.

[0042] For example, when the supplier is a specific service provider, the test cases can be used as a test plan for that specific service. The supplier can use the test model to test its own service and obtain various test data of the service, such as service efficiency, error rate, service quality, etc.

[0043] In detail, the business test cases can be pre-built by the purchaser, and can be specifically built according to different target business scenarios.

[0044] For example, when purchasing fire-fighting equipment for high-temperature operations, business test cases can include the equipment's high-temperature resistance; while when purchasing fire-fighting equipment for chemical material production, business test cases can include the equipment's corrosion resistance.

[0045] In this embodiment of the invention, business test cases can be sent to the supplier backend system corresponding to each candidate account. Then, after each supplier tests its own business according to the business test cases, the business test data returned by each supplier backend system according to the business test cases can be obtained.

[0046] S4. Perform data anonymization processing on the business test data corresponding to each candidate account to obtain anonymized test data.

[0047] In this embodiment of the invention, in order to achieve objective and accurate analysis of business test data, it is necessary to use data desensitization processing to remove data content related to each supplier from the business test data.

[0048] For example, it is necessary to remove the product names, product types, and supplier identifiers of each supplier from the business test data.

[0049] In this embodiment of the invention, the step of performing data anonymization processing on the business test data corresponding to each candidate account to obtain anonymized test data excluding supplier identifiers includes: Retrieve the regular expressions for the entities corresponding to each supplier; The entity regular expression is used to traverse and match the business test data corresponding to each candidate account to obtain the entity fields in the business test data corresponding to each supplier. The entity fields contained in the business test data corresponding to each candidate account are deleted to obtain the de-identified test data.

[0050] In detail, the entity regular expression can be pre-provided by each supplier and used to match and identify entity fields in the business test data that are related to each supplier. The entity fields are custom-named entity data contents such as product name, product type, and supplier identifier that are associated with each supplier in the business test data.

[0051] For example, if the obtained entity regular expression is "AAA" generated based on the supplier name of supplier A, then the entity field that is the same as "AAA" can be matched from the business test data using this entity regular expression (that is, the supplier name of supplier A can be matched).

[0052] Specifically, regular expressions can be used to quickly identify and match entity fields related to various suppliers in business test data without the need for semantic analysis of actual business test data using large models, thus avoiding complex data identification and processing and greatly improving data processing efficiency.

[0053] S5. Standardize the anonymized test data corresponding to each candidate account according to the preset data format, and distribute the standardized anonymized test data to the supplier backend system corresponding to each candidate account in a staggered manner. Obtain the test score returned by each supplier backend system based on the received anonymized test data.

[0054] In the actual application scenario of this invention, although step S4 removes data content related to each supplier from the business test data and obtains desensitized test data, different suppliers often use their own unique data formats when conducting data testing using test cases. Therefore, simply removing the sensitive content cannot truly hide the source of the data (which supplier's test case it comes from).

[0055] In summary, to further improve the objectivity of subsequent analysis and evaluation of the anonymized test data, the anonymized test data corresponding to each candidate account can be standardized according to a preset data format to obtain data content in a fixed and uniform format.

[0056] The preset format can be determined in advance by the purchaser.

[0057] Furthermore, since each supplier is in the R&D field of the industry, their evaluation of test results is more accurate. In order to achieve an objective evaluation of the anonymized test data corresponding to each candidate account and avoid the influence of human factors on the evaluation results, a staggered evaluation method can be used to distribute the standardized anonymized test data to the back-end system of each candidate account. This way, each supplier does not evaluate the anonymized test data corresponding to its own test cases. Since each supplier cannot know which supplier the anonymized test data comes from, a more objective and accurate evaluation result can be given.

[0058] In detail, the misalignment assessment method involves sending the anonymized test data corresponding to each supplier to other suppliers for evaluation. For example, there are suppliers A, B, and C, where supplier A generates anonymized test data 'a', supplier B generates anonymized test data 'b', and supplier C generates anonymized test data 'c'.

[0059] When evaluating desensitized test data, desensitized test data b can be sent to supplier C, desensitized test data c to supplier A, and desensitized test data a to supplier B, thus creating a staggered evaluation.

[0060] Alternatively, desensitized test data b can be sent to supplier A, desensitized test data c to supplier B, and desensitized test data a to supplier C, thus creating a misaligned assessment.

[0061] In this embodiment of the invention, the step of misallocating the standardized de-identified test data to the supplier backend system corresponding to each candidate account includes: The anonymized test data corresponding to each candidate account is sequentially encoded to obtain the first code; Each candidate account is encoded one by one according to the same order of the corresponding de-identified test data to obtain the second code; Select any de-identified test data as the target test data, and match any candidate account with the target test data; If the first code corresponding to the target test data is the same as the second code of the selected candidate account, then return to the step of selecting any candidate account to match the target test data; If the first code corresponding to the target test data is different from the second code of the selected candidate account, then the target test data will be assigned to the supplier backend system corresponding to the selected candidate account.

[0062] For example, there are suppliers A, B, and C, where supplier A generates de-identified test data a, supplier B generates de-identified test data b, and supplier C generates de-identified test data c.

[0063] When evaluating desensitized test data, desensitized test data a can be assigned the first code 1, desensitized test data b can be assigned the first code 2, and desensitized test data c can be assigned the first code 3.

[0064] At the same time, supplier A assigns a second code 1, supplier B assigns a second code 2, and supplier C assigns a second code 3.

[0065] Then, arbitrarily select de-identified test data 'a' as the target test data, and randomly select supplier A to match de-identified test data 'a'. At this time, the first code 1 of de-identified test data 'a' is the same as the second code 1 of supplier A. Then return to the previous step and randomly select supplier B to match de-identified test data 'a' again. At this time, the first code 1 of de-identified test data 'a' is different from the second code 2 of supplier B. Then de-identified test data 'a' can be sent to supplier B's supplier back-end system.

[0066] Furthermore, the test score returned by each supplier's backend system based on the received anonymized test data is obtained. This test score can identify the comprehensive score of the product or service corresponding to the test data in terms of quality, price, and other aspects. Its specific content is determined by the business test cases given in advance by the purchaser in the target business scenario.

[0067] S6. Convert the account information into multiple numerical parameters for each procurement account, calculate the account feature value for each procurement account based on the numerical parameters and the test score, and select the account with the largest account feature value as the query result account corresponding to the procurement data query request.

[0068] In this embodiment of the invention, since the account information includes multiple data related to the payment ability of each supplier account, such as the cash flow stability, expected payment probability, and whether there is a guarantee, in order to comprehensively consider the data content of the account information during the final account selection, the account information can be converted into numerical parameters for each purchasing account, and then the numerical parameters can be used as relevant parameters for data analysis.

[0069] In this embodiment of the invention, converting the account information into multiple numerical parameters for each purchasing account includes: Select the account information corresponding to any procurement account as the target information; The target information is split into multiple data fields using a pre-defined large language model. The data fields are vectorized to obtain the field vector corresponding to each data field. Select any field vector as the target vector one by one, and calculate the distance between the target vector and the parameter vectors corresponding to each parameter in the preset parameter table; The parameter corresponding to the parameter vector with the smallest distance value is determined as the numerical parameter of the target vector, until multiple numerical parameters of the account information corresponding to each procurement account are determined.

[0070] In detail, the target information can be split into data fields using a pre-trained data analysis model, which includes BERT model, NLP (Natural Language Processing) model, etc.

[0071] For example, if the target information is data related to the supplier's payment ability, such as cash flow stability, expected payment probability, and the existence of guarantees, a pre-defined large language model can be used to identify the data semantics of each part of the target information. Then, according to their different data semantics, the target information can be broken down into separate data representing cash flow stability, data representing expected payment probability, and data representing the existence of guarantees. Subsequently, in the subsequent solution, the account information corresponding to each purchasing account can be analyzed based on different data content, improving the accuracy of data analysis.

[0072] Specifically, data fields can be vectorized using a preset vector encoder (such as a Transformer encoder) to obtain the field vector corresponding to each data field.

[0073] In this embodiment of the invention, the preset parameter table can be a table pre-set by the supplier, containing multiple numerical parameters and parameter labels corresponding to each numerical parameter. The parameter labels can be semantic labels such as cash flow stability, expected payment probability, and whether there is a guarantee. The numerical parameters can be the supplier's preset score values ​​for each semantic label.

[0074] In detail, the distance between the target vector and the parameter vectors corresponding to each parameter in the preset parameter table can be calculated using Euclidean distance algorithm, cosine distance algorithm, etc., and then the numerical parameters of the account information corresponding to each procurement account can be determined based on the distance value.

[0075] In this embodiment of the invention, calculating the account characteristic value of each purchasing account based on the numerical parameters and the test score includes: Query the parameter weight of each of the numerical parameters from the preset parameter weight table; The numerical parameters and the test scores are weighted and summed according to the parameter weights to obtain the account characteristic value of each purchasing account.

[0076] In detail, the parameter weight table can be a form pre-set by the supplier, containing multiple numerical parameters and the weight corresponding to each numerical parameter.

[0077] For example, the parameter weight table contains numerical parameters for cash flow stability and expected payment probability. However, this supplier values ​​expected payment probability highly but pays less attention to cash flow stability. Therefore, the parameter weight for expected payment probability can be preset to 2, and the parameter weight for cash flow stability can be preset to 0.5.

[0078] The parameter weight of each numerical parameter can be retrieved from the preset parameter weight table using preset SQL or Python query statements.

[0079] In this embodiment of the invention, the numerical parameters and test scores are weighted and summed according to the parameter weights to generate the account feature value of each procurement account. Then, the accounts with the largest number of account feature values ​​are selected as target accounts, which are the query result accounts corresponding to the procurement data query request.

[0080] like Figure 3 The diagram shown is a functional block diagram of a scenario-based multi-source concurrent data acquisition device provided in an embodiment of the present invention.

[0081] This disclosure provides a scenario-based multi-source concurrent data acquisition device, which corresponds one-to-one with the scenario-based multi-source concurrent data acquisition method described in the above embodiments. For example... Figure 3 As shown, this scenario-based multi-source concurrent data acquisition device 100 can be installed in an electronic device. According to its functions, the scenario-based multi-source concurrent data acquisition device 100 includes a data acquisition module 101, a data analysis module 102, a scoring calculation module 103, and an account filtering module 104. Detailed descriptions of each functional module are as follows: Data acquisition module 101: When receiving a procurement data query request from a target user for a target business scenario, it extracts supplier identifiers of multiple suppliers from a preset supplier database according to the procurement data query request, obtains the procurement account corresponding to each supplier identifier, and collects the account information of each procurement account using a preset multi-source data engine. Data analysis module 102: Obtain business test cases under the target business scenario, send the business test cases to the supplier backend system corresponding to each candidate account, obtain the business test data returned by each supplier backend system according to the business test cases, perform data desensitization processing on the business test data corresponding to each candidate account, and obtain desensitized test data. Scoring Calculation Module 103: Standardizes the anonymized test data corresponding to each candidate account according to a preset data format, distributes the standardized anonymized test data to the supplier backend system corresponding to each candidate account in a staggered manner, and obtains the test score returned by each supplier backend system based on the received anonymized test data; Account filtering module 104: Converts the account information into multiple numerical parameters for each procurement account, calculates the account feature value for each procurement account based on the numerical parameters and the test score, and selects the account with the largest account feature value as the query result account corresponding to the procurement data query request.

[0082] In this invention, specific limitations regarding a scenario-based multi-source concurrent data acquisition device can be found in the above-described limitations of the scenario-based multi-source concurrent data acquisition method, and will not be repeated here. Each module in the aforementioned scenario-based multi-source concurrent data acquisition device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0083] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external clients via a network connection. When the computer program is executed by the processor, it implements the functions or steps of the server-side method for scenario-based multi-source concurrent data acquisition.

[0084] In one embodiment, a computer device is provided, which may be a client, and its internal structure diagram may be as follows: Figure 5As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the client-side functions or steps of a scenario-based multi-source concurrent data acquisition method.

[0085] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: When a procurement data query request for a target business scenario is received from a target user, the supplier identifiers of multiple suppliers are extracted from a preset supplier database according to the procurement data query request. Obtain the procurement account corresponding to each supplier identifier, and collect the account information of each procurement account using a preset multi-source data engine; Obtain business test cases under the target business scenario, send the business test cases to the supplier backend system corresponding to each candidate account, and obtain the business test data returned by each supplier backend system based on the business test cases; Data anonymization processing is performed on the business test data corresponding to each candidate account to obtain anonymized test data; The anonymized test data for each candidate account is standardized according to a preset data format. The standardized anonymized test data is then misaligned and distributed to the supplier backend system for each candidate account. The test score returned by each supplier backend system based on the received anonymized test data is then obtained. The account information is converted into multiple numerical parameters for each procurement account. Based on the numerical parameters and the test score, the account feature value of each procurement account is calculated, and the account with the largest account feature value is selected as the query result account corresponding to the procurement data query request.

[0086] In the several embodiments provided by this invention, it should be understood that the disclosed devices and apparatuses can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0087] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0088] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0089] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.

[0090] The readable storage medium of the present invention stores a computer program, which, when executed by a processor of an electronic device, can perform the following: When a procurement data query request for a target business scenario is received from a target user, the supplier identifiers of multiple suppliers are extracted from a preset supplier database according to the procurement data query request. Obtain the procurement account corresponding to each supplier identifier, and collect the account information of each procurement account using a preset multi-source data engine; Obtain business test cases under the target business scenario, send the business test cases to the supplier backend system corresponding to each candidate account, and obtain the business test data returned by each supplier backend system based on the business test cases; Data anonymization processing is performed on the business test data corresponding to each candidate account to obtain anonymized test data; The anonymized test data for each candidate account is standardized according to a preset data format. The standardized anonymized test data is then misaligned and distributed to the supplier backend system for each candidate account. The test score returned by each supplier backend system based on the received anonymized test data is then obtained. The account information is converted into multiple numerical parameters for each procurement account. Based on the numerical parameters and the test score, the account feature value of each procurement account is calculated, and the account with the largest account feature value is selected as the query result account corresponding to the procurement data query request.

[0091] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0092] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0093] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0094] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0095] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Furthermore, any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory.

[0096] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0097] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0098] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

[0099] It should be noted that any AI models, software tools, or components not belonging to this company appearing in the embodiments of this application are merely illustrative examples and do not represent actual use. All user personal information involved in the embodiments of this application has been authorized (with the knowledge and consent) by the relevant parties or has been fully authorized by all parties, and the executing entity may obtain it through various legal and compliant means. The collection, storage, use, processing, transmission, provision, and disclosure of the information, data, and signals involved all comply with relevant laws and regulations and do not violate public order and good morals.

Claims

1. A scenario-based multi-source concurrent data acquisition method, characterized in that, The method includes: When a procurement data query request for a target business scenario is received from a target user, the supplier identifiers of multiple suppliers are extracted from a preset supplier database according to the procurement data query request. Obtain the procurement account corresponding to each supplier identifier, and collect the account information of each procurement account using a preset multi-source data engine; Obtain business test cases under the target business scenario, send the business test cases to the supplier backend system corresponding to each candidate account, and obtain the business test data returned by each supplier backend system based on the business test cases; Data anonymization processing is performed on the business test data corresponding to each candidate account to obtain anonymized test data; The anonymized test data for each candidate account is standardized according to a preset data format. The standardized anonymized test data is then misaligned and distributed to the supplier backend system for each candidate account. The test score returned by each supplier backend system based on the received anonymized test data is then obtained. The account information is converted into multiple numerical parameters for each procurement account. Based on the numerical parameters and the test score, the account feature value of each procurement account is calculated, and the account with the largest account feature value is selected as the query result account corresponding to the procurement data query request.

2. The scenario-based multi-source concurrent data acquisition method as described in claim 1, characterized in that, The step of retrieving supplier identifiers from a preset supplier database based on the procurement data query request includes: Extract the request header of the procurement data query request, parse the fields of the request header to obtain multiple request message fields contained in the request header; A decision tree model is constructed using multiple request message fields. Collect supplier data and supplier identifiers from a pre-defined supplier database; The decision tree model is used to filter the supplier data of each supplier, and the supplier identifiers corresponding to all the filtered supplier data are collected.

3. The scenario-based multi-source concurrent data acquisition method as described in claim 1, characterized in that, The method of collecting account information for each procurement account using a preset multi-source data engine includes: Select any procurement account one by one, and use the preset multi-source data engine to collect the basic information of the selected procurement account from multiple preset channels; Determine whether the data in the preset location field within the basic information corresponding to each preset channel is consistent; If they match, then the preset position field is deduplicated. If there is a discrepancy, the preset position field is corrected for outliers, and then the preset position field is deduplicated after the outlier correction. The process continues until all basic information corresponding to each preset channel has been removed, resulting in the account information of the selected procurement account.

4. The scenario-based multi-source concurrent data acquisition method as described in claim 1, characterized in that, The process of anonymizing the business test data corresponding to each candidate account to obtain anonymized test data includes: Obtain the entity regular expression corresponding to each supplier; The entity regular expression is used to traverse and match the business test data corresponding to each candidate account to obtain the entity fields in the business test data corresponding to each supplier. The entity fields contained in the business test data corresponding to each candidate account are deleted to obtain the de-identified test data.

5. The scenario-based multi-source concurrent data acquisition method as described in claim 1, characterized in that, The step of misallocating the standardized de-identified test data to the supplier backend system corresponding to each candidate account includes: The anonymized test data corresponding to each candidate account is sequentially encoded to obtain the first code; Each candidate account is encoded one by one according to the same order of the corresponding de-identified test data to obtain the second code; Select any de-identified test data as the target test data, and match any candidate account with the target test data; If the first code corresponding to the target test data is the same as the second code of the selected candidate account, then return to the step of selecting any candidate account to match the target test data; If the first code corresponding to the target test data is different from the second code of the selected candidate account, then the target test data will be assigned to the supplier backend system corresponding to the selected candidate account.

6. The scenario-based multi-source concurrent data acquisition method as described in claim 1, characterized in that, The step of converting the account information into multiple numerical parameters for each purchasing account includes: Select the account information corresponding to any procurement account as the target information; The target information is split into multiple data fields using a pre-defined large language model. The data fields are vectorized to obtain the field vector corresponding to each data field. Select any field vector as the target vector one by one, and calculate the distance between the target vector and the parameter vectors corresponding to each parameter in the preset parameter table; The parameter corresponding to the parameter vector with the smallest distance value is determined as the numerical parameter of the target vector, until multiple numerical parameters of the account information corresponding to each procurement account are determined.

7. The scenario-based multi-source concurrent data acquisition method as described in claim 1, characterized in that, The calculation of the account characteristic value for each purchasing account based on the numerical parameters and the test score includes: The parameter weight of each numerical parameter is retrieved from the preset parameter weight table; The numerical parameters and the test scores are weighted and summed according to the parameter weights to obtain the account characteristic value of each purchasing account.

8. A scenario-based multi-source concurrent data acquisition device, characterized in that, The device includes: Data acquisition module: When a target user requests procurement data for a target business scenario, the module extracts supplier identifiers of multiple suppliers from a preset supplier database based on the procurement data query request, obtains the procurement account corresponding to each supplier identifier, and collects the account information of each procurement account using a preset multi-source data engine. Data Analysis Module: Obtain business test cases under the target business scenario, send the business test cases to the supplier backend system corresponding to each candidate account, obtain the business test data returned by each supplier backend system according to the business test cases, and perform data desensitization processing on the business test data corresponding to each candidate account to obtain desensitized test data; Scoring Calculation Module: Standardizes the anonymized test data corresponding to each candidate account according to a preset data format, distributes the standardized anonymized test data to the supplier backend system corresponding to each candidate account in a staggered manner, and obtains the test score returned by each supplier backend system based on the received anonymized test data; Account filtering module: Converts the account information into multiple numerical parameters for each procurement account, calculates the account feature value for each procurement account based on the numerical parameters and the test score, and selects the account with the largest account feature value as the query result account corresponding to the procurement data query request.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the scenario-based multi-source concurrent data acquisition method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the scenario-based multi-source concurrent data acquisition method as described in any one of claims 1 to 7.

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