A big data-based tire recycling data visualization management method

Abstract

The application discloses a kind of based on big data's tire recycling data visualization management method, obtains waste tyre initial trajectory, render into the first level flow direction line segment of the whole piece;After entity enters processing node, cut off rendering, based on broken data to establish asynchronous rendering queue, the physical output beat in queue is mapped as rendering time sequence, according to this data entity fission is multiple second level flow direction line segment of the second level flow direction line segment of the second level flow direction line segment of the second level flow direction line segment of the second level flow direction line segment of the second level flow direction line segment of the second level flow direction line segment of the second level flow direction line segment of the second level flow direction line segment of the second level flow direction line segment of the second level flow direction line segment of the second level flow direction line segment of the second level flow direction line segment of the second level flow direction line segment of the second level flow direction line segment of the second level flow direction line segment of the second level flow direction line segment of the second level flow direction

Description

Technical Field

[0001] This invention relates to the field of tire recycling management technology, specifically a data visualization management method for tire recycling based on big data. Background Technology

[0002] With the development of Industrial Internet of Things (IIoT) technology, data visualization of the solid waste recycling chain has become an important means to improve production line management efficiency. Existing industrial data monitoring systems generally adopt point-to-point trajectory tracking models based on discrete entities. In this traditional mode, the data source is usually mapped to a single, constant-shape flow line segment to represent the flow status of standard parts between various physical nodes.

[0003] However, the waste tire recycling business exhibits typical discrete-to-continuous phase transition characteristics. After being crushed and sorted, the whole tire input at the front end transforms from a single entity into bulk materials with varying yields, such as rubber powder and steel wire. Traditional visualization models, lacking a mapping mechanism for these physical transformations, cannot express the fission logic before and after the data source. Faced with this scenario, existing systems typically can only mechanically allocate parallel, independent rendering channels to all newly generated bulk material data downstream of the processing nodes, passively tracking the material trajectory.

[0004] This mechanical parallel rendering mechanism has significant drawbacks in actual big data global monitoring. When production line sensors report bulk material data at high frequency, the front-end interface passively generates an extremely large number of independent trajectory line segments. If the monitoring view is at a macroscopic zoom level or the data push of local nodes reaches its peak, the line segment rendering density within the visible area of ​​the screen will instantly saturate. The massive number of line segments interweaving and obscuring each other not only causes severe visual congestion, making it difficult for business personnel to intuitively obtain the true conversion logic and mixed output ratio of various bulk materials; at the same time, maintaining this exponentially increasing number of independent rendering commands will greatly consume the computing resources of the front-end graphics interface, causing rendering pipeline congestion, and thus leading to interface lag or even crashes.

[0005] Therefore, providing a visualization management method that can adapt to different data densities and view levels and effectively balance the display of complex material fission relationships with system rendering performance is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0006] To address at least one of the technical deficiencies mentioned in the background art, the present invention aims to provide a data visualization management method for tire recycling based on big data, a computer-readable storage medium, and a program product.

[0007] The first aspect of this invention provides a data visualization management method for tire recycling based on big data, comprising the following steps:

[0008] The initial recycling trajectory data of waste tires is obtained and rendered in the visualization interface as a first-level flow line segment pointing to the entity processing node. The first-level flow line segment represents a tire with a physical form as a whole piece.

[0009] After the data entity corresponding to the first-level flow line segment is detected to enter the entity processing node, the rendering of the first-level flow line segment is interrupted; an asynchronous rendering queue is established based on the real-time collected crushing data, and the physical output rhythm of various bulk materials in the asynchronous rendering queue is mapped to the rendering sequence. According to the rendering sequence, the data entity is split into multiple second-level flow line segments representing different bulk materials.

[0010] The current view zoom level, line density within the visible area, and data push frequency of the visualization interface are obtained, and a dynamic visual congestion index is calculated by combining these factors.

[0011] If the dynamic visual congestion index is not less than the preset dimensionality reduction threshold, the independent drawing instructions for each second-level flow line segment are suspended, their spatial vector data are merged and rendered as a single composite line segment; if the dynamic visual congestion index is less than the dimensionality reduction threshold, each second-level flow line segment is rendered independently based on the downstream node coordinate set.

[0012] A second aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a main control chip, implements the method as described in any of the preceding claims.

[0013] A third aspect of the present invention provides a program product that, when executed by a main control chip, implements the method described in any of the preceding claims.

[0014] Compared with existing technologies, this invention maps the physical output rhythm of various loose materials to the rendering sequence through an asynchronous rendering queue and a frame fission mechanism, which can effectively avoid the surge in rendering instructions caused by the concentrated instantiation of massive loose material line segments.

[0015] Meanwhile, by comprehensively considering the view zoom level, line segment density, and data push frequency, a dynamic visual congestion index is calculated in real time. Based on this, the system adaptively merges lines into a single composite line segment using multi-channel alternating texture mapping to represent the mixed output ratio in congested conditions and restores independent dynamic flow rendering in idle conditions. This effectively resolves visual congestion caused by the interlacing and occlusion of high-density lines, significantly reduces the number of graphics drawing instructions, avoids rendering pipeline blockage and interface lag, and thus achieves adaptive and smooth monitoring of complex production line data under different load conditions. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the main process of a big data-based tire recycling data visualization management method disclosed in an embodiment of the present invention;

[0017] Figure 2 This is a flowchart illustrating step 2 of the present invention.

[0018] Figure 3 This is a flowchart illustrating step 3 of the present invention.

[0019] Figure 4 This is a flowchart illustrating step 4 of an embodiment of the present invention;

[0020] Figure 5 This is a schematic diagram of the structure of a computer-readable storage medium disclosed in an embodiment of the present invention. Detailed Implementation

[0021] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.

[0022] This invention provides a data visualization management method for tire recycling based on big data. This method can be deployed in a visualization monitoring system or data management platform in the tire recycling industry to perform real-time visualization tracking of the entire life cycle of waste tires from the recycling end to the sorting and processing node. Furthermore, it provides an intuitive presentation of the mixed output ratio and destination of various bulk materials (such as rubber powder, steel wire, and fiber) generated after crushing and processing, so as to support business personnel in comprehensive perception and efficient decision-making regarding the production line operation status.

[0023] Reference Figure 1 As shown, the method of the present invention includes the following steps:

[0024] Step 1: Obtain the initial recycling trajectory data of waste tires and render it as a first-level flow line segment pointing to the entity processing node in the visualization interface. The first-level flow line segment represents a tire with a physical form as a whole piece.

[0025] In this embodiment, the vehicle receives in real-time latitude and longitude coordinate sequences, timestamps corresponding to each coordinate point, and unique identifiers of the transport vehicles reported by the vehicle-mounted positioning terminal at a preset collection frequency, as well as tire weight load data and tire coding information collected by the weighbridge or RFID radio frequency identification device. This information collectively constitutes the initial recycling trajectory data of the waste tires. It should be understood that this initial recycling trajectory data forms an ordered data stream sequence in the time dimension, recording the entire path movement process of each batch of waste tires from loading at the recycling site, transportation to the final arrival at the sorting center or physical processing node.

[0026] After acquiring the initial recycling trajectory data, in the visualization interface canvas, using the starting point coordinates of the current recycling trajectory as the starting point and the entry coordinates of the entity processing node as the ending point, a continuous and smooth line segment, namely the first-level flow direction line segment, is generated by calling drawing commands through a graphics application interface (such as WebGL or DirectX) according to the order of the latitude and longitude coordinate sequence in the trajectory data. Visually, the first-level flow direction line segment is drawn with a highlighted, thick line indicating direction. The line color can be a dark color associated with the physical properties of waste tires (such as dark gray or dark green), and the arrow of the line segment points to the entity processing node to clearly indicate the overall logistics direction of the waste tires.

[0027] It should be noted that the first-level flow line segment represents a tire in its physical form as a whole unit. That is, at this rendering stage, the data entity corresponding to the line segment has not yet undergone any fragmentation or sorting processing, and its physical form remains a complete, undisassembled waste tire. By giving this line segment a complete and continuous visual style (e.g., without setting breakpoints, without segmentation, and without introducing multiple material textures), the morphological semantics of the current flow object as a whole tire can be conveyed to business monitoring personnel.

[0028] In addition, when monitoring multiple recycling links at the same time, the first-level flow segments corresponding to different links remain independent of each other and are not merged or cross-fused in rendering, so as to ensure that the entire flow trajectory of each batch of waste tires from the source to the physical processing node can be tracked and identified separately.

[0029] Step 2: After the data entity corresponding to the first-level flow line segment is detected to enter the entity processing node, the rendering of the first-level flow line segment is interrupted; an asynchronous rendering queue is established based on the real-time collected crushing data, and the physical output cycle of various bulk materials in the asynchronous rendering queue is mapped to the rendering sequence. According to the rendering sequence, the data entity is split into multiple second-level flow line segments representing different bulk materials.

[0030] Once the complete tire arrives at the sorting center and enters the physical processing node, the physical form of the waste tire changes. The complete tire is transformed into various loose materials such as rubber blocks, steel wires, and fibers after crushing and sorting processes. During this process, production line sensors continuously report the output data of various loose materials at high frequency. If the front-end interface uses a traditional synchronous rendering method, a flow line segment will be generated independently for each piece of loose material data. This results in a massive number of line segment instantiation requests being generated in a very short time, which in turn causes the main rendering loop to block, the interface to freeze, or even crash.

[0031] To address the aforementioned issues, this embodiment employs an asynchronous rendering queue and a staggered frame splitting strategy to decouple high-concurrency data reporting from low-coupling line segment instantiation in a timely manner. This effectively alleviates the pressure of single-frame rendering while ensuring the completeness of material flow visualization.

[0032] Specifically, the entry status of each entity processing node is continuously monitored. When it is detected that the data entity corresponding to a certain first-level flow segment has arrived and entered the entity processing node, the rendering of that first-level flow segment is cut off, that is, the extension of that segment in its original direction is stopped in the visualization interface. It should be noted that since the physical object corresponding to this data entity has entered the crushing and processing stage, its original whole tire shape no longer exists. If the complete shape of the first-level flow segment is retained, it will convey incorrect material status information to the business monitoring personnel, causing visual misleading.

[0033] While severing the first-level flow segments, an asynchronous rendering queue is established based on the real-time collected fragmented data. This asynchronous rendering queue runs independently of the main rendering loop and is used to temporarily store and cache various types of loose material output data reported by entity processing nodes, and to classify, organize, and time-series-calibrate the data. Next, the physical output rhythm of various types of loose materials in the asynchronous rendering queue is extracted, that is, the output rate and output interval of various types of loose materials on the actual production line, and the physical output rhythm is mapped to a rendering sequence that can be recognized by the rendering engine. Finally, according to the rendering sequence, the data entity originally corresponding to the whole tire is split into multiple second-level flow segments representing different loose materials. Each second-level flow segment corresponds to a specific type of loose material (for example, a rubber block corresponds to one second-level flow segment, a steel wire corresponds to another second-level flow segment, and so on), and the second-level flow segments corresponding to various types of loose materials are presented sequentially according to their respective independent rendering sequences.

[0034] In this way, the large number of scattered line segments that originally needed to be generated in a concentrated burst within the same frame are transformed into a staggered instantiation process based on asynchronous queue caching and timing mapping, thereby avoiding rendering pipeline blockage caused by a surge in the number of rendering instructions per frame.

[0035] As an optional implementation method, such as Figure 2 As shown, an asynchronous rendering queue is established based on the fragmented data collected in real time, specifically including steps 21 and 22:

[0036] Step 21: Parse the material classification identifier contained in the high-frequency crushing data stream reported by the entity processing node, and instantiate multiple independent buffer channels corresponding to each type of bulk material in the asynchronous rendering queue according to the material classification identifier.

[0037] In this embodiment, a sensor array and data acquisition device are deployed at the physical processing node to monitor the output of various bulk materials by equipment such as crushers, vibrating screens, and magnetic separators in real time. During operation, the above-mentioned equipment continuously pushes high-frequency crushing data streams to the visual monitoring system according to a preset data reporting cycle.

[0038] Upon receiving the data stream, its protocol is parsed to extract the material classification identifiers. These identifiers uniquely distinguish different types of bulk materials produced after crushing and sorting; for example, "01" can represent rubber blocks, "02" steel wire, and "03" fiber. Based on the number of material classification identifiers obtained, multiple independent buffer channels are instantiated in the asynchronous rendering queue. Each independent buffer channel corresponds one-to-one with a type of bulk material and is specifically used to store and cache the crushing data packets for that type of bulk material.

[0039] In this way, data of different bulk material types are physically isolated at the queue level, avoiding data competition and processing conflicts caused by multiple types of data being mixed and written into the same buffer.

[0040] Step 22: Press the crushing data carrying the equipment timestamp into the corresponding independent buffer channel according to the material classification identifier, and extract the equipment timestamp difference of adjacent data packets in the same independent buffer channel as the physical output cycle of the corresponding bulk material.

[0041] When parsing the fragmented data stream, the device timestamp carried in each data packet is also extracted. The device timestamp is marked in real-time by the data acquisition device of the physical processing node at the moment of data generation, accurately recording the actual output time of each fragmented data in the physical world. Based on the material classification identifier obtained from the aforementioned parsing, each fragmented data packet is pushed into an independent buffer channel corresponding to that identifier, and the data packets in each channel are sorted according to the order of the device timestamps.

[0042] Based on this, for each independent buffer channel, the device timestamp difference between adjacent data packets within that channel is extracted. For example, for an independent buffer channel corresponding to rubber blocks, if the device timestamps of two consecutive data packets within the channel are T1 and T2 respectively, then the difference ΔT = T2 - T1 is the physical production rhythm of the rubber blocks at the current moment, representing the time interval between the production of two adjacent batches of rubber blocks. For independent buffer channels corresponding to other bulk materials such as steel wire and fiber, their respective physical production rhythms are calculated in the same way. This allows for the capture of the differentiated production rhythms of various bulk materials at the physical level.

[0043] After completing the construction of the asynchronous rendering queue and obtaining the physical output ticks of various materials, the next step is to map the physical output ticks to the rendering sequence and, based on this, split the data entity into multiple secondary flow line segments, specifically including steps 23 to 25:

[0044] Step 23: Based on the material classification identifier of each type of bulk material, query the preset material characteristic lag mapping table to obtain the corresponding initial rendering lag frame number, and convert the physical output beat into a continuous basic frame interval.

[0045] A material property lag mapping table is pre-constructed, storing the corresponding initial rendering lag frame counts for different bulk material types. The initial rendering lag frame count reflects the offset of the rendering start time caused by differences in the physical processing timing at the entity processing node for different bulk materials. It should be noted that in an actual tire recycling production line, different types of bulk materials are not all produced at the same time, but rather in a sequential order. For example, rubber blocks are quickly discharged after the initial crushing by the crusher, while steel wires require subsequent magnetic separation to be separated from the mixture, resulting in a significant lag in their production time compared to rubber blocks. The initial rendering lag frame count in the material property lag mapping table is a quantitative expression of this physical timing difference; the larger the value, the greater the lag in the rendering start time of this type of bulk material relative to the baseline bulk material.

[0046] Based on the material classification identifiers of various bulk materials obtained from the aforementioned analysis, each type of bulk material is queried in the mapping table to obtain the initial rendering lag frame count corresponding to each type of bulk material.

[0047] Simultaneously, the physical output beats (measured in actual time units, such as seconds or milliseconds) of various types of loose materials calculated above are converted into a coherent base frame interval. The base frame interval is a re-expression of the physical output beat in units of rendering frames, and its conversion method is: Base frame interval = Physical output beat × Current rendering frame rate. For example, if the physical output beat of a rubber block is 2 seconds and the current rendering frame rate is 60 frames / second, then the base frame interval for the rubber block is 120 frames, meaning that in the rendering sequence, there should be a 120-frame interval between the instantiation trigger frames of two adjacent rubber blocks flowing towards the line segment. Through the above conversion, the output beats of various types of loose materials in the physical time dimension are uniformly mapped to the rendering frame dimension.

[0048] Step 24: At the starting rendering frame of the data entity at the fission node, with the initial rendering lag frame number as the bias reference, the target trigger frame of each second-level flow line segment is allocated by accumulating the calculation according to the basic frame interval.

[0049] The rendering frame corresponding to the moment when the data entity is at the fission node (i.e., at the exit of the entity processing node, the critical position where the data entity splits from a whole form into a loose form) is used as the starting rendering frame. For each type of loose material, the starting rendering frame is used as the time origin, the initial rendering lag frame number corresponding to this type of loose material obtained from the previous query is used as the bias benchmark, and the basic frame interval corresponding to this type of loose material is used as the step size for cumulative calculation. The target trigger frame is then assigned to each secondary flow line segment of this type of loose material.

[0050] For example, assuming the initial rendering frame is frame 1000, the initial rendering lag of the rubber block is 30 frames, and the base frame interval is 120 frames, then the target trigger frame for the first second-level flow line segment corresponding to the rubber block is frame 1030 (1000+30), the second is frame 1150 (1030+120), the third is frame 1270 (1150+120), and so on. Further assuming the initial rendering lag of the wire is 80 frames and the base frame interval is 180 frames, then the target trigger frame for the first second-level flow line segment corresponding to the wire is frame 1080 (1000+80), the second is frame 1260 (1080+180), the third is frame 1440 (1260+180), and so on.

[0051] Through the above allocation process, the second-level flow segments corresponding to different material types each form an independent equally spaced trigger sequence in the rendering timing, and the different sequences are staggered from each other due to the difference in the number of frames of initial rendering lag.

[0052] Step 25: When the main rendering loop advances to the corresponding target trigger frame, the corresponding second-level flow line segment is instantiated in the mis-frame to complete the fission of the data entity into the loose material line segment.

[0053] In each frame of the main rendering loop, it checks whether the current frame number matches the target trigger frame of any assigned secondary flow segment. When the main rendering loop advances to a target trigger frame, the secondary flow segment corresponding to that target trigger frame is immediately instantiated, i.e., the segment is actually drawn in the visualization interface. Since different bulk material types and different batches of secondary flow segments each have different target trigger frames, they will be instantiated sequentially in different rendering frames, resulting in the visual effect of misaligned frames.

[0054] By using the aforementioned staggered instantiation method, the large number of loose material segments that would normally need to be generated centrally within the same frame are distributed across multiple rendering frames for gradual instantiation. This keeps the number of new segment instantiation instructions in each frame at a controllable low level, significantly reducing the computational load of single-frame rendering and the number of graphics interface calls. It effectively avoids rendering pipeline congestion and interface lag caused by a large number of instantiated segments at once. Simultaneously, because the secondary flow segments of various loose materials are precisely mapped and staggered in time according to their physical production rhythm and initial rendering lag frame number, the segment appearance rhythm presented in the visualization interface is basically consistent with the actual production rhythm of various loose materials on the production line. Business monitoring personnel can still intuitively judge the real-time production status of various loose materials by observing the frequency and order of segment appearance.

[0055] As the sorting production line operates at full capacity and the downstream secondary flow segments of each physical processing node are continuously instantiated, the number of segments within the visible area of ​​the visualization interface will accumulate rapidly with the increase in the number of bulk material output batches. When the monitoring view is at a macro zoom level, a large number of segments will interweave and overlap in the limited visible area, causing severe visual congestion; when the data push reaches its peak, the dense segment updates and redraws will further exacerbate the computational pressure on the graphics rendering pipeline. To perceive the above congestion state in real time and adaptively adjust the rendering strategy accordingly, the following step 3 is executed.

[0056] Step 3: Obtain the current view zoom level, line density within the visible area, and data push frequency of the visualization interface, and calculate the dynamic visual congestion index.

[0057] In this embodiment, the Dynamic Visual Congestion Index is a quantitative indicator used to comprehensively measure the visual clutter level of the current rendering scene and the system rendering load pressure. This indicator is comprehensively evaluated from the following three dimensions: 1) Current view zoom level, which characterizes the perspective choice of business monitoring personnel between macroscopic situation and microscopic details. Under different zoom levels, the visual congestion level generated by the same number of line segments within the visible area varies significantly; 2) Line segment density within the visible area, which characterizes the degree of overlap and intersection of each secondary flow line segment within the current display range in spatial location, and is a direct physical quantity for measuring visual congestion; 3) Data push frequency, which characterizes the rate at which entity processing nodes report fragmented data in real time. The higher the frequency, the more intensive the demand for instantiation of new line segments and updating of existing line segments, and the greater the dynamic pressure on the rendering system. The Dynamic Visual Congestion Index is obtained by comprehensively calculating the information from the above three dimensions.

[0058] As an alternative implementation method, such as Figure 3 As shown, this step specifically includes steps 31 and 32:

[0059] Step 31: Determine the pixel aggregation radius within the visible area based on the current view zoom level. Using the pixel aggregation radius as a reference, perform spatial bounding box collision detection on each second-level flow line segment within the visible area, count the number of overlapping layers of line segments within the same pixel aggregation radius, and take the maximum value of the number of overlapping layers of line segments as the line segment density within the visible area.

[0060] Retrieves the current view zoom level of the visualization interface. The view zoom level is determined by the user using the mouse wheel, gesture zoom, or interface zoom controls, and its value reflects the current display scale.

[0061] The pixel aggregation radius within the visible area is determined based on the current view zoom level. It's important to note that the pixel aggregation radius is negatively correlated with the current view zoom level. Specifically, when the view zoom level is high (i.e., the user zooms in to observe local details), the spatial range in the image is smaller. In this case, the pixel aggregation radius is correspondingly reduced to ensure sufficient sensitivity in determining the degree of line overlap at a fine scale, accurately reflecting subtle density differences within local areas. Conversely, when the view zoom level is low (i.e., the user zooms out to observe the overall picture), the spatial range in the image is larger. In this case, the pixel aggregation radius is correspondingly increased to aggregate and statistically analyze line overlap within a larger spatial range at a macro scale, avoiding inaccurate density assessments due to an excessively small judgment window.

[0062] After determining the pixel aggregation radius, spatial bounding box collision detection is performed on each secondary flow line segment within the viewport, using this radius as a reference. Specifically, each pixel within the viewport is used as the detection center, and a circular detection area is defined by the pixel aggregation radius. The number of secondary flow line segments falling within this detection area is counted; this number represents the line segment overlap layer at that pixel location. All pixel locations within the viewport are traversed, and the corresponding line segment overlap layer number at each location is recorded. The maximum value of the line segment overlap layer across all locations is taken as the line segment density within the viewport. It should be understood that a higher line segment density value indicates a greater number of overlapping secondary flow line segments within a certain local area of ​​the viewport, resulting in more severe visual congestion.

[0063] Step 32: Extract the real-time data push frequency of the entity processing node and discretize it into the corresponding rendering refresh rating. Use the line segment density in the visible area and the rendering refresh rating as a joint input index to query in the preset congestion state mapping matrix and obtain the corresponding matrix element value as the dynamic visual congestion index.

[0064] After calculating the line segment density within the visible area, the real-time data push frequency of the entity processing nodes is further extracted. The real-time data push frequency refers to the number of broken data packets reported per second by the entity processing node to the visualization monitoring system; its value reflects the rate of bulk material production and the urgency of rendering update requirements. This continuous real-time data push frequency value is converted into a corresponding rendering refresh rating according to a preset discretization rule. For example, the data push frequency can be divided into multiple level ranges: a frequency below 50 packets / second corresponds to rating level 1, between 50 and 200 packets / second corresponds to rating level 2, and above 200 packets / second corresponds to rating level 3. The higher the rendering refresh rating, the greater the refresh pressure on the rendering system from the current data push.

[0065] Next, the calculated line segment density within the visible area and the aforementioned rendering refresh rating are used as a joint input index to query a pre-defined congestion state mapping matrix. It should be understood that the congestion state mapping matrix is ​​a pre-calibrated two-dimensional lookup table, where row indices correspond to different levels of line segment density, column indices correspond to different levels of rendering refresh rating, and each element value in the matrix represents a pre-calibrated dynamic visual congestion index under the combined conditions of that density level and refresh rating. The calibration of this mapping matrix can be obtained through regression analysis based on performance indicators such as rendering frame latency and interface operation response delay from historical operating data, or it can be pre-set through offline simulation testing.

[0066] By mapping the currently calculated line segment density and rendering refresh rating to the corresponding row and column indices, the unique matrix element is located in the matrix, and the value of that element is read as the current dynamic visual congestion index.

[0067] Step 4: If the dynamic visual congestion index is not less than the preset dimensionality reduction threshold, then suspend the independent drawing instructions of each second-level flow direction segment, merge their spatial vector data and render them as a single composite line segment; if the dynamic visual congestion index is less than the dimensionality reduction threshold, then render each second-level flow direction segment independently based on the downstream node coordinate set.

[0068] In this embodiment, the dimensionality reduction threshold is a critical value used to determine whether the current rendering scene needs to initiate a dimensionality reduction merging strategy. Its value can be set and adjusted according to the graphics processing capabilities of the target device, the resolution of the visualization interface, and the level of detail required by the business monitoring. The dynamic visual congestion index is compared with the aforementioned dimensionality reduction threshold, and the corresponding rendering branch is executed based on the comparison result. Specifically:

[0069] When the dynamic visual congestion index is not less than the dimensionality reduction threshold, it indicates that the density of line segments in the current visible area has exceeded the preset tolerance limit. Continuing to render independently will lead to severe visual chaos and performance degradation. Therefore, the dimensionality reduction and merging rendering mode is entered. Specifically, the independent drawing instructions for each second-level flow line segment are suspended, that is, the operation of calling the graphics drawing interface for each second-level flow line segment is paused. At the same time, the spatial vector data of each second-level flow line segment are merged, integrating multiple independent line segment paths into a unified geometric path, and rendering it as a single composite line segment in the visualization interface.

[0070] Through the above-mentioned dimensionality reduction and merging process, the number of independent line segment instances in the visible area is reduced from dozens or even hundreds to one, which can effectively solve the visual congestion caused by dense interlacing and overlapping of line segments, while greatly reducing the number of times graphics drawing instructions are called, effectively alleviating the computational pressure on the rendering pipeline.

[0071] Optionally, the single composite line segment employs multi-channel alternating texture mapping, and the dynamic texture ratio is used to characterize the mixed output ratio of various bulk materials.

[0072] When multiple secondary flow segments are merged into a single composite segment, the visual features corresponding to various loose materials also merge. Traditional segment merging schemes typically use simple color averaging or transparency mixing, causing the visual features of different loose material types to cancel each other out after merging, resulting in a loss of distinction and ultimately appearing as unrecognizable gray or blurry blocks. Alternatively, they may use dynamic image stitching in the CPU, but this introduces additional image synthesis calculations, which can exacerbate lag in severe cases. To provide business monitoring personnel with visual information on the output ratio of various loose materials even in the dimensionality reduction merging state, this embodiment uses a multi-channel alternating texture mapping technology based on graphics shaders to dynamically present alternating texture images on a single composite segment, with the length ratio corresponding to the mixed output ratio of various loose materials.

[0073] Specifically, the multi-channel alternating texture mapping scheme stores textures corresponding to different fragment types in different texture channels. During the fragment rendering stage of the graphics shader, the corresponding texture channel is dynamically selected and activated for pixel color sampling by detecting the relative position of the current fragment on a single composite line segment. Since the entire texture mapping and blending decision-making process is completed entirely within the graphics shader without any CPU intervention in image compositing operations, this scheme can present a clear and distinct alternating texture effect on a single composite line segment while maintaining high-performance rendering.

[0074] As an optional implementation method, such as Figure 4 As shown, the multi-channel alternating texture mapping specifically includes steps 41 and 42:

[0075] Step 41: Extract the real-time data corresponding to each of the second-level flow segments participating in the merging, calculate the mixed output ratio of various bulk materials, convert the mixed output ratio into a ratio distribution array with a defined continuous scale interval, and pass it as a global variable into the graphics shader.

[0076] After entering the dimension reduction and merging rendering mode, real-time data corresponding to each secondary flow line segment participating in the merging is obtained. This real-time data includes the material type identifier represented by each secondary flow line segment and its corresponding current or cumulative output. Based on this real-time data, the mixed output ratio of various materials is calculated, reflecting the relative share of each type of material in the total output within the current merging rendering scope.

[0077] After calculating the mixed output ratio of various bulk materials, it is converted into a ratio distribution array limited to a continuous scale interval. The ratio distribution array is a one-dimensional array whose elements are a monotonically increasing sequence of scale values ​​in the interval [0,1]. The interval length between adjacent scale values ​​corresponds to the mixed output ratio of the corresponding bulk material type.

[0078] Taking three types of bulk materials as an example, if the mixed production ratios of rubber blocks, steel wires, and fibers are 50%, 30%, and 20% respectively, then this is converted into a proportional distribution array [0, 0.5, 0.8, 1.0], where the interval [0, 0.5] corresponds to rubber blocks, the interval [0.5, 0.8] corresponds to steel wires, and the interval [0.8, 1.0] corresponds to fibers. This proportional distribution array is then passed as a global variable to the graphics shader for use in the fragment rendering stage.

[0079] Step 42: In the fragment rendering stage of the graphics shader, extract the fractional values ​​of the current fragment flow UV coordinates within the texture repetition cycle as a detection probe, perform threshold hit detection within the continuous scale interval defined by the proportional distribution array, and activate the corresponding single loose texture channel based on the hit interval index to perform pixel color sampling, so as to dynamically generate an alternating texture image on the single composite line segment whose length ratio matches the mixed output ratio.

[0080] During the fragment rendering stage of the graphics shader, the flow direction UV coordinates of the current fragment on a single composite line segment are extracted, where the U component represents the relative position of the current fragment along the line segment direction. The fractional part of this flow direction UV coordinate within the texture repetition cycle is then obtained as a detection probe. The fractional value ranges from [0,1), representing the proportion of the current fragment's position within the current texture repetition cycle.

[0081] Next, using this score as a detection probe, threshold hit detection is performed within the continuous scale interval defined by the proportional distribution array. Specifically, it determines which interval in the proportional distribution array the current score falls into and obtains the corresponding index number of that interval. For example, for the proportional distribution array [0, 0.5, 0.8, 1.0], if the current score is 0.3, it falls into the interval [0, 0.5], and the corresponding interval index is 0; if the score is 0.65, it falls into the interval [0.5, 0.8], and the corresponding interval index is 1; if the score is 0.9, it falls into the interval [0.8, 1.0], and the corresponding interval index is 2.

[0082] After threshold hit detection is completed and the hit interval index is obtained, the corresponding single loose material texture channel is activated for pixel color sampling based on the interval index. A corresponding single loose material texture is prepared in advance for each type of loose material, and each texture is stored in a different texture channel. For example, texture channel 0 stores the rubber block texture, texture channel 1 stores the steel wire texture, and texture channel 2 stores the fiber texture. When the hit interval index is 0, texture channel 0 is activated for color sampling; when the hit interval index is 1, texture channel 1 is activated for color sampling; and so on. In this way, at different positions along the length direction of a single composite line segment, the corresponding loose material type's texture pattern will be presented according to the interval segment into which its UV coordinate values ​​fall. Since the length ratio of each interval segment corresponds perfectly to the mixed production ratio of various loose materials, the presentation length ratio of different textures on a single composite line segment also reflects the mixed production ratio of various loose materials in real time.

[0083] It should be noted that due to the texture repetition cycle, the aforementioned alternating textures will repeat periodically along the length of a single composite line segment, forming a continuous visual effect of alternating texture segments. Furthermore, the length of each texture segment is proportional to the output ratio of the corresponding material type. When the mixed output ratio changes, simply updating the ratio distribution array passed to the graphics shader will automatically render the updated alternating texture ratio in subsequent rendering frames, without needing to regenerate or upload any texture resources.

[0084] Therefore, by using the aforementioned GPU-based interval lookup table and texture sampling method, alternating texture images with length ratios matching the mixed output ratios and clear, sharp boundaries between different material textures are dynamically generated on a single composite line segment. Even in the dimensionality reduction and merging state, intuitive visual information on the output ratios of various materials can still be retained for business monitoring personnel.

[0085] When the dynamic visual congestion index is less than the dimensionality reduction threshold, it indicates that the line segment density of the current interface is within a controllable range. As an optional implementation method, each second-level flow line segment is rendered independently based on the downstream node coordinate set, specifically including steps 43 and 44:

[0086] Step 43: Extract the spatial coordinates of the current fission node as the starting coordinates, and traverse the coordinate set of the downstream nodes to obtain the corresponding ending coordinates. Use the starting coordinates and the ending coordinates as control points to construct the Bézier curve path of each second-level flow segment.

[0087] When it is determined that dimensionality reduction merging is not required and independent rendering mode is entered, for each second-level flow line segment generated by the current fission node, the spatial coordinates of that fission node are extracted as the starting coordinates. The spatial coordinates of the fission node are the pixel coordinates or world coordinates of the entity processing node's exit position in the visualization interface.

[0088] Simultaneously, the downstream node coordinate set is traversed to obtain the endpoint coordinates corresponding to each second-level flow segment. The downstream node coordinate set is a pre-configured set of position coordinates corresponding to the positions of each downstream processing device (e.g., rubber powder silo, wire baler, fiber collection device, etc.) in the visualization interface. Based on the type of bulk material represented by each second-level flow segment, the corresponding coordinates are selected from the downstream node coordinate set as the endpoint coordinates.

[0089] After obtaining the starting and ending coordinates, using these coordinates as control points, a smooth curved path for each secondary flow segment is constructed using cubic or quadratic Bézier curve interpolation algorithms. It should be understood that using Bézier curves instead of straight lines for path construction allows each secondary flow segment to visually present a smooth, curved shape, more realistically reflecting the actual physical flow trajectory of bulk materials in sorting, conveyor belt transfer, and other processes. This also improves the readability and aesthetics of the visualization interface.

[0090] Step 44: Match the corresponding basic flow texture according to the bulk material properties represented by each second-level flow line segment, and map the basic flow texture along the corresponding Bézier curve path; and, based on the preset normal rendering rate, drive the UV coordinates of the basic flow texture mapped on the Bézier curve path to generate periodic continuous offset in the graphics shader to complete the independent dynamic flow rendering of each second-level flow line segment.

[0091] After constructing the Bézier curve paths for each secondary flow segment, the corresponding basic flow texture is matched according to the bulk material attributes represented by each segment. Basic flow textures with visual differentiation are pre-configured for different types of bulk materials (e.g., dark particle texture for rubber blocks, bright metallic texture for steel wires, and light filamentous texture for fibers), so that business monitoring personnel can intuitively distinguish the flow direction of different bulk materials through the color and pattern of the texture.

[0092] The basic flow texture is mapped along the corresponding Bézier curve path. Specifically, the length of the Bézier curve path is normalized to the U component of the texture coordinates, and the normal or width direction of the curve path is mapped to the V component of the texture coordinates. The basic flow texture is then fitted onto the surface of the curve path using a texture sampler. Simultaneously, based on a preset normal rendering rate, the UV coordinates of the basic flow texture mapped onto the Bézier curve path are driven to produce periodic continuous offsets in the graphics shader. It should be understood that the normal rendering rate is a preset standard texture animation speed suitable for low-load scenes, used to control the speed at which the texture flows along the path. Through the periodic continuous offset of the UV coordinates, each secondary flow segment exhibits a flowing animation effect that continuously moves along the path direction.

[0093] It should be noted that the texture pattern continuously moves along the line segment path from the starting coordinate to the ending coordinate, which can intuitively express the dynamic process of various bulk materials being continuously transported from the fission node to the downstream node. This allows business monitoring personnel to clearly grasp the real-time flow direction and transportation status of various bulk materials. When the bulk material production rate changes, the instantiation frequency of each secondary flow line segment can be indirectly affected by adjusting the aforementioned mapping relationship between physical production cycle and rendering timing, thereby dynamically reflecting the changes in production line rhythm in independent rendering mode.

[0094] like Figure 5 As shown, embodiments of the present invention also provide a computer-readable storage medium storing a computer program or instructions. When the computer program or instructions are executed by a main control chip or processor, they can implement some or all of the steps of the visualization management method described in any of the foregoing embodiments.

[0095] Computer-readable storage media include, but are not limited to, non-volatile memory (e.g., read-only memory, flash memory, solid-state drive), volatile memory (e.g., random access memory, cache), magnetic storage media (e.g., hard disk, floppy disk, magnetic tape), optical storage media (e.g., optical disc, digital versatile optical disc), and storage devices composed of any combination thereof. By deploying the computer program or instructions on the server or industrial control computer of the tire recycling monitoring system, existing general-purpose computing devices can be transformed into dedicated devices for tire recycling data visualization management, possessing the functions of adaptive rendering switching, asynchronous queue splitting, and multi-channel texture mapping described in the foregoing embodiments.

[0096] This invention also provides a program product, which includes a computer program or instructions. When the computer program or instructions are executed by a main control chip or processor, the method described in any of the foregoing embodiments is implemented.

[0097] The program product can be packaged as a standalone software installation package and provided to target users via network distribution or physical media (such as optical discs or Universal Serial Bus storage devices). Alternatively, it can be integrated as part of a software development kit into existing monitoring platforms or data management systems in the tire recycling industry, extending the platform or system's functionality to include the visualization management capabilities described in the aforementioned embodiments. When executed, the program product can acquire real-time data on the recycling trajectory and breakage of waste tires, dynamically calculate the visual congestion index, and adaptively switch rendering strategies, thereby achieving the technical effect of mitigating visual congestion and system rendering overload.

[0098] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A data visualization management method for tire recycling based on big data, characterized in that, include: Step 1: Obtain the initial recycling trajectory data of waste tires and render it as a first-level flow line segment pointing to the entity processing node in the visualization interface. The first-level flow line segment represents a tire with a physical form as a whole piece. Step 2: After the data entity corresponding to the first-level flow line segment is detected to enter the entity processing node, the rendering of the first-level flow line segment is interrupted; an asynchronous rendering queue is established based on the real-time collected crushing data, and the physical output cycle of various bulk materials in the asynchronous rendering queue is mapped to the rendering sequence. According to the rendering sequence, the data entity is split into multiple second-level flow line segments representing different bulk materials. Step 3: Obtain the current view zoom level, line density within the visible area, and data push frequency of the visualization interface, and calculate the dynamic visual congestion index. Step 4: If the dynamic visual congestion index is not less than the preset dimensionality reduction threshold, then suspend the independent drawing instructions of each second-level flow direction segment, merge their spatial vector data and render them as a single composite line segment; if the dynamic visual congestion index is less than the dimensionality reduction threshold, then render each second-level flow direction segment independently based on the downstream node coordinate set.

2. The method for visual management of tire recycling data based on big data according to claim 1, characterized in that, An asynchronous rendering queue is established based on real-time acquired fragmented data, including: Step 21: Parse the material classification identifier contained in the high-frequency crushing data stream reported by the entity processing node, and instantiate multiple independent buffer channels corresponding to each type of bulk material in the asynchronous rendering queue according to the material classification identifier. Step 22: Press the crushing data carrying the equipment timestamp into the corresponding independent buffer channel according to the material classification identifier, and extract the equipment timestamp difference of adjacent data packets in the same independent buffer channel as the physical output cycle of the corresponding bulk material.

3. The method for visual management of tire recycling data based on big data according to claim 2, characterized in that, The physical output cycle of various types of loose materials in the asynchronous rendering queue is mapped to a rendering sequence. Based on this rendering sequence, the data entity is split into multiple second-level flow segments representing different loose materials, including: Step 23: Based on the material classification identifier of each type of bulk material, query the preset material characteristic lag mapping table to obtain the corresponding initial rendering lag frame number, and convert the physical output beat into a continuous basic frame interval. Step 24: At the starting rendering frame of the data entity at the fission node, with the initial rendering lag frame number as the bias reference, the target trigger frame of each second-level flow line segment is allocated by accumulating the calculation according to the basic frame interval. Step 25: When the main rendering loop advances to the corresponding target trigger frame, the corresponding second-level flow line segment is instantiated in the mis-frame to complete the fission of the data entity into the loose material line segment.

4. The method for visual management of tire recycling data based on big data according to claim 1, characterized in that, The system obtains the current view zoom level, line segment density within the visible area, and data feed frequency of the visualization interface, and calculates a dynamic visual congestion index, including: Step 31: Determine the pixel aggregation radius within the visible area based on the current view zoom level. Using the pixel aggregation radius as a reference, perform spatial bounding box collision detection on each second-level flow line segment within the visible area, count the number of overlapping layers of line segments within the same pixel aggregation radius, and take the maximum value of the number of overlapping layers of line segments as the line segment density within the visible area. Step 32: Extract the real-time data push frequency of the entity processing node and discretize it into the corresponding rendering refresh rating. Use the line segment density in the visible area and the rendering refresh rating as a joint input index to query in the preset congestion state mapping matrix and obtain the corresponding matrix element value as the dynamic visual congestion index.

5. The method for visual management of tire recycling data based on big data according to claim 4, characterized in that, The pixel aggregation radius is negatively correlated with the current view zoom level.

6. The method for visual management of tire recycling data based on big data according to claim 1, characterized in that, The single composite line segment employs multi-channel alternating texture mapping, and the dynamic texture ratio is used to characterize the mixed output ratio of various bulk materials.

7. The method for visualizing and managing tire recycling data based on big data according to claim 6, characterized in that, Multi-channel alternating texture mapping, specifically including: Step 41: Extract the real-time data corresponding to each of the second-level flow segments participating in the merging, calculate the mixed output ratio of various bulk materials, convert the mixed output ratio into a ratio distribution array with a defined continuous scale interval, and pass it as a global variable into the graphics shader. Step 42: In the fragment rendering stage of the graphics shader, extract the fractional values ​​of the current fragment flow UV coordinates within the texture repetition cycle as a detection probe, perform threshold hit detection within the continuous scale interval defined by the proportional distribution array, and activate the corresponding single loose texture channel based on the hit interval index to perform pixel color sampling, so as to dynamically generate an alternating texture image on the single composite line segment whose length ratio matches the mixed output ratio.

8. The method for visual management of tire recycling data based on big data according to claim 7, characterized in that, Each second-level flow segment is rendered independently based on the downstream node coordinate set, including: Step 43: Extract the spatial coordinates of the current fission node as the starting coordinates, and traverse the coordinate set of the downstream nodes to obtain the corresponding ending coordinates. Use the starting coordinates and the ending coordinates as control points to construct the Bézier curve path of each second-level flow segment. Step 44: Match the corresponding basic flow texture according to the bulk material properties represented by each second-level flow line segment, and map the basic flow texture along the corresponding Bézier curve path; and, based on the preset normal rendering rate, drive the UV coordinates of the basic flow texture mapped on the Bézier curve path to generate periodic continuous offset in the graphics shader to complete the independent dynamic flow rendering of each second-level flow line segment.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the main control chip, it implements the method as described in any one of claims 1-8.

10. A program product, characterized in that, When the program product is executed by the main control chip, it implements the method as described in any one of claims 1-8.

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