Business data access method and device, storage medium and equipment

Abstract

The application discloses a business data access method and device, a storage medium and equipment, and belongs to the technical field of clouds, and specifically relates to a management tool in the technical field of clouds. The method realizes automatic and intelligent business data access based on a cost management system, does not depend on manual implementation, only needs user input of business data, and then the cost management system can automatically and intelligently complete business data access, thereby saving a large amount of manpower cost and time cost and having high access efficiency.

Description

Technical Field

[0001] This application relates to the field of cloud technology, and in particular to a business data access method, apparatus, storage medium and device. Background Technology

[0002] Cost management refers to the overall scientific management of costs, including cost accounting, cost analysis, cost decision-making, and cost control, within an enterprise. Correspondingly, a cost management system is a management tool that integrates these elements into a single system, providing employees with functions such as querying, modifying, and approving costs.

[0003] In the field of cloud technology, the primary task of cost management is to integrate business data from business systems into the cost management system. Currently, related technologies rely on manual processing for this integration, resulting in significant manpower and time costs and low efficiency. Summary of the Invention

[0004] This application provides a business data access method, apparatus, storage medium, and device. This solution, based on a cost management system, achieves automated and intelligent business data access. This solution does not rely on manual intervention; only user input of business data is required, after which the cost management system can automatically and intelligently complete the business data access, saving significant labor and time costs and achieving high access efficiency. The technical solution is as follows:

[0005] On the one hand, a business data access method is provided, applied to a cost management system, the method comprising:

[0006] The system acquires input business data, performs data mapping processing on the business data, and obtains data fields that the cost management system can recognize.

[0007] The data fields are categorized according to different classification methods; the matching degree of a single category is obtained based on the data fields included in a single category.

[0008] The matching degree of a single category is used to characterize the degree of matching between the single category and the target category; the target category is either a basic category or an advanced category, and the basic category and the advanced category are divided according to the importance of the data.

[0009] Based on the matching degree of the single category, the matching degree of the business data is obtained, and the matching degree of the business data is used to characterize the degree of matching between the business data and the target category;

[0010] In response to the fact that the matching degree of the business data is greater than the first threshold corresponding to the basic category, the business data is determined to be basic category data, and the business data is connected to the cost management system.

[0011] On the other hand, a business data access device is provided, the device comprising:

[0012] The data acquisition unit is configured to acquire input business data;

[0013] The data mapping unit is configured to perform data mapping processing on the business data to obtain data fields that can be recognized by the cost management system.

[0014] The data processing unit is configured to classify the data fields according to different classification methods; and to obtain the matching degree of a single category based on the data fields included in a single category.

[0015] The matching degree of a single category is used to characterize the degree of matching between the single category and the target category; the target category is either a basic category or an advanced category, and the basic category and the advanced category are divided according to the importance of the data.

[0016] The data processing unit is further configured to obtain the matching degree of the business data based on the matching degree of the single category, wherein the matching degree of the business data is used to characterize the degree of matching between the business data and the target category;

[0017] The data processing unit is further configured to, in response to the matching degree of the business data being greater than the first threshold corresponding to the basic category, determine the business data as basic category data and connect the business data to the cost management system.

[0018] In one possible implementation, the data processing unit is configured to perform one or more of the following:

[0019] Based on the necessity of the data fields, the obtained data fields are classified into non-essential fields and essential fields;

[0020] Based on the confidentiality of the data fields, the obtained data fields are classified into fields that must be encrypted, fields that do not need to be encrypted, and public fields.

[0021] Based on the validity of the data fields, the obtained data fields are classified into permanent validity fields and time-sensitive fields;

[0022] Based on the sensitivity of the data fields, the obtained data fields are classified into sensitive fields and non-sensitive fields;

[0023] Based on whether the data fields have been anonymized, the obtained data fields are classified into anonymized fields and non-anonymized fields.

[0024] In one possible implementation, the data processing unit is configured as follows:

[0025] For a single category, obtain the input parameters of the data fields included in the category, and obtain the calculation parameters corresponding to the single parameter in the target category;

[0026] Based on the value of a single parameter under the category and the value of the corresponding calculation parameter, obtain the matching degree corresponding to the single parameter under the category;

[0027] The matching degree of the category is obtained based on the matching degree of all parameters under the category.

[0028] In one possible implementation, the data processing unit is further configured as follows:

[0029] Identify the existing M1 basic categories in the cost management system;

[0030] Obtain the N1 basic categories after this calculation, where N1 is the number of basic categories obtained after calculating the matching degree of a single category; M1 and N1 are both positive integers.

[0031] In response to the ratio of N1 to M1 being less than the first threshold, a feedback indication message is sent, which is used to indicate that the business data should be re-entered.

[0032] In one possible implementation, the data processing unit is further configured as follows:

[0033] In response to the fact that the matching degree of the business data is less than the first threshold, N2 advanced categories are obtained after this calculation, where N2 is the number of advanced categories obtained after calculating the matching degree of a single category; M2 and N2 are both positive integers.

[0034] From the N2 high-level categories, obtain the M2 high-level categories that need to be compared;

[0035] Obtain the calculated data from the M2 high-level categories, and determine the maximum matching degree among the matching degrees of the M2 high-level categories;

[0036] In response to the maximum matching degree being greater than the second threshold corresponding to the advanced category, the business data is determined to be advanced category data, and the business data is connected to the cost management system.

[0037] In one possible implementation, the data processing unit is further configured as follows:

[0038] In response to the maximum matching degree being less than the second threshold, the business data is added to the data configuration queue; wherein, the data in the data configuration queue needs to be manually reviewed.

[0039] In one possible implementation, the device further includes:

[0040] The data adjustment unit is configured to fit the matching degree data of a single category obtained this time and the existing matching degree data of a single category to form new matching degree data for a single category; update the value of the first threshold based on the new matching degree data of the single category to obtain a new first threshold; and fit the maximum matching degree data obtained this time and the existing maximum matching degree data to form a new second threshold.

[0041] On the other hand, a computer device is provided, the device including a processor and a memory, the memory storing at least one piece of program code, the at least one piece of program code being loaded and executed by the processor to implement the above-described business data access method.

[0042] On the other hand, a computer-readable storage medium is provided, wherein at least one piece of program code is stored in the storage medium, and the at least one piece of program code is loaded and executed by a processor to implement the above-described business data access method.

[0043] On the other hand, a computer program product or computer program is provided, which includes computer program code stored in a computer-readable storage medium. The processor of a computer device reads the computer program code from the computer-readable storage medium and executes the computer program code, causing the computer device to perform the aforementioned business data access method.

[0044] The beneficial effects of the technical solutions provided in this application are:

[0045] This application embodiment achieves automated and intelligent business data access based on a cost management system. Specifically, after the user inputs business data, the cost management system automatically performs data mapping to obtain multiple recognizable data fields. Then, it acquires various classification attributes and related matching degrees for each data field. Finally, by summarizing the matching degrees of multiple categories, it categorizes the input business data and determines whether to directly access it into the cost management system. Since this solution does not rely on manual implementation, requiring only user input of business data, the cost management system can automatically and intelligently complete the business data access, thus saving significant manpower and time costs and achieving high access efficiency. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a schematic diagram of the implementation environment involved in a service data access method provided in an embodiment of this application;

[0048] Figure 2 This is a schematic diagram of the structure of a cost management system provided in an embodiment of this application;

[0049] Figure 3 This is a flowchart of a service data access method provided in an embodiment of this application;

[0050] Figure 4 This is an overall execution flowchart of a business data access method provided in an embodiment of this application;

[0051] Figure 5 This is a schematic diagram of the structure of a service data access device provided in an embodiment of this application;

[0052] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0054] In this application, the terms "first," "second," etc., are used to distinguish identical or similar items that have essentially the same function. It should be understood that there is no logical or temporal dependency between "first," "second," and "nth," nor does it limit the quantity or execution order. It should also be understood that although the following description uses the terms "first," "second," etc., to describe various elements, these elements should not be limited by the terms.

[0055] These terms are simply used to distinguish one element from another. For example, without departing from the various examples, the first field can be referred to as the second field, and similarly, the second field can be referred to as the first field. Both the first and second fields can be fields, and in some cases, they can be separate and distinct fields.

[0056] "At least one" refers to one or more fields. For example, at least one field can be one field, two fields, three fields, or any integer number of fields greater than or equal to one. "Multiple" refers to two or more fields. For example, multiple fields can be two fields, three fields, or any integer number of fields greater than or equal to two.

[0057] The business data access solution provided in this application relates to the field of cloud technology.

[0058] For example, this business data access solution involves management tools in the field of cloud technology.

[0059] Cloud technology refers to a hosting technology that unifies a series of resources such as hardware, software, and networks within a wide area network or local area network to achieve data computing, storage, processing, and sharing.

[0060] Cloud technology, in particular, is a collective term for network technology, information technology, integration technology, management platform technology, and application technology applied to the cloud computing business model. It can form resource pools, providing flexible and convenient on-demand access. Cloud computing technology will become a crucial support. The backend services of technical network systems require substantial computing and storage resources, such as video websites, image websites, and many portal websites. With the rapid development and application of the internet industry, every item may have its own identification mark in the future, requiring transmission to backend systems for logical processing. Data at different levels will be processed separately, and various industry data will all require robust system support, which can only be achieved through cloud computing.

[0061] The following is an explanation of some terms and concepts that may be involved in the embodiments of this application.

[0062] Cost management refers to the overall scientific management practices involved in cost accounting, cost analysis, cost decision-making, and cost control during an enterprise's production and operation. Cost management comprises four components: cost planning, cost calculation, cost control, and performance evaluation.

[0063] Data standardization: In large-scale data analysis projects, data come from different sources and have different dimensions and units. To make them comparable, standardization methods are needed to eliminate the biases introduced by these differences. After data standardization, the raw data is aligned to the same order of magnitude, making it suitable for comprehensive comparative evaluation. This is data standardization.

[0064] Figure 1 This is a schematic diagram of the implementation environment involved in a business data access method provided in an embodiment of this application.

[0065] See Figure 1 The business data access method provided in this application embodiment is applied to the cost management system 101.

[0066] The cost management system 101 integrates cost accounting, cost analysis, cost decision-making, and cost control into a single system. It provides employees with management tools that allow them to query, modify, and audit, thereby reducing waste, ensuring the scientific nature of decision-making, and ultimately improving corporate efficiency.

[0067] In addition, the business data accessed by the cost management system 101 comes from the business system 102.

[0068] In one possible implementation, the cost management system 101 is a server. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing 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, CDN (Content Delivery Network), and big data and artificial intelligence platforms. This application does not impose any restrictions on these aspects.

[0069] Based on the aforementioned implementation environment, this application embodiment implements an automated and intelligent business data access scheme based on a cost management system 101. This scheme only requires the user (e.g., a developer on the business side) 103 to input the business data to be accessed into the cost management system 101, after which the cost management system 101 can automatically and intelligently complete the business data access. Specifically, after the user 103 inputs the business data to be accessed, the cost management system 101 automatically performs data mapping to obtain multiple data fields that it can identify. Then, it categorizes the input business data by summarizing the matching degrees of multiple categories and decides whether to directly access the input business data into the cost management system or to go through a manual data review process. Furthermore, after each new business data is accessed, the cost management system can dynamically adjust existing threshold parameters, continuously adaptively adjusting the threshold parameters to achieve better data matching and automated access of related business data.

[0070] Figure 2 This is a schematic diagram of the structure of a cost management system provided in an embodiment of this application.

[0071] See Figure 2 The cost management system 101 includes: a data access module 101-1, a data mapping module 101-2, a data classification and calculation module 101-3, and a data adaptive module 101-4.

[0072] The data access module 101-1 is used to acquire business data input by the user. For example, the business-side developers can input the business data to be accessed. The cost management system 101 will provide certain input format requirements, which the user can follow when inputting the business data.

[0073] The data mapping module 101-2 is used to perform data mapping processing, mapping user-input business data into data fields that the cost management system 101 can recognize. Specifically, the data mapping module 101-2 can achieve efficient mapping; even when new business data is accessed for the first time, it can complete the mapping based on historical business data to obtain data fields that the cost management system 101 can recognize.

[0074] The data classification and calculation module 101-3 is used to classify and calculate the data fields obtained after data mapping, and to determine whether the input business data meets the access requirements of the cost management system.

[0075] For example, the classification calculation here refers to classifying the input business data into basic classification data or advanced classification data. Basic classification usually favors more general and common data, which are of higher importance; while advanced classification favors data that is unique to different business systems.

[0076] The data adaptation module 101-4 is used to perform new fitting after new business data is accessed into the cost management system 101, forming new thresholds corresponding to the basic classification and new thresholds corresponding to the advanced classification.

[0077] Figure 3 This is a flowchart illustrating a business data access method provided in an embodiment of this application. This method is applied to a cost management system; see [link / reference]. Figure 3 The method flow provided in this application embodiment includes:

[0078] 301. Obtain business data input by the user.

[0079] This step is by Figure 2 The data access module is now complete.

[0080] For example, the business side developers can input the business data to be integrated. The cost management system will provide specific input format requirements, which the user must follow. Furthermore, after receiving the business data entered by the user according to the required format, the data access module will forward the data to... Figure 2 The data mapping module performs data mapping processing.

[0081] In one possible implementation, the business data input by the user is in the form of a data packet, such as a data traffic package.

[0082] 302. Perform data mapping processing on the business data input this time to obtain multiple data fields that the cost management system can recognize.

[0083] This step is by Figure 2 The data mapping module in the middle is complete.

[0084] In this embodiment of the application, data mapping processing is performed on the input business data, including but not limited to: mapping the input business data into data fields that the cost management system can recognize through methods such as historical data fuzzy matching, related field association, and business system mapping content.

[0085] Among them, historical data fuzzy matching refers to querying historical business data that roughly matches the input business data from the historical business data already stored in the cost management system. For example, related field association can refer to whether the traffic package name is related (e.g., there was a previous x-live 1T traffic package, and now a new x-live 5T traffic package has been added), whether the configured expiration time expires at the same time, whether the configuration personnel are consistent, and other related data. Business system mapping involves mapping different business data from the same business system.

[0086] 303. Classify the multiple data fields obtained according to different classification methods; obtain the matching degree of a single category based on the data fields included in a single category.

[0087] In one possible implementation, embodiments of this application classify the obtained data fields according to different classification methods, including one or more of the following:

[0088] Based on the necessity of the data fields, the obtained multiple data fields are classified into non-essential fields and essential fields;

[0089] Based on the confidentiality of the data fields, the obtained data fields are classified into fields that must be encrypted, fields that do not need to be encrypted, and public fields.

[0090] Based on the validity of the data fields, the resulting data fields are classified into permanent validity fields and time-sensitive fields;

[0091] Based on the sensitivity of the data fields, the obtained data fields are classified into sensitive fields and non-sensitive fields;

[0092] Based on whether the data fields have been de-identified, the resulting multiple data fields are classified into de-identified fields and non-de-identified fields.

[0093] The first point to note is that, based on the different classification methods mentioned above, each data field may have multiple classification attributes. For example, a certain data field may be both a necessary field and a sensitive field.

[0094] In addition, each classification attribute has corresponding calculation parameters and related verification intervals. The calculation parameters refer to the physical quantities used in the calculation process to represent each classification attribute. For example, data fields can be classified according to the aforementioned verification intervals; for instance, data fields with a sensitivity below a certain threshold are considered non-sensitive data.

[0095] The second point to note is that the cost management system can further divide the above classifications into basic classifications and advanced classifications based on the importance of the data.

[0096] For example, the distinction between basic and advanced categories is configured by the cost management system administrators. In one possible implementation, basic categories typically favor more general and common data with higher importance, such as order numbers; while advanced categories favor data unique to different business systems. For instance, advanced categories might refer to data specific to a particular business system and not found in other business systems, such as expiration dates.

[0097] In this embodiment of the application, the matching degree of a single category is obtained by... Figure 2 The data classification and calculation module in the system completes the task. For example... Figure 4 As shown, the data classification calculation module first calculates the matching degree of a single category. It should be noted that a single category here includes, but is not limited to, multiple categories obtained after classifying multiple mapped data fields based on their necessity, confidentiality, effectiveness, sensitivity, and whether they have been anonymized.

[0098] For example, the matching degree of a single category is used to characterize the degree of matching between a single category and a target category; wherein, the target category is a basic category or a high-level category. In one possible implementation, the matching degree data of a single category is calculated as follows:

[0099] 3031. For a single category, obtain the input parameters of the data fields included in that category; and obtain the calculated parameters corresponding to the single parameter in the target category.

[0100] The input parameters are the business data itself, and each input parameter has a value.

[0101] 3032. Based on the value of a single parameter under this category and the value of the corresponding calculated parameter, obtain the matching degree corresponding to the single parameter under this category.

[0102] For example, the data fields included in a single category can be used to record monetary data, personnel data, or business type data, etc. In one possible implementation, the matching degree calculation method includes, but is not limited to: proximity, semantic distance, and similarity. Semantic distance is divided into Hamming distance, Euclidean distance, Tchaikovsky distance, and Chebyshev distance; similarity includes the min-max method, arithmetic mean method, geometric mean method, correlation coefficient method, and exponential method. Furthermore, the greater the proximity and similarity, the greater the matching degree; the smaller the semantic distance, the greater the matching degree.

[0103] 3033. Based on the matching degree of all parameters under this category, obtain the matching degree of this category.

[0104] In this embodiment, weight values ​​can be set for each parameter under the category according to their importance. Then, when summarizing the matching degree of the category, a weighted average method is adopted to calculate the matching degree of the category. This embodiment does not impose specific limitations on this.

[0105] 304. Based on the matching degree of a single category, obtain the matching degree of the business data input this time; in response to the matching degree of the business data input this time being greater than the first threshold corresponding to the basic category, determine that the business data input this time is the basic category data, and connect the business data input this time to the cost management system.

[0106] In this step, the matching degree of the business data summarized is used to characterize the degree of matching between the input business data and the target category. In this embodiment, a weight value can be set for each category according to its importance, and then a weighted average method is used to calculate the matching degree of the input business data when summarizing the matching degree.

[0107] In addition, the first threshold corresponding to the basic category is dynamically updated. For example, the value of this threshold is updated every time new business data is added.

[0108] In one possible implementation, before performing step 304 above, the method provided in this application embodiment further includes step 305 below.

[0109] 305. Determine the existing M1 basic categories in the cost management system; obtain the N1 basic categories after this calculation, where N1 is the number of basic categories obtained after calculating the matching degree of a single category; in response to the ratio of N1 to M1 being greater than the first threshold, execute step 304 above again.

[0110] 306. In response to the ratio of N1 to M1 being less than the first threshold, a feedback indication message is sent, which is used to indicate that the business data is re-entered (insufficient basic data).

[0111] In another possible implementation, if the matching degree of the input business data is less than the first threshold corresponding to the basic category, then a data comparison for advanced classification is performed.

[0112] In this application, steps 304 to 306 are also referred to as basic classification calculation.

[0113] 307. In response to the fact that the matching degree of the business data input this time is less than the first threshold, obtain N2 advanced categories after this calculation, where N2 is the number of advanced categories obtained after calculating the matching degree of a single category.

[0114] 308. Among the N2 high-level categories, obtain the M2 high-level categories that need to be compared; obtain the calculated data from the M2 high-level categories, and determine the maximum matching degree among the matching degrees of the M2 high-level categories.

[0115] For example, the calculation data could include information such as how the business expiration time should be allocated and the allocation criteria. This step involves determining the maximum matching degree among the M2 high-level categories calculated in this step.

[0116] 309. In response to the fact that the maximum matching degree obtained this time is greater than the second threshold corresponding to the advanced category, the business data input this time is determined to be advanced category data, and the business data input this time is connected to the cost management system.

[0117] 310. In response to the maximum matching degree obtained this time being less than the second threshold, the business data input this time is added to the data configuration queue; the data in the data configuration queue needs to be manually reviewed.

[0118] This step involves adding the entered business data to a data configuration queue that requires manual review, so that the cost management system administrators can review whether to add the entered business data to the cost management system.

[0119] In this application, steps 307 to 310 are also referred to as advanced classification calculation.

[0120] In another possible implementation, embodiments of this application also support [the following]: Figure 2 The data adaptive adjustment module in the middle adjusts the threshold parameters that the relevant calculation results pass through.

[0121] Scenario 1: Fit the obtained matching data of a single category and the existing matching data of a single category to form new matching data for a single category.

[0122] Example 1: If business data is integrated into the cost management system through basic classification calculation, the matching degree data of each category obtained in this calculation will be transferred to the matching degree data pool. The matching degree data pool is used to store the existing matching degree data of a single category. Then, all the matching degree data will be fitted to form new matching degree data for a single category.

[0123] The fitting method can be either linear fitting or polynomial fitting, and this application does not specifically limit the method.

[0124] Example 2: If business data is integrated into the cost management system through advanced classification calculation, the matching degree data of each category obtained in this calculation will be stored in the matching degree data pool. The matching degree data pool is used to store the existing matching degree data of a single category. Then, all the matching degree data will be fitted to form new matching degree data for a single category.

[0125] Scenario 2: Update the value of the first threshold based on the new matching data for a single category to obtain a new first threshold.

[0126] In this process, after determining the new matching degree data for a single category, a new first threshold can be generated based on the new matching degree data for a single category. That is, a new first threshold is generated after the business data input this time is connected to the cost management system.

[0127] Scenario 3: Fit the obtained maximum matching degree data and the existing maximum matching degree data to form a new second threshold.

[0128] If business data is integrated into the cost management system through advanced classification calculation, the maximum matching degree data obtained in this calculation will be passed into the maximum matching degree data pool. The maximum matching degree data pool is used to store all the maximum matching degrees that have passed the verification, and then a new second threshold is fitted.

[0129] The method provided in this application has at least the following beneficial effects:

[0130] This application embodiment achieves automated and intelligent business data access based on a cost management system. Specifically, after a user inputs business data, the cost management system automatically performs data mapping to obtain multiple recognizable data fields. Then, it acquires various classification attributes and related matching degrees for each data field. Finally, by summarizing the matching degrees of multiple categories, it categorizes the input business data and decides whether to directly access it into the cost management system. Since this solution does not rely on manual implementation, requiring only user input of business data, the cost management system can automatically and intelligently complete the business data access, thus saving significant manpower and time costs and achieving high access efficiency. Furthermore, after each new business data access, the cost management system can dynamically adjust existing threshold parameters, continuously adaptively adjusting these parameters to achieve better data matching and automated access of related business data.

[0131] Figure 4 This is an overall execution flowchart of a business data access method provided in an embodiment of this application. This method is applied to a cost management system. See also... Figure 4 The method flow provided in this application embodiment includes:

[0132] 401. The data access module obtains the business data entered by the user according to the input format requirements.

[0133] 402. The data mapping module performs data mapping on the input business data to map the input business data into data fields that the cost management system can recognize.

[0134] 403. Calculation of matching degree for a single category.

[0135] Please refer to step 303 above for instructions on how to perform this step.

[0136] 404. Calculate the matching degree of the business data input this time.

[0137] Please refer to step 304 above for instructions on how to perform this step.

[0138] 405. Determine whether the matching degree of the input business data is greater than the first threshold corresponding to the basic category; if yes, proceed to step 408 below; if no, proceed to step 406 below.

[0139] 406. Calculate the maximum matching degree of the advanced classification.

[0140] Please refer to steps 307 and 308 above for the implementation of this step.

[0141] 407. Determine whether the maximum matching degree calculated in this step is greater than the second threshold. If yes, proceed to step 408. If no, proceed to step 410.

[0142] 408. Connect the business data entered this time to the cost management system.

[0143] 409. After new business data is integrated into the cost management system, the data adaptive module fits new threshold parameters.

[0144] 410. Follow the manual data review process.

[0145] This application embodiment achieves automated and intelligent business data access based on a cost management system. Specifically, after a user inputs business data, the cost management system automatically performs data mapping to obtain multiple recognizable data fields. Then, it acquires various classification attributes and related matching degrees for each data field. Finally, by summarizing the matching degrees of multiple categories, it categorizes the input business data and decides whether to directly access it into the cost management system. Since this solution does not rely on manual implementation, requiring only user input of business data, the cost management system can automatically and intelligently complete the business data access, thus saving significant manpower and time costs and achieving high access efficiency. Furthermore, after each new business data access, the cost management system can dynamically adjust existing threshold parameters, continuously adaptively adjusting these parameters to achieve better data matching and automated access of related business data.

[0146] Figure 5 This is a schematic diagram of the structure of a service data access device provided in an embodiment of this application. See also... Figure 5 The device includes:

[0147] The data acquisition unit 501 is configured to acquire input business data;

[0148] The data mapping unit 502 is configured to perform data mapping processing on the business data to obtain data fields that can be recognized by the cost management system.

[0149] The data processing unit 503 is configured to classify the data fields according to different classification methods; and to obtain the matching degree of a single category based on the data fields included in a single category.

[0150] The matching degree of a single category is used to characterize the degree of matching between the single category and the target category; the target category is either a basic category or an advanced category, and the basic category and the advanced category are divided according to the importance of the data.

[0151] The data processing unit 503 is further configured to obtain the matching degree of the business data based on the matching degree of the single category, wherein the matching degree of the business data is used to characterize the degree of matching between the business data and the target category;

[0152] The data processing unit 503 is further configured to, in response to the matching degree of the business data being greater than the first threshold corresponding to the basic category, determine the business data as basic category data and connect the business data to the cost management system.

[0153] This application embodiment achieves automated and intelligent business data access based on a cost management system. Specifically, after a user inputs business data, the cost management system automatically performs data mapping to obtain multiple recognizable data fields. Then, it acquires various classification attributes and related matching degrees for each data field. Finally, by summarizing the matching degrees of multiple categories, it categorizes the input business data and decides whether to directly access it into the cost management system. Since this solution does not rely on manual implementation, requiring only user input of business data, the cost management system can automatically and intelligently complete the business data access, thus saving significant manpower and time costs and achieving high access efficiency. Furthermore, after each new business data access, the cost management system can dynamically adjust existing threshold parameters, continuously adaptively adjusting these parameters to achieve better data matching and automated access of related business data.

[0154] In one possible implementation, the data processing unit is configured to perform one or more of the following:

[0155] Based on the necessity of the data fields, the obtained data fields are classified into non-essential fields and essential fields;

[0156] Based on the confidentiality of the data fields, the obtained data fields are classified into fields that must be encrypted, fields that do not need to be encrypted, and public fields.

[0157] Based on the validity of the data fields, the obtained data fields are classified into permanent validity fields and time-sensitive fields;

[0158] Based on the sensitivity of the data fields, the obtained data fields are classified into sensitive fields and non-sensitive fields;

[0159] Based on whether the data fields have been anonymized, the obtained data fields are classified into anonymized fields and non-anonymized fields.

[0160] In one possible implementation, the data processing unit is configured as follows:

[0161] For a single category, obtain the input parameters of the data fields included in the category, and obtain the calculation parameters corresponding to the single parameter in the target category;

[0162] Based on the value of a single parameter under the category and the value of the corresponding calculation parameter, obtain the matching degree corresponding to the single parameter under the category;

[0163] The matching degree of the category is obtained based on the matching degree of all parameters under the category.

[0164] In one possible implementation, the data processing unit is further configured as follows:

[0165] Identify the existing M1 basic categories in the cost management system;

[0166] Obtain the N1 basic categories after this calculation, where N1 is the number of basic categories obtained after calculating the matching degree of a single category; M1 and N1 are both positive integers.

[0167] In response to the ratio of N1 to M1 being less than the first threshold, a feedback indication message is sent, which is used to indicate that the business data should be re-entered.

[0168] In one possible implementation, the data processing unit is further configured as follows:

[0169] In response to the fact that the matching degree of the business data is less than the first threshold, N2 advanced categories are obtained after this calculation, where N2 is the number of advanced categories obtained after calculating the matching degree of a single category; M2 and N2 are both positive integers.

[0170] From the N2 high-level categories, obtain the M2 high-level categories that need to be compared;

[0171] Obtain the calculated data from the M2 high-level categories, and determine the maximum matching degree among the matching degrees of the M2 high-level categories;

[0172] In response to the maximum matching degree being greater than the second threshold corresponding to the advanced category, the business data is determined to be advanced category data, and the business data is connected to the cost management system.

[0173] In one possible implementation, the data processing unit is further configured as follows:

[0174] In response to the maximum matching degree being less than the second threshold, the business data is added to the data configuration queue; wherein, the data in the data configuration queue needs to be manually reviewed.

[0175] In one possible implementation, the device further includes:

[0176] The data adjustment unit is configured to fit the matching degree data of a single category obtained this time and the existing matching degree data of a single category to form new matching degree data for a single category; update the value of the first threshold based on the new matching degree data of the single category to obtain a new first threshold; and fit the maximum matching degree data obtained this time and the existing maximum matching degree data to form a new second threshold.

[0177] All of the above-mentioned optional technical solutions can be combined in any way to form optional embodiments of this disclosure, and will not be described in detail here.

[0178] It should be noted that the service data access device provided in the above embodiments is only illustrated by the division of the above functional modules when accessing service data. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the system can be divided into different functional modules to complete all or part of the functions described above. In addition, the service data access device and the service data access method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0179] Figure 6 This diagram illustrates a structural block diagram of a computer device 600 provided in an exemplary embodiment of this application. Typically, the computer device 600 includes a processor 601 and a memory 602.

[0180] Processor 601 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 601 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 601 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 601 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 601 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0181] The memory 602 may include one or more computer-readable storage media, which may be non-transitory. The memory 602 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 602 are used to store at least one program code, which is executed by the processor 601 to implement the service data access method provided in the method embodiments of this application.

[0182] In some embodiments, the computer device 600 may also optionally include a peripheral device interface 603 and at least one peripheral device. The processor 601, memory 602, and peripheral device interface 603 can be connected via a bus or signal lines. Each peripheral device can be connected to the peripheral device interface 603 via a bus, signal lines, or a circuit board. Specifically, the peripheral device includes a power supply 604.

[0183] Peripheral interface 603 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 601 and memory 602. In some embodiments, processor 601, memory 602 and peripheral interface 603 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 601, memory 602 and peripheral interface 603 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0184] Those skilled in the art will understand that Figure 6 The structure shown does not constitute a limitation on the computer device 600, and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0185] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including program code, which can be executed by a processor in a terminal to complete the service data access method in the above embodiments. For example, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0186] In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer program code stored in a computer-readable storage medium. The processor of a computer device reads the computer program code from the computer-readable storage medium and executes the computer program code, causing the computer device to perform the above-described business data access method.

[0187] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0188] The above description is merely an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A service data access method characterized by, The method is applied to a cost management system and comprises the following steps: Obtaining input business data, performing data mapping processing on the business data to obtain data fields recognizable by the cost management system; Classifying the data fields according to different classification manners to obtain single classifications to which the data fields belong, each single classification being obtained based on a classification manner and each data field corresponding to at least one single classification; For each single classification, obtaining a matching degree of the single classification according to an incoming parameter of a data field included in the single classification and a calculation parameter corresponding to the single classification in a target classification, the matching degree of the single classification representing a matching degree of the single classification with the target classification, the target classification being a basic classification or a high-level classification, the basic classification and the high-level classification being classified according to data importance, the basic classification indicating general data of a business system and the high-level classification indicating unique data of different business systems; Determining M1 basic classifications already existing in the cost management system, obtaining N1 basic classifications after the calculation, N1 being a number of basic classifications obtained after the calculation of the matching degrees of the single classifications, and in response to a ratio of N1 to M1 being less than a first threshold value corresponding to the basic classification, feeding back an indication message indicating that business data needs to be input again due to a small amount of basic data, M1 and N1 being positive integers; In response to the ratio of N1 to M1 being greater than the first threshold value, obtaining a matching degree of the business data according to the matching degrees of the single classifications, the matching degree of the business data representing a matching degree of the business data with the target classification; In response to the matching degree of the business data being greater than the first threshold value, determining that the business data is basic classification data and connecting the business data to the cost management system; In response to the matching degree of the business data being less than the first threshold value, obtaining N2 high-level classifications after the calculation, N2 being a number of high-level classifications obtained after the calculation of the matching degrees of the single classifications, obtaining M2 high-level classifications that need to be compared in the N2 high-level classifications, obtaining calculation data in the M2 high-level classifications, and determining a maximum matching degree in the matching degrees of the M2 high-level classifications, M2 and N2 being positive integers; In response to the maximum matching degree being greater than a second threshold value corresponding to the high-level classification, determining that the business data is high-level classification data and connecting the business data to the cost management system.

2. The method of claim 1, wherein, The classification of the data fields according to different classification manners comprises one or more of the following: Classifying the obtained data fields into necessary fields and non-necessary fields according to necessity of the data fields; Classifying the obtained data fields into fields that must be encrypted, fields that need not be encrypted, and public fields according to confidentiality of the data fields; Classifying the obtained data fields into permanent validity fields and time validity fields according to validity of the data fields; Classifying the obtained data fields into sensitive fields and non-sensitive fields according to sensitivity of the data fields; Classifying the obtained data fields into desensitized fields and non-desensitized fields according to whether the data fields have been desensitized.

3. The method of claim 1, wherein, The method comprises the following steps: For each single classification, the incoming parameters of the data fields included in the single classification and the calculation parameters corresponding to the single classification in the target classification are obtained to obtain the matching degree of the single classification. For each single classification, the incoming parameters of the data fields included in the single classification and the calculation parameters corresponding to the single classification in the target classification are obtained to obtain the matching degree of the single classification. The method further comprises the following steps:

4. The method of claim 1, wherein, In response to the maximum matching degree being less than the second threshold value, the business data is added to a data configuration queue; wherein the data in the data configuration queue needs to be manually audited. The method further comprises the following steps:

5. The method of claim 1, wherein, The matching degree data of the single classification obtained this time and the existing matching degree data of the single classification are fitted to form new matching degree data of the single classification. The value of the first threshold value is updated according to the new matching degree data of the single classification to obtain a new first threshold value. The maximum matching degree data obtained this time and the existing maximum matching degree data are fitted to form a new second threshold value. The device comprises:

6. A service data access device characterized by comprising: a data obtaining unit configured to obtain input business data; a data mapping unit configured to perform data mapping processing on the business data to obtain data fields recognizable by a cost management system; a data processing unit configured to classify the data fields according to different classification manners to obtain single classifications to which the data fields belong, each single classification being obtained based on a classification manner, and one data field corresponding to at least one single classification; for each single classification, the incoming parameters of the data fields included in the single classification and the calculation parameters corresponding to the single classification in the target classification are obtained to obtain the matching degree of the single classification; the matching degree of the single classification is used to represent the matching degree of the single classification and the target classification, the target classification being a basic classification or a high-level classification, the basic classification and the high-level classification being divided according to data importance, the basic classification indicating general data of a business system, and the high-level classification indicating unique data of different business systems; the data processing unit is further configured to determine M1 basic classifications already existing in the cost management system, obtain N1 basic classifications calculated this time, N1 being the number of basic classifications obtained after the matching degrees of the single classifications are calculated, and in response to the ratio of N1 to M1 being less than a first threshold value corresponding to the basic classification, feed back an instruction message used to instruct that the business data needs to be re-input due to a small amount of basic data; M1 and N1 are both positive integers; the data processing unit is further configured to, in response to the ratio of N1 to M1 being greater than the first threshold value, obtain the matching degree of the business data according to the matching degrees of the single classifications, the matching degree of the business data being used to represent the matching degree of the business data and the target classification; and the data processing unit is further configured to, in response to the ratio of N1 to M1 being greater than the first threshold value, obtain the matching degree of the business data according to the matching degrees of the single classifications, the matching degree of the business data being used to represent the matching degree of the business data and the target classification. The data processing unit is further configured to, in response to the matching degree of the business data being greater than the first threshold, determine that the business data is basic classification data, and access the business data to the cost management system; The data processing unit is further configured to, in response to the matching degree of the business data being less than the first threshold, obtain N2 senior classifications after this calculation, N2 being the number of senior classifications obtained after the matching degrees of single classifications are calculated; obtain M2 senior classifications that need to be compared in the N2 senior classifications; obtain calculation data in the M2 senior classifications, determine a maximum matching degree in the matching degrees of the M2 senior classifications; M2 and N2 are positive integers; and in response to the maximum matching degree being greater than a second threshold corresponding to the senior classification, determine that the business data is senior classification data, and access the business data to the cost management system.

7. The apparatus of claim 6, wherein, The data processing unit is configured to perform one or more of the following: According to the necessity of the data field, the obtained data field is classified into a non-essential field and an essential field; According to the confidentiality of the data field, the obtained data field is classified into a must-encrypt field, a non-must-encrypt field, and a public field; According to the validity of the data field, the obtained data field is classified into a permanent validity field and a time validity field; According to the sensitivity of the data field, the obtained data field is classified into a sensitive field and a non-sensitive field; According to whether the data field has been desensitized, the obtained data field is classified into a desensitized field and a non-desensitized field.

8. The apparatus of claim 6, wherein, The data processing unit is configured to: For a single classification, obtain an incoming parameter of a data field included in the single classification, and obtain a calculation parameter corresponding to the single parameter in a target classification; According to the value of the single parameter in the single classification and the value of the corresponding calculation parameter, obtain a matching degree corresponding to the single parameter in the single classification; According to the matching degrees corresponding to all parameters in the single classification, obtain the matching degree of the single classification.

9. The apparatus of claim 6, wherein, The data processing unit is further configured to: In response to the maximum matching degree being less than the second threshold, add the business data to a data configuration queue; wherein the data in the data configuration queue needs to be manually audited.

10. The apparatus of claim 6, wherein, The device further includes a data adjustment unit configured to: fit the matching degree data of the single classification obtained this time and the matching degree data of the single classification already obtained to form new matching degree data of the single classification; update the value of the first threshold according to the new matching degree data of the single classification to obtain a new first threshold; fit the maximum matching degree data obtained this time and the maximum matching degree data already obtained to form a new second threshold.

11. A computer device, comprising: The device includes a processor and a memory, the memory storing at least one program code, the at least one program code being loaded and executed by the processor to implement the business data access method according to any one of claims 1 to 5.

12. A computer-readable storage medium, characterized in that, The storage medium has at least one program code stored therein, the at least one program code is loaded and executed by the processor to implement the business data access method as claimed in any one of claims 1 to 5.

13. A computer program product, the computer program product comprising computer program code stored in a computer readable storage medium, the computer program code being read by a processor of a computer device from the computer readable storage medium, the processor executing the computer program code to cause the computer device to perform to implement the business data access method as claimed in any one of claims 1 to 5.

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