Data visualization methods, devices, computer equipment, and storage media

Abstract

This disclosure relates to the field of data processing technology, and discloses a method, apparatus, computer equipment, and storage medium for visualizing data. The method includes: acquiring target data to be displayed and multiple types of chart information corresponding to the target data, the chart information being used to visualize the target data in chart form; associating the multiple types of chart information according to a preset data structure to generate target chart information corresponding to the target data; bitmapping the target chart information to generate a data topology corresponding to the target chart information; and visualizing the target data based on the data topology. By implementing the technical solution of this disclosure, the chaotic storage of chart information is avoided, orderly storage of chart information is achieved, effective data traceability can be realized by combining data topology, and the credibility of data visualization is improved.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, specifically to a data visualization method, apparatus, computer equipment, and storage medium. Background Technology

[0002] With the development of digitalization, data, as a crucial element, is most directly used for data visualization. Data visualization takes various forms, including charts and forms. However, data visualization is often fragmented, which hinders data traceability, easily leads to data confusion, and affects the credibility of the data visualization. Summary of the Invention

[0003] In view of this, the present disclosure provides a data visualization method, apparatus, computer equipment, and storage medium to solve the problem of data scattering and difficulty in data traceability during the data visualization process.

[0004] In a first aspect, embodiments of this disclosure provide a data visualization method, comprising: acquiring target data to be displayed and multiple types of chart information corresponding to the target data, wherein the chart information is used to visualize the target data in the form of charts; associating multiple types of chart information according to a preset data structure to generate target chart information corresponding to the target data; bitmapping the target chart information to generate a data topology corresponding to the target chart information; and visualizing the target data based on the data topology.

[0005] The data visualization method provided in this disclosure, by setting a corresponding data structure to associate different types of chart information, can statistically integrate chart information used for data visualization into a single target chart, avoiding chaotic storage of chart information and achieving orderly storage. Furthermore, by determining the corresponding data topology through bitmap formatting, visualization of the target data is implemented according to the data topology. Thus, effective data traceability can be achieved by combining the data topology, improving the credibility of the data visualization.

[0006] Secondly, embodiments of this disclosure provide a data visualization display device, comprising: an acquisition module, configured to acquire target data to be displayed and multiple types of chart information corresponding to the target data, the chart information being used to visualize the target data in the form of charts; an association module, configured to associate multiple types of chart information according to a preset data structure to generate target chart information corresponding to the target data; a bitmap conversion module, configured to convert the target chart information into a bitmap to generate a data topology corresponding to the target chart information; and a visualization module, configured to visualize the target data based on the data topology.

[0007] Thirdly, this disclosure provides a computer device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the data visualization method described in the first aspect or any corresponding embodiment.

[0008] Fourthly, embodiments of this disclosure provide a computer-readable storage medium storing computer instructions for causing a computer to execute the data visualization method described in the first aspect or any corresponding embodiment. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the specific embodiments of this disclosure or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0010] Figure 1 This is a flowchart illustrating a method for visualizing data according to some embodiments of the present disclosure;

[0011] Figure 2 This is a flowchart illustrating another data visualization method according to some embodiments of the present disclosure;

[0012] Figure 3 This is a schematic diagram of target icon information according to some embodiments of the present disclosure;

[0013] Figure 4 This is a schematic diagram of another target icon information according to some embodiments of this disclosure;

[0014] Figure 5 This is a schematic diagram of a bitmapped data topology according to some embodiments of this disclosure;

[0015] Figure 6 This is another graphical representation of a data topology according to some embodiments of the present disclosure;

[0016] Figure 7 This is a structural block diagram of a data visualization display device according to an embodiment of the present disclosure;

[0017] Figure 8 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present disclosure. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0019] In data visualization within related technologies, data metrics and detailed tables are often managed in a decentralized manner, requiring extensive reading of technical documents to locate the corresponding data, which hinders alignment with business logic. Data metrics are derived from detailed table statistics; however, due to this decentralized management, real-time data analysis is difficult, leading to discrepancies between the detailed tables and statistical results. Tracing the source of problematic data is challenging when investigating data issues. While data is stored in an orderly fashion, data integrity verification and comparison are not adequately considered. Comparative analysis of two sets of statistical metrics and detailed tables requires establishing separate data checking logic, which is time-consuming. Furthermore, the relationship between statistical metrics and detailed table fields necessitates separate documentation for each, often resulting in delayed updates and data inconsistencies due to timeliness concerns.

[0020] Based on this, the technical solution can integrate the statistical information of charts used for data visualization into a single target chart, avoiding the chaotic storage of chart information, achieving orderly storage of chart information, and enabling effective data traceability by combining data topology, thereby improving the credibility of data visualization.

[0021] According to an embodiment of this disclosure, a method for visualizing data is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0022] This embodiment provides a method for visualizing data, which can be used on computer devices such as computers, laptops, and servers. Figure 1 This is a flowchart of a data visualization method according to an embodiment of the present disclosure, such as... Figure 1 As shown, the process includes the following steps:

[0023] Step S101: Obtain the target data to be displayed and multiple types of chart information corresponding to the target data. The chart information is used to visualize the target data in the form of charts.

[0024] The target data is the data that needs to be visualized on computer devices. This target data is associated with multiple indicator data from different charts and other data such as detailed data from different forms.

[0025] The type of chart information is used to represent different types of forms, visually displaying target data. Specifically, this type can include charts, detailed lists, etc. Chart information is used to represent data information in charts related to the target data, as well as list data in detailed lists.

[0026] The target data to be displayed has a corresponding data source, which can store data in a categorized manner and support structured extraction of detailed data. Computer devices can access the data source to extract the target data to be displayed, and at the same time, extract the indicators and detailed data associated with the target data, and determine the chart information such as charts and detailed lists where the associated indicators and detailed data are located.

[0027] Step S102: Associate multiple types of chart information according to a preset data structure to generate target chart information corresponding to the target data.

[0028] The default data structure is a pre-designed structure for linking different types of chart information. Different types of chart information have corresponding data storage structures, resulting in fragmented data storage and making it difficult to effectively trace the source of the target data.

[0029] The target chart information is a table containing association information for different types of charts. In other words, this target chart information includes various types of chart information corresponding to the target data. Therefore, the associated indicator data and detailed data can be identified through this target chart information. When the indicator data or detailed data changes, this target data can be updated accordingly in real time.

[0030] Specifically, the data in each type of chart information is processed according to a preset data structure to obtain structured data corresponding to each chart information. The structured data corresponding to different types of chart information are then associated according to the preset data structure to merge the structured data corresponding to different types of chart information into the same target chart information.

[0031] Step S103: Convert the target chart information into a bitmap to generate the data topology corresponding to the target chart information.

[0032] Data topology is used to represent the position of each field in the data structure within the target chart information and the corresponding numerical information of each field. Specifically, the target chart information is generated based on a preset data structure, which includes the fields of the preset data structure. A bitmap algorithm can be used to convert the target chart information into a bitmap, mapping each field in the preset data structure to a specific bit in the bitmap. Then, according to the mapping relationship between each field and each bit in the bitmap, the data topology of the target chart information can be formed.

[0033] Step S104: Visualize the target data based on the data topology.

[0034] According to the data topology, the location and numerical information of each field associated with the target data can be determined. When the target data is visualized, the location of the field associated with the target data can be used to determine the chart or detailed list where the field is located, and the corresponding numerical information and indicator information of the field can be displayed, so that the target data can be accurately traced when there are anomalies.

[0035] The data visualization method provided in this embodiment, by setting an appropriate data structure to associate different types of chart information, can statistically integrate chart information used for data visualization into a single target chart, avoiding chaotic storage of chart information and achieving orderly storage. Furthermore, by determining the corresponding data topology through bitmap representation, visualization of the target data is implemented according to the data topology. Thus, effective data traceability can be achieved by combining the data topology, improving the credibility of the data visualization.

[0036] This embodiment provides a method for visualizing data, which can be used on computer devices such as computers, laptops, and servers. Figure 2 This is a flowchart of a data visualization method according to an embodiment of the present disclosure, such as... Figure 2 As shown, the process includes the following steps:

[0037] Step S201: Obtain the target data to be displayed and multiple types of chart information corresponding to the target data. The chart information is used to visualize the target data in chart form. For detailed explanation, please refer to the relevant descriptions in the above embodiments; they will not be repeated here.

[0038] Step S202: Associate multiple types of chart information according to a preset data structure to generate target chart information corresponding to the target data.

[0039] Specifically, the preset data structure includes a parameter structure and a chart arrangement structure. Accordingly, step S202 above may include:

[0040] Step S2021: Extract chart attribute parameters from each type of chart information. These chart attribute parameters include one or more of the following: chart identifier, chart metric, and chart value.

[0041] Chart attribute parameters are used to characterize all the characteristic parameters that make up a chart. Specifically, computer devices can parse the various types of chart information they acquire, determine the content contained in the chart information, and identify the chart attribute parameters used to characterize the chart attributes.

[0042] Specifically, chart attribute parameters include at least one or more of the following: chart identifier, chart metric, and chart value. Among them, the chart identifier is used to uniquely identify the chart, such as the chart name or chart ID.

[0043] Chart metrics are used to represent the metrics and metric elements recorded in the chart. Taking a chart that records multimedia playback information as an example, its chart metrics can be video, audio, images, etc. The metric elements corresponding to video metrics can be video playback count, video playback duration, etc. The metric elements corresponding to audio metrics can be audio playback count, audio view count, etc. The metric elements corresponding to image metrics can be image download count, image click count, etc.

[0044] The numerical values ​​in the charts represent the data stored within them. Examples include the specific duration of a video playback, the number of viewers for an audio view, and the number of downloads for an image.

[0045] Step S2022: Perform structured processing on the chart attribute parameters according to the parameter structure to obtain the structured parameters corresponding to each type of chart information.

[0046] The parameter structure is a data structure defined for parameter values ​​and parameter text in different types of chart information. Chart attribute parameters of different types of chart information are structured according to the parameter structure to achieve a structured representation of chart labels, chart indicators, and chart values. The structured representation of chart labels, chart indicators, and chart values ​​is then defined as structured parameters.

[0047] In an optional implementation, the chart attribute parameters may include a chart identifier, a chart data indicator, and indicator parameter values; the parameter structure may include an identifier structure, an indicator structure, and a value structure. Accordingly, step S2022 above may include:

[0048] Step a1: Perform structuring on the chart identifiers according to the identifier structure to obtain structured chart identifiers.

[0049] The identifier structure is a data structure based on the naming conventions for chart identifiers. Here, names can be created according to the chart identifiers, and the naming content is the chart name or chart ID, such as Name A, Name B, ID1, ID2, etc. By structuring each chart identifier in the chart information according to the identifier structure, structured chart identifiers generated according to a unified naming convention can be obtained.

[0050] Step a2: Obtain the indicator name and indicator element of the chart data indicator, designate the indicator name as the first field, and designate the indicator element as the second field, and connect the first field and the second field using a preset connector.

[0051] Step a3: Perform structured processing on the first and second fields according to the indicator structure to obtain structured indicators.

[0052] The indicator structure follows the naming conventions for chart indicators. Here, indicators can be named by starting the indicator name in the first field and the indicator element in the second field. This design facilitates the management of the correspondence between data indicators and indicator details. In application, indicators with the same first field are considered the same indicator and its related details.

[0053] The indicator name is the name that the chart indicator has, and the indicator element is the element that makes up the chart indicator. For example, if the indicator name of the chart indicator is "video", the corresponding indicator element for the "video" indicator is "video views". The indicator name is placed at the beginning of the first field, and the indicator element is placed at the beginning of the second field. The first and second fields are connected by a preset connector, which can be "-", ",", "_", etc., without specific limitations here.

[0054] By structuring the various chart indicators and their elements according to the indicator structure, structured indicators generated according to a unified naming convention can be obtained. Taking the hyphen "-" as the default connector, the structured indicators obtained after structuring according to the indicator structure can be represented as "Indicator A - Element A", "Indicator A - Element B", etc.

[0055] Step a4: The index parameter values ​​are processed according to the numerical structure to obtain structured values.

[0056] The numerical structure is a standardized data structure for storing chart values, and it is consistent with the data storage structure of the data source. Here, by directly storing the indicator parameter values ​​according to the data storage structure of the data source, the corresponding structured values ​​can be obtained.

[0057] It should be noted that for indicator-type data storage types, the storage format can include three types: text, integer, and decimal. Here, the numerical structure of indicator-type data can be determined according to the data storage type.

[0058] In the above embodiments, by structuring the chart identifiers, chart data indicators, and indicator parameter values, different types of chart information can be associated, making it easier to integrate different types of chart information.

[0059] In an alternative implementation, the chart attribute parameters may further include supplementary text data, and the method further includes:

[0060] Step b1: Use a preset connector to connect the chart identifier and the supplementary text data to obtain the target chart identifier.

[0061] Step b2: Perform structuring processing on the target chart identifier according to the identifier structure to obtain the target structured chart identifier.

[0062] Supplementary text refers to text that requires additional explanation of the chart labels, such as the statistical time period and the data platform on which the chart is located. Preset connectors are pre-defined connectors used to connect chart labels and supplementary text. These pre-defined connectors can be "-", ",", "_", etc., without specific limitations here. For example, connecting the chart label (name A) and the supplementary text data (August) using "_" results in the target chart label "Name A_August".

[0063] By structuring the target chart identifiers obtained above according to the identifier structure, we can obtain the target structured chart identifiers generated according to the unified naming convention, thereby achieving the unification of chart identifiers in terms of data structure.

[0064] Step S2023: Arrange the structured parameters according to the chart arrangement structure to obtain the storage location corresponding to each type of chart information.

[0065] The chart arrangement structure is a data structure set for the arrangement position of different types of chart information. This chart arrangement structure can be a horizontal splicing structure or a vertical splicing structure. There is no specific limitation here. It can be determined according to the type of database (row database or column database) used for data storage.

[0066] Storage location is used to characterize the arrangement position of each structured parameter within the target chart information. Specifically, by arranging the structured parameters corresponding to each chart information obtained above according to the chart arrangement structure, the storage location of each type of chart information can be determined. For example, the first group stores the indicator name and its corresponding value, and the second group stores the indicator element (i.e., details) and its value, such as... Figure 3 and Figure 4 As shown, Figure 3 Is Figure 4 The same data units were merged on the basis.

[0067] In an optional implementation, the chart arrangement structure is an alternating arrangement structure, and accordingly, step S2023 may include:

[0068] Step c1: Obtain the sequence length and preset sequence separator value corresponding to the structured parameters.

[0069] The sequence length is the maximum length of the data units required by the structured parameters. For example... Figure 3 As shown, the structured parameters include indicator A, indicator B, the element corresponding to indicator A, and the element corresponding to indicator B. The maximum length of the data unit required by all structured parameters is 6, that is, the sequence length is 6.

[0070] The preset sequence separator value is a pre-defined interval value used to divide the sequence length. This interval value can be fixed or variable, and can be determined according to actual needs without specific limitations.

[0071] Step c2: Based on the sequence length and the preset sequence separation value, determine the permutation step size corresponding to the structured parameters.

[0072] By taking the modulo between the sequence length and the preset sequence separator value, the permutation step size corresponding to each structured parameter can be determined. Figure 3 For example, the indicator values ​​corresponding to indicators A and B occupy 6 data units. If the preset sequence separation value is 3, the 6 data units can be calculated by taking the modulo of 3. At this time, the 6 data units can be divided into 2 parts, resulting in 3 data units corresponding to indicator A for storing its corresponding indicator value, and 3 data units corresponding to indicator B for storing its corresponding indicator value.

[0073] Step c3: Arrange the structured parameters alternately according to the arrangement step size to determine the start and end positions of the structured parameters. The start and end positions are the storage locations of the chart information.

[0074] The structured parameters are arranged alternately according to the permutation step size. This means that different types of chart information are stored in an alternating manner. For example, the first group stores the indicators and their values, and the second group stores the indicator elements and their values. Figure 3 and Figure 4 As shown.

[0075] The starting position is the sequence number at the beginning of the structured parameter storage, and the ending position is the sequence number at the end of the structured parameter storage. For example... Figure 3 As shown, the starting position of index A is number 1, and the ending position is number 2; the starting position of the elements of index A is number 3, and the ending position is number 4.

[0076] In an alternative implementation, the method may further include associating the start position and the end position with structured parameters.

[0077] The start and end positions are associated with the structured parameters, and a preset connector is used to link the start and end positions to the indicator names or indicator element names in the structured parameters, thus enabling the indexing of the storage location for the structured parameters. For example... Figure 3 The symbols "Indicator A-1-2" and "Indicator A-Element A-3-4" are shown.

[0078] In the above embodiments, by combining the sequence length with a preset sequence separator value to determine the corresponding permutation step size, the start and end positions corresponding to the structured parameters can be determined, thereby enabling the ordered storage of different types of chart information in the same data unit. Simultaneously, associating the start and end positions with the structured parameters facilitates indexing of each structured parameter based on its start and end positions.

[0079] Step S2024: Fill in the structured parameters according to the storage location to generate the target chart information.

[0080] The structured parameters corresponding to different types of chart information are sequentially filled into the same structured chart according to the storage locations determined above, generating target chart information containing various types of chart information.

[0081] Step S203: Convert the target chart information into a bitmap to generate the data topology corresponding to the target chart information.

[0082] Specifically, step S203 above may include:

[0083] Step S2031: Perform bitmap-based processing on each data structure unit in the target chart information to determine the data structure state of each data structure unit.

[0084] The state of a data structure characterizes whether a value exists within a data structure unit. Each data structure unit corresponds to a specific data structure field. A bitmap algorithm is used to represent each field within a data structure unit as a bitmap, ensuring that each field corresponds to a single bit in the bitmap. Specifically, the presence or absence of a value in each field can be mapped to 0 or 1 within the data structure, thereby determining the state of each data structure unit. Figure 5 As shown, if a data structure unit has a value, it is mapped to 1; otherwise, it is mapped to 0.

[0085] It should be noted that text (such as indicator A-1-2) can be mapped to 1 or 0 depending on actual needs.

[0086] Step S2032: Based on the data structure status of each data structure unit, determine the data topology corresponding to the target chart information.

[0087] By processing the data in each data structure unit according to its state, the corresponding data topology can be obtained. For example... Figure 5 The data topology of the first column data structure unit corresponding to the name A shown is shown, where X represents 1 or 0 and is used to characterize the mapping value of "index A-1-2".

[0088] In an optional implementation, the above method may further include: performing data integrity verification on each data structure unit based on the data structure state.

[0089] Integrity verification is used to check whether the data structure units in each chart are complete. Specifically, based on whether each data structure unit has a value, a binary combination of 0s and 1s can be concatenated. This binary combination is then converted into ASCII characters, and the characters in each row are arranged horizontally to obtain a feature string representing the integrity of the data structure. This feature string is used to perform data integrity verification on the data structure units.

[0090] In an optional implementation, the above method may further include:

[0091] Step c1: Obtain similar chart information corresponding to the target chart information.

[0092] Step c2: Based on the data structure state target chart information, compare it with similar chart information to generate chart comparison results.

[0093] Similar chart information refers to charts that have the same indicators and indicator elements as the target chart information. For example, if the target chart information is a video playback statistics table for May, the similar chart information is a video playback statistics table for June.

[0094] The target chart information is compared with similar chart information, and the comparison results are used to determine the consistency of the data structure state in the target chart information and similar chart information, such as... Figure 5 The first column of data structure units corresponding to name A in the data structure is... Figure 6 By comparing the data structure units in the first column corresponding to name B, it can be determined that the value storage of the data structure units in the first column corresponding to name A is different from that of the data structure units in the first column corresponding to name B. At this time, a comparison result can be generated so that technicians can perform value missing detection.

[0095] In the above implementation, the data consistency between two data tables can be quickly verified, avoiding data comparison errors caused by missing data, achieving fast data comparison, and reducing the read and write pressure on the database.

[0096] Step S204: Visualize the target data based on the data topology. For detailed explanation, please refer to the relevant descriptions in the above embodiments; they will not be repeated here.

[0097] The data visualization method provided in this embodiment achieves consistency in the data structure of chart attribute parameters by structuring the chart attribute parameters through parameter structures; it also achieves orderly storage of different types of chart information by arranging the structured parameters through chart arrangement structures. By combining the storage locations corresponding to each type of chart information to generate target chart information, it achieves the fusion of different types of chart information and the effective management of various types of chart information. Furthermore, by bitmapping each data structure unit, it facilitates the generation of data topology based on the data structure state, enabling effective data traceability through structured data topology.

[0098] This embodiment also provides a data visualization display device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0099] This embodiment provides a data visualization display device, such as... Figure 7 As shown, it includes:

[0100] The acquisition module 301 is used to acquire the target data to be displayed and the chart information of multiple types corresponding to the target data. The chart information is used to visualize the target data in the form of charts.

[0101] The association module 302 is used to associate multiple types of chart information according to a preset data structure to generate target chart information corresponding to the target data.

[0102] Bitmap module 303 is used to bitmap the target chart information and generate the data topology corresponding to the target chart information.

[0103] The visualization module 304 is used to visualize target data based on data topology.

[0104] In some alternative embodiments, the preset data structure includes a parameter structure and a chart arrangement structure. Accordingly, the aforementioned association module 302 may include:

[0105] The parameter extraction unit is used to extract chart attribute parameters from various types of chart information. These chart attribute parameters include one or more of the following: chart identifier, chart metric, and chart value.

[0106] The first structured processing unit is used to perform structured processing on the chart attribute parameters according to the parameter structure, so as to obtain the structured parameters corresponding to each type of chart information.

[0107] The second structured processing unit is used to arrange the structured parameters according to the chart arrangement structure to obtain the storage location corresponding to each type of chart information.

[0108] The generation unit is used to populate the structured parameters according to the storage location to generate the target chart information.

[0109] In one optional implementation, the chart attribute parameters may include chart identifiers, chart data metrics, and metric parameter values; the parameter structure may include an identifier structure, a metric structure, and a value structure. Accordingly, the first structured processing unit may include:

[0110] The identifier is a structured subunit, used to perform structured processing on the chart identifiers according to the identifier structure, so as to obtain structured chart identifiers.

[0111] The field connection sub-unit is used to obtain the indicator name and indicator element of the chart data indicator. The indicator name is determined as the first field, and the indicator element is determined as the second field. The first field and the second field are connected by a preset connector.

[0112] The indicator structuring sub-unit is used to perform structuring processing on the first and second fields according to the indicator structure to obtain structured indicators.

[0113] The numerical structuring subunit is used to perform structuring processing on index parameter values ​​according to numerical structure to obtain structured numerical values.

[0114] In an optional implementation, the chart attribute parameters may further include supplementary text data, and the first structured processing unit further includes:

[0115] The identifier connection sub-unit is used to connect the chart identifier and supplementary text data using a preset connector to obtain the target chart identifier.

[0116] The structured sub-unit is used to perform structured processing on the target chart identifier according to the identifier structure, so as to obtain the target structured chart identifier.

[0117] In an optional implementation, the chart arrangement structure is an alternating arrangement structure, and accordingly, the second structuring processing unit described above may include:

[0118] Get sub-units, used to obtain the sequence length and preset sequence separator value corresponding to the structured parameters.

[0119] The step size determination subunit is used to determine the permutation step size corresponding to the structured parameters based on the sequence length and the preset sequence separation value.

[0120] The arrangement sub-unit is used to alternately arrange the structured parameters according to the arrangement step size, and determine the start and end positions of the structured parameters. The start and end positions are the storage locations of the chart information.

[0121] In an optional implementation, the aforementioned associated module 302 may further include:

[0122] The position association unit is used to associate the start and end positions with structured parameters.

[0123] In an alternative implementation, the bitmap module 303 may further include:

[0124] Bitmap unit is used to bitmap each data structure unit in the target chart information and determine the data structure state of each data structure unit.

[0125] The data topology determination unit is used to determine the data topology corresponding to the target chart information based on the data structure status of each data structure unit.

[0126] In an optional embodiment, the above-described apparatus may further include:

[0127] The integrity verification module is used to perform data integrity verification on each data structure unit based on the data structure status.

[0128] In an optional embodiment, the above-described apparatus may further include:

[0129] The similar chart acquisition module is used to obtain similar chart information that corresponds to the target chart information.

[0130] The comparison module is used to compare the target chart information with similar chart information based on the data structure status and generate chart comparison results.

[0131] The further functional descriptions of each module and unit are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0132] In this embodiment, the data visualization device is presented in the form of functional units. Here, a unit refers to an ASIC circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0133] The data visualization device provided in this embodiment, by setting a corresponding data structure, associates different types of chart information, enabling the statistical fusion of chart information used for data visualization into a single target chart, avoiding chaotic storage of chart information and achieving orderly storage. Furthermore, by determining the corresponding data topology through bitmap representation, visualization of the target data is implemented according to the data topology. Thus, effective data traceability can be achieved by combining the data topology, improving the credibility of the data visualization.

[0134] This disclosure also provides a computer device having the above-described features. Figure 7 The data visualization display device shown.

[0135] Please see Figure 8 , Figure 8 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of this disclosure, such as... Figure 8 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 8 Take a processor 10 as an example.

[0136] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0137] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0138] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0139] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0140] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0141] This disclosure also provides a computer-readable storage medium in which the methods described in this disclosure can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium after being downloaded over a network. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium may be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium may also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code that, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0142] Although embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for visualizing data, characterized in that, include: Obtain the target data to be displayed and multiple types of chart information corresponding to the target data. The chart information is used to visualize the target data in the form of charts. The multiple types of chart information are associated according to a preset data structure to generate target chart information corresponding to the target data; The target chart information is bitmapped to generate the data topology corresponding to the target chart information; The target data is visualized based on the data topology.

2. The method according to claim 1, characterized in that, The preset data structure includes a parameter structure and a chart arrangement structure; The step of associating the multiple types of chart information according to a preset data structure to generate target chart information corresponding to the target data includes: Extract chart attribute parameters from the chart information of each type, wherein the chart attribute parameters include one or more of chart identifier, chart metric, and chart value; The chart attribute parameters are structured according to the parameter structure to obtain the structured parameters corresponding to each type of chart information; The structured parameters are arranged according to the chart arrangement structure to obtain the storage location corresponding to each type of chart information; The structured parameters are filled according to the storage location to generate the target chart information.

3. The method according to claim 2, characterized in that, The chart attribute parameters include chart identifiers, chart data indicators, and indicator parameter values, and the parameter structure includes an identifier structure, an indicator structure, and a numerical structure. The step of structuring the chart attribute parameters according to the parameter structure to obtain the structured parameters corresponding to each type of chart information includes: The chart identifier is structured according to the aforementioned identifier structure to obtain a structured chart identifier; Obtain the indicator name and indicator element of the chart data indicator, determine the first field corresponding to the indicator name and the second field corresponding to the indicator element, and connect the first field and the second field with a preset connector; The first field and the second field are processed according to the index structure to obtain structured indicators; The index parameter values ​​are processed in a structured manner according to the data structure to obtain structured numerical values.

4. The method according to claim 3, characterized in that, The chart attribute parameters also include supplementary text data, and the method further includes: The target chart identifier is obtained by connecting the chart identifier and the supplementary text data using a preset connector. The target chart identifier is structured according to the aforementioned identifier structure to obtain the target structured chart identifier.

5. The method according to claim 2, characterized in that, The chart arrangement structure is an alternating arrangement structure. The arrangement of the structured parameters according to the chart arrangement structure to obtain the storage locations corresponding to each type of chart information includes: Obtain the sequence length and preset sequence separator value corresponding to the structured parameters; Based on the sequence length and the preset sequence separation value, the permutation step size corresponding to the structured parameters is determined; The structured parameters are arranged alternately according to the alternating arrangement structure based on the arrangement step size, and the starting position and ending position corresponding to the structured parameters are determined. The starting position and the ending position are the storage locations of the chart information.

6. The method according to claim 5, characterized in that, Also includes: The start and end positions are associated with the structured parameters.

7. The method according to claim 1, characterized in that, The step of bitmapping the target chart information to generate the corresponding data topology includes: Each data structure unit in the target chart information is bitmapped to determine the data structure state of each data structure unit. Based on the data structure state of each data structure unit, the data topology corresponding to the target chart information is determined.

8. The method according to claim 7, characterized in that, Also includes: Data integrity is verified for each data structure unit based on the state of the data structure.

9. The method according to claim 7, characterized in that, Also includes: Obtain similar chart information corresponding to the target chart information; Based on the state of the data structure, the target chart information is compared with the similar chart information to generate a chart comparison result.

10. A data visualization display device, characterized in that, include: The acquisition module is used to acquire the target data to be displayed and multiple types of chart information corresponding to the target data. The chart information is used to visualize the target data in the form of charts. The association module is used to associate the multiple types of chart information according to a preset data structure to generate target chart information corresponding to the target data; The bitmap module is used to bitmap the target chart information and generate the data topology corresponding to the target chart information. The visualization module is used to visualize the target data based on the data topology.

11. A computer device, characterized in that, include: A memory and a processor are interconnected, the memory stores computer instructions, and the processor executes the computer instructions to perform the data visualization method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the data visualization method according to any one of claims 1 to 9.

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