Index data processing method and device, electronic equipment and readable medium

Abstract

The invention provides an index data processing method and device, electronic equipment and a readable medium, and the method comprises the steps: determining parameter items and parameter values in an original report of index data; in response to an instruction of drilling down or rolling up the index data, calculating the numerical precision of the index data according to a preset scaling model; according to the drill-down instruction or the roll-up instruction, performing numerical unit matching processing on the index data; and updating the original report of the index data according to the numerical value precision and the matched numerical value unit to obtain a target report. According to the embodiment of the invention, the dynamic adjustment of the numerical value precision of the index data is realized, and the numerical value processing amount after business change is reduced.

Description

Background Art

[0002] Currently, in the data processing technology industry, after data is rolled up or drilled down, its data accuracy and unit are fixed. After data is rolled up, the data usually becomes very large, or after data is drilled down, the data becomes very small, which is inconvenient for the next step of data processing and reading.

[0003] In the related art, special processing is performed on the chart, or a relatively balanced data unit is found to take into account the terminal hierarchical units of roll-up and drill-down.

[0004] However, the existing unit processing method is relatively fixed and coupled with business indicators. When the business changes, the numerical accuracy cannot meet the usage requirements, which may cause additional data processing.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0006] The purpose of the present disclosure is to provide an indicator data processing method, device, electronic device and readable medium, which are used to overcome, at least to a certain extent, the problem that the data accuracy of indicator data cannot be dynamically adjusted due to the limitations and defects of related technologies.

[0007] According to a first aspect of an embodiment of the present disclosure, there is provided an indicator data processing method, comprising: determining parameter items and parameter values ​​in an original report of indicator data; in response to an instruction to drill down or roll up the indicator data, calculating the numerical precision of the indicator data according to a preset scaling model; performing numerical unit matching processing on the indicator data according to the drill down instruction or the roll up instruction; and updating the original report of the indicator data according to the numerical precision and the numerical unit after matching processing to obtain a target report.

[0008] In an exemplary embodiment of the present disclosure, the parameter item includes an indicator name and an indicator dimension, and in response to an instruction to drill down or roll up the indicator data, calculating the numerical precision of the indicator data according to a preset scaling model includes:

[0009] In response to an instruction to drill down or roll up the indicator data, determining a zoom factor according to the preset zoom model;

[0010] Update the indicator dimension corresponding to any of the indicator names according to the zoom multiple;

[0011] The numerical precision of the indicator data is calculated according to the updated indicator dimension.

[0012] In an exemplary embodiment of the present disclosure, the parameter item includes an indicator type and an indicator unit, and updating an indicator dimension corresponding to any indicator name according to the zoom factor includes:

[0013] Determine the correspondence between the indicator dimension and the indicator unit of any indicator name to obtain a zoomed report;

[0014] Associating the original report with the zoomed report according to the indicator type;

[0015] Determine the zoom factor corresponding to the indicator dimension in the original report according to the association result;

[0016] The indicator dimension corresponding to any of the indicator names is updated according to the zoom factor.

[0017] In an exemplary embodiment of the present disclosure, calculating the numerical precision of the indicator data according to the updated indicator dimension includes:

[0018] Determine an indicator dimension before performing the drill-down or the roll-up, recorded as a first indicator dimension;

[0019] Determine an indicator dimension after the drill-down or the roll-up, recorded as a second indicator dimension;

[0020] The indicator data is scaled according to the first indicator dimension and the second indicator dimension to update the numerical precision of the indicator data.

[0021] In an exemplary embodiment of the present disclosure, calculating the numerical precision of the indicator data according to the updated indicator dimension further includes:

[0022] After calculating the numerical precision, the indicator data with the same indicator dimension are merged and calculated.

[0023] In an exemplary embodiment of the present disclosure, scaling the indicator data according to the first indicator dimension and the second indicator dimension to update the numerical precision of the indicator data includes:

[0024] If the instruction is an instruction to roll up the indicator data, determining a plurality of transition dimensions between the first indicator dimension and the second indicator dimension;

[0025] According to the order of the first indicator dimension, the transition dimension sorted from small to large, and the second indicator dimension, the indicator data between any two adjacent indicator dimensions are amplified in sequence;

[0026] The numerical precision of the index data is updated according to the result of the amplification process.

[0027] In an exemplary embodiment of the present disclosure, scaling the indicator data according to the first indicator dimension and the second indicator dimension to update the numerical precision of the indicator data includes:

[0028] If the instruction is an instruction to drill down on the indicator data, determining a plurality of transition dimensions between the first indicator dimension and the second indicator dimension;

[0029] According to the order of the first indicator dimension, the transition dimension sorted from large to small, and the second indicator dimension, the indicator data between any two adjacent indicator dimensions are sequentially reduced;

[0030] The numerical precision of the index data is updated according to the result of the reduction process.

[0031] According to a second aspect of an embodiment of the present disclosure, there is provided an indicator data processing device, including:

[0032] A determination module, configured to determine parameter items and parameter values ​​in an original report of indicator data;

[0033] a calculation module, configured to calculate the numerical accuracy of the indicator data according to a preset scaling model in response to an instruction to drill down or roll up the indicator data;

[0034] a matching module, configured to perform numerical unit matching processing on the indicator data according to the drill-down instruction or the roll-up instruction;

[0035] The updating module is configured to update the original report of the indicator data according to the numerical precision and the numerical unit after matching processing to obtain a target report.

[0036] According to a third aspect of the present disclosure, there is provided an electronic device, comprising: a memory; and a processor coupled to the memory, wherein the processor is configured to execute any one of the methods described above based on instructions stored in the memory.

[0037] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a program is stored, and when the program is executed by a processor, the indicator data processing method as described in any one of the above items is implemented.

[0038] The disclosed embodiment determines parameter items and parameter values ​​in an original report of indicator data, and in response to an instruction to drill down or roll up the indicator data, calculates the numerical precision of the indicator data according to a preset scaling model, and performs numerical unit matching processing on the indicator data according to the drill down instruction or the roll up instruction, and then updates the original report of the indicator data according to the numerical precision and the numerical unit after matching processing to obtain a target report, thereby realizing dynamic adjustment of the numerical precision of the indicator data and reducing the amount of numerical processing after business changes.

[0039] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0041] Figure 1 A schematic diagram showing an exemplary system architecture to which the indicator data processing solution of an embodiment of the present invention can be applied;

[0042] Figure 2 is a flow chart of an indicator data processing method in an exemplary embodiment of the present disclosure;

[0043] Figure 3 is a flow chart of another indicator data processing method in an exemplary embodiment of the present disclosure;

[0044] Figure 4 is a flow chart of another indicator data processing method in an exemplary embodiment of the present disclosure;

[0045] Figure 5 is a flow chart of another indicator data processing method in an exemplary embodiment of the present disclosure;

[0046] Figure 6 is a flow chart of another indicator data processing method in an exemplary embodiment of the present disclosure;

[0047] Figure 7 is a flow chart of another indicator data processing method in an exemplary embodiment of the present disclosure;

[0048] Figure 8 is a flow chart of another indicator data processing method in an exemplary embodiment of the present disclosure;

[0049] Fig. 9is a schematic diagram of a dynamic data accuracy calculation process of another indicator data processing method in an exemplary embodiment of the present disclosure;

[0050] Fig.10 is a block diagram of an indicator data processing device in an exemplary embodiment of the present disclosure;

[0051] Fig.11 is a block diagram of an electronic device in an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0052] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as being limited to the examples set forth herein; on the contrary, these embodiments are provided so that the present disclosure will be more comprehensive and complete, and the concepts of the example embodiments are fully conveyed to those skilled in the art. The described features, structures, or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0053] In addition, the accompanying drawings are only schematic diagrams of the present disclosure, and the same reference numerals in the drawings represent the same or similar parts, so their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities, which do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0054] Figure 1 A schematic diagram showing an exemplary system architecture to which the indicator data processing solution according to an embodiment of the present invention can be applied.

[0055] like Figure 1 As shown, the system architecture 100 may include one or more of terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0056] It should be understood that Figure 1The number of terminal devices, networks and servers in the embodiment is only for illustration. According to the implementation requirements, there may be any number of terminal devices, networks and servers. For example, the server 105 may be a server cluster composed of multiple servers.

[0057] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Terminal devices 101, 102, 103 can be various electronic devices with display screens, including but not limited to smart phones, tablet computers, portable computers, desktop computers, etc.

[0058] In some embodiments, the index data processing method provided in the embodiments of the present invention is generally executed by the server 105, and accordingly, the index data processing device is generally set in the terminal device 103 (or the terminal device 101 or 102). In other embodiments, some terminals may have functions similar to those of the server device to execute the method.

[0059] The exemplary embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0060] Figure 2 is a flow chart of an indicator data processing method in an exemplary embodiment of the present disclosure.

[0061] refer to Figure 2 , the indicator data processing method may include:

[0062] Step S202: determining parameter items and parameter values ​​in the original report of indicator data.

[0063] Step S204 , in response to an instruction to drill down or roll up the indicator data, calculate the numerical precision of the indicator data according to a preset scaling model.

[0064] Step S206: performing numerical unit matching processing on the indicator data according to the drill-down instruction or the roll-up instruction.

[0065] Step S208: updating the original report of the indicator data according to the numerical precision and the numerical unit after matching processing to obtain a target report.

[0066] The disclosed embodiment determines parameter items and parameter values ​​in an original report of indicator data, and in response to an instruction to drill down or roll up the indicator data, calculates the numerical precision of the indicator data according to a preset scaling model, and performs numerical unit matching processing on the indicator data according to the drill down instruction or the roll up instruction, and then updates the original report of the indicator data according to the numerical precision and the numerical unit after matching processing to obtain a target report, thereby realizing dynamic adjustment of the numerical precision of the indicator data and reducing the amount of numerical processing after business changes.

[0067] Next, each step of the indicator data processing method is described in detail.

[0068] In an exemplary embodiment of the present disclosure, Figure 3 As shown, the parameter items include an indicator name and an indicator dimension. In response to an instruction to drill down or roll up the indicator data, the numerical accuracy of the indicator data is calculated according to a preset scaling model, including:

[0069] Step S302, in response to an instruction to drill down or roll up the indicator data, determining a zoom factor according to the preset zoom model.

[0070] Step S304: update the indicator dimension corresponding to any of the indicator names according to the zoom factor.

[0071] Step S306, calculating the numerical precision of the indicator data according to the updated indicator dimension.

[0072] In the above embodiment, by responding to an instruction to drill down or roll up the indicator data, determining the zoom factor according to the preset zoom model, and updating the indicator dimension corresponding to any indicator name according to the zoom factor, and then calculating the numerical accuracy of the indicator data according to the updated indicator dimension, dynamic adjustment of the indicator data is achieved.

[0073] In an exemplary embodiment of the present disclosure, Figure 4 As shown, the parameter items include indicator type and indicator unit, and updating the indicator dimension corresponding to any indicator name according to the zoom multiple includes:

[0074] Step S402, determining the correspondence between the indicator dimension and the indicator unit of any indicator name to obtain a zoomed report.

[0075] Step S404: Associating the original report with the zoomed report according to the indicator type.

[0076] Step S406, determining the zoom factor corresponding to the indicator dimension in the original report according to the association result.

[0077] Step S408: updating the indicator dimension corresponding to any of the indicator names according to the zoom factor.

[0078] In the above embodiment, by associating the original report and the zoomed report according to the indicator type, and determining the zoom factor corresponding to the indicator dimension in the original report according to the association result, when the indicator dimension is adjusted in response to business needs, the numerical accuracy of the indicator data is dynamically adjusted according to the correspondence between the zoom factor and the indicator dimension.

[0079] In an exemplary embodiment of the present disclosure, Figure 5 As shown, calculating the numerical precision of the indicator data according to the updated indicator dimension includes:

[0080] Step S502, determining an indicator dimension before performing the drill-down or the roll-up, recorded as a first indicator dimension.

[0081] Step S504, determining the indicator dimension after the drill-down or the roll-up, recorded as the second indicator dimension.

[0082] Step S506: scaling the indicator data according to the first indicator dimension and the second indicator dimension to update the numerical precision of the indicator data.

[0083] In an exemplary embodiment of the present disclosure, Figure 6 As shown, calculating the numerical precision of the indicator data according to the updated indicator dimension also includes:

[0084] Step S602, merging and calculating the indicator data with the same indicator dimension after calculating the numerical precision.

[0085] In an exemplary embodiment of the present disclosure, Figure 7 As shown, scaling the indicator data according to the first indicator dimension and the second indicator dimension to update the numerical precision of the indicator data includes:

[0086] Step S702: If the instruction is an instruction to roll up the indicator data, a plurality of transition dimensions between the first indicator dimension and the second indicator dimension are determined.

[0087] Step S704, according to the order of the first indicator dimension, the transition dimension sorted from small to large, and the second indicator dimension, the indicator data between any two adjacent indicator dimensions are amplified in sequence.

[0088] Step S706: updating the numerical precision of the indicator data according to the result of the amplification process.

[0089] In an exemplary embodiment of the present disclosure, Figure 8 As shown, scaling the indicator data according to the first indicator dimension and the second indicator dimension to update the numerical precision of the indicator data includes:

[0090] Step S802: If the instruction is an instruction to drill down on the indicator data, determine a plurality of transition dimensions between the first indicator dimension and the second indicator dimension.

[0091] Step S804, according to the order of the first indicator dimension, the transition dimension sorted from large to small, and the second indicator dimension, the indicator data between any two adjacent indicator dimensions are reduced in sequence.

[0092] Step S806: updating the numerical precision of the indicator data according to the result of the reduction process.

[0093] Below Fig. 9 As an example, the dynamic data accuracy calculation process 900 in the indicator data processing solution is specifically described.

[0094] like Fig. 9 As shown, the dynamic data accuracy calculation process 900 includes four stages: establishing a basic data model 902, designing an indicator scaling model 904, data fusion 906 and dynamic calculation 908. The specific processing steps are as follows.

[0095] Step 1. Establish a basic data model: Establish data model TB_A, whose type is t1, and define the drill-down dimensions as: Dimension 1, Dimension 2, Dimension 3. The basic data model structure is shown in Table 1.

[0096] Table 1

[0097] Indicator name Indicator Type unit Dimension 1 Dimension 2 Dimension 3 value Idx_1 t1 u_1 dim1_1 dim2_1 dim3_1 v_1 Idx_1 t1 u_1 dim1_1 dim2_1 dim3_2 v_2 Idx_1 t1 u_1 dim1_1 dim2_2 dim3_3 v_3

[0098] Among them, "Indicator Name" is the indicator item to which the data belongs, and each indicator is a data model. "Indicator Type" defines the indicator attribute, which is used to distinguish indicators of different categories and define different scaling values ​​for different units. "Unit" is the measurement unit of the current indicator data. "Dimension 1" is the data description dimension of the current indicator. Drill-down is performed on Dimension 1, Dimension 2, and Dimension 3. "Value" is the measurement of the current data indicator.

[0099] Step 2. Design the indicator scaling model: In this stage, the scaling model F is defined. The indicator scaling model is the key factor to control the accuracy of automatic scaling of indicator measurement data. According to the scaling model, the data unit is controlled when drilling down, and the data accuracy is dynamically calculated. The scaling model structure is shown in Table 2.

[0100] Table 2

[0101] Indicator Type unit Dimensions Zoom ratio t1 u_1 Dimension 1 r1 t1 u_2 Dimension 2 r2 t1 u_3 Dimension 3 r3 t2 u_4 Dimension 1 r4 t2 u_5 Dimension 2 r5 t2 u_6 Dimension 3 r6

[0102] When using, it is necessary to convert the scaled model columns to rows, extract the model t1, and the view converted according to the "unit" and "dimension" is Va, and its structure is shown in Table 3 below.

[0103] Table 3

[0104] Indicator Type Dimension 1 Unit 1 Dimension 2 Unit 2 Dimension 3 Unit 3 t1 r1 u_1 r2 u_2 r3 u_3 t2 r4 u_4 r5 u_5 r6 u_6

[0105] Step 3: Build a scalable data model: Expand the basic data model TB_A to make it a scalable data view. The specific expansion method is: associate the scalable view Va obtained in step 2 with the model TB_A, and the association is based on the "indicator type", and the following model M is obtained as shown in Table 4.

[0106] Table 4

[0107]

[0108] Step 4: Dynamically calculate data accuracy: The drill-down levels are: Dimension 1, Dimension 2, Dimension 3. At each drill-down / roll-up level, the output result unit is required to be the unit corresponding to the drill-down / roll-up dimension. At the same time, the value is the value after scaling calculation under the current unit.

[0109] For the model M obtained in step 3, roll up the dimensions according to the indicator name. For example, roll up dim3_1 of dimension 3 of idx_1. The result is the grouped aggregation value of dimension 2. The result unit uses unit 2. The result value is calculated based on the scaling factor r2 corresponding to dimension 2 by scaling v_1. The results are shown in Tables 5 and 6 below.

[0110] Table 5

[0111] Indicator name Indicator Type Dimension 1 Dimension 2 unit value Idx_1 t1 dim1_1 dim2_1 u_2 (v_1+v_2)*r2 Idx_1 t1 dim1_1 dim2_2 u_2 v_3*r2

[0112] Table 6

[0113] Indicator name Indicator Type Dimension 1 unit value Idx_1 t1 dim1_1 u_1 (v_1+v_2+v_3)*r2

[0114] Drilling down is the reverse operation of rolling up. Drilling down is performed on the above results, that is, the aggregation dimension is dimension 2, and the measurement data accuracy is processed according to the unit corresponding to dimension 2. The results are shown in Table 7 below.

[0115] Table 7

[0116] Indicator name Indicator Type Dimension 1 Dimension 2 unit value Idx_1 t1 dim1_1 dim2_1 u_2 (v_1+v_2)*r2 Idx_1 t1 dim1_1 dim2_2 u_2 v_3*r2

[0117] The following is an example of existing express delivery business data to illustrate the embodiments of the present disclosure. The data is divided into four dimensions: region, area, and site, and divided into multiple analysis indicators according to demand. Here, "the number of collection orders" and "the number of couriers" are listed as examples.

[0118] The first step is to construct a basic data model (the data are all test data, and only the first three data of each indicator are listed) as shown in Table 8 below.

[0119] Table 8

[0120]

[0121]

[0122] Step 2. Design the indicator scaling model: The scaling model is a general model as shown in Table 9 below, covering any indicator type in the business field.

[0123] Table 9

[0124] Indicator Type unit Dimensions Zoom ratio Business Volume Million orders Region 1000000 Business Volume Ten thousand orders area 10000 Business Volume one Site 1 Human Resources 10,000 people Region 10000 Human Resources Thousands area 1000 Human Resources people Site 1

[0125] The third step is to construct a telescoping model as shown in Table 10.

[0126] Table 10

[0127]

[0128]

[0129] Step 4: Dynamically calculate data accuracy: For the result model in step 3, aggregate the sites and roll them up to the regions, as shown in Table 11 below.

[0130] Table 11

[0131] Indicator name Indicator Type unit Region area date value Number of orders collected Business Volume Ten thousand orders North China Region A 2022-08 200 Number of orders collected Business Volume Ten thousand orders North China Area B 2022-08 380 Number of couriers Human Resources Thousands North China Region A 2022-08 23 Number of couriers Human Resources Thousands North China Area B 2022-08 42

[0132] In the results shown in Table 11 above, the value units have changed, and the values ​​themselves are calculated with the scaling factor corresponding to the roll-up dimension, so the results meet the readability requirements.

[0133] Corresponding to the above method embodiments, the present disclosure also provides an indicator data processing device, which can be used to execute the above method embodiments.

[0134] Fig.10 It is a block diagram of an indicator data processing device in an exemplary embodiment of the present disclosure.

[0135] refer to Fig.10 , the indicator data processing device 1000 may include:

[0136] The determination module 1002 is configured to determine parameter items and parameter values ​​in an original report of indicator data.

[0137] The calculation module 1004 is configured to calculate the numerical accuracy of the indicator data according to a preset scaling model in response to an instruction to drill down or roll up the indicator data.

[0138] The matching module 1006 is configured to perform numerical unit matching processing on the indicator data according to the drill-down instruction or the roll-up instruction.

[0139] The updating module 1008 is configured to update the original report of the indicator data according to the numerical precision and the numerical unit after matching processing to obtain a target report.

[0140] In an exemplary embodiment of the present disclosure, the calculation module 1004 is also configured to: in response to an instruction to drill down or roll up the indicator data, determine a zoom factor according to the preset zoom model; update the indicator dimension corresponding to any of the indicator names according to the zoom factor; and calculate the numerical accuracy of the indicator data according to the updated indicator dimension.

[0141] In an exemplary embodiment of the present disclosure, the parameter items include indicator types and indicator units, and the calculation module 1004 is also configured to: determine the correspondence between the indicator dimensions of any of the indicator names and the indicator units to obtain a zoomed report; associate the original report and the zoomed report according to the indicator type; determine the zoom multiple corresponding to the indicator dimension in the original report according to the association result; and update the indicator dimension corresponding to any of the indicator names according to the zoom multiple.

[0142] In an exemplary embodiment of the present disclosure, the calculation module 1004 is further configured to: determine the indicator dimension before performing the drill-down or the roll-up, recorded as the first indicator dimension; determine the indicator dimension after performing the drill-down or the roll-up, recorded as the second indicator dimension; and scale the indicator data according to the first indicator dimension and the second indicator dimension to update the numerical accuracy of the indicator data.

[0143] In an exemplary embodiment of the present disclosure, the calculation module 1004 is further configured to: merge and calculate the indicator data with the same indicator dimension after calculating the numerical precision.

[0144] In an exemplary embodiment of the present disclosure, the calculation module 1004 is further configured to: if the instruction is an instruction to roll up the indicator data, then determine multiple transition dimensions between the first indicator dimension and the second indicator dimension; sort the indicator data between any two adjacent indicator dimensions in order according to the first indicator dimension, the transition dimension sorted from small to large, and the second indicator dimension; and update the numerical precision of the indicator data according to the result of the amplification process.

[0145] In an exemplary embodiment of the present disclosure, the calculation module 1004 is further configured to: if the instruction is an instruction to drill down on the indicator data, determine multiple transition dimensions between the first indicator dimension and the second indicator dimension; sort the first indicator dimension, the transition dimension sorted from large to small, and the second indicator dimension in order, and perform reduction processing on the indicator data between any two adjacent indicator dimensions in turn; and update the numerical precision of the indicator data according to the result of the reduction processing.

[0146] Since the functions of the indicator data processing device 1000 have been described in detail in the corresponding method embodiments, the present disclosure will not elaborate on them here.

[0147] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0148] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

[0149] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as a "circuit", "module" or "system".

[0150] Refer to the following Fig.11 The electronic device 1100 according to this embodiment of the present invention is described. Fig.11 The electronic device 1100 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0151] like Fig.11As shown, the electronic device 1100 is in the form of a general computing device. The components of the electronic device 1100 may include but are not limited to: at least one processing unit 1110, at least one storage unit 1120, and a bus 1130 connecting different system components (including the storage unit 1120 and the processing unit 1110).

[0152] The storage unit stores program codes, which can be executed by the processing unit 1110, so that the processing unit 1110 performs the steps according to various exemplary embodiments of the present invention described in the above “Exemplary Method” section of this specification. For example, the processing unit 1110 can perform the method shown in the embodiment of the present disclosure.

[0153] The storage unit 1120 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 11201 and / or a cache storage unit 11202 , and may further include a read-only storage unit (ROM) 11203 .

[0154] The storage unit 1120 may also include a program / utility 11204 having a set (at least one) of program modules 11205, such program modules 11205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0155] Bus 1130 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0156] The electronic device 1100 may also communicate with one or more external devices 1140 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 1100, and / or communicate with any device that enables the electronic device 1100 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 1150. Furthermore, the electronic device 1100 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 1160. As shown, the network adapter 1160 communicates with other modules of the electronic device 1100 via a bus 1130. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 1100, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0157] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0158] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present specification.

[0159] The program product for implementing the above method according to an embodiment of the present invention can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0160] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0161] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0162] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0163] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0164] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0165] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are to be considered exemplary only, and the true scope and concept of the present disclosure are indicated by the claims.

Claims

1. A method for processing index data, characterized in that: include: Determine the parameter items and parameter values ​​in the original report of the indicator data; In response to an instruction to drill down or roll up the indicator data, calculating the numerical precision of the indicator data according to a preset scaling model; According to the drill-down instruction or the roll-up instruction, performing numerical unit matching processing on the indicator data; The original report of the indicator data is updated according to the numerical precision and the numerical unit after matching processing to obtain a target report.

2. The indicator data processing method according to claim 1, characterized in that: The parameter items include an indicator name and an indicator dimension. In response to an instruction to drill down or roll up the indicator data, the numerical precision of the indicator data is calculated according to a preset scaling model, including: In response to an instruction to drill down or roll up the indicator data, determining a zoom factor according to the preset zoom model; Update the indicator dimension corresponding to any of the indicator names according to the zoom multiple; The numerical precision of the indicator data is calculated according to the updated indicator dimension.

3. The indicator data processing method according to claim 2, characterized in that: The parameter items include indicator type and indicator unit, and updating the indicator dimension corresponding to any indicator name according to the scaling factor includes: Determine the correspondence between the indicator dimension and the indicator unit of any indicator name to obtain a zoomed report; Associating the original report with the zoomed report according to the indicator type; Determine the zoom factor corresponding to the indicator dimension in the original report according to the association result; The indicator dimension corresponding to any of the indicator names is updated according to the zoom factor.

4. The indicator data processing method according to claim 2, characterized in that: Calculating the numerical precision of the indicator data according to the updated indicator dimension includes: Determine an indicator dimension before performing the drill-down or the roll-up, recorded as a first indicator dimension; Determine an indicator dimension after the drill-down or the roll-up, recorded as a second indicator dimension; The indicator data is scaled according to the first indicator dimension and the second indicator dimension to update the numerical precision of the indicator data.

5. The indicator data processing method according to claim 2, characterized in that: Calculating the numerical precision of the indicator data according to the updated indicator dimension also includes: After calculating the numerical precision, the indicator data with the same indicator dimension are merged and calculated.

6. The indicator data processing method according to claim 4, characterized in that: Scaling the indicator data according to the first indicator dimension and the second indicator dimension to update the numerical precision of the indicator data includes: If the instruction is an instruction to roll up the indicator data, determining a plurality of transition dimensions between the first indicator dimension and the second indicator dimension; According to the order of the first indicator dimension, the transition dimension sorted from small to large, and the second indicator dimension, the indicator data between any two adjacent indicator dimensions are amplified in sequence; The numerical precision of the index data is updated according to the result of the amplification process.

7. The indicator data processing method according to claim 4, characterized in that: Scaling the indicator data according to the first indicator dimension and the second indicator dimension to update the numerical precision of the indicator data includes: If the instruction is an instruction to drill down on the indicator data, determining a plurality of transition dimensions between the first indicator dimension and the second indicator dimension; According to the order of the first indicator dimension, the transition dimension sorted from large to small, and the second indicator dimension, the indicator data between any two adjacent indicator dimensions are sequentially reduced; The numerical precision of the index data is updated according to the result of the reduction process.

8. An indicator data processing device, characterized in that: include: A determination module, configured to determine parameter items and parameter values ​​in an original report of indicator data; a calculation module, configured to calculate the numerical accuracy of the indicator data according to a preset scaling model in response to an instruction to drill down or roll up the indicator data; a matching module, configured to perform numerical unit matching processing on the indicator data according to the drill-down instruction or the roll-up instruction; The updating module is configured to update the original report of the indicator data according to the numerical precision and the numerical unit after matching processing to obtain a target report.

9. An electronic device, characterized in that: include: Memory; as well as A processor coupled to the memory, the processor being configured to execute the indicator data processing method according to any one of claims 1 to 7 based on instructions stored in the memory.

10. A computer-readable storage medium having a program stored thereon, wherein when the program is executed by a processor, the indicator data processing method according to any one of claims 1 to 7 is implemented.

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