Methods, devices, electronic equipment and storage media for processing vehicle cost business data

Abstract

The present disclosure provides a whole vehicle cost business data processing method and device, electronic equipment and computer readable storage medium, relating to the technical field of big data, which comprises the following steps: collecting business data corresponding to a plurality of target business systems; obtaining basic data participating in whole vehicle cost data calculation in the business data to obtain push data; obtaining standard element data, and generating fusion data according to the standard element data and the push data; calling a plurality of dimensional target algorithm models, and determining the whole vehicle cost data according to the fusion data and the plurality of dimensional target algorithm models. The present disclosure realizes data fusion between different business systems and also realizes distributed calculation of different business type data.

Description

Technical Field

[0001] This disclosure relates to the field of big data technology, and in particular to a method, apparatus, electronic device, and computer-readable storage medium for processing vehicle cost business data. Background Technology

[0002] Traditional data analysis largely relies on maintaining spreadsheets and files. Analyzing data involves manual editing, calculations, or generating spreadsheets. This approach limits the amount of data that can be analyzed. When dealing with large datasets or data from different business systems, it fails to achieve the integration and real-time computation of massive amounts of data across these systems, and cannot meet the requirements of systematic data management and structured data management mechanisms.

[0003] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] The purpose of this disclosure is to provide a method, apparatus, electronic device, and computer-readable storage medium for processing vehicle cost business data, which can at least to some extent realize the fusion of large amounts of data and real-time calculation services between different business systems.

[0005] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0006] According to one aspect of this disclosure, a method for processing vehicle cost business data is provided, comprising:

[0007] Collect business data corresponding to multiple target business systems;

[0008] Obtain the basic data involved in the calculation of the whole vehicle cost data from the above business data, and get the push data;

[0009] Acquire standard element data and generate fused data based on the aforementioned standard element data and the aforementioned pushed data; wherein, the aforementioned standard element data is obtained by parsing the supplementary data table involved in the calculation of the whole vehicle cost data;

[0010] The target algorithm model with multiple dimensions is invoked, and the vehicle cost data is determined based on the above fused data and the target algorithm model with multiple dimensions.

[0011] Optionally, the steps of obtaining the basic data involved in the calculation of vehicle cost data from the above-mentioned business data and obtaining the push data include: storing the above-mentioned business data to generate a dataset; selecting the basic data involved in the calculation of vehicle cost data corresponding to each of the above-mentioned target business systems from the above-mentioned dataset to obtain the push data corresponding to each of the above-mentioned target business systems.

[0012] Optionally, the steps of storing the aforementioned business data and generating a dataset include: preprocessing the aforementioned business data to obtain preprocessed data, wherein the preprocessing includes at least one of data extraction, data cleaning, data transformation, and data loading; filtering the aforementioned preprocessed data to obtain the basic data for calculating the vehicle cost data corresponding to each of the aforementioned target business systems; and generating the aforementioned dataset based on the basic data for calculating the vehicle cost data corresponding to each of the aforementioned target business systems.

[0013] Optionally, the steps of generating fused data based on the standard element data and the push data include: constructing a data dictionary table structure based on the push data; verifying the push data based on the data dictionary table structure and the business data; and generating fused data based on the standard element data and the push data if the push data verification passes.

[0014] Optionally, the above-mentioned method for processing vehicle cost business data further includes: performing verification processing on the above-mentioned fused data based on the above-mentioned business data and the above-mentioned standard element data, wherein the verification processing includes verifying at least one of the following: consistency, real-time performance, completeness, and accuracy of the data.

[0015] Optionally, the above-mentioned whole vehicle cost business data processing method further includes: when a data operation event is detected, performing target operation processing on the above-mentioned standard element data and / or the above-mentioned pushed data according to the data operation event, wherein the target operation processing includes at least one of data addition, data deletion, data update and data query.

[0016] Optionally, after the steps of calling the target algorithm model of multiple dimensions and determining the vehicle cost data based on the fused data and the target algorithm model of multiple dimensions, the vehicle cost business data processing method further includes: obtaining the vehicle cost accounting value from the vehicle cost data; if the vehicle cost accounting value is within the expected vehicle cost accounting value range, generating vehicle reporting result data indicators based on the vehicle cost data; if the vehicle cost accounting value is not within the expected vehicle cost accounting value range, returning to the steps of determining the vehicle cost data based on the fused data and the target algorithm model of multiple dimensions.

[0017] According to another aspect of this disclosure, a vehicle cost business data processing device is provided, configured in a vehicle cost management system, the vehicle cost business data processing device comprising:

[0018] The data acquisition module is used to collect business data corresponding to multiple target business systems;

[0019] The data extraction module is used to obtain the basic data involved in the calculation of the whole vehicle cost data from the above business data and to obtain the push data;

[0020] The data management module is used to acquire standard element data and generate fused data based on the aforementioned standard element data and the aforementioned pushed data; wherein, the aforementioned standard element data is obtained by parsing the supplementary data table that participates in the calculation of the whole vehicle cost data;

[0021] The data calculation module is used to call the target algorithm model in multiple dimensions and determine the vehicle cost data based on the above fused data and the target algorithm model in multiple dimensions.

[0022] According to another aspect of this disclosure, an electronic device is provided, the electronic device 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 implement the whole vehicle cost business data processing method as described in the above embodiments.

[0023] According to another aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the whole vehicle cost business data processing method as described in the above embodiments.

[0024] The vehicle cost business data processing method, apparatus, electronic device, and computer-readable storage medium provided in the embodiments of this disclosure have the following technical effects:

[0025] This disclosure employs a technical solution that involves collecting business data from multiple target business systems, obtaining basic data from the business data used in calculating vehicle cost data, generating push data, acquiring standard element data, generating fused data based on the standard element data and push data, calling target algorithm models in multiple dimensions, and determining the vehicle cost data based on the fused data and the target algorithm models in multiple dimensions. This not only achieves data fusion between different business systems but also enables distributed computing of data of different business types, which is beneficial to improving the calculation efficiency of vehicle cost data.

[0026] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0027] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0028] Figure 1 A flowchart illustrating a method for processing vehicle cost business data in an exemplary embodiment of this disclosure is shown.

[0029] Figure 2 The application deployment architecture diagram of the vehicle cost management system disclosed herein is shown;

[0030] Figure 3 An exemplary flowchart corresponding to step S120 in the whole vehicle cost business data processing method of this disclosure is shown;

[0031] Figure 4 This diagram illustrates an exemplary process for generating fused data in the vehicle cost business data processing method disclosed herein.

[0032] Figure 5 This paper presents an exemplary flowchart illustrating the generation of vehicle reporting result data indicators in the vehicle cost business data processing method disclosed herein.

[0033] Figure 6 This diagram illustrates the business logic flow of the vehicle cost management system disclosed herein.

[0034] Figure 7 This diagram illustrates the interaction between the front-end application and the business model data calculation service in the vehicle cost management system disclosed herein.

[0035] Figure 8 A schematic diagram of the structure of a vehicle cost business data processing apparatus according to an exemplary embodiment of the present disclosure is shown;

[0036] Figure 9 A schematic diagram of the structure of an electronic device in an exemplary embodiment of the present disclosure is shown. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this disclosure clearer, the embodiments of this disclosure will be described in further detail below with reference to the accompanying drawings.

[0038] In the following description, when referring to the accompanying drawings, the same numbers in different drawings denote the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0039] It should be noted that the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this disclosure, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0040] The following is an embodiment of the vehicle cost business data processing method provided in this disclosure. Wherein, Figure 1 A flowchart illustrating a method for processing vehicle cost business data in an exemplary embodiment of this disclosure is shown. Figure 1 As shown, an embodiment of the method disclosed herein provides a vehicle cost business data processing method applied to a vehicle cost management system. This system supports OLTP (On-Line Transaction Processing) and OLAP (On-Line Analytical Processing) applications across different business scenarios, and supports ultra-large-scale data volume computation modes to achieve the sharing and reuse of business data model API (Application Programming Interface) services in different scenarios. The vehicle cost management system includes a data lake, front-end applications, and business model data computation services. It also includes a data platform tool with data acquisition capabilities, capable of collecting business data from various business systems, including SAP (systems, applications, and products in data processing), attendance systems, OA systems, EAS (Enterprise Application Suites), MES (Manufacturing Execution System), and so on. Figure 2 As shown, Figure 2 The application deployment architecture diagram of the vehicle cost management system disclosed herein is shown. The front-end application is deployed on the application server. Figure 2The computing service center in this context refers to the business model data computing service, which can perform distributed computing on business data from different business systems. The data lake can distribute and store the business data from different business systems and the data calculated by the business model data computing service. The above-mentioned vehicle cost business data processing method is applied to the vehicle cost management system, and this method mainly includes:

[0041] Step S110: Collect business data corresponding to multiple target business systems.

[0042] In one exemplary embodiment, a data platform tool collects business data corresponding to multiple target business systems using a "T+1" data collection method. Here, "T" represents the data collection day, and "T+1" represents the data collection day plus one day. The "T+1" data collection method enables asynchronous data collection; the business data corresponding to each target business system is "T+1" data, which is asynchronous. The business data corresponding to multiple target business systems includes financial data from the SAP system, attendance data from the attendance system, organizational data from the OA system, EAS data from the EAS system, MES data from the MES system, and so on. After collecting the business data corresponding to multiple target business systems, the individual business data for each target business system is obtained. This data is then sent to a data lake, where it is processed and stored in the lake, thus achieving distributed data storage. Alternatively, one of Sqoop, Kettle, and Flume can be used to replace the data platform tool, i.e., one of these three tools can be used to collect business data corresponding to multiple target business systems using a "T+1" data collection method.

[0043] Step S120: Obtain the basic data involved in the calculation of the whole vehicle cost data from the above business data, and get the push data.

[0044] After receiving the business data from each target business system, the data lake stores the foundational data used for calculating the vehicle cost data for each system. Foundational data not used in the calculation is removed. In other words, the foundational data used for calculating the vehicle cost data for each target business system is the push data corresponding to that system's subsequent participation in the vehicle cost data calculation; the push data is the foundational data used for the vehicle cost data calculation. Furthermore, the push data for each target business system is distributed and stored, and then sent to the front-end application for structured data management.

[0045] Step S130: Obtain standard feature data and generate fused data based on the standard feature data and the pushed data.

[0046] During or after the front-end application receives push data from the data lake corresponding to each target business system, it also acquires the standard element data corresponding to each target business system, thus obtaining the standard element data for each target business system. This standard element data is obtained by parsing the supplementary data table used in the calculation of vehicle cost data. In other words, the standard element data is the data in the supplementary data table and also participates in the implementation of data structured management. The standard element data can compensate for other missing data in the push data. After acquiring the standard element data and push data corresponding to each target business system, the front-end application merges them to obtain the merged data for each target business system. This merged data is the complete data required for the calculation of vehicle cost data. For example, if the target business system is an attendance system, the push data for the attendance system includes the employee's name, expected / actual attendance days, overtime days, leave days, and clock-in time, but not the employee's department and start date. After the front-end application obtains the standard element data corresponding to the attendance system, which includes the employee's department and start date, it merges the push data from the attendance system with the standard element data. The resulting merged data is the complete data required for calculating the overall vehicle cost, encompassing all data from both the push data and the standard element data. The front-end application then sends the merged data to the business model data calculation service.

[0047] Step S140: Call the target algorithm model of multiple dimensions, and determine the vehicle cost data based on the above fused data and the target algorithm model of multiple dimensions.

[0048] The multi-dimensional target algorithm models are pre-built. Through model construction, a data fusion model is established, encompassing fused business data from various target business systems, such as the fused data from the OA system, attendance system, and SAP system. After receiving the fused data from each target business system, the business model data calculation service invokes the multi-dimensional target algorithm models. Invoking the target algorithm models can be based on the data type of the fused data or on user actions. On one hand, since there are multiple target business systems, the fused data received by the business model data calculation service also includes multiple dimensions, thus enabling the invocation of multi-dimensional target algorithm models. On the other hand, the business model data calculation service provides users with the option to create algorithm models. Users can combine the business data corresponding to the target business systems and actual needs, triggering the algorithm model creation option, which will then allow the business model data calculation service to invoke the multi-dimensional target algorithm models.

[0049] Different dimensional target algorithm models are used for calculations of data from different business types. For example, multi-dimensional target algorithm models include labor cost algorithm models, manufacturing cost algorithm models, fuel cost algorithm models, and unit cost algorithm models, etc. The labor cost algorithm model is a business logic algorithm model built based on the social insurance and housing fund contributions, basic salary, commissions, and other benefits of formal and informal employees within the factory organization, spanning different time dimensions (year, month, day) and organizational levels. The manufacturing cost algorithm model is a business logic algorithm model built based on the actual production data of different brands in the MES system of the vehicle assembly plant, spanning different time dimensions (year, month, day) and organizational levels. The fuel cost algorithm model is a business logic algorithm model built based on fuel (water, electricity, gas, steam) costs in the vehicle assembly plant, spanning different time dimensions (year, month, day) and organizational levels. The unit cost algorithm model is a business logic algorithm model built based on the production dimension of a single vehicle, spanning different time dimensions (year, month, day) and organizational levels.

[0050] Among them, the labor cost algorithm model, manufacturing cost algorithm model, fuel cost algorithm model, and unit cost algorithm model are all generated through corresponding calculation formulas, as follows:

[0051] Labor cost algorithm model = monthly labor cost budget + annual labor cost budget formula + historical data. Monthly labor cost budget = first intermediate data + second intermediate data. First intermediate data = labor cost process monitoring data - efficiency achievement data. Second intermediate data = actual labor cost - actual monthly labor cost data = daily labor cost monitoring data + (actual labor cost - actual annual labor cost data).

[0052] Manufacturing cost algorithm model = Manufacturing cost - Monthly actual cost = Monthly process monitoring details + Historical data + Monthly target data; Monthly process monitoring details = Manufacturing cost breakdown data + Single unit manufacturing cost data.

[0053] Fuel cost algorithm model = (monthly actual fuel cost per unit - monthly actual per unit data) + (annual actual fuel cost per unit - annual actual fuel cost data) + (fuel cost process monitoring data - fuel cost process monitoring table data).

[0054] Single-unit cost algorithm model = actual single-unit manufacturing cost accounting data + monthly single-unit manufacturing cost process monitoring data at each level; actual single-unit manufacturing cost accounting data = actual single-unit cost data - financial data = annual single-unit manufacturing cost monitoring report data + monthly financial data and internal control data comparison report data; monthly single-unit manufacturing cost process monitoring data at each level = monthly single-unit cost process monitoring data - internal control data.

[0055] After constructing multi-dimensional target algorithm models, a single-dimensional target algorithm model is used to calculate the fused data corresponding to a matching target business system, resulting in one type of business data. This achieves distributed computation of different business data types. For example, a manufacturing cost algorithm model is used to calculate the fused data corresponding to the MES system, yielding manufacturing cost data. Based on the above calculation method, multiple business data types are calculated through multi-dimensional target algorithm models, and then these multiple business data types are aggregated to obtain the overall vehicle cost data. Furthermore, for the business categories corresponding to different business data types, cost consistency monitoring and deviation analysis are performed according to budgeting, accounting, and process monitoring, shifting deviation issues to proactive early warning, allowing for early prediction and strategy adjustment, which helps to reduce cost risks to the latest level.

[0056] This embodiment, based on the above technical solution, collects business data corresponding to multiple target business systems, obtains basic data involved in the calculation of vehicle cost data from the business data, obtains push data, acquires standard element data, generates fused data based on the standard element data and push data, calls target algorithm models of multiple dimensions, and determines the technical solution of vehicle cost data based on the fused data and target algorithm models of multiple dimensions. This not only realizes the data fusion between different business systems, but also realizes the distributed computing of data of different business types, which is conducive to improving the calculation efficiency of vehicle cost data.

[0057] For example, Figure 3 An exemplary flowchart corresponding to step S120 in the vehicle cost business data processing method of this disclosure is shown. Optionally, based on the above method embodiment, step S120 includes the following scheme:

[0058] Step S121: Store the above business data to generate a dataset;

[0059] Step S122: Select the basic data for calculating the whole vehicle cost data corresponding to each of the above target business systems from the above dataset, and obtain the push data corresponding to each of the above target business systems.

[0060] After receiving the business data corresponding to each target business system, the data lake performs distributed storage on this data, resulting in a dataset for each target business system. In other words, the dataset for each target business system is stored in a distributed manner within the data lake. After storing the business data for each target business system, the data lake retrieves the foundational data for calculating the overall vehicle cost data from this dataset, thus obtaining the push data for each target business system. This push data is then sent to the front-end application.

[0061] Optionally, step S121 includes the following options:

[0062] The aforementioned business data is preprocessed to obtain preprocessed data. The preprocessing includes at least one of data extraction, data cleaning, data transformation, and data loading.

[0063] The preprocessed data is filtered to obtain the basic data for calculating the whole vehicle cost corresponding to each of the above target business systems.

[0064] The datasets mentioned above are generated based on the fundamental data used in calculating the overall vehicle cost data for each of the aforementioned target business systems.

[0065] It should be understood that after receiving the business data corresponding to each target business system, the data lake uses at least one of the following methods—data extraction, data cleaning, data transformation, and data loading—to preprocess the received business data for each target business system, resulting in preprocessed data for each target business system. Then, according to preset conditions, the preprocessed data for each target business system is filtered out, thereby removing the basic data that does not participate in the calculation of the overall vehicle cost data, resulting in basic cost data and cost basic data fields. In other words, the push data corresponding to each target business system is generated using the basic cost data and cost basic data fields, which constitute the basic data participating in the calculation of the overall vehicle cost data. Finally, the dataset for each target business system is generated using the push data corresponding to each target business system; that is, the data in the dataset is the basic data participating in the calculation of the overall vehicle cost data.

[0066] Optionally, after collecting business data corresponding to multiple target business systems, the above-mentioned method for processing vehicle cost business data further includes: synchronizing the business data to the front-end application through the data lake. Specifically, after receiving the business data corresponding to each target business system, the data lake also synchronizes the business data corresponding to each target business system to the front-end application, so that the front-end application can perform data verification using the synchronized business data.

[0067] For example, Figure 4 This diagram illustrates an exemplary workflow for generating fused data in the vehicle cost business data processing method of this disclosure. Optionally, the generation of fused data based on the aforementioned standard element data and the aforementioned push data includes the following schemes:

[0068] Step S131: Construct a data dictionary table structure based on the above-mentioned push data;

[0069] Step S132: Verify the pushed data based on the above data dictionary table structure and the above business data;

[0070] Step S133: If the above-mentioned push data verification passes, generate fused data based on the above-mentioned standard element data and the above-mentioned push data.

[0071] After receiving the push data for each target business system from the data lake, the front-end application constructs a data dictionary table structure based on the cost base data and cost base data fields in the push data for each target business system. This data dictionary table structure includes the correspondence between cost base data and cost base data fields, which can be understood as cost base data field - cost base data. Since the data lake has pre-synchronized the business data for each target business system to the front-end application, the front-end application verifies the corresponding push data using the business data for each target business system and the data dictionary table structure. If the cost base data and cost base data fields in the data dictionary table structure for each target business system are the same as those in the business data, then the push data for that target business system has passed verification. The standard element data and push data for each target business system are then merged to generate the merged data for each target business system.

[0072] Optionally, the above-mentioned method for processing vehicle cost business data also includes the following solutions:

[0073] Based on the aforementioned business data and standard element data, the aforementioned fused data is verified.

[0074] After the front-end application obtains the business data and standard element data corresponding to each target business system, it synchronizes these data to the business model data calculation service. This allows the business model data calculation service to perform data verification using the synchronized business data and standard element data. Upon receiving the fused data corresponding to each target business system, the business model data calculation service verifies the corresponding fused data using the synchronized business data and standard element data. Verification processing includes checking at least one of the following: consistency, real-time performance, completeness, and accuracy. If the push data in the fused data is included within the business data, and the standard element data in the fused data is the same as the synchronized standard element data, then the fused data verification for that target business system is considered successful. If the push data in the fused data is not included within the business data, and the standard element data in the fused data is different from the synchronized standard element data, then the fused data verification for that target business system is considered unsuccessful, and an anomaly notification is issued.

[0075] Optionally, the above-mentioned method for processing vehicle cost business data also includes the following solutions:

[0076] Upon detecting a data operation event, target operation processing is performed on the aforementioned standard element data and / or the aforementioned pushed data based on the aforementioned data operation event.

[0077] It should be understood that the vehicle cost management system also provides an application programming interface (API) service, which interacts with the front-end application. The front-end application provides a data manipulation page for users. If it detects a data manipulation event input by the user through this page, it retrieves the target operation corresponding to that event. This target operation may be an operation performed on any one of the standard element data or pushed data corresponding to each target business system. The system then processes this target operation on any one of the standard element data or pushed data corresponding to each target business system to meet business needs, thereby supporting OLTP and OLAP scenario services. The target operation processing includes at least one of the following: data addition, data deletion, data update, and data query.

[0078] For example, Figure 5 This diagram illustrates an exemplary process for generating vehicle reporting result data indicators in the vehicle cost business data processing method of this disclosure. Optionally, after step S140, which involves calling a multi-dimensional target algorithm model and determining the vehicle cost data based on the fused data and the multi-dimensional target algorithm model, the vehicle cost business data processing method further includes the following:

[0079] Step S150: Obtain the calculated vehicle cost from the above vehicle cost data;

[0080] Step S160: If the above-mentioned vehicle cost calculation value is within the expected vehicle cost calculation value range, generate vehicle reporting result data indicators based on the above-mentioned vehicle cost data;

[0081] Step S170: If the above-mentioned vehicle cost calculation value is not within the expected vehicle cost calculation value range, return to the above-mentioned step of determining the vehicle cost data based on the above-mentioned fused data and the above-mentioned target algorithm model of multiple dimensions.

[0082] It should be understood that users can operate the business model data calculation service through the interactive page provided by the vehicle cost management system. For example, the interactive page displays selection options related to vehicle cost accounting, such as button A for "Monthly Budget Calculation of Vehicle Cost" and button B for "Target Budget Generation". After the user triggers button A, the business model data calculation service obtains the vehicle cost accounting value from the vehicle cost data, and then determines whether the vehicle cost accounting value is within the expected vehicle cost accounting value range. The expected vehicle cost accounting value range is preset. If the vehicle cost accounting value is within the expected vehicle cost accounting value range, the obtained vehicle cost data is considered to meet business requirements and is displayed on the interactive page. Then, the user can trigger button B to generate the final vehicle reporting result data indicators based on the vehicle cost data. If the vehicle cost accounting value is not within the expected vehicle cost accounting value range, the obtained vehicle cost data is considered not to meet business requirements, and the process returns to execute the vehicle cost data determined based on the above-mentioned fused data and the target algorithm model of the above-mentioned multiple dimensions, and recalculates the vehicle cost data.

[0083] Optionally, after step S170, the above-mentioned method for processing vehicle cost business data further includes the following: displaying the above-mentioned vehicle reporting result data indicators through an application programming interface (API) service. It should be understood that after generating the vehicle reporting result data indicators, data sharing and services are performed through the API service, thereby achieving a visual display of the API service.

[0084] Based on the above exemplary embodiments, such as Figure 6 As shown, Figure 6 The diagram illustrates the business logic flow of the vehicle cost management system disclosed herein. This system enables sharing and reuse among various branches within a group, meaning each branch can use the same system for vehicle cost management, eliminating the need for each branch to develop its own independent system. The business processing flow of this vehicle cost management system is as follows:

[0085] Figure 6The upstream business systems in the process include multiple target business systems, such as SAP systems, MES systems, EAS systems, attendance systems, OA systems, etc. JDBC is a Java API used to execute SQL statements.

[0086] In the first stage of the business processing flow of the vehicle cost management system, JDBC development is performed for each target business system. If each target business system has authorized JDBC access (i.e., JDBC authorized access), then the system accesses the databases of each target business system via JDBC. Specifically, the vehicle cost management system, through its provided data platform tools and based on JDBC, collects business data from the databases of multiple target business systems in a "T+1" data collection manner. For example, it collects business data from SAP systems, MES systems, EAS systems, attendance systems, OA systems, etc. Once the business data corresponding to each target business system is collected, the individual business data for each target business system is obtained and then sent to the data lake for data ingestion. The data lake performs ETL (Extraction-Transformation-Loading) on ​​the business data corresponding to each target business system, obtaining preprocessed data for each system. Then, it filters this preprocessed data according to preset conditions, removing essential data that is not used in the vehicle cost calculation, resulting in basic cost data and its fields. This basic cost data and its fields are then used to generate the push data for each target business system, forming the foundational data for the vehicle cost calculation. Next, the push data for each target business system is used to generate its own dataset, which contains the foundational data for the vehicle cost calculation. These datasets are then distributed and stored. After storing the datasets for each target business system, the data lake performs data push integration. This involves retrieving push data from the datasets of each target business system based on specified conditions and sending the corresponding push data for each system to the front-end application, thus entering the second stage of the vehicle cost management system's business processing flow.

[0087] The second stage of the business process of the vehicle cost management system involves structured data management. For example... Figure 7 As shown, Figure 7This diagram illustrates the interaction between the front-end application and the business model data calculation service in the vehicle cost management system of this disclosure. Data is written to the master database through the network management system of the vehicle cost management system, and then synchronized to the slave database and distributed backup database in real time using a master-slave replication relationship. Simultaneously, data verification is performed between the synchronization mechanisms, and the distributed backup database of the business model data calculation service receives data from upstream business systems and the data lake. After receiving the business data corresponding to each target business system, the data lake also synchronizes the corresponding business data of each target business system to the front-end application.

[0088] In the second stage of the vehicle cost management system's business processing flow, while the front-end application receives push data from the data lake corresponding to each target business system, it also acquires the standard element data corresponding to each target business system entered by the user. After receiving the push data for each target business system from the data lake, the front-end application constructs a data dictionary table structure based on the cost base data and cost base data fields in the push data for each target business system. This data dictionary table structure includes the correspondence between cost base data and cost base data fields, which can be understood as cost base data fields - cost base data. Since the data lake has pre-synchronized the business data corresponding to each target business system to the front-end application, the front-end application verifies the corresponding push data using the business data and data dictionary table structure for each target business system. Once the push data for that target business system passes verification, the front-end application merges the standard element data and push data for each target business system to generate merged data for that target business system, and then sends the merged data for each target business system to the business model data calculation service.

[0089] After obtaining the business data and standard element data corresponding to each target business system, the front-end application synchronizes this data to the business model data calculation service. Specifically, the front-end application synchronizes the business data corresponding to each target business system to the distributed backup database of the business model data calculation service through the backup database of the whole vehicle cost management system. The business model data calculation service verifies the consistency, real-time performance, completeness, and accuracy of the pushed data in the fused data corresponding to each target business system at preset intervals (e.g., 1 hour). If any anomalies are found, an anomaly notification is issued. The front-end application synchronizes the standard element data corresponding to each target business system to the distributed backup database of the business model data calculation service. The business model data calculation service verifies the consistency, real-time performance, completeness, and accuracy of the standard element data in the fused data corresponding to each target business system at preset intervals (e.g., 1 hour). If any anomalies are found, an anomaly notification is issued.

[0090] In the third stage of the business process of the vehicle cost management system, the business model data calculation service constructs a model based on user operations, thereby building a fusion model that integrates business data from various target business systems and calls target algorithm models across multiple dimensions. After the business model data calculation service calls the target algorithm models across multiple dimensions, the user can verify the calculation results of the constructed target algorithm models. For example, the vehicle cost management system provides an interactive page displaying selection options for vehicle cost accounting data, such as button A for "Monthly Budget Calculation of Vehicle Cost" and button B for "Target Budget Generation". When the user triggers button A, the business model data calculation service performs a trial calculation of the vehicle cost accounting value. If the vehicle cost accounting value is not within the expected range, it is considered that the obtained vehicle cost accounting value does not meet business requirements, and the vehicle cost accounting value is recalculated until the vehicle cost accounting value meets business requirements. If the calculated vehicle cost falls within the expected range, and is considered to meet business requirements, then a multi-dimensional target algorithm model is used to calculate the fused data from each target business system to obtain vehicle cost data. This vehicle cost data is then used to generate the final vehicle reporting result data indicator. Users can trigger button B to generate this final indicator based on the vehicle cost data. After generating the indicator, data sharing and services are provided through an API interface, supporting front-end application interaction and visualization. Furthermore, the vehicle cost management system can interact with the API interface service through the front-end application. Users can add, delete, update, and query data from any of the standard element data and pushed data corresponding to each target business system to meet business needs and support OLTP and OLAP scenarios.

[0091] The following are embodiments of the apparatus disclosed herein, which can be used to execute embodiments of the method disclosed herein. For details not disclosed in the apparatus embodiments of this disclosure, please refer to the embodiments of the method disclosed herein.

[0092] in, Figure 8 A schematic diagram of a vehicle cost business data processing apparatus that can be applied according to an embodiment of this disclosure is shown. Please refer to... Figure 8 The vehicle cost business data processing device shown in the figure can be implemented as all or part of an electronic device through software, hardware, or a combination of both, or it can be integrated as an independent module into an electronic device or server.

[0093] The vehicle cost business data processing device 800 in this embodiment is configured in the vehicle cost management system, and the vehicle cost business data processing device 800 includes:

[0094] The data acquisition module 810 is used to collect business data corresponding to multiple target business systems;

[0095] The data extraction module 820 is used to obtain the basic data involved in the calculation of the whole vehicle cost data from the above business data and to obtain the push data;

[0096] The data management module 830 is used to acquire standard element data and generate fused data based on the standard element data and the pushed data; wherein, the standard element data is obtained by parsing the supplementary data table that participates in the calculation of the whole vehicle cost data;

[0097] The data calculation module 840 is used to call the target algorithm model of multiple dimensions and determine the whole vehicle cost data based on the above fused data and the target algorithm model of multiple dimensions.

[0098] In an exemplary embodiment, based on the foregoing scheme, the data extraction module 820 includes:

[0099] The data storage unit is used to store the aforementioned business data and generate datasets;

[0100] The data sending unit is used to select the basic data for calculating the whole vehicle cost data corresponding to each of the above target business systems from the above dataset, and obtain the push data corresponding to each of the above target business systems.

[0101] In an exemplary embodiment, based on the foregoing scheme, the data storage unit includes:

[0102] The data preprocessing subunit is used to preprocess the aforementioned business data to obtain preprocessed data. The preprocessing includes at least one of data extraction, data cleaning, data transformation, and data loading.

[0103] The data filtering subunit is used to perform conditional filtering on the above preprocessed data to obtain the basic data for calculating the whole vehicle cost data corresponding to each of the above target business systems.

[0104] The dataset generation subunit is used to generate the aforementioned datasets based on the basic data involved in calculating the vehicle cost data corresponding to each of the aforementioned target business systems.

[0105] In an exemplary embodiment, based on the foregoing scheme, the data management module 830 includes the following aspects in generating fused data according to the aforementioned standard element data and the aforementioned push data:

[0106] The data structure construction unit is used to construct a data dictionary table structure based on the aforementioned pushed data.

[0107] The first data verification unit is used to verify the pushed data according to the above data dictionary table structure and the above business data.

[0108] The data fusion unit is used to generate fused data based on the standard element data and the push data, provided that the push data verification is passed.

[0109] In an exemplary embodiment, based on the foregoing solution, the above-mentioned vehicle cost business data processing device further includes:

[0110] The second data verification unit is used to perform verification processing on the above-mentioned fused data based on the above-mentioned business data and the above-mentioned standard element data. The verification processing includes verifying at least one of the following: consistency, real-time performance, integrity, and accuracy of the data.

[0111] In an exemplary embodiment, based on the foregoing solution, the above-mentioned vehicle cost business data processing device further includes:

[0112] The data operation unit is used to perform target operation processing on the standard element data and / or the pushed data according to the data operation event when a data operation event is detected. The target operation processing includes at least one of data addition, data deletion, data update and data query.

[0113] In an exemplary embodiment, based on the foregoing solution, the above-mentioned vehicle cost business data processing device further includes:

[0114] The data acquisition unit is used to obtain the calculated value of the vehicle cost from the above-mentioned vehicle cost data;

[0115] The first judgment unit is used to generate vehicle reporting result data indicators based on the above vehicle cost data when the above-mentioned vehicle cost accounting value is within the expected vehicle cost accounting value range.

[0116] The second judgment unit is used to return to the above-mentioned step of determining the vehicle cost data based on the above-mentioned fused data and the above-mentioned target algorithm model of multiple dimensions when the above-mentioned vehicle cost calculation value is not in the expected vehicle cost calculation value range.

[0117] It should be noted that the vehicle cost business data processing device provided in the above embodiments is only illustrated by the division of the above functional modules when executing the vehicle cost business data processing method. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the vehicle cost business data processing device and the vehicle cost business data processing method embodiments provided in the above embodiments belong to the same concept. Therefore, for details not disclosed in the device embodiments of this disclosure, please refer to the embodiments of the vehicle cost business data processing method of this disclosure, which will not be repeated here.

[0118] The sequence numbers of the embodiments disclosed above are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0119] This disclosure also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described in the foregoing embodiments. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0120] This disclosure also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of any of the methods described above.

[0121] Figure 9 A schematic diagram of the electronic device is shown. Please refer to [link / reference]. Figure 9 As shown, the electronic device 900 includes a processor 901 and a memory 902.

[0122] In this embodiment, the processor 901 is the control center of the computer system, and can be a processor of a physical machine or a processor of a virtual machine. The processor 901 may include one or more processing cores, such as a 4-core processor or an 8-core processor. The processor 901 can be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 901 may also include a main processor and a coprocessor. The main processor, also known as the 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.

[0123] In this embodiment of the disclosure, the processor 901 is specifically used for: collecting business data corresponding to multiple target business systems; obtaining basic data involved in the calculation of vehicle cost data from the aforementioned business data to obtain push data; obtaining standard element data and generating fused data based on the aforementioned standard element data and the aforementioned push data; wherein, the aforementioned standard element data is obtained by parsing a supplementary data table involved in the calculation of vehicle cost data; calling a target algorithm model of multiple dimensions, and determining vehicle cost data based on the aforementioned fused data and the aforementioned target algorithm model of multiple dimensions.

[0124] Furthermore, the processor 901 is also used to: store the aforementioned business data to generate a dataset; select the basic data corresponding to each of the aforementioned target business systems for calculating the whole vehicle cost data from the aforementioned dataset, and obtain the push data corresponding to each of the aforementioned target business systems.

[0125] Furthermore, the processor 901 is also used to: preprocess the aforementioned business data to obtain preprocessed data, wherein the preprocessing includes at least one of data extraction, data cleaning, data transformation, and data loading; perform conditional filtering on the aforementioned preprocessed data to obtain the basic data for calculating the vehicle cost data corresponding to each of the aforementioned target business systems; and generate the aforementioned dataset based on the basic data for calculating the vehicle cost data corresponding to each of the aforementioned target business systems.

[0126] Furthermore, the processor 901 is also used to: construct a data dictionary table structure based on the pushed data; verify the pushed data based on the data dictionary table structure and the business data; and generate fused data based on the standard element data and the pushed data if the pushed data verification passes.

[0127] Furthermore, the processor 901 is also used to: perform verification processing on the fused data based on the business data and the standard element data, wherein the verification processing includes verifying at least one of the consistency, real-time performance, integrity and accuracy of the data.

[0128] Furthermore, the processor 901 is also configured to: upon detecting a data operation event, perform target operation processing on the standard element data and / or the pushed data according to the data operation event, wherein the target operation processing includes at least one of data addition, data deletion, data update, and data query.

[0129] Furthermore, the processor 901 is also configured to: obtain the vehicle cost calculation value from the vehicle cost data; if the vehicle cost calculation value is within the expected vehicle cost calculation value range, generate vehicle reporting result data indicators based on the vehicle cost data; if the vehicle cost calculation value is not within the expected vehicle cost calculation value range, return to execute the steps of determining the vehicle cost data based on the fused data and the target algorithm model of the multiple dimensions.

[0130] Memory 902 may include one or more computer-readable storage media, which may be non-transitory. Memory 902 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 of this disclosure, the non-transitory computer-readable storage media in memory 902 is used to store at least one instruction, which is executed by processor 901 to implement the methods in the embodiments of this disclosure.

[0131] In some embodiments, the electronic device 900 further includes a peripheral device interface 903 and at least one peripheral device. The processor 901, memory 902, and peripheral device interface 903 are connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 903 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of a display screen 904, a camera 905, and an audio circuit 906.

[0132] Peripheral device interface 903 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 901 and memory 902. In some embodiments of this disclosure, processor 901, memory 902, and peripheral device interface 903 are integrated on the same chip or circuit board; in other embodiments of this disclosure, any one or two of processor 901, memory 902, and peripheral device interface 903 can be implemented on separate chips or circuit boards. This disclosure does not specifically limit the scope of the embodiments.

[0133] Display screen 904 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 904 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 901 for processing. In this case, display screen 904 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments of this disclosure, there may be one display screen 904, which serves as the front panel of electronic device 900; in other embodiments, there may be at least two display screens 904, respectively disposed on different surfaces of electronic device 900 or in a folded design; in still other embodiments, display screen 904 may be a flexible display screen, disposed on a curved or folded surface of electronic device 900. Furthermore, display screen 904 may be configured as a non-rectangular irregular shape, i.e., a non-rectangular screen. Display screen 904 may be made of materials such as LCD (Liquid Crystal Display) or OLED (Organic Light-Emitting Diode).

[0134] Camera 905 is used to acquire images or videos. Optionally, camera 905 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is located on the front panel of the electronic device 900, and the rear-facing camera is located on the back of the electronic device 900. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, VR (Virtual Reality) shooting, or other fusion shooting functions. In some embodiments of this disclosure, camera 905 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cool light flash, which can be used for light compensation at different color temperatures.

[0135] The audio circuit 906 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals that are input to the processor 901 for processing. For stereo sound acquisition or noise reduction purposes, there may be multiple microphones, each located in a different part of the electronic device 900. The microphone may also be an array microphone or an omnidirectional microphone.

[0136] Power supply 907 is used to supply power to various components in electronic device 900. Power supply 907 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 907 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired line, while a wireless rechargeable battery is a battery that is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0137] The structural block diagram of the electronic device 900 shown in the embodiments of this disclosure does not constitute a limitation on the electronic device 900. The electronic device 900 may include more or fewer components than shown, or combine certain components, or adopt different component arrangements.

[0138] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in the embodiments of this specification are all authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the object characteristics, interactive behavior characteristics, and user information involved in this specification were all obtained under full authorization.

[0139] In the description of this disclosure, it should be understood that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this disclosure based on the specific circumstances. Furthermore, in the description of this disclosure, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0140] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, equivalent variations made in accordance with the claims of this disclosure are still within the scope of this disclosure.

Claims

1. A total cost of ownership business data processing method, characterized by, The method comprises the following steps: Collecting business data corresponding to a plurality of target business systems; Distributively storing the business data corresponding to each target business system to obtain a data set corresponding to each target business system; Obtaining basic data participating in the calculation of whole vehicle cost data from the data set corresponding to each target business system to obtain push data corresponding to each target business system; Obtaining standard element data corresponding to each target business system; wherein the standard element data is obtained by analyzing a supplementary data table participating in the calculation of whole vehicle cost data, the standard element data can make up other matter data that does not exist in the push data, and the supplementary data table is input by a user in advance; Constructing a data dictionary table structure according to cost basis data and cost basis data fields in the push data, wherein the data dictionary table structure comprises the corresponding relationship between the cost basis data and the cost basis data fields; If the cost basis data and the cost basis data fields in the data dictionary table structure are the same as the cost basis data and the cost basis data fields in the corresponding business data, fusing the standard element data and the push data to generate fused data; If the push data in the fused data is included in the corresponding business data, and the standard element data in the fused data is the same as the standard element data synchronized to the business model data calculation service, it is determined that the fused data passes the verification; Calling a plurality of dimensional target algorithm models, and determining whole vehicle cost data according to the fused data and the plurality of dimensional target algorithm models.

2. The method of claim 1, wherein, The whole vehicle cost business data processing method further comprises: Preprocessing the business data to obtain preprocessed data, wherein the preprocessing comprises at least one of data extraction, data cleaning, data conversion, and data loading; Conditionally filtering the preprocessed data to obtain basic data participating in the calculation of whole vehicle cost data corresponding to each target business system; Generating a data set corresponding to each target business system according to the basic data participating in the calculation of whole vehicle cost data corresponding to each target business system.

3. The TCOD processing method of any one of claims 1 or 2, wherein, The whole vehicle cost business data processing method further comprises: In the case of listening to a data operation event, performing target operation processing on the standard element data and / or the push data according to the data operation event, wherein the target operation processing comprises at least one of data addition, data deletion, data update, and data query.

4. The TCOD processing method of any one of claims 1 or 2, wherein, After the step of calling a plurality of dimensional target algorithm models and determining whole vehicle cost data according to the fused data and the plurality of dimensional target algorithm models, the whole vehicle cost business data processing method further comprises: Obtaining a whole vehicle cost accounting value from the whole vehicle cost data; In the case that the whole vehicle cost accounting value is in an expected whole vehicle cost accounting value interval, generating whole vehicle submission result data indicators according to the whole vehicle cost data; In the case that the whole vehicle cost accounting value is not in the expected whole vehicle cost accounting value interval, returning to perform the step of determining whole vehicle cost data according to the fused data and the plurality of dimensional target algorithm models.

5. A total cost of ownership business data processing apparatus characterized by comprising: The vehicle cost management system is configured to include a vehicle cost business data processing device, which comprises: a data acquisition module configured to acquire business data corresponding to a plurality of target business systems; a data storage unit configured to store the business data corresponding to each target business system in a distributed manner to obtain a data set corresponding to each target business system; a data sending unit configured to obtain basic data participating in vehicle cost data calculation from the data set corresponding to each target business system to obtain push data corresponding to each target business system; a data management module configured to obtain standard element data corresponding to each target business system, the standard element data being obtained by analyzing a supplementary data table participating in vehicle cost data calculation, the standard element data being capable of making up other matter data not existing in the push data, the supplementary data table being input by a user in advance, constructing a data dictionary table structure according to cost basis data and a cost basis data field in the push data, the data dictionary table structure including a corresponding relationship between the cost basis data and the cost basis data field, and fusing the standard element data and the push data to generate fused data if the cost basis data and the cost basis data field in the data dictionary table structure are the same as cost basis data and a cost basis data field in corresponding business data; a second data verification unit configured to determine that the fused data passes verification if the push data in the fused data is included in corresponding business data and the standard element data in the fused data is the same as the standard element data synchronized to a business model data calculation service; a data calculation module configured to call a plurality of dimensional target algorithm models and determine vehicle cost data according to the fused data and the plurality of dimensional target algorithm models.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the vehicle cost business data processing method according to any one of claims 1 to 4.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to implement the vehicle cost business data processing method according to any one of claims 1 to 4.

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