Data index visualization method, device, equipment, medium and product

Abstract

This disclosure relates to the field of data analysis technology, and in particular to a data indicator visualization method, apparatus, device, medium, and product. The method includes: acquiring a user request; acquiring request data based on the user request; sending the request data to a backend according to an established communication method; receiving the request data sent by a frontend; parsing and validating the request data; acquiring multi-source data from a database according to the request data; performing unified standardization processing on the multi-source data to obtain time-series data; calculating the difference ratio between the time-series data at each time point and a preset baseline value; marking abnormal time points with a difference ratio greater than a preset threshold as alarms; and encapsulating the time-series data, the difference ratio, and the alarm markers into response data and sending the response data to the frontend. The frontend, by dynamically selecting a communication method (short polling / long connection / SSE), can balance real-time performance and performance, which is beneficial for improving frontend response speed.

Description

Technical Field

[0001] This disclosure relates to the field of data analysis technology, and in particular to a method, apparatus, device, medium and product for visualizing data indicators. Background Technology

[0002] Geographic Information Systems (GIS) typically categorize display methods into base maps and thematic maps based on the level of interest in the features. Features refer to natural or man-made entities on the Earth's surface, such as rivers, roads, buildings, and forests, possessing clearly defined spatial locations and attributes. Base maps are used as supplementary backgrounds to display features with large data volumes, low change frequency, or those not of particular interest, such as land, buildings, mountains, and rivers. Thematic maps involve overlaying several layers of target objects of interest onto the base map according to specific needs, such as water conservancy themes, geological disaster themes, and commercial themes.

[0003] However, due to the limitations of the browser / server (B / S) architecture, for traditional GIS, when the data volume is large, loading all the data at once will cause the front-end page to freeze, the response to be slow, and the user experience to be poor. Summary of the Invention

[0004] This disclosure is made in view of the above-mentioned problems and provides a method, apparatus, device, medium and product for visualizing data indicators.

[0005] According to one aspect of this disclosure, a data metric visualization method is provided for application in a front-end, comprising: Obtain user requests, and based on user requests, obtain request data; user requests represent the user's operational intent; Based on the established communication method, request data is sent to the backend; the communication method includes any one of short polling, long connection, and SSE connection.

[0006] The beneficial effects of this solution are as follows: the front-end can intelligently decide on different communication methods based on the scenario. For example, for initial loading or changes in filtering conditions (non-real-time data), a short polling connection can be established; for real-time alarms and dynamic updates, a long connection can be established; and for unidirectional real-time data streams (such as continuously updated trend charts), an SSE connection can be established. By dynamically selecting the communication method (short polling / long connection / SSE), real-time performance and performance can be balanced, which helps improve the front-end response speed.

[0007] Furthermore, the data indicator visualization method according to one aspect of this disclosure also includes: Receive response data returned by the backend; The response data is parsed and validated, and the validated response data is converted into a format that matches the visualization library to obtain formatted data; Based on formatted data, a main view and a sub-view are generated; the main view is used to display the differences in core indicators, and the sub-view is used to display the decomposition of related dimensions. Based on formatted data, a dynamic flow diagram and a timeline animation are generated; the dynamic flow diagram is used to display network topology changes, and the timeline animation is used to display historical trend evolution. Dynamic visualization charts are generated based on the main view, sub-view, dynamic flow diagram, and timeline animation.

[0008] In one or more embodiments, the beneficial effects of this solution are as follows: the front end can perform color encoding and size mapping by applying visualization principles, such as mapping colors according to the difference ratio (red→yellow→green gradient). The larger the difference ratio, the larger the graphic elements (such as bubble radius). Multi-dimensional collaborative display is achieved through the main view and sub-views, and real-time flow expression is achieved through dynamic flow diagrams and timeline animations. The final dynamic visualization chart can support real-time updates and interactions, which is conducive to improving the user experience.

[0009] Furthermore, the data indicator visualization method according to one aspect of this disclosure also includes: Based on the request data, check if there is matching historical cached data on the front end; If no historical cached data exists, set the request frequency and send request data to the backend based on the request frequency; or, set the minimum request interval for window scaling and send request data to the backend based on the minimum request interval.

[0010] In one or more embodiments, the beneficial effects of this solution are as follows: by checking the cache, repeated loading can be reduced, alleviating backend pressure; by setting request frequency to control debouncing, frequent triggering of timeline drag requests can be avoided. For example, when a user triggers the same operation repeatedly and quickly (such as frantically dragging the timeline), only the last one is executed. By setting the minimum request interval for window scaling, the request frequency can be controlled. For example, by setting an interval (such as 1 second), no matter how frequently the user triggers the operation, at most one request is sent per second, avoiding excessive resource consumption and improving the frontend response speed.

[0011] According to one aspect of this disclosure, a data metric visualization method is provided for application in a backend, comprising: Receive request data sent by the front end, and parse and validate the request data; If the verification passes, retrieve multi-source data from the database according to the requested data; perform unified standardization processing on the multi-source data to obtain time series data; wherein, the unified standardization processing includes unifying the multi-source data onto the same time axis; Calculate the difference ratio between the time series data at each time point and the preset baseline value, and mark abnormal time points with a difference ratio greater than the preset threshold as alarm points; The time series data, difference ratio, and alarm flags are encapsulated into response data and sent to the front end.

[0012] The beneficial effects of this solution are: the backend can support real-time difference calculation requests through request reception, request parsing and difference calculation, and achieve timely response while ensuring security, forming an efficient closed loop with the frontend visualization components.

[0013] Furthermore, according to a data metric visualization method of one aspect of this disclosure, response data is sent to the front end, including: Based on the request data, determine the scenario type of the response; the scenario type includes initial full loading and real-time incremental update; Based on the scenario type, the response data is compressed and optimized. Send compressed and optimized response data to the front end.

[0014] In one or more embodiments, the beneficial effects of this solution are as follows: initial full loading returns data across all requested time ranges, while real-time incremental updates only return data that has changed since the last request (such as the latest value, amount of change, and trends over the last three time points). By compressing and optimizing the response data, network transmission volume can be reduced, and the front-end response speed can be improved.

[0015] Furthermore, the data indicator visualization method according to one aspect of this disclosure also includes: If the verification passes, check if there is cached data in the pre-computed cache that matches the requested data; the pre-computed cache is data cached by the backend according to a preset time granularity. In response to the existence of cached data, time-series data is generated based on the cached data; If no cached data is found, retrieve multi-source data from the database according to the requested data.

[0016] In one or more embodiments, the beneficial effect of this solution is that by caching data with a preset time granularity, results can be returned directly to the client, reducing the response time from seconds to milliseconds and achieving efficient support.

[0017] According to another aspect of this disclosure, a data indicator visualization device is provided, including a front-end and a back-end, the front-end including: The acquisition module is used to acquire user requests and, based on these requests, acquire the request data; the user request represents the user's operational intent. The sending module is used to send request data to the backend according to the established communication method; the communication method includes any one of short polling, long connection, and SSE connection; The backend includes: The receiving module is used to receive request data sent by the front end, and to parse and verify the request data; The data processing module is used to retrieve multi-source data from the database according to the requested data after the verification is passed; and to perform unified standardization processing on the multi-source data to obtain time series data; wherein, the unified standardization processing includes unifying the multi-source data onto the same time axis; The difference calculation module is used to calculate the difference ratio between the time series data at each time point and the preset baseline value, and to mark abnormal time points with a difference ratio greater than a preset threshold as alarm points. The results return module is used to encapsulate time series data, difference ratios, and alarm flags into response data and send the response data to the front end.

[0018] According to another aspect of this disclosure, a computer device is provided, including a memory, a processor, and a computer program stored in the memory, the processor executing the computer program to implement the method of one aspect above.

[0019] According to another aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method of one aspect above.

[0020] According to another aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method of the above-described aspect.

[0021] It should be understood that both the foregoing general description and the following detailed description are exemplary and intended to provide further illustration of the claimed technology. Attached Figure Description

[0022] The above and other objects, features, and advantages of this disclosure will become more apparent from the more detailed description of the embodiments thereof in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0023] Figure 1 This is a system architecture diagram illustrating a data metric visualization method according to an embodiment of the present disclosure.

[0024] Figure 2This is a flowchart illustrating the processing of the business logic layer of an application according to an embodiment of this disclosure.

[0025] Figure 3 This is a flowchart illustrating a data metric visualization method according to an embodiment of the present disclosure.

[0026] Figure 4 This is a schematic diagram of the structure of a data index visualization device according to an embodiment of the present disclosure.

[0027] Figure 5 This is a schematic diagram illustrating the structure of a computer device according to an embodiment of the present disclosure.

[0028] Figure 6 This is a schematic diagram illustrating a computer program product according to an embodiment of the present disclosure. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this disclosure more apparent, exemplary embodiments according to this disclosure will now be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this disclosure, and not all embodiments of this disclosure. It should be understood that this disclosure is not limited to the exemplary embodiments described herein.

[0030] Geographic Information Systems (GIS) typically categorize display methods into base maps and thematic maps based on the level of interest in the features. Features refer to natural or man-made entities on the Earth's surface, such as rivers, roads, buildings, and forests, possessing clearly defined spatial locations and attributes. Base maps are used as supplementary backgrounds for features with large data volumes, low change frequency, or those not of particular interest, such as land, buildings, mountains, and rivers. These GIS systems usually employ rasterized vector data, converting complex vector data into simple images for display. Thematic maps involve overlaying several layers of target objects of interest onto the base map, based on specific needs, such as water conservancy themes, geological disaster themes, or commercial themes. Under normal circumstances, the spatial locations of these data are fixed, and they are typically displayed using rasterized (WMS) or direct vector (WFS) formats.

[0031] However, due to the limitations of the browser / server (B / S) architecture, for traditional GIS, when the data volume is large, loading all the data at once will cause the front-end page to freeze, the response to be slow, and the user experience to be poor.

[0032] The above description, with reference to the accompanying drawings, illustrates a data indicator visualization method, apparatus, device, medium, and product according to embodiments of the present disclosure. The front end transmits data to the back end after conversion via an interactive control panel, and selects different communication methods according to different situations. This enables the front end to achieve dynamic visualization with a response time of up to seconds, and to efficiently collaborate with the back end's computing engine, thereby meeting the user's needs for real-time data difference analysis.

[0033] By decoupling the request receiving layer, business logic layer, and computing engine layer into layers, the backend can support real-time differential computing requests and achieve timely responses while ensuring security, forming an efficient closed loop with the frontend visualization components.

[0034] Through real-time incremental updates, the backend can ensure that the returned data not only meets the high-performance rendering requirements of the frontend's dynamic visualization, but also maintains flexibility and maintainability in complex business scenarios.

[0035] To facilitate understanding of this embodiment, a data indicator visualization method disclosed in this disclosure will first be described in detail. The execution subject of the data indicator visualization method provided in this disclosure is generally a computer device with certain computing capabilities, such as a terminal device, a server, or other processing device. In some possible implementations, the data indicator visualization method can be implemented by a processor calling computer-readable instructions stored in memory.

[0036] like Figure 1 The diagram shown is a system architecture diagram of the data indicator visualization method provided in this embodiment of the present disclosure, including a front-end 1 and a back-end 2. The front-end 1 includes a dynamic visualization engine 11 and an interactive control panel 12, and the back-end includes a request receiving layer 21, a business logic layer 22, and a computing engine layer 23. This embodiment provides a detailed description of each module according to the data processing flow as follows: (I) Front-end 1: The ETL (Extract-Transform-Load) tool extracts, cleans, and transforms multi-source data into a standardized format, defining key metrics (such as sales revenue, user activity, and response latency). The dynamic visualization engine 11 loads the basic configuration, and the interactive control panel 12 renders interface controls, such as timelines and filters. The specific front-end workflow includes: Generate request data: When a user performs an operation on the interactive control panel 12 and issues a user request, such as when the user selects "Indicator A", the front end captures this action and generates a front end request object (i.e., request data) that encapsulates Indicator A, time range, filter conditions, etc.

[0037] Scene recognition: Based on user actions and the front-end system status, establish the following communication methods, and send request data to the back-end according to the selected communication method: (1) Short polling: used for non-real-time needs such as initial loading and changes in filtering conditions.

[0038] (2) Long connection (WebSocket): Used for scenarios that require bidirectional real-time communication, such as real-time difference alarms and dynamic data updates.

[0039] (3) Server-Sent Events (SSE) connection: used for one-way real-time data streams, such as continuously updated trend data.

[0040] This embodiment also provides the following request optimization techniques: (1) Debouncing: Request frequency control Debouncing: Set the request frequency and send request data to the backend based on the request frequency; that is, if the user frequently drags the timeline, the user request will only be sent after the user stops dragging, which can avoid the user frequently triggering the timeline drag request.

[0041] (2) Throttling: Set the minimum request interval for window scaling and send request data to the backend based on the minimum request interval; for example, set an interval (such as 1 second) so that no matter how frequently the user triggers it, at most one request will be sent per second to avoid excessive consumption of resources and improve the response speed of the front end.

[0042] (3) Data caching and reuse: Cache the relevant data of historical user requests. Based on the request data, check whether there is matching historical cached data on the front end. If there is historical cached data and it has not expired, users can use it more conveniently.

[0043] (4) Monitoring: Record request time / failure rate by embedding points on the front end, and display loading animation for requests that take more than 1 second.

[0044] (ii) Backend 2: Backend 2 processes the request data sent by frontend 1 through a layered processing model, which can be divided into a request receiving layer 21, a business logic layer 22, and a computing engine layer 23. Request receiving layer 21: Parses the request data and performs preliminary validation, such as parameter format and permission checks. If the validation fails, it directly returns an error response and terminates the process. If the validation passes, it enters the business logic layer 22.

[0045] Business logic layer 22: such as Figure 2 As shown, the processing flow of the business logic layer includes: S201: Check if there is cached data in the pre-computed cache that matches the requested data.

[0046] Among them, the pre-computed cache is the data cached by the backend according to the preset time granularity. For example, the system has pre-calculated the "indicator A" for each day in "hour" granularity and stored it in the cache. If the user requests "hour-level" data, it can be read directly from the cache to generate time series data. If the cache is not hit (for example, the user requests more granular "minute-level" data), the original multi-source data is queried from the database associated with the backend.

[0047] Since real-time calculation of differences is very time-consuming (especially when dealing with massive amounts of data), and many queries are repetitive (e.g., many people query "today vs. yesterday"), pre-calculation caching can directly return results, reducing response time from seconds to milliseconds, thus achieving "high-efficiency support".

[0048] S202: Data Cleaning and Alignment: Obtain multi-source data (potentially multiple tables and formats) from the backend database, perform unified standardization processing on the multi-source data to obtain time-series data (a list of timestamps + corresponding values). Unified standardization processing includes: Resampling: Unifying timestamps from different sources onto the same timeline. For example, if database A records one record every 5 minutes and database B records one record every 1 minute, and a user requests to display data by the minute, then the data from database A needs to be interpolated or aggregated to the minute-by-minute format.

[0049] Missing value handling: Fill in the missing time points with forward padding (filling the gap with the previous valid value) or other strategies.

[0050] Outlier filtering: Based on the standard deviation method, remove obviously abnormal data points (such as a value that suddenly appears 100 times higher than usual, which may be a sensor malfunction).

[0051] S203: Calculate the difference ratio: Step 1: Obtain the pre-stored baseline value. The baseline value can be a pre-set fixed value or dynamically obtained comparison period data (such as data from the same time period yesterday).

[0052] Step 2: For each time point in the time series data, calculate the difference ratio of indicator A. The calculation formula is as follows: = (MN) / M × 100% in, M represents the difference ratio of indicator A at time point i, M represents the current value of indicator A at time point i, and N represents the baseline value.

[0053] Step 3: Mark abnormal time points with a difference ratio greater than a preset threshold as alarm points and enter them into the dataset.

[0054] Step 4: Update baseline status: If business rules require this data to be used as a new baseline (e.g., for comparison tomorrow), then the baseline value is updated.

[0055] S204: Result Encapsulation: The dataset containing time series data, difference ratios, and alarm tags is encapsulated into response data and returned to the front end. The process of returning the response data includes: ① Confirm the scenario type of the response: Initial full load: Returns data for all requested time periods.

[0056] Real-time incremental update: Only returns data that has changed since the last request (latest value, amount of change, trend of the last 3 time points).

[0057] ② Compress the response data (e.g., Gzip) to reduce network transmission volume.

[0058] ③ Perform specialized compression format conversion on heat map data and time series data (such as using differential encoding).

[0059] Computation Engine Layer 23: Serves as a performance accelerator for the request receiving layer 21 and the business logic layer 22, and is automatically activated when the data volume is large. Vectorized computation (SIMD): When calculating the differences of a large number of metrics, the CPU processes multiple data points at once, rather than looping through them one by one.

[0060] Parallel Computing (CUDA): For large-scale multidimensional data difference analysis, CUDA enables parallel computation of the difference matrix, increasing speed by tens of times.

[0061] Pre-computed cache: This is the pre-computed cache mentioned above, which calculates and stores the results of frequently used queries in advance.

[0062] This embodiment decouples the request receiving layer, business logic layer, and computing engine layer into layers, enabling the backend to support real-time differential computing requests and achieve timely responses while ensuring security, forming an efficient closed loop with the frontend visualization components.

[0063] (III) Front-end 1: ① Receive response data returned from the backend and parse the response data, for example, parse it into a JavaScript object.

[0064] ② Check if the data format meets expectations (e.g., fields are complete and types are correct). If the validation fails, trigger the retry logic with backoff (e.g., exponential backoff algorithm to re-request).

[0065] ③ Convert the validated response data into a format that matches visualization libraries such as ECharts (e.g., assemble it into a series of data, coordinate axis configuration) to obtain formatted data that supports direct rendering by visualization components; optionally, store the formatted data as new historical cache data in the front-end data cache for users to reuse later.

[0066] ④ Apply the principle of visualization: Color coding: Map colors based on the percentage difference, for example: red (abnormal difference) → yellow (warning) → green (normal) gradient.

[0067] Size mapping: The greater the difference ratio, the larger the graphic element (such as the bubble radius).

[0068] ⑤ Multi-dimensional collaborative display: Based on formatted data, a main view and a sub-view are generated. The main view is used to display the differences in core indicators, and the sub-view is used to display the decomposition of related dimensions.

[0069] ⑥ Based on formatted data, generate dynamic flow diagrams and timeline animations; dynamic flow diagrams are used to display network topology changes, and timeline animations are used to display historical trend evolution. ⑦ Call libraries such as ECharts to render charts and bind interactive events (such as clicking to drill down and dragging the timeline) to generate dynamic visual charts that support real-time updates and interactions.

[0070] Based on the above embodiments, this embodiment also provides a data indicator visualization method, such as... Figure 3 The diagram shows a flowchart of a data metric visualization method, including S301-S306. S301-S302 are applied to the front-end, and S303-S306 are applied to the back-end. S301: Obtain user request; based on user request, obtain request data.

[0071] Among them, user requests represent the user's intention to perform an action.

[0072] S302: Send request data to the backend according to the established communication method.

[0073] The communication methods include any one of short polling, long connection, and SSE connection.

[0074] S303: Receives request data sent by the front end, and parses and verifies the request data.

[0075] S304: If the verification passes, retrieve multi-source data from the database according to the requested data; perform unified standardization processing on the multi-source data to obtain time series data.

[0076] Among them, the unified standardization process includes unifying multi-source data onto the same timeline.

[0077] S305: Calculate the difference ratio between the time series data at each time point and the preset baseline value, and mark abnormal time points with a difference ratio greater than the preset threshold as alarm points; S306: Encapsulate time series data, difference ratio, and alarm flags into response data and send the response data to the front end.

[0078] In one or more embodiments, the front end is further used for: Receive response data returned by the backend; The response data is parsed and validated, and the validated response data is converted into a format that matches the visualization library to obtain formatted data; Based on formatted data, a main view and a sub-view are generated; the main view is used to display the differences in core indicators, and the sub-view is used to display the decomposition of related dimensions. Based on formatted data, a dynamic flow diagram and a timeline animation are generated; the dynamic flow diagram is used to display network topology changes, and the timeline animation is used to display historical trend evolution. Dynamic visualization charts are generated based on the main view, sub-view, dynamic flow diagram, and timeline animation.

[0079] In one or more embodiments, the front end is further used for: Based on the request data, check if there is matching historical cached data on the front end; If no historical cached data exists, set the request frequency and send request data to the backend based on the request frequency; or, set the minimum request interval for window scaling and send request data to the backend based on the minimum request interval.

[0080] In one or more embodiments, sending response data to the front end includes: Based on the request data, determine the scenario type of the response; the scenario type includes initial full loading and real-time incremental update; Based on the scenario type, the response data is compressed and optimized. Send compressed and optimized response data to the front end.

[0081] In one or more embodiments, the backend is further configured to: If the verification passes, check if there is cached data in the pre-computed cache that matches the requested data; the pre-computed cache is data cached by the backend according to a preset time granularity. In response to the existence of cached data, time-series data is generated based on the cached data; If no cached data is found, retrieve multi-source data from the database according to the requested data.

[0082] In one or more embodiments, According to another aspect of the embodiments of this disclosure, a data indicator visualization device is provided, such as... Figure 4 As shown, the device includes: The acquisition module 401 is used to acquire user requests and, based on user requests, acquire request data; the user request represents the user's operational intent; The sending module 402 is used to send request data to the backend according to the established communication method; wherein the communication method includes any one of short polling, long connection, and SSE connection; The receiving module 403 is used to receive request data sent by the front end, and to parse and verify the request data; The data processing module 404 is used to retrieve multi-source data from the database according to the requested data after the verification is passed; and to perform unified standardization processing on the multi-source data to obtain time series data; wherein, the unified standardization processing includes unifying the multi-source data onto the same time axis; The difference calculation module 405 is used to calculate the difference ratio between the time series data at each time point and the preset baseline value, and to mark abnormal time points with a difference ratio greater than a preset threshold as alarm points. The result return module 406 is used to encapsulate time series data, difference ratios, and alarm flags into response data and send the response data to the front end.

[0083] The data indicator visualization device and the data indicator visualization method provided in this disclosure are based on the same inventive concept and have the same beneficial effects as the methods they adopt, operate or implement.

[0084] This disclosure also provides a computer device for performing the above-described data indicator visualization method. Please refer to... Figure 5 It illustrates a schematic diagram of a computer device provided by some embodiments of this disclosure. For example... Figure 5 As shown, the computer device 5 includes: a processor 500, a memory 501, a bus 502, and a communication interface 503. The processor 500, the communication interface 503, and the memory 501 are connected via the bus 502. The memory 501 stores a computer program that can run on the processor 500. When the processor 500 runs the computer program, it executes the data index visualization method provided in any of the foregoing embodiments of this disclosure.

[0085] The memory 501 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this device network element and at least one other network element is achieved through at least one communication interface 503 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0086] Bus 502 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory 501 is used to store programs. After receiving an execution instruction, the processor 500 executes the program. The data index visualization method disclosed in any of the foregoing embodiments of this disclosure can be applied to the processor 500, or implemented by the processor 500.

[0087] The processor 500 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 500 or by instructions in software form. The processor 500 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPTA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this disclosure. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this disclosure can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 501. The processor 500 reads the information in memory 501 and, in conjunction with its hardware, completes the steps of the above method.

[0088] The computer device and the data indicator visualization method provided in this disclosure are based on the same inventive concept and have the same beneficial effects as the methods they employ, operate, or implement.

[0089] This disclosure also provides a computer-readable storage medium corresponding to the data indicator visualization method provided in the foregoing embodiments. The computer-readable storage medium is an optical disc, on which a computer program (i.e., a computer program product) is stored. When the computer program is run by a processor, it executes the data indicator visualization method provided in any of the foregoing embodiments.

[0090] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0091] The computer-readable storage medium provided in the above embodiments of this disclosure and the data indicator visualization method provided in the embodiments of this disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0092] This disclosure also provides a computer program product; please refer to [reference needed]. Figure 6 The computer program product 600 carries program code, namely computer program 601. The instructions included in the computer program 601 can be used to execute the steps of the data indicator visualization method described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0093] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0094] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0095] The block diagrams of devices, apparatuses, devices, and systems disclosed herein are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0096] Additionally, as used herein, the "or" used in a list of items beginning with "at least one" indicates a separate list, such that a list of, for example, "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not imply that the described example is preferred or better than other examples.

[0097] It should also be noted that in the systems and methods of this disclosure, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions to this disclosure.

[0098] Various changes, substitutions, and modifications can be made to the technology described herein without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufactures, events, means, methods, or actions that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Therefore, the appended claims include such processes, machines, manufactures, events, means, methods, or actions within their scope.

[0099] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0100] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for visualizing data indicators, characterized in that, Applied to front-end, including: Obtain a user request, and based on the user request, obtain request data; the user request represents the user's operational intent; According to the established communication method, the request data is sent to the backend; wherein, the communication method includes any one of short polling, long connection, and SSE connection.

2. The data indicator visualization method as described in claim 1, characterized in that, Also includes: Receive the response data returned by the backend; The response data is parsed and verified, and the verified response data is converted into a format that matches the visualization library to obtain formatted data; Based on the formatted data, a main view and a sub-view are generated; wherein, the main view is used to display the differences in core indicators, and the sub-view is used to display the decomposition of related dimensions; Based on the formatted data, a dynamic flow diagram and a timeline animation are generated; wherein, the dynamic flow diagram is used to display network topology changes, and the timeline animation is used to display historical trend evolution; A dynamic visualization chart is generated based on the main view, the sub-view, the dynamic flow diagram, and the timeline animation.

3. The data indicator visualization method as described in claim 1, characterized in that, Also includes: Based on the requested data, check whether the front-end has matching historical cached data; If the historical cached data does not exist, set the request frequency and send the request data to the backend based on the request frequency; or, set the minimum request interval for window scaling and send the request data to the backend based on the minimum request interval.

4. A method for visualizing data indicators, characterized in that, Applied to the backend, including: Receive request data sent by the front end, and parse and verify the request data; If the verification passes, multi-source data is retrieved from the database according to the requested data; the multi-source data is then subjected to unified standardization processing to obtain time series data; wherein, the unified standardization processing includes unifying the multi-source data onto the same time axis; Calculate the difference ratio between the time series data at each time point and the preset baseline value, and mark abnormal time points where the difference ratio is greater than the preset threshold as alarm points; The time series data, the difference ratio, and the alarm flag are encapsulated into response data, and the response data is sent to the front end.

5. The data indicator visualization method as described in claim 4, characterized in that, Sending the response data to the front end includes: Based on the requested data, the scenario type of the response is determined; the scenario type includes initial full loading and real-time incremental update. Based on the scenario type, the response data is compressed and optimized. The compressed and optimized response data is sent to the front end.

6. The data indicator visualization method as described in claim 4, characterized in that, Also includes: If the verification passes, check if there is cached data in the pre-computed cache that matches the requested data; wherein, the pre-computed cache is data cached by the backend according to a preset time granularity; In response to the existence of the cached data, time series data is generated based on the cached data; In response to the absence of the cached data, multi-source data is retrieved from the database according to the requested data.

7. A data indicator visualization device, characterized in that, It includes a front-end and a back-end, wherein the front-end includes: The acquisition module is used to acquire user requests and, based on the user requests, acquire request data; the user requests represent the user's operational intent. The sending module is used to send the request data to the backend according to the established communication method; wherein, the communication method includes any one of short polling, long connection, and SSE connection; The backend includes: The receiving module is used to receive request data sent by the front end, and to parse and verify the request data; The data processing module is used to retrieve multi-source data from the database according to the requested data, provided that the verification passes; and to perform unified standardization processing on the multi-source data to obtain time series data; wherein, the unified standardization processing includes unifying the multi-source data onto the same time axis; The difference calculation module is used to calculate the difference ratio between the time series data at each time point and the preset baseline value, and to mark abnormal time points where the difference ratio is greater than a preset threshold. The result return module is used to encapsulate the time series data, the difference ratio, and the alarm flag into response data and send the response data to the front end.

8. A computer embedded device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 6.

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