Stock market index system custom display method and system based on component reuse

Abstract

The invention provides a stock market index system user-defined display method and system based on component multiplexing, and relates to the technical field of component multiplexing, and the method comprises the steps: obtaining multi-source index data and layout parameter data defining display rules, and mapping the index data into a structured data unit set according to frequency characteristics and parameters; time-sharing and frequency-division multiplexing is adopted, data units with different frequency characteristics are distributed to independent virtual transmission channels, and intelligent scheduling of multiple paths of data streams is achieved; the scheduled data stream is input into a rendering component, a fragment processing unit of the rendering component analyzes the data stream to generate spatial distribution information and a rendering parameter set, and then the spatial distribution information and the rendering parameter set are synthesized into a pixel array; and finally, performing view synthesis and parameter adjustment on the pixel array by using a rendering component, and generating and customizing a target view containing a multi-dimensional stock market index system in real time. According to the method, the fluency and the self-definition degree of visualized display of the mass stock market data are improved.

Description

Technical Field

[0001] This application relates to the field of component reuse technology, and in particular to a method and system for customizing the display of stock market indicator systems based on component reuse. Background Technology

[0002] With the rapid development of financial information technology, securities trading venues need to visualize massive, multi-source, and high-frequency stock market indicator data in real time. These application scenarios require the display system to be able to process data streams with different update frequencies, such as intraday trading and fundamentals, and to support users in flexibly adjusting the rendering density and display style of the view according to their personalized needs. This places high demands on the real-time performance of data processing and the customizable capabilities of view rendering.

[0003] One existing technical solution uses a fixed pipeline-based graphics rendering architecture. This solution uses a predefined data parsing module to convert stock market indicator data from different sources into a standardized format. Then, a fixed rendering pipeline generates visual charts according to a preset view template. In the data processing stage, data of different frequencies are sorted and transmitted through a static priority queue. In the rendering stage, the standard interface of the graphics library is called to complete the drawing and combination of graphic elements.

[0004] However, this existing solution exhibits certain limitations when dealing with high-concurrency data streams. For example, when users need to adjust the visual attributes of a view or the degree of data aggregation, the entire rendering process often needs to be reconfigured due to the fixed rendering pipeline structure, resulting in a delay in view updates. At the same time, static data transmission strategies are prone to uneven distribution of rendering resources when faced with sudden changes in data streams, which in turn affects the smoothness of displaying multiple metrics simultaneously. Furthermore, standardized rendering components have relatively limited richness and adjustability of visual elements when representing complex data relationships. Summary of the Invention

[0005] This application provides a method and system for customizing the display of stock market indicators based on component reuse, in order to solve the problems of low fluency and low degree of customization in the visualization of massive stock market data in the prior art.

[0006] To address the aforementioned technical problems, firstly, this application provides a method for customizing the display of a stock market indicator system based on component reuse, including:

[0007] Acquire multi-source indicator data and layout parameter data;

[0008] Based on the layout parameter data, the multi-source indicator data is mapped to generate a data unit set;

[0009] The data units in the data unit set are allocated to different virtual transmission channels according to frequency characteristics by using time division multiplexing and frequency division multiplexing, and data streams of the multiple virtual transmission channels are controlled by scheduling.

[0010] The data stream controlled by scheduling is input into a rendering component, and a fragment processing unit in the rendering component generates spatial distribution information and a rendering parameter set according to the data stream controlled by scheduling, and generates a pixel array based on the spatial distribution information and the rendering parameter set.

[0011] The rendering component multiplexes the pixel array, performs view synthesis processing on the pixel array, and generates and customizes a target view for display.

[0012] Optionally, the data units in the data unit set are allocated to different virtual transmission channels according to frequency characteristics by using time division multiplexing and frequency division multiplexing, and data streams of the multiple virtual transmission channels are controlled by scheduling, including:

[0013] The feature identifier of each data unit is extracted from the data unit set.

[0014] Based on frequency division multiplexing, data units with different feature identifiers are allocated to virtual transmission channels with corresponding bandwidths.

[0015] Based on time division multiplexing, each virtual transmission channel is allocated an independent transmission time slot, and a time slot allocation scheme is generated.

[0016] Using a dynamic allocation algorithm, the time slot allocation scheme of each virtual transmission channel is adjusted according to the data request frequency of the real-time rendering engine.

[0017] Based on the adjusted time slot allocation scheme, the data streams of the multiple virtual transmission channels are processed to generate a data stream controlled by scheduling.

[0018] In a second aspect, the application provides a stock market index system self-defined display system based on component multiplexing, including:

[0019] An acquisition module is configured to acquire multi-source index data and layout parameter data.

[0020] A mapping module is configured to perform mapping processing on the multi-source index data based on the layout parameter data to generate a data unit set.

[0021] An allocation module is configured to allocate data units in the data unit set to different virtual transmission channels according to frequency characteristics by using time division multiplexing and frequency division multiplexing, and control data streams of the multiple virtual transmission channels by scheduling.

[0022] The input module is configured to input the data stream after the scheduling control to the rendering component, and generate spatial distribution information and a rendering parameter set by a fragment processing unit in the rendering component according to the data stream after the scheduling control, and generate a pixel array based on the spatial distribution information and the rendering parameter set.

[0023] The synthesis module is configured to multiplex the rendering component, perform view synthesis processing on the pixel array, and generate and customize a display target view.

[0024] In a third aspect, the present application provides an electronic device, comprising:

[0025] A memory configured to store a computer program.

[0026] A processor configured to execute the computer program to implement the steps of the stock market index system customized display method based on component multiplexing according to the first aspect.

[0027] In a fourth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the stock market index system customized display method based on component multiplexing according to the first aspect.

[0028] The technical scheme provided by the present application has the following beneficial effects:

[0029] The present application introduces a dynamic adjustable data stream scheduling mechanism combined with a rendering component to form a collaborative processing flow; by intelligently allocating channels according to data characteristics, it ensures that multiple data streams can be efficiently and stably supplied to the rendering link; at the same time, by utilizing the flexibility of the rendering component, the data attributes are directly mapped to pixel information, and the user can adjust the rendering parameters in real time, finally generating an integrated visual view that integrates multi-dimensional indicators, and supporting real-time interaction and customized adjustment of the view effect by the user.

[0030] Further, the present application constructs a dynamic adaptive data scheduling mechanism, which can intelligently allocate data transmission resources according to the actual load of the downstream rendering engine, thereby improving the collaborative efficiency between data supply and rendering consumption, and ultimately ensuring that multiple data streams can be stably and low-delay transmitted under complex views.

[0031] These aspects and other aspects of the present application will be more apparent in the following description of the embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and all other embodiments obtained by a person of ordinary skill in the art without creative work on the basis of these embodiments also belong to the protection scope of the present application.

[0033] Figure 1 A flow chart of a stock market index system self-defined display method based on component reuse provided by an embodiment of the present application is shown in the figure.

[0034] Figure 2 A specific implementation schematic diagram of a stock market index system self-defined display method based on component reuse provided by an embodiment of the present application is shown in the figure.

[0035] Figure 3 A structural schematic diagram of a stock market index system self-defined display system based on component reuse provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0036] In view of the problems in the prior art, the present application provides a stock market index system self-defined display method based on component reuse, the core of which is to introduce a dynamic adjustable data flow scheduling mechanism combined with a collaborative processing flow of rendering components; by intelligently allocating channels according to data characteristics, it ensures that multiple data can be efficiently and stably supplied to the rendering link; at the same time, by using the flexibility of the rendering component, the data attributes are directly mapped to pixel information, and the user can adjust the rendering parameters in real time. With the deep cooperation of data scheduling and rendering presentation, the present application effectively overcomes the rigidity of the fixed pipeline architecture, so that the view can quickly respond to the user's self-defined operation, and maintain a smooth and consistent display effect in a complex data environment, thereby improving the adaptability of the system in the face of diversified display requirements.

[0037] In order to enable the persons skilled in the art to better understand the present application, the present application will be further described in detail below in combination with the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work also belong to the protection scope of the present application.

[0038] The core of the present application is to provide a stock market index system self-defined display method based on component reuse, and a flow chart of one specific embodiment is shown in the figure. Figure 1 The method comprises the following steps.

[0039] Step 101: Obtain multi-source index data and layout parameter data.

[0040] In step 101, multi-source indicator data refers to stock market-related information from different channels and with different update frequencies, such as real-time changing stock transaction prices and transaction volume data, and regularly released company financial data; layout parameter data is a set of rules predefined by the user to guide subsequent data processing and visualization presentation. This data specifies the density and structure of different types of data to be arranged on the screen.

[0041] In this embodiment of the application, two types of information are read simultaneously from a specified data source: one is the original market data, which serves as multi-source indicator data, and the other is the user-defined display specifications, which serve as layout parameter data, thereby providing complete input for subsequent mapping processing.

[0042] Step 102: Based on the layout parameter data, perform mapping processing on the multi-source indicator data to generate a data unit set.

[0043] In step 102, the data unit set is a sequence of multiple data units arranged in chronological order. The data unit is a standardized data block formed after feature mapping. Each data unit not only contains the original data content, but also has a density level label determined by its update frequency and a structure type label determined by its data type.

[0044] In the embodiments of this application, the specific implementation process of step 102 can be referred to steps 601 to 605, which will not be repeated here.

[0045] Step 103: Using time-division multiplexing and frequency-division multiplexing, the data units in the data unit set are allocated to different virtual transmission channels according to their frequency characteristics, and the data streams of multiple virtual transmission channels are scheduled and controlled.

[0046] In step 103, frequency division multiplexing is a logical channel partitioning technique that classifies data units according to their frequency characteristics and sends them to different processing pipelines.

[0047] Frequency characteristics refer to the rate of change characteristics of data identified through digital signal processing analysis of multi-source indicator data. This characteristic comes from the result of quantitative analysis of the fluctuation frequency of stock market indicator data over time. Specifically, frequency characteristics can include data classifications of different frequency ranges such as high-frequency fluctuation data, low-frequency trend data, and macroeconomic indicator data.

[0048] A virtual transmission channel refers to a logically defined independent transmission path for data streams of different frequency categories; time-division multiplexing refers to allocating a specific time slice to each virtual transmission channel to transmit its data, ensuring that different types of data can be transmitted alternately and in an orderly manner on the same physical link.

[0049] In the embodiments of this application, the specific implementation process of step 103 can be referred to steps 201 to 205, which will not be repeated here.

[0050] For example, time-sharing price and volume data units with a density level of "high" are assigned to a virtual transmission channel called "Channel Fast," while earnings per share data units with a density level of "low" are assigned to a virtual transmission channel called "Channel Slow." Initially, "Channel Fast" is set to receive a transmission opportunity every 10 milliseconds, while "Channel Slow" receives one transmission opportunity every 100 milliseconds. When the rendering engine is detected to be idle and able to process data quickly, a digital signal processing algorithm is used to adjust the transmission opportunity of "Channel Fast" to once every 5 milliseconds, thereby accelerating the transmission speed of high-frequency data. After adjustment, the data from the two channels are integrated and sorted to ensure that the data stream can be smoothly and efficiently delivered to the next step.

[0051] Step 104: Input the data stream after scheduling and control to the rendering component. Through the fragment processing unit in the rendering component, generate spatial distribution information and rendering parameter set according to the data stream after scheduling and control. Generate pixel array based on the spatial distribution information and the rendering parameter set.

[0052] In step 104, the rendering component is a software module in a computer graphics system that allows developers to customize the graphics rendering logic; the fragment processing unit is the core part of the rendering component, responsible for calculating the final color and transparency of each pixel on the screen;

[0053] Spatial distribution information refers to the calculation results of which pixel positions on the screen each data unit should correspond to. The rendering parameter set is a set of parameter values ​​used to control the visual appearance of pixels. The pixel array is a two-dimensional array composed of the final color and transparency values ​​of all pixels arranged in order of their screen positions. This array is the direct data basis for generating images.

[0054] In the embodiments of this application, the specific implementation process of step 104 can be referred to steps 401 to 405, which will not be repeated here.

[0055] Step 105: Reuse the rendering component to perform view composition processing on the pixel array, generate and customize the target view for display.

[0056] In step 105, the target view refers to the complete visualization interface that is ultimately presented to the user and contains all the specified stock market indicator information.

[0057] In the embodiments of this application, the specific implementation process of step 105 can be referred to steps 701 to 704, which will not be repeated here.

[0058] This method efficiently transforms multi-source, heterogeneous stock market data into a high-density visualization view that can be flexibly customized, achieving efficient, flexible, and intuitive display of stock market indicator data.

[0059] To address potential congestion, latency, and resource contention issues during the transmission of massive, multi-frequency stock market indicator data to the rendering engine, and to further improve the efficiency and stability of data supply, some embodiments include step 103: employing time-division multiplexing and frequency-division multiplexing to allocate data units in the data unit set to different virtual transmission channels according to their frequency characteristics, and scheduling and controlling the data streams of multiple virtual transmission channels, such as... Figure 2 As shown, it includes:

[0060] Step 201: Extract the feature identifier of each data unit from the data unit set.

[0061] In step 201, the feature identifier is a label carried by each data unit in the data unit set. This label has been assigned in the previous mapping process stage and is used to indicate the update speed category of the stock market indicator data represented by the data unit, such as high frequency, medium frequency or low frequency.

[0062] In this embodiment, each data unit in the data unit set is read one by one, and a pre-marked feature identifier is extracted from its metadata information, thereby preparing for the subsequent diversion of data with different characteristics to different processing channels.

[0063] Step 202: Based on frequency division multiplexing, data units with different characteristic identifiers are allocated to virtual transmission channels with corresponding bandwidths.

[0064] In step 202, the corresponding bandwidth refers to the data transmission capability allocated to different virtual transmission channels that matches the frequency characteristics of the data they carry. For example, high-frequency data channels obtain high bandwidth, and low-frequency data channels obtain low bandwidth.

[0065] In this embodiment, based on the feature identifier extracted from the data unit, frequency division multiplexing is used for logical routing. Specifically, data units identified as high frequency are guided to a high-bandwidth virtual transmission channel, while data units identified as low frequency are guided to a low-bandwidth virtual transmission channel, thereby achieving preliminary classification and isolated transmission of data.

[0066] For example, virtual transmission channel 1 can provide the corresponding bandwidth for data units of frequency characteristic A, virtual transmission channel 2 can provide the corresponding bandwidth for data units of frequency characteristic B, virtual transmission channel 3 can provide the corresponding bandwidth for data units of frequency characteristic C, ..., virtual transmission channel N can provide the corresponding bandwidth for data units of frequency characteristic N.

[0067] Step 203: Based on time-division multiplexing, allocate independent transmission time slots for each virtual transmission channel and generate a time slot allocation scheme.

[0068] In step 203, a transmission time slot refers to a specific time window allocated to each virtual transmission channel for transmitting its data; the time slot allocation scheme is a planning document that specifies the sequence of time windows and their durations occupied by each virtual transmission channel on a common physical transmission path.

[0069] In the embodiments of this application, after the data units are allocated to their respective virtual transmission channels, based on the principle of time-division multiplexing, an independent, periodically occurring transmission time slot is allocated to each virtual transmission channel. For example, frequent but short time slots are allocated to high-bandwidth channels, and time slots with longer intervals but slightly longer durations are allocated to low-bandwidth channels. Based on this, an initial time slot allocation scheme is generated to specify the transmission opportunities of each channel on the time axis.

[0070] Step 204: Using a dynamic allocation algorithm, adjust the time slot allocation scheme of each virtual transmission channel according to the data request frequency of the real-time rendering engine.

[0071] In step 204, the data request frequency of the real-time rendering engine refers to the number of times the downstream rendering component requests data from the data scheduling module per unit time.

[0072] In the embodiments of this application, the specific implementation process of step 204 can be referred to steps 301 to 305, which will not be repeated here.

[0073] Step 205: Based on the adjusted time slot allocation scheme, process the data streams of multiple virtual transmission channels to generate a data stream after scheduling and control.

[0074] In this embodiment of the application, based on the latest time slot allocation scheme adjusted in step 204, the data streams to be transmitted in multiple virtual transmission channels are first prioritized to ensure that the data in high-priority channels can get the transmission opportunity first. Then, conflict detection is performed to ensure that the data in each channel will not interfere with each other during the transmission in the adjusted time slots. Finally, a smooth, efficient and conflict-free data stream after scheduling and control is output for use by subsequent rendering components.

[0075] Here is a specific example:

[0076] Continuing from the data unit set generated in the aforementioned embodiments, which includes second-level intraday price and volume data units and earnings per share data units for stock X, the second-level intraday price and volume data units are marked as high frequency and the earnings data units are marked as low frequency. First, the feature identifier of each data unit is extracted from the data unit set to clearly distinguish between high frequency and low frequency data. Based on frequency division multiplexing, all high frequency data units are allocated to a high-bandwidth virtual transmission channel, which is called channel fast, and all low frequency data units are allocated to a low-bandwidth virtual transmission channel, which is called channel slow.

[0077] Then, based on time-division multiplexing, the initial transmission time slots for the fast channel are allocated as one time slot with a duration of 1 millisecond every 10 milliseconds, and the initial transmission time slots for the slow channel are allocated as one time slot with a duration of 5 milliseconds every 100 milliseconds, and an initial time slot allocation scheme is generated accordingly.

[0078] Subsequently, it was observed that the data request frequency of the real-time rendering engine increased from the initial 60 times per second to 120 times per second. Therefore, a dynamic allocation algorithm was used to adjust the time slot allocation scheme for each virtual transmission channel. The formula for calculating the new time slot interval required for each channel is as follows: ,in This indicates that the adjusted time slot interval is in milliseconds. This indicates that the original time slot interval is 10 milliseconds. This indicates that the original request frequency is 60 times per second. This means the new request frequency is 120 times per second. This represents the load safety factor, such as a value of 0.9. In a specific example, This equals 10 × 60 / 120 × 0.9 = 4.5 milliseconds, therefore the time slot interval of the faster channel is adjusted from 10 milliseconds to 4.5 milliseconds;

[0079] Finally, based on this adjusted time slot allocation scheme, the data streams of fast and slow channels are prioritized to ensure that the data of fast channels is transmitted first, and conflict detection is performed to avoid time slot overlap. In the end, a data stream after scheduling and control is generated and smoothly delivered to the subsequent rendering components.

[0080] In this embodiment of the application, this scheme constructs an adaptive data transmission mechanism through operations such as data stream classification, channelization, dynamic time slot scheduling, and conflict resolution, laying a solid data supply foundation for the subsequent generation of smooth and real-time custom views.

[0081] To further improve the responsiveness and adaptability of the time slot allocation scheme to changes in downstream rendering demands and achieve optimal dynamic configuration of transmission resources, in some embodiments, step 204: using a dynamic allocation algorithm, the time slot allocation scheme of each virtual transmission channel is adjusted according to the data request frequency of the real-time rendering engine, including:

[0082] Step 301: Monitor the data request signal emitted by the real-time rendering engine and extract the timestamp sequence from the data request signal.

[0083] In step 301, the data request signal is an electronic signal sent by the real-time rendering engine to the data scheduling module when new data is needed to refresh or generate a view; the timestamp sequence is an ordered list extracted from a series of consecutive data request signals, recording the time when each signal occurs.

[0084] In this embodiment, the system continuously monitors the data request signals sent by the real-time rendering engine. Whenever a signal is received, the system records the precise time when the signal occurs and arranges these time points in chronological order to form a timestamp sequence, thereby objectively reflecting the actual rhythm of the rendering engine requesting data.

[0085] Step 302: Calculate the data request frequency based on the timestamp sequence.

[0086] In step 302, the data request frequency refers to the number of times the real-time rendering engine sends a data request signal per unit time. This frequency is a key indicator for measuring the urgency of the rendering engine's data processing needs.

[0087] In this embodiment of the application, after obtaining the timestamp sequence, the data request frequency is obtained by analyzing the interval distribution of adjacent timestamps in the sequence and calculating the average number of times the request signal occurs within a certain time window. This data request frequency can quantify the real-time data consumption rate of the rendering engine.

[0088] Step 303: Compare the data request frequency with the current allocated capacity of each virtual transmission channel to generate channel status information.

[0089] In step 303, the current allocated capacity of each virtual transmission channel is derived from the time slot allocation scheme, which means that under the current allocation scheme, each virtual transmission channel is allocated a fixed amount of time resources per unit time. The channel status information is a descriptive conclusion about whether each channel is in a light-load, full-load, or overload state, generated by comparing the data request frequency with the current allocated capacity of the channel.

[0090] In this embodiment, the calculated data request frequency is compared one by one with the transmission capacity calculated for each virtual transmission channel based on its currently allocated number of time slots and time length. Based on the comparison results, it is determined whether the transmission capacity of each channel is higher than, equal to or lower than the current rendering requirements, thereby generating clear channel status information.

[0091] Step 304: Based on the channel status information, dynamically calculate the adjustment value required for each virtual transmission channel using the dynamic allocation algorithm.

[0092] In step 304, the adjustment value is a suggested number of transmission slots to be increased or decreased calculated for each virtual transmission channel, and its sign and magnitude depend on the load status information of that channel.

[0093] In this embodiment of the application, the dynamic allocation algorithm makes decisions based on the channel status information generated in step 303. For channels with excessive load, the algorithm calculates the positive adjustment value that needs to be increased, while for channels with insufficient load, it calculates the negative adjustment value that needs to be reduced. This calculation process is dynamic and aims to achieve a balance of load on each channel.

[0094] Step 305: According to the adjustment value, reallocate the positions of the transmission time slots corresponding to the time slot allocation scheme of each virtual transmission channel on the time axis to obtain the adjusted time slot allocation scheme.

[0095] In this embodiment of the application, according to the adjustment value of each channel calculated in step 304, a corresponding number of transmission time slots are added or deleted on the time axis, and the positions of these time slots are rearranged to ensure that the time slots of each channel are evenly distributed and without conflict, and finally an updated time slot allocation scheme that is more adapted to the needs of real-time rendering is formed.

[0096] In this embodiment of the application, this solution improves the timeliness and efficiency of the entire data stream transmission by sensing rendering needs in real time, accurately assessing channel load, intelligently calculating adjustment amounts, and reconstructing time slot distribution.

[0097] To efficiently convert an ordered data stream into displayable pixel information and achieve accurate mapping from data attributes to visual attributes, in some embodiments, step 104 involves: inputting the scheduled and controlled data stream into a rendering component; generating spatial distribution information and a rendering parameter set based on the scheduled and controlled data stream using a fragment processing unit within the rendering component; and generating a pixel array based on the spatial distribution information and the rendering parameter set, including:

[0098] Step 401: Input the data stream after scheduling and control to the rendering component, and parse the data stream after scheduling and control through the fragment processing unit in the rendering component to obtain the density attribute and structural attribute of the data unit.

[0099] In step 401, the density attribute is a numerical identifier in the data unit that represents the speed of updating the corresponding stock market indicator data, and the structural attribute is a category identifier in the data unit that represents its predetermined visual appearance.

[0100] In this embodiment of the application, the data stream after scheduling control is sent to the rendering component. The fragment processing unit in the component reads the data units in the data stream one by one, and parses the pre-assigned density attribute value and structural attribute identifier from the metadata of each data unit.

[0101] Step 402: Based on the density attribute, calculate the coverage area of ​​each data unit in the screen coordinate system using a spatial mapping function, and generate spatial distribution information based on the coverage area.

[0102] In step 402, the screen coordinate system refers to a two-dimensional coordinate system with the upper left corner of the physical screen of the display device as the origin, the positive X-axis pointing horizontally to the right, and the positive Y-axis pointing vertically downward. It is used to define the absolute position of a pixel on the screen; the coverage area refers to a continuous area affected by a data unit in the screen coordinate system.

[0103] In this embodiment, the fragment processing unit calls a predefined spatial mapping function based on the density attribute value of the data unit. This function maps the density value to a region of a specific size on the screen coordinate system. For example, high-density data is mapped to a smaller dense region, and low-density data is mapped to a larger sparse region. Based on the calculated region boundary coordinates, the spatial distribution information of the data unit is generated, clearly indicating the range of pixels it covers on the screen.

[0104] It should be noted that the specific expression of the space mapping function is not specifically limited in the embodiments of this application, and can be set accordingly according to the actual situation.

[0105] Step 403: Query the preset mapping table to generate rendering parameter sets corresponding to different structural attributes.

[0106] In step 403, the mapping table is a predefined lookup table that specifies a set of visual representation parameters corresponding to each structural attribute identifier.

[0107] In this embodiment, the fragment processing unit queries a preset mapping table based on the structural attribute identifier parsed from the data unit. The mapping table then returns a complete set of material parameter values ​​corresponding to the structural identifier. The fragment processing unit then assembles these parameter values ​​into a rendering parameter set specific to the data unit.

[0108] Step 404: Combine the spatial distribution information with the rendering parameter set to calculate the final color value and transparency value for each pixel position.

[0109] In step 404, the final color value refers to the color that a pixel should ultimately present after all visual calculations, usually composed of red, green, and blue components; the transparency value refers to the degree of transparency of the pixel, which affects its blending effect with the background or other layers; and the pixel position refers to the specific position point of a minimum display unit on the screen, determined by the X and Y coordinates in the screen coordinate system.

[0110] In this embodiment of the application, the specific implementation process of step 404 can be referred to steps 501 to 505, and will not be repeated here.

[0111] Step 405: Arrange the final color and transparency values ​​of all pixels in screen coordinate order to generate a pixel array.

[0112] In step 405, the screen coordinate order refers to the order in which all pixels are traversed from left to right and then from top to bottom, following the natural arrangement rules of screen pixels.

[0113] In this embodiment, after the final color and transparency values ​​of all affected pixels have been calculated, these values ​​are arranged sequentially according to the screen coordinate order, that is, the first row from left to right, then the second row, and so on until the last row, thereby generating a complete pixel array that matches the screen resolution.

[0114] For example, when an orderly data stream enters the rendering component, for the time-sharing price and volume data unit, the fragment processing unit calculates the position of a set of closely adjacent pixels on the screen according to its honeycomb grid coordinates, and defines its rendering parameter set as red gradient and highlight effect according to preset rules. For the earnings per share data unit, it calculates its pixel position in the rectangular area in the upper right corner of the screen, and defines the material parameters as blue and semi-transparent effect.

[0115] Next, the fragment processing unit calculates for each pixel that needs to be rendered. For example, for the pixel corresponding to the time-sharing data, it interpolates in the red spectrum based on its price value to determine the base color, and then superimposes the highlight effect to finally obtain the accurate color of the pixel. After all pixels have been calculated, a complete pixel array with a width of 1920 pixels and a height of 1080 pixels is formed.

[0116] In this embodiment of the application, this scheme transforms the information flow containing stock market data semantics into a pixel array containing rich visual details through parsing, mapping, querying and calculation, providing the core image data foundation for finally realizing a highly expressive custom stock market indicator view.

[0117] To further improve the precision and realism of pixel color and transparency calculations, and to make the generated visual elements more expressive, in some embodiments, step 404: combining the spatial distribution information and the rendering parameter set, calculates the final color and transparency values ​​for each pixel location, including:

[0118] Step 501: Determine the basic color value from the preset color lookup table based on the coordinate data in the spatial distribution information.

[0119] In step 501, the coordinate data in the spatial distribution information comes from the logical coordinates assigned to each data unit during the mapping process. This coordinate data defines the relative position of the data unit in the screen space. The color lookup table is a predefined table used to map different data values ​​or position coordinates to a set of basic color values. The basic color values ​​refer to the initial colors that have not undergone effects such as lighting and saturation.

[0120] In this embodiment of the application, for the pixel position where the color value needs to be calculated, a preset color lookup table is queried based on the coordinate data of the pixel in the spatial distribution information to return a corresponding basic color value according to the coordinate position. For example, in a heat map, the central area is mapped to red and the edge area is mapped to blue.

[0121] Step 502: Extract the surface gloss parameter and color saturation parameter from the rendering parameter set.

[0122] In step 502, the surface gloss parameter is a value that controls the intensity of surface reflected light in the rendering parameters, and the color saturation parameter is a value that controls the vividness of the color.

[0123] Step 503: Based on the surface gloss parameters, calculate the highlight component using the lighting model, and superimpose the highlight component with the base color value to generate an intermediate color value.

[0124] In step 503, the highlight component is the color value that simulates the bright spot effect produced when light shines on a smooth surface. The lighting model is a mathematical formula in computer graphics that simulates the interaction between light and surface. The intermediate color value refers to the color value that has been superimposed with the highlight effect but has not yet been adjusted for saturation.

[0125] It should be noted that the embodiments of this application do not impose specific limitations on the setting of the mathematical formula of the lighting model, and can be set accordingly according to the actual situation.

[0126] In this embodiment of the application, the highlight component is calculated based on the surface gloss parameter using a simplified lighting model. The calculation process takes into account the position and intensity of the virtual light source, as well as the gloss level of the surface. Then, the color value of the calculated highlight component is added to the base color value obtained in step 501 component by component to generate a brighter and more three-dimensional intermediate color value.

[0127] Step 504: Based on the color saturation parameter, adjust the saturation of the intermediate color value to generate the final color value at the pixel position.

[0128] In this embodiment of the application, the saturation of intermediate color values ​​is adjusted based on the color saturation parameter. For example, if the saturation parameter is greater than 1, the vividness of the color is increased, and if it is less than 1, the vividness is reduced. The proportional relationship of each component of the color is adjusted through mathematical transformation, thereby generating the final color value of the pixel position.

[0129] Step 505: Calculate the transparency value of the pixel position using the transparency mapping function based on the density weight value in the spatial distribution information.

[0130] In step 505, the density weight value is a weight coefficient contained in the spatial distribution information that is related to the density attribute of the data unit corresponding to the pixel position, and the transparency mapping function is a rule that converts the density weight value into a transparency value.

[0131] It should be noted that the embodiments of this application do not impose specific limitations on the setting of the mathematical formula of the transparency mapping function, and can be set accordingly according to the actual situation.

[0132] In this embodiment, the transparency value of a pixel is calculated using a predefined transparency mapping function based on the density weight value contained in the spatial distribution information. Generally, areas with higher density have lower transparency, and areas with lower density have higher transparency, thereby visually distinguishing the density of the data.

[0133] In this embodiment of the application, this solution transforms abstract data attributes into visual information with a sense of illumination, hierarchy, and rich color expression through step-by-step, fine-grained color and transparency calculations, thereby improving the aesthetics and information delivery effect of the visualization results.

[0134] To transform the raw multi-source stock market data into structured data with spatial and classification features, laying the foundation for subsequent visualization processing, in some embodiments, step 102: based on the layout parameter data, the multi-source indicator data is mapped to generate a data unit set, including:

[0135] Step 601: Extract the hierarchical classification rules and structural template library from the layout parameter data.

[0136] In step 601, the grading rules are a set of predefined standards used to classify data into different categories based on the speed of data updates, such as high frequency, medium frequency, and low frequency; the structure template library is a collection containing various predefined spatial arrangement patterns, such as grid, tree, and honeycomb structures, used to define the layout of data on the screen.

[0137] In this embodiment of the application, during the process of reading and parsing the layout parameter data, key configuration information is first extracted from the data. This configuration information mainly includes two parts: hierarchical classification rules and structural template library. This information provides a clear basis for subsequent data classification and spatial arrangement, thereby supporting the smooth progress of the entire layout process.

[0138] Step 602: According to the level division rules, the multi-source indicator data is divided into data blocks of different density levels according to the update frequency.

[0139] In step 602, the update frequency refers to the number of times stock market indicator data changes within a unit of time. This update frequency is a quantitative parameter obtained by statistical analysis of the historical update intervals of multi-source indicator data. The density level is a classification label assigned to the data block according to the classification rules, such as "high density", "medium density" and "low density". The data block is the original data set grouped according to the update time window.

[0140] In this embodiment of the application, by traversing the acquired multi-source indicator data and checking the update frequency of each data item according to the specific requirements in the level classification rules extracted in step 601, the data whose update frequency meets the high-frequency standard are classified into high-density data blocks, while the data that meets the low-frequency standard are classified into low-density data blocks, thereby realizing the preliminary classification of multi-source indicator data.

[0141] Step 603: Based on the structure template library, perform spatial structure encoding on the data blocks at each density level.

[0142] In step 603, the encoding scheme is a specific spatial arrangement rule selected from the structure template library for data blocks of a specific density level.

[0143] In this embodiment, based on the density level of each data block, a corresponding encoding scheme is matched from the structure template library. For example, a honeycomb-shaped dense encoding scheme is matched for high-density data blocks, while a grid-shaped loose encoding scheme is matched for low-density data blocks. Subsequently, the position of each data point in the data block is calculated based on the matched encoding scheme, thereby generating the spatial coordinate information corresponding to each data point.

[0144] Step 604: Bind the spatially structured data blocks to the corresponding density level identifiers to form data units with a unified format.

[0145] Step 605: Group all data units according to the time series to generate a data unit set.

[0146] In step 605, the time series refers to the sequence of data units arranged in chronological order of data acquisition time. This time series is formed by extracting the timestamp information contained in each data unit and sorting them in chronological order.

[0147] In this embodiment of the application, each data unit is marked with its corresponding data acquisition timestamp, and then all data units are sorted and grouped according to the order of the timestamps. For example, data units generated within the same second are grouped into the same group, thereby forming a set of data units that are sequentially coherent for use in subsequent steps.

[0148] For example, the system retrieves continuous intraday price and volume data for stock X on a certain trading morning from data source A, and retrieves the latest earnings per share data for the stock from database B. In addition, it reads a layout configuration file pre-set by the user, which defines that high-frequency data is arranged in a dense honeycomb pattern and low-frequency data is displayed in a striking icon style.

[0149] Then, based on this layout configuration file, the density level of high-frequency data such as continuous intraday price and volume data is marked as "high," and the row and column position coordinates of each data point in the virtual cell grid are calculated using a honeycomb structure template. Meanwhile, the density level of low-frequency data such as earnings per share data is marked as "low," and a fixed rectangular area in the upper right corner of the screen is allocated to it. Next, these data with density labels and coordinate information are packaged together with the original values ​​into multiple data units, arranged in chronological order, and finally forming a set of data units.

[0150] In this embodiment of the application, this scheme transforms the chaotic raw stock market data into a standardized set of data units with clear semantic and spatial attributes through rule-based classification and spatial coding, providing a well-structured and complete input for subsequent differentiated data transmission scheduling and precise visualization rendering.

[0151] In order to composite the rendered pixel array into the final user interface and support real-time interaction and customization of the view effect by the user, in some embodiments, step 105: reusing the rendering component to perform view compositing processing on the pixel array, generating and customizing the target view for display, including:

[0152] Step 701: Invoke the view composition unit in the rendering component, and map the different data channels in the pixel array to the corresponding target display area according to the view layout parameters configured by the user.

[0153] In step 701, the view composition unit is a functional module in the rendering component responsible for combining multiple image elements into a complete screen; the view layout parameters are user-defined rules about how different indicator data are arranged on the screen, such as the position of the main chart, the position of the sub-chart, and the size; the target display area is a rectangular or other shaped area on the screen that is divided to display specific types of stock market data.

[0154] In this embodiment, the view composition unit in the rendering component is invoked to read the view layout parameters pre-configured by the user. Then, the unit accurately places the data channels representing different stock market indicators in the pixel array onto the corresponding predetermined display area on the screen based on these parameters.

[0155] Step 702: Process multiple target display areas using the layer blending unit in the rendering component to generate an initial view.

[0156] In step 702, the layer blending unit is a functional module in the rendering component used to handle how multiple overlapping image layers are combined. The initial view is a preliminary composite image after the layer blending is completed but before it has been adjusted by the user in real time.

[0157] In this embodiment of the application, after multiple target display areas are placed, they may overlap or be adjacent to each other. At this time, the layer blending unit in the rendering component performs transparency blending on the image layers corresponding to these areas, and feathers or gradients are applied to the edges of the areas to eliminate harsh boundaries, thereby generating an initial view that is visually unified and harmonious.

[0158] Step 703: Modify the rendering parameters of the initial view in real time through the parameter adjustment unit in the rendering component to obtain the target view.

[0159] In step 703, the parameter adjustment unit is a functional module in the rendering component that allows real-time modification of the visual attributes of the image. The rendering parameters are variables that control the appearance of the view, such as color, brightness, contrast, and transparency.

[0160] In this embodiment, the parameter adjustment unit in the rendering component receives instructions from the user via the interactive interface. These instructions modify the rendering parameters of the initial view in real time, such as adjusting the color bands of the heatmap or changing the thickness and color of the technical indicator lines. Subsequently, the parameter adjustment unit immediately recalculates and updates the screen based on the new parameters, thereby generating a target view that meets the user's personalized needs.

[0161] Step 704: Refresh the target view to the display device via the display driver to complete the custom display process.

[0162] In step 704, the display driver is a software component in the operating system or graphics system responsible for sending image data to the physical display device. The display device is the hardware used for the final image presentation, such as a monitor or screen.

[0163] In this embodiment of the application, after the target view is generated, its final image data is sent to the display driver through the display driver interface; the display driver is responsible for converting this image data into signals that the display device can recognize, and controlling the display device to present the target view at a set refresh rate, thereby completing the entire custom display process.

[0164] For example, the pixel array is fed into the frame buffer, and the rendering component blends the heat map formed by the intraday price and volume data with the information frame layer of the earnings per share data in terms of transparency, and adds a ruler and stock name label around it to create the initial view. At this time, the user changes the color theme of the heat map from "red-yellow" to "blue-green" through the interface. This instruction is received by the rendering component, which then quickly recalculates the color values ​​of the corresponding pixels in the intraday data and updates the image in the frame buffer, so that the final target view after the color style change is immediately displayed on the display device.

[0165] In this embodiment, the solution transforms the original pixel array into a final visual interface that is reasonably laid out, visually harmonious, and can be customized in real time according to user interaction through the coordinated work of view composition, layer blending, parameter adjustment, and driving display, thereby improving the interactivity of the system and the user experience.

[0166] Figure 3 This application provides a schematic diagram of the structure of a custom display system for stock market indicators based on component reuse, and the specific implementation details are as follows:

[0167] The acquisition module 31 is used to acquire multi-source indicator data and layout parameter data.

[0168] The mapping module 32 is used to perform mapping processing on the multi-source indicator data based on the layout parameter data to generate a data unit set.

[0169] The allocation module 33 is used to allocate data units in the data unit set to different virtual transmission channels according to frequency characteristics using time-division multiplexing and frequency-division multiplexing, and to schedule and control the data streams of multiple virtual transmission channels.

[0170] The input module 34 is used to input the data stream after scheduling and control to the rendering component. The fragment processing unit in the rendering component generates spatial distribution information and a rendering parameter set according to the data stream after scheduling and control, and generates a pixel array based on the spatial distribution information and the rendering parameter set.

[0171] The compositing module 35 is used to reuse the rendering components to perform view compositing processing on the pixel array, generate and customize the target view for display.

[0172] The custom display system for stock market indicators based on component reuse in this application is used to implement the aforementioned custom display method for stock market indicators based on component reuse. Therefore, the specific implementation of the custom display system for stock market indicators based on component reuse can be found in the embodiment section of the custom display method for stock market indicators based on component reuse described above. The specific implementation can be referred to the description of the corresponding embodiments, which will not be repeated here.

[0173] This application also provides an electronic device, including: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the custom display method for a stock market indicator system based on component reuse as described above.

[0174] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described custom display methods for a stock market indicator system based on component reuse.

[0175] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0176] The embodiments of this application also provide a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps in any of the above embodiments of the custom display method for a stock market indicator system based on component reuse.

[0177] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0178] The foregoing has provided a detailed description of a custom display method and system for a stock market indicator system based on component reuse, as provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for customizing the display of a stock market indicator system based on component reuse, characterized in that, include: Acquire multi-source indicator data and layout parameter data; Based on the layout parameter data, the multi-source indicator data is mapped to generate a data unit set; By employing time-division multiplexing and frequency-division multiplexing, the data units in the data unit set are allocated to different virtual transmission channels according to their frequency characteristics, and the data streams of multiple virtual transmission channels are scheduled and controlled. The data stream after scheduling and control is input to the rendering component. The fragment processing unit in the rendering component generates spatial distribution information and a set of rendering parameters based on the data stream after scheduling and control. A pixel array is generated based on the spatial distribution information and the set of rendering parameters. The rendering component is reused to perform view composition processing on the pixel array, generating and customizing the target view for display.

2. The method according to claim 1, characterized in that, The method employs time-division multiplexing and frequency-division multiplexing to allocate data units in the data unit set to different virtual transmission channels according to frequency characteristics, and to schedule and control the data streams of multiple virtual transmission channels, including: Extract the feature identifier of each data unit from the set of data units; Based on frequency division multiplexing, data units with different characteristic identifiers are allocated to virtual transmission channels with corresponding bandwidths; Based on time-division multiplexing, an independent transmission time slot is allocated to each virtual transmission channel, and a time slot allocation scheme is generated; Using a dynamic allocation algorithm, the time slot allocation scheme for each virtual transmission channel is adjusted according to the data request frequency of the real-time rendering engine; Based on the adjusted time slot allocation scheme, the data streams of multiple virtual transmission channels are processed to generate a data stream after scheduling and control.

3. The method according to claim 2, characterized in that, The method of using a dynamic allocation algorithm to adjust the time slot allocation scheme of each virtual transmission channel according to the data request frequency of the real-time rendering engine includes: Monitor the data request signals emitted by the real-time rendering engine and extract the timestamp sequence from the data request signals; Calculate the data request frequency based on the timestamp sequence; The data request frequency is compared with the current allocated capacity of each virtual transmission channel to generate channel status information; Based on the channel status information, the adjustment value required for each virtual transmission channel is dynamically calculated using the dynamic allocation algorithm; Based on the adjustment value, the time slot allocation scheme of each virtual transmission channel is reallocated, and the corresponding transmission time slot is positioned on the time axis to obtain the adjusted time slot allocation scheme.

4. The method according to claim 1, characterized in that, The process of inputting the scheduled and controlled data stream into the rendering component, generating spatial distribution information and a rendering parameter set based on the scheduled and controlled data stream through the fragment processing unit in the rendering component, and generating a pixel array based on the spatial distribution information and the rendering parameter set includes: The data stream after scheduling and control is input to the rendering component, and the fragment processing unit in the rendering component parses the data stream after scheduling and control to obtain the density and structural properties of the data units. Based on the density attribute, the coverage area of ​​each data unit in the screen coordinate system is calculated using a spatial mapping function, and spatial distribution information is generated based on the coverage area. Query the preset mapping table to generate a set of rendering parameters corresponding to different structural attributes; By combining the spatial distribution information with the rendering parameter set, the final color value and transparency value are calculated for each pixel position; Arrange the final color and transparency values ​​of all pixels in screen coordinate order to generate a pixel array.

5. The method according to claim 4, characterized in that, The step of combining the spatial distribution information with the rendering parameter set to calculate the final color and transparency values ​​for each pixel location includes: Based on the coordinate data in the spatial distribution information, determine the basic color value in a preset color lookup table; Surface gloss parameters and color saturation parameters are extracted from the rendering parameter set; Based on the surface gloss parameters, the highlight component is calculated using a lighting model, and the highlight component is superimposed on the base color value to generate an intermediate color value. Based on the color saturation parameter, the saturation of the intermediate color value is adjusted to generate the final color value at the pixel position; The transparency value of the pixel location is calculated and generated using a transparency mapping function based on the density weight value in the spatial distribution information.

6. The method according to claim 1, characterized in that, The step of mapping the multi-source indicator data based on the layout parameter data to generate a data unit set includes: The hierarchical classification rules and structural template library are extracted from the layout parameter data; According to the classification rules, the multi-source indicator data is divided into data blocks of different density levels according to the update frequency; Based on the aforementioned structural template library, spatial structure encoding is performed on data blocks at each density level; Data blocks that have undergone spatial structure encoding are bound to identifiers of corresponding density levels to form data units with a unified format; All data units are grouped according to time series to generate a data unit set.

7. The method according to claim 1, characterized in that, The reused rendering component performs view composition processing on the pixel array to generate and customize the target view for display, including: The view composition unit in the rendering component is invoked to map different data channels in the pixel array to the corresponding target display area according to the view layout parameters configured by the user. The initial view is generated by processing multiple target display areas using the layer blending unit in the rendering component. The rendering parameters of the initial view are modified in real time by the parameter adjustment unit in the rendering component to obtain the target view; The target view is refreshed to the display device via the display driver to complete the custom display process.

8. A customizable display system for stock market indicator systems based on component reuse, characterized in that, include: The acquisition module is used to acquire multi-source indicator data and layout parameter data; The mapping module is used to perform mapping processing on the multi-source indicator data based on the layout parameter data to generate a data unit set; The allocation module is used to allocate data units in the data unit set to different virtual transmission channels according to frequency characteristics using time-division multiplexing and frequency-division multiplexing, and to schedule and control the data streams of multiple virtual transmission channels. The input module is used to input the data stream after scheduling and control to the rendering component. The fragment processing unit in the rendering component generates spatial distribution information and a rendering parameter set according to the data stream after scheduling and control. Based on the spatial distribution information and the rendering parameter set, a pixel array is generated. The compositing module is used to reuse the rendering components, perform view compositing on the pixel array, and generate and customize the target view for display.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the custom display method for a stock market indicator system based on component reuse as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the customized display method of a stock market indicator system based on component reuse as described in any one of claims 1 to 7.

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