Maintenance fund data management method and device, equipment, medium and product

Abstract

The invention provides a maintenance fund data management method and device, equipment, a medium and a product. Relates to the technical field of data processing. The method comprises the following steps: acquiring index design demand data and maintenance fund data of a plurality of cells; determining at least three index dimensions according to the index design demand data, and determining a main body and a to-be-combined attribute of each index dimension; storing the maintenance fund data of each cell as an independent attribute table in an attribute value manner according to the main body of each index dimension and the to-be-combined attribute; obtaining user query request data, and determining a plurality of to-be-combined attributes of the at least three index dimensions according to the user query request data; generating corresponding index query conditions according to the to-be-combined attributes of the at least three index dimensions; and carrying out association processing on the attribute table of the index dimension corresponding to each to-be-combined attribute and the business process data to obtain an index result meeting the index query condition. The method has the technical effect of improving the flexibility and real-time performance of maintenance fund data processing.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus, equipment, medium and product for maintenance fund data management. Background Technology

[0002] Traditional maintenance fund data management processes, such as storage and retrieval, require processing and calculation based on fixed methods. With business development and ever-changing requirements, traditional maintenance fund data management methods can no longer meet users' significantly increased demands for flexibility, real-time performance, and scalability of indicators. Furthermore, traditional maintenance fund data management methods still suffer from low flexibility, real-time performance, and scalability.

[0003] Therefore, there is an urgent need for a maintenance fund data management method to improve the flexibility, real-time nature, and scalability of maintenance fund data management. Summary of the Invention

[0004] This application provides a method, apparatus, equipment, medium, and product for managing maintenance fund data, in order to improve the flexibility, real-time performance, and scalability of maintenance fund data management.

[0005] In a first aspect, embodiments of this application provide a method for managing maintenance fund data, including:

[0006] Obtain data on indicator design requirements and maintenance fund data for multiple residential communities;

[0007] Based on the design requirements data of the indicators, at least three indicator dimensions are determined, and the subject and attributes to be combined for each indicator dimension are determined.

[0008] The maintenance fund data of each community is stored as an independent attribute table according to the subject and the attribute to be combined of each indicator dimension in the form of attribute values;

[0009] Obtain user query request data, and based on the user query request data, determine multiple attributes to be combined across at least three indicator dimensions;

[0010] Generate corresponding indicator query conditions based on the attributes to be combined from the at least three indicator dimensions;

[0011] The attribute tables of the indicator dimensions corresponding to each attribute to be combined are associated with the business process data to obtain the indicator results that meet the indicator query conditions.

[0012] In one possible implementation, determining at least three indicator dimensions based on the indicator design requirement data includes: performing a requirement classification extraction operation based on the indicator design requirement data to obtain at least three indicator categories; and determining three indicator dimensions—time dimension, physical dimension, and business dimension—based on each indicator category, wherein one indicator dimension contains at least one subject and at least one attribute to be combined under the subject.

[0013] In one possible implementation, determining multiple attributes to be combined across at least three indicator dimensions based on the user query request data includes: determining the category of the query dimension and the attribute to be combined based on the user query request data using a pre-trained natural language processing model, wherein the pre-trained natural language processing model is obtained by inputting sample data of three-dimensional indicator dimensions (time dimension, physical dimension, and business dimension) into an open-source natural language processing model and then training the model; and determining multiple attributes to be combined across at least three indicator dimensions based on the category of the query dimension and the attribute to be combined.

[0014] In one possible implementation, the business process data includes time-dimensional process data, physical-dimensional process data, and business-dimensional process data. The attribute tables of the indicator dimensions corresponding to each combined attribute are associated with the business process data to obtain indicator results that satisfy the indicator query conditions. This includes: associating the attribute tables of the indicator dimensions corresponding to each combined attribute with the corresponding time-dimensional process data, physical-dimensional process data, and business-dimensional process data in the business process data one-to-one through a preset multi-table cross-dimensional association combination query, so as to generate indicator results composed of multiple independent attribute tables based on the indicator query conditions for the corresponding time-dimensional process data, physical-dimensional process data, and business-dimensional process data in the business process data.

[0015] In one possible implementation, the indicator query conditions include time dimension query conditions, physical dimension query conditions, and business dimension query conditions. The step of generating an indicator result composed of multiple independent attribute tables from the corresponding time dimension process data, physical dimension process data, and business dimension process data in the business process data according to the indicator query conditions includes: aggregating and calculating the time dimension data in the business process data according to time dimension statistical conditions and inputting it into an independent time attribute table corresponding to the time dimension; aggregating and calculating the physical dimension data in the business process data according to physical dimension statistical conditions and inputting it into an independent physical attribute table corresponding to the physical dimension; aggregating and calculating the business dimension data in the business process data according to business dimension query conditions and inputting it into an independent business attribute table corresponding to the business dimension; and combining the time attribute table, physical attribute table, and business attribute table to generate the indicator result.

[0016] In one possible implementation, after associating the attribute table of the indicator dimension corresponding to each combined attribute with the business process data to obtain the indicator result that satisfies the indicator query conditions, the method further includes: obtaining user query adjustment data; determining at least one adjusted combined attribute of at least one dimension based on the user query adjustment data; associating the attribute table of the indicator dimension corresponding to the adjusted combined attribute with the business process data to obtain an updated attribute table corresponding to the adjusted combined attribute; and merging the updated attribute table into the indicator result to obtain the adjusted indicator result.

[0017] Secondly, embodiments of this application provide a maintenance fund data management device, comprising:

[0018] The acquisition module is used to acquire indicator design requirements data and maintenance fund data for multiple communities;

[0019] The dimension creation module is used to determine at least three indicator dimensions based on the indicator design requirement data, and to determine the subject and attributes to be combined for each indicator dimension.

[0020] The dimension creation module is used to store the maintenance fund data of each community as an independent attribute table according to the subject and the attribute to be combined of each indicator dimension in the form of attribute values.

[0021] The indicator query module is used to obtain user query request data and determine multiple attributes to be combined in at least three indicator dimensions based on the user query request data.

[0022] The indicator query module is also used to generate corresponding indicator query conditions based on the attributes to be combined of the at least three indicator dimensions.

[0023] The indicator query module is also used to associate the attribute table of the indicator dimension corresponding to each combined attribute with the business process data to obtain the indicator results that meet the indicator query conditions.

[0024] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0025] The memory stores computer-executed instructions;

[0026] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0027] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0028] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0029] The maintenance fund data management method, apparatus, equipment, medium, and product provided in this application embodiment first acquire indicator design requirement data and maintenance fund data from multiple communities, storing them in independent attribute tables. These independent attribute tables store the relevant maintenance fund data for each indicator dimension and each attribute to be combined. Then, by acquiring user query data, multiple attributes to be combined for at least three indicator dimensions are determined. These attributes are then correlated with business process data to obtain indicator results that meet the query conditions. This approach, similar to a building block model, combines the maintenance fund data required by the user, improving the flexibility and real-time performance of maintenance fund data processing. Attached Figure Description

[0030] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0031] Figure 1 A schematic diagram illustrating an application scenario for a maintenance fund data management method provided in this application;

[0032] Figure 2 A flowchart illustrating a maintenance fund data management method provided in this application embodiment;

[0033] Figure 3 A schematic diagram of a maintenance fund data management device provided in this application;

[0034] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0035] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0036] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0037] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0038] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0039] In related technologies, maintenance fund indicators are processed and calculated in a fixed manner. Most similar indicators need to be recalculated for different dimensions. Indicators are built based on business needs. Every time the statistical time range or statistical dimension changes, new indicators need to be calculated to meet business needs. Response speed, scalability and real-time performance are all affected by the recalculation and thus reduced.

[0040] Therefore, in order to solve the above problems, the embodiments of the present invention propose the following technical concept:

[0041] The dimensional decomposition and combination mechanism based on a building block model enables dynamic construction and flexible expansion of indicators by decomposing indicators into attribute modules of three dimensions: time, physical, and business. Indicators are split into independently definable dimensions (such as time, physical, and business), each containing an expandable set of attributes (such as "monthly" and "quarterly" attributes in the time dimension). Based on business process data (such as daily fund changes), data is stored in a unified format, supporting dynamic association of multi-dimensional attributes. Indicator query conditions are dynamically generated through user-inputted dimensional attribute combinations, reusing process indicator data to generate result indicators, avoiding redundant calculations. This improves the real-time performance, flexibility, and scalability of the maintenance fund data management method of this invention.

[0042] The following will describe the steps and principles of the present invention in detail with reference to the accompanying drawings and specific embodiments.

[0043] Figure 1 This application provides an illustration of a maintenance fund data management method, such as... Figure 1As shown, the specific application scenarios of this application include: terminal 101 and server 102.

[0044] The terminal 101 is used for users to input user query request data and send the input data to the server 102. The terminal 101 may include a mouse, keyboard, or touchscreen, or other devices capable of responding to user actions, generating corresponding instructions such as user query request data, and transmitting the user query request data to the server 102. The server 102 is used to receive user query request data submitted from the terminal 101, as well as maintenance fund data and indicator design requirement data for tenants in each community obtained from other servers or the cloud, and executes the relevant steps of the maintenance fund data management method to obtain indicator results that meet user needs.

[0045] Figure 2 This is a flowchart illustrating a maintenance fund data management method provided in an embodiment of this application, such as... Figure 2 As shown, an embodiment of this application provides a method for managing maintenance fund data. The executing entity of this embodiment can be... Figure 2 The server 102 in the illustrated embodiment can be a physical server, a cloud server, or other computer-related devices; this embodiment does not impose any particular limitations on this. The method is described in detail below:

[0046] S201: Obtain indicator design requirements data and maintenance fund data for multiple communities.

[0047] In this embodiment, the indicator design requirement data can be configuration data related to indicator creation requirements input by management personnel, such as design requirements for at least three indicator dimensions: physical dimension, time dimension, and business dimension. In this embodiment, the indicator design requirement data can be an indicator design document or a piece of text. The maintenance fund data for multiple communities can be expenditure data on the maintenance of related facilities for at least three or more communities. This data can be obtained from the maintenance logs or historical maintenance fund database of each community.

[0048] S202: Based on the indicator design requirements data, determine at least three indicator dimensions, and determine the subject and attributes to be combined for each indicator dimension.

[0049] In this embodiment, based on the indicator design requirement data, at least three indicator dimensions are determined. These dimensions can be parsed using a Natural Language Processing (NLP) model to extract the time, physical, and business dimensions from the indicator design requirement data. Simultaneously, the subject and attributes to be combined for each indicator dimension can also be parsed.

[0050] Specifically, in an optional embodiment of this application, step S202 includes:

[0051] S202a: Perform requirement classification and extraction operations based on the indicator design requirement data to obtain at least three indicator categories.

[0052] In this embodiment, step S202a can be a process of parsing and classifying the indicator design requirement data from the pre-trained natural language large model to obtain indicator classifications in at least three indicator dimensions, such as time dimension, physical dimension, and business dimension.

[0053] S202b: Based on the classification of each indicator, determine three indicator dimensions: time dimension, physical dimension, and business dimension. One indicator dimension contains at least one subject and at least one attribute to be combined under the subject.

[0054] In this embodiment, each indicator category may include indicator design requirement description information in terms of time dimension, physical dimension, and business dimension. Based on these indicator categories, the indicator design can be specifically divided into three indicator dimensions: time dimension, physical dimension, and business dimension. The subject of each indicator dimension can be a statistical term that summarizes the scope of that indicator dimension. For example, the subject of the time dimension can be a point in time, a time period, or a duration. If the subject is a time period, the corresponding attributes to be combined can be detailed statistical terms such as year, month, day, week, etc., representing the time period.

[0055] S203: Store the maintenance fund data of each community as an independent attribute table according to the subject and the attribute to be combined of each indicator dimension in the form of attribute values.

[0056] In this embodiment, the maintenance fund data of each community is stored as an independent attribute table according to the subject and the attribute to be combined of each indicator dimension, in the form of attribute values, for subsequent user queries.

[0057] For example, the time dimension is based on time. The statistics are based on the time when the repair business occurred, and the attributes are broken down according to time. For example, if the business occurred on May 18, 2024, the attribute breakdown is shown in the table below:

[0058]

[0059] If new attribute rules are needed for the time dimension, there is no need to recalculate business metrics; simply perform the statistics according to the new attribute dimension.

[0060] Similarly, the physical and business dimensions are designed in the same way. The subject of the statistics remains unchanged. Different combinations of indicators can be created based on the statistical needs of different attributes, just like building blocks that can be assembled according to different attributes.

[0061] S204: Obtain user query request data, and based on the user query request data, determine multiple attributes to be combined across at least three indicator dimensions.

[0062] In this embodiment, user query request data can be parsed using an open-source natural language processing model or a trained natural language processing model to determine user query needs and obtain multiple attributes to be combined across at least three indicator dimensions.

[0063] Specifically, in an optional embodiment of this application, step S204 includes:

[0064] S204a: Based on the user query request data, the query dimension category and query combination attribute are determined by a pre-trained natural language processing model. The pre-trained natural language processing model is obtained by inputting sample data of three-dimensional indicator dimensions of time, physical and business dimensions into an open-source natural language processing model and then training the model.

[0065] S204b: Based on the category of the dimension to be queried and the combined attributes to be queried, determine multiple combined attributes of at least three indicator dimensions.

[0066] S205: Generate corresponding indicator query conditions based on the attributes to be combined from at least three indicator dimensions.

[0067] In this embodiment, at least three dimensions of attributes to be combined can generate combined index query conditions. The combined index can be time dimension + physical dimension + business dimension + process statistics index, where the process statistics index refers to the statistical value (amount) corresponding to each dimension of attributes to be combined.

[0068] For example, we can calculate the "total amount of expenditures incurred in Hongfa Community in May 2024". Such metrics can be generated by combining different metrics using this model.

[0069] S206: Associate the attribute tables of the indicator dimensions corresponding to each attribute to be combined with the business process data to obtain the indicator results that meet the indicator query conditions.

[0070] Association processing refers to associating the attribute tables of the corresponding indicator dimensions for each attribute to be combined with the occurrence amounts in the business process, so as to fill the specific values ​​into the specific positions in the corresponding attribute tables. The resulting combined attribute table is the indicator result that the user wants to query.

[0071] In an optional embodiment of this application, the business process data includes time-dimensional process data, physical-dimensional process data, and business-dimensional process data. Accordingly, step S206 includes: associating the attribute tables of the indicator dimensions corresponding to each combined attribute with the corresponding time-dimensional process data, physical-dimensional process data, and business-dimensional process data in the business process data one-to-one through a preset multi-table cross-dimensional association combination query, so as to generate an indicator result composed of multiple independent attribute tables based on the indicator query conditions for the corresponding time-dimensional process data, physical-dimensional process data, and business-dimensional process data in the business process data.

[0072] Furthermore, in an optional embodiment of this application, the indicator query conditions include time-dimension query conditions, physical-dimension query conditions, and business-dimension query conditions. Accordingly, step S206 generates an indicator result composed of multiple independent attribute tables by combining the time-dimension process data, physical-dimension process data, and business-dimension process data corresponding to the business process data according to the indicator query conditions, including:

[0073] S206a: Aggregate and calculate the time dimension data in the business process data according to the time dimension statistical conditions, and input it into an independent time attribute table corresponding to the time dimension.

[0074] S206b: Aggregate and calculate the physical dimension data in the business process data according to the physical dimension statistical conditions, and input the data into an independent physical attribute table corresponding to the physical dimension.

[0075] S206c: Aggregate and calculate the business dimension data in the business process data according to the business dimension query conditions, and input the data into an independent business attribute table corresponding to the business dimension.

[0076] S206d: Combines the time attribute table, physical attribute table, and business attribute table to generate indicator results.

[0077] In this embodiment, steps S206 to S206c represent the independent calculation and statistical analysis of the maintenance fund data from each indicator dimension. Step S206d refers to combining the business attribute tables corresponding to each indicator dimension in a modular fashion to obtain the final required indicator result.

[0078] Based on the above embodiments, in an optional embodiment of this application, step S206 further includes:

[0079] Step A: Obtain user query and adjustment data.

[0080] In this embodiment, the user query for adjusted data can be a query request information input by the user indicating that one or more attributes to be combined have changed. For example, "total amount of expenditure transactions in Hongfa Community in May 2024" can be adjusted to "total amount of expenditure transactions in Hongfa Community in the first half of 2024".

[0081] Step B: Adjust the data based on the user query and determine at least one attribute to be adjusted for at least one dimension.

[0082] In this embodiment, in step B, determining at least one adjustable attribute to be combined for at least one dimension can also be achieved by parsing the attribute to be combined using a natural language processing model and comparing it with the attribute to be combined parsed from historical query request data to obtain at least one attribute to be combined that has changed as the attribute to be combined for adjustment.

[0083] Step C: Associate the attribute table of the indicator dimension corresponding to the attribute to be combined with the business process data to obtain the updated attribute table corresponding to the attribute to be combined.

[0084] In this embodiment, if the business dimension and physical dimension remain unchanged, it is unnecessary to statistically analyze the indicator data of the attributes to be combined corresponding to these two indicator dimensions. Only the changes in the attribute tables and business process data of the indicator dimensions corresponding to the adjusted attributes to be combined need to be analyzed. For example, if neither the community nor the equipment under maintenance has changed, only the relevant data for the corresponding month needs to be selected for correlation statistics.

[0085] Step D: Merge the updated attribute table into the indicator results to obtain the adjusted indicator results.

[0086] In this embodiment, the attribute table before the update is merged with the previous index results to obtain the adjusted index results. This process is similar to replacing a certain attribute module in a building block model, resulting in a building block model with the adjusted index results.

[0087] In an optional embodiment of this application, the adjustments mentioned in steps A to D can be the replacement, deletion, or addition of the attributes to be combined. Specific details are not limited here. For example, the account change of maintenance funds in a certain community on a certain day is a process indicator, which can be created by considering the community, date, public account, direction of change, and amount. Statistics based on different dimensions of community, date, and public account business can be performed without recalculating such process indicators. Based on time-dimension, physical-dimension, and business-dimension statistical conditions, the process indicator values ​​that meet the conditions can be queried. The same business process indicator can query statistical indicator values ​​from different dimensions, allowing users to build their desired indicator results like building blocks.

[0088] In summary, the maintenance fund data management method provided in this application first obtains indicator design requirement data and maintenance fund data from multiple communities and stores them in independent attribute tables. These independent attribute tables store the relevant maintenance fund data for each indicator dimension and each attribute to be combined. Then, by obtaining user query data, it determines multiple attributes to be combined for at least three indicator dimensions and associates these attributes with business process data to obtain indicator results that meet the query conditions. This method, similar to a building block model, combines the maintenance fund data required by the user, improving the flexibility and real-time performance of maintenance fund data processing.

[0089] At the same time, it can also adjust the data of the corresponding attributes to be combined by adjusting the query request data of the user, and quickly combine the required indicator results by replacing one or several modules in the building block model, instead of recalculating all the indicator dimensions and the corresponding indicator results of the attributes to be combined, which further improves the flexibility, real-time and scientific scalability of maintenance fund data processing.

[0090] Figure 3 A schematic diagram of a maintenance fund data management device provided in this application is shown below. Figure 3 As shown, the maintenance fund data management device provided in this embodiment includes: an acquisition module 31, a dimension creation module 32, and an indicator query module 33.

[0091] Among them, the acquisition module 31 is used to acquire indicator design requirement data and maintenance fund data of multiple communities.

[0092] The dimension creation module 32 is used to determine at least three indicator dimensions based on the indicator design requirements data, and to determine the subject and attributes to be combined for each indicator dimension.

[0093] The dimension creation module 32 is used to store the maintenance fund data of each community as an independent attribute table according to the subject and the attribute to be combined of each indicator dimension in the form of attribute values.

[0094] The indicator query module 33 is used to obtain user query request data and determine multiple attributes to be combined in at least three indicator dimensions based on the user query request data.

[0095] The indicator query module 33 is also used to generate corresponding indicator query conditions based on the attributes to be combined from at least three indicator dimensions.

[0096] The indicator query module 33 is also used to associate the attribute table of the indicator dimension corresponding to each combined attribute with the business process data to obtain the indicator results that meet the indicator query conditions.

[0097] In an optional embodiment of this application, the dimension creation module 32 is specifically used to: perform requirement classification extraction operation based on indicator design requirement data to obtain at least three indicator categories; and determine three indicator dimensions—time dimension, physical dimension, and business dimension—based on each indicator category, wherein one indicator dimension contains at least one subject and at least one attribute to be combined under the subject.

[0098] In an optional embodiment of this application, the indicator query module 33 is specifically used to: determine the category of the dimension to be queried and the combined attributes to be queried based on the user query request data through a pre-trained natural language processing model, wherein the pre-trained natural language processing model is obtained by inputting three-dimensional indicator dimension sample data of time dimension, physical dimension and business dimension into an open source natural language processing model and training the model; and determine multiple combined attributes of at least three indicator dimensions based on the category of the dimension to be queried and the combined attributes to be queried.

[0099] In an optional embodiment of this application, the business process data includes time-dimensional process data, physical-dimensional process data, and business-dimensional process data; the indicator query module 33 is specifically used to: associate the attribute tables of the indicator dimensions corresponding to each combined attribute with the corresponding time-dimensional process data, physical-dimensional process data, and business-dimensional process data in the business process data one-to-one through a preset multi-table cross-dimensional association combination query, so as to generate an indicator result composed of multiple independent attribute tables according to the indicator query conditions of the corresponding time-dimensional process data, physical-dimensional process data, and business-dimensional process data in the business process data.

[0100] In an optional embodiment of this application, the indicator query conditions include time dimension query conditions, physical dimension query conditions, and business dimension query conditions. Accordingly, the indicator query module 33 is specifically used to: aggregate and calculate the time dimension data in the business process data according to the time dimension statistical conditions and input it into an independent time attribute table corresponding to the time dimension; aggregate and calculate the physical dimension data in the business process data according to the physical dimension statistical conditions and input it into an independent physical attribute table corresponding to the physical dimension; aggregate and calculate the business dimension data in the business process data according to the business dimension query conditions and input it into an independent business attribute table corresponding to the business dimension; and combine the time attribute table, physical attribute table, and business attribute table to generate indicator results.

[0101] In an optional embodiment of this application, the indicator query module 33 is further specifically used for: obtaining user query adjustment data; determining at least one adjustment attribute to be combined for at least one dimension based on the user query adjustment data; associating the attribute table of the indicator dimension corresponding to the adjustment attribute with the business process data to obtain an updated attribute table corresponding to the adjustment attribute; and merging the updated attribute table into the indicator result to obtain the adjusted indicator result.

[0102] The maintenance fund data management provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0103] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device provided in this embodiment includes at least one processor 401 and a memory 402. Optionally, the electronic device further includes a communication component 403. The processor 401, memory 402, and communication component 403 are connected via a bus.

[0104] In a specific implementation, at least one processor 401 executes computer execution instructions stored in memory 402, causing at least one processor 401 to perform the above-described method.

[0105] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0106] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0107] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0108] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0109] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0110] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0111] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0112] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0113] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0114] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0115] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0116] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0117] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0118] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method of managing maintenance fund data, characterized by, The method comprises the following steps: obtaining index design requirement data and maintenance fund data of a plurality of cells; determining at least three index dimensions according to the index design requirement data, and determining subjects and to-be-combined attributes of each index dimension; storing the maintenance fund data of each cell as an independent attribute table in the form of attribute values according to the subjects and to-be-combined attributes of each index dimension; obtaining user query request data, and determining a plurality of to-be-combined attributes of at least three index dimensions according to the user query request data; generating corresponding index query conditions according to the to-be-combined attributes of the at least three index dimensions; associating the attribute table of the index dimension corresponding to each to-be-combined attribute with business process data to obtain an index result satisfying the index query conditions.

2. The method of claim 1, wherein, The method comprises the following steps: performing requirement classification extraction according to the index design requirement data to obtain at least three index classifications; determining three index dimensions of time dimension, physical dimension and business dimension according to each index classification, wherein one index dimension comprises at least one subject and at least one to-be-combined attribute under the subject.

3. The method of claim 1, wherein, The method comprises the following steps: performing to-be-queried dimension category and to-be-queried combined attribute according to the user query request data by using a pre-trained natural language processing model, wherein the pre-trained natural language processing model is obtained by inputting three-dimensional index dimension sample data of time dimension, physical dimension and business dimension into an open source natural language processing model and then performing model training; determining a plurality of to-be-combined attributes of at least three index dimensions according to the to-be-queried dimension category and the to-be-queried combined attribute.

4. The method of claim 1, wherein, The business process data comprises time dimension process data, physical dimension process data and business dimension process data. The method comprises the following steps: performing one-to-one association between the attribute table of the index dimension corresponding to each combined attribute and the corresponding time dimension process data, physical dimension process data and business dimension process data in the business process data by using a pre-set multi-table cross-dimension association combined query, so as to generate an index result composed of a plurality of independent attribute tables from the corresponding time dimension process data, physical dimension process data and business dimension process data in the business process data according to the index query conditions.

5. The method of claim 4, wherein, The index query conditions comprise time dimension query conditions, physical dimension query conditions and business dimension query conditions. The method comprises the following steps: aggregating and calculating the time dimension data in the business process data according to time dimension statistical conditions to input an independent time attribute table corresponding to the time dimension; aggregating and calculating the physical dimension data in the business process data according to physical dimension statistical conditions to input an independent physical attribute table corresponding to the physical dimension; The business dimension data in the business process data is aggregated according to a business dimension query condition to generate an independent business attribute table corresponding to the business dimension; The time attribute table, the physical attribute table and the business attribute table are combined to generate an index result.

6. The method according to any one of claims 1 to 5, characterized in that, After the attribute table of the index dimension corresponding to each combined attribute is associated with the business process data to obtain an index result satisfying the index query condition, the method further includes: Obtaining user query adjustment data; According to the user query adjustment data, at least one adjustment combined attribute of at least one dimension is determined; The attribute table of the index dimension corresponding to the adjustment combined attribute is associated with the business process data to obtain an updated attribute table corresponding to the adjustment combined attribute; The updated attribute table is merged into the index result to obtain an adjusted index result.

7. A maintenance fund data management device characterized by comprising: The method includes: An acquisition module is configured to acquire index design requirement data and maintenance fund data of a plurality of cells; A dimension creation module is configured to determine at least three index dimensions according to the index design requirement data, and determine a subject and a combined attribute of each index dimension; The dimension creation module is configured to store the maintenance fund data of each cell as an independent attribute table in the form of an attribute value according to the subject and the combined attribute of each index dimension; An index query module is configured to acquire user query request data, and determine a plurality of combined attributes of the at least three index dimensions according to the user query request data; The index query module is further configured to generate an index query condition corresponding to the combined attributes of the at least three index dimensions; The index query module is further configured to associate the attribute table of the index dimension corresponding to each combined attribute with the business process data to obtain an index result satisfying the index query condition.

8. An electronic device, comprising: The method includes: A memory and a processor; The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory, so that the processor executes the method in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to implement the method in any one of claims 1 to 6.

10. A computer program product, characterised in that, The computer program is executed by the processor to implement the method in any one of claims 1 to 6.

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