Real-time temperature field interpolation rendering method and system

Abstract

The invention discloses a real-time temperature field interpolation rendering method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining a to-be-monitored region, dividing the region into a plurality of sub-regions, obtaining a temperature node array, and obtaining a temperature change sequence; the change rate is obtained, and the maximum value of the change rate serves as a representative change rate; defining a core area or a non-core area, obtaining the closing proportion of the non-core area, closing the temperature collection points in the temperature node array of each non-core area according to the closing proportion, and taking the unclosed temperature collection points as interpolation reference collection points, taking all temperature acquisition points in the temperature node array of the core area as interpolation reference acquisition points; and acquiring real-time temperature data of the interpolation reference acquisition points at the current moment, and performing interpolation rendering on the blank area according to the real-time temperature data of all the interpolation reference acquisition points to form a real-time temperature field. The method has the advantages of adaptive scheduling, good gradient interpolation effect and real-time core region conversion.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a real-time temperature field interpolation rendering method and system. Background Technology

[0002] In the field of real-time monitoring of industrial equipment or ambient temperature fields, especially in large-scale scenarios with stringent temperature control and safety requirements such as data centers, power facilities, or production workshops, it is necessary to closely monitor temperature field data.

[0003] Specifically, to ensure accurate detection of sudden local thermal faults (such as equipment overheating and ignition points), sensors must be densely deployed and all nodes must operate at high frequency to achieve full field coverage. This directly leads to a sharp increase in the load (computing resources, storage space, communication bandwidth) and data processing latency, making it difficult to meet real-time requirements. However, if a fixed sparse layout or simply random shutdown of some sensors is adopted to save resources, it is easy to miss temperature anomaly areas or cause serious distortion of the temperature field after interpolation rendering due to insufficient sampling points in critical areas (areas with rapid temperature fluctuations) or the blindness of the shutdown strategy (such as continuously shutting down adjacent nodes, which disrupts spatial continuity). This is because uniform sparsity cannot dynamically distinguish between temperature stable areas and potential risk areas, while the concentrated shutdown of adjacent nodes will cause the interpolation algorithm to lose effective spatial gradient references in blank areas. Ultimately, a contradiction arises between the needs of security monitoring and the efficiency of resource consumption. Specifically, it is impossible to find an adaptive, dynamic optimization balance between the redundant data transmission caused by high-density sensor deployment (especially the continuous generation of data streams in areas that occupy most of the monitoring area and are stable over a long period of time) and the potential thermal risk monitoring blind spots and interpolation distortion under the sparse sampling strategy. This balance would allow for a significant reduction in resource consumption and latency to meet the actual needs of large-scale deployment, while ensuring high sensitivity to abnormal temperature changes and spatial accuracy of temperature field visualization, thereby achieving synergistic optimization of resource efficiency and security reliability. Summary of the Invention

[0004] To address the technical problems mentioned above, this invention provides a real-time temperature field interpolation rendering method and system.

[0005] A real-time temperature field interpolation rendering method includes: acquiring a region to be monitored, uniformly dividing the region into multiple sub-regions, acquiring temperature node arrays set in each sub-region, and acquiring the temperature change sequence of each temperature acquisition point in each temperature node array within the previous acquisition window at the current time; acquiring the rate of change of each temperature acquisition point in each temperature node array based on the temperature change sequence, and taking the maximum value of the rate of change of each temperature acquisition point in the same temperature node array as the representative rate of change of that temperature node array; defining the sub-region where each temperature node array is located as a core region or a non-core region based on the representative rate of change of each temperature node array, acquiring the closing ratio of each non-core region based on the representative rate of change of the temperature node array in each non-core region, closing the temperature acquisition points in the temperature node array of each non-core region according to the closing ratio of each non-core region, and taking the unclosed temperature acquisition points as interpolation reference acquisition points, and taking all temperature acquisition points in the temperature node array of the core region as interpolation reference acquisition points; acquiring the real-time temperature data of the interpolation reference acquisition points at the current time, and performing interpolation rendering on the blank area based on the real-time temperature data of all interpolation reference acquisition points to form a real-time temperature field.

[0006] Optionally, obtaining the rate of change of each temperature acquisition point in each temperature node array based on the temperature change sequence of each temperature acquisition point in each temperature node array includes: obtaining the absolute difference between adjacent temperature data in the temperature change sequence of each temperature acquisition point in each temperature node array; summing all the absolute differences in the temperature change sequence of each temperature acquisition point in each temperature node array and taking the average value to obtain the average variable; dividing the average variable of the temperature change sequence of each temperature acquisition point in each temperature node array by the average value of the temperature data in the temperature change sequence, and using the obtained value as the rate of change of each temperature acquisition point in each temperature node array.

[0007] Optionally, defining the sub-region where each temperature node array is located as a core region or a non-core region based on the representative rate of change of each temperature node array includes: if the representative rate of change of the i-th temperature node array exceeds a preset standard rate of change, then the sub-region where the i-th temperature node array is located is defined as a core region; if the representative rate of change of the i-th temperature node array does not exceed a preset standard rate of change, then the sub-region where the i-th temperature node array is located is defined as a non-core region.

[0008] Optionally, obtaining the shut-off ratio of each non-core region based on the representative rate of change of the temperature node array in each non-core region includes: subtracting the representative rate of change of the temperature node array in each non-core region from the preset standard rate of change to obtain the rate of change difference of each non-core region; and defining the rate of change difference of each non-core region as the shut-off ratio of each non-core region.

[0009] Optionally, closing the temperature acquisition points in the temperature node array of each non-core region according to the closure ratio of each non-core region and using the unclosed temperature acquisition points as interpolation reference acquisition points includes: obtaining the number of temperature acquisition points that need to be closed in the temperature node array of the j-th non-core region according to the closure ratio of the j-th non-core region; selecting temperature acquisition points in the temperature node array to close non-continuously according to the number of temperature acquisition points that need to be closed in the j-th non-core region, and making the closed temperature acquisition points spatially spaced in the sub-region; and using the unclosed temperature acquisition points in the j-th non-core region as interpolation reference acquisition points for the j-th non-core region.

[0010] Optionally, interpolating and rendering the blank area to form a real-time temperature field based on the real-time temperature data of all interpolation reference acquisition points includes: identifying all locations within the area to be monitored that are not interpolation reference acquisition points and forming blank areas; using the real-time temperature data of the interpolation reference acquisition points and their spatial positional relationship within the area to be monitored, applying spatial interpolation with uniform gradient to calculate the calculated temperature value at each location within the blank area; and generating a continuous and visualized real-time temperature field covering the entire area to be monitored and reflecting the spatial distribution of temperature at the current moment, based on the calculated temperature values ​​at each location within the blank area and the real-time temperature data of the interpolation reference acquisition points.

[0011] A real-time temperature field interpolation rendering system is also provided. The system includes: an acquisition module for acquiring the area to be monitored, uniformly dividing the area into multiple sub-regions, acquiring temperature node arrays set in each sub-region, and acquiring the temperature change sequence of each temperature acquisition point in each temperature node array within the previous acquisition window at the current time; a data processing module for acquiring the rate of change of each temperature acquisition point in each temperature node array based on the temperature change sequence of each temperature acquisition point in each temperature node array, and taking the maximum value of the rate of change of each temperature acquisition point in the same temperature node array as the representative rate of change of that temperature node array; and an interpolation preprocessing module for... The representative rate of change of the point array defines the sub-region where each temperature node array is located as a core region or a non-core region. Based on the representative rate of change of the temperature node array in each non-core region, the closure ratio of each non-core region is obtained. The temperature acquisition points in the temperature node array of each non-core region are closed according to the closure ratio of each non-core region, and the unclosed temperature acquisition points are used as interpolation reference acquisition points. All temperature acquisition points in the temperature node array of the core region are used as interpolation reference acquisition points. The interpolation rendering processing module is used to obtain the real-time temperature data of the interpolation reference acquisition points at the current moment. Based on the real-time temperature data of all interpolation reference acquisition points, the blank area is interpolated and rendered to form a real-time temperature field.

[0012] Optionally, the data processing module is further configured to: obtain the absolute difference between adjacent temperature data in the temperature change sequence of each temperature acquisition point in each temperature node array; add up all the absolute differences in the temperature change sequence of each temperature acquisition point in each temperature node array and take the average value to obtain the average variable; divide the average variable of the temperature change sequence of each temperature acquisition point in each temperature node array by the average value of the temperature data in the temperature change sequence, and use the obtained value as the rate of change of each temperature acquisition point in each temperature node array.

[0013] Optionally, the interpolation preprocessing module is further configured to: define the sub-region where the i-th temperature node array is located as a core region if the representative rate of change of the i-th temperature node array exceeds the preset standard rate of change; and define the sub-region where the i-th temperature node array is located as a non-core region if the representative rate of change of the i-th temperature node array does not exceed the preset standard rate of change.

[0014] Optionally, the interpolation preprocessing module is also used to: subtract the representative rate of change of the temperature node array in each non-core region from the preset standard rate of change to obtain the rate of change difference of each non-core region; and define the rate of change difference of each non-core region as the closing ratio of each non-core region.

[0015] The beneficial effects of this invention are reflected in: In the entire real-time temperature field interpolation rendering method, firstly, a dynamic classification mechanism based on the regional core degree (representing the rate of change, the maximum rate of change in the region) and a calculation of the shutdown ratio driven by the rate of change difference are used to achieve quantifiable resource compression in non-core regions. The resource savings are precisely matched to the regional thermal stability level, significantly reducing transmission bandwidth and computational load compared to the full-node working mode. At the same time, the extraction strategy of the maximum rate of change and the full-node retention mechanism in the core region are used to ensure zero omission of key thermal hazards. Secondly, a shutdown strategy based on the spatial interval distribution of temperature acquisition points in non-core regions (such as checkerboard retention) is used to maintain the uniform discrete distribution of reference points in sub-regions, thus preserving the spatial distribution of the temperature field. The integrity of gradient information ensures that the interpolation algorithm can accurately reconstruct non-homogeneous temperature distributions (such as gradient decay from pipe bends to support frames) even with limited reference points, avoiding the risk of interpolation distortion caused by continuous shutdown. Finally, real-time temperature field rendering, combined with data collected from interval reference points and spatial interpolation uniform gradient calculation, forms a closed-loop dynamic optimization capability while ensuring the spatiotemporal continuity of full-field visualization. When a sudden temperature anomaly occurs in a non-core area (such as a pipe weld leak in the next acquisition window), the core area conversion mechanism triggered by the rate of change will immediately restore full-node monitoring, achieving a dynamic balance between resource conservation and safety early warning at the level, and improving the engineering feasibility of real-time monitoring of large-scale temperature fields. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0017] Figure 1 This is a partial flowchart of S1 to S3 in the real-time temperature field interpolation rendering method of the present invention; Figure 2 This is a partial flowchart of S3 in the real-time temperature field interpolation rendering method of the present invention; Figure 3 This is a partial flowchart of S4 in the real-time temperature field interpolation rendering method of the present invention; Figure 4 This is a schematic diagram illustrating the steps of the real-time temperature field interpolation rendering method of the present invention; Figure 5 This is a schematic diagram of part of step S2 in the real-time temperature field interpolation rendering method of the present invention; Figure 6 This is a schematic diagram of part of step S3 in the real-time temperature field interpolation rendering method of the present invention; Figure 7 This is a schematic diagram of another part of the steps in S3 of the real-time temperature field interpolation rendering method of the present invention; Figure 8 This is a schematic diagram of another part of step S3 in the real-time temperature field interpolation rendering method of the present invention; Figure 9 This is a schematic diagram of part of step S4 in the real-time temperature field interpolation rendering method of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0019] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0020] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] like Figures 1 to 4 As shown, a real-time temperature field interpolation rendering method is provided. In one embodiment, the method includes: S1. Obtain the area to be monitored, and divide the area to be monitored into multiple sub-areas evenly. Obtain the temperature node arrays set in each sub-area respectively, and obtain the temperature change sequence of each temperature acquisition point in each temperature node array within the previous acquisition window at the current time. S2. Obtain the rate of change of each temperature acquisition point in each temperature node array based on the temperature change sequence of each temperature acquisition point in each temperature node array, and take the maximum value of the rate of change of each temperature acquisition point in the same temperature node array as the representative rate of change of that temperature node array. S3. Define the sub-region where each temperature node array is located as a core region or a non-core region according to the representative rate of change of each temperature node array. Obtain the closing ratio of each non-core region according to the representative rate of change of each temperature node array in each non-core region. Close the temperature acquisition points in the temperature node array of each non-core region according to the closing ratio of each non-core region and use the unclosed temperature acquisition points as interpolation reference acquisition points. Use all temperature acquisition points in the temperature node array of the core region as interpolation reference acquisition points. S4. Obtain the real-time temperature data of the interpolation reference acquisition point at the current moment, and perform interpolation rendering on the blank area based on the real-time temperature data of all interpolation reference acquisition points to form a real-time temperature field.

[0022] In this embodiment, it should be noted that in S1, the basic spatial framework for adaptive temperature monitoring is constructed. In industrial-grade temperature field monitoring scenarios (such as data center cabinet clusters or chemical plant reactor arrays), the area to be monitored is first uniformly divided into several sub-regions (for example, each sub-region corresponds to a set of equipment cabinets or a section of pipeline). This division can be a simple geometric cut. The "temperature node array" deployed in each sub-region is essentially a group of sensors arranged in a fixed spatial pattern (typically a 5×5 grid layout). Its physical distribution density must meet the sampling requirements and prevent spatial overlap. Then, the temperature change sequence is used as the basis for dynamic control. It does not only focus on the current instantaneous temperature value, but also obtains the time series data of all temperature acquisition points within the previous "acquisition window" (such as a 30-second cycle). For example, in power equipment monitoring, the sensor array corresponding to a circuit breaker cabinet in a sub-region may contain 16 acquisition points. Each acquisition point records 10 sampling values ​​within the window (within a 3-second time interval), thus forming 16 time series matrices of length 10. This step implies the preliminary capture of the dynamic characteristics of temperature changes—only by basing our understanding on the patterns of change over time, rather than on single-point instantaneous values, can we support the subsequent quantitative assessment of regional thermal activity.

[0023] Furthermore, let's illustrate the implementation of S1 using a temperature control scenario in a chemical reactor workshop as an example. Assume a workshop 100 meters long and 50 meters wide is divided into 50 10m × 10m sub-regions (e.g., each sub-region covers one reactor unit), with a 4×4 temperature node array (16 collection points) deployed on top of each sub-region. When executing S1: First, locate the end time of the (t-1)th collection window (e.g., 09:00:00), and backtrack to extract historical data from all collection points within that window (08:59:30 to 09:00:00). For example, a sensor located in the sub-region corresponding to reactor A might have a temperature change sequence of [82.1, 82.0, 82.3, 82.5, 82.4, 82.6, 82.8, 83.0, 83.2, 83.5] (unit: °C). The other 15 collection points in the same array each generate independent sequences. These sequences constitute the original input dataset for this sub-region, and their sampling frequency (e.g., 2Hz) must be sufficient to capture typical thermal changes in the equipment (e.g., the temperature rise rate of a pipe leak). It is worth noting that the window length setting requires a trade-off between two factors: too short a window will fail to distinguish between noise and real changes (e.g., cooling fan start-stop disturbances), while too long a window will reduce response speed. This design allows subsequent steps to accurately quantify local thermal inertia—if all sequences of a reactor unit fluctuate only slightly (e.g., ±0.1℃), it suggests a stable region, potentially becoming a non-core region in S3; conversely, if a sequence at a sampling point undergoes a sudden change (e.g., jumps from 80℃ to 85℃), the representative rate of change calculation in S2 will drive that sub-region to be marked as a core region.

[0024] In S2, the dynamic characteristics of temperature changes are quantified to provide an objective basis for subsequent region classification. This step first calculates the specific rate of change for each temperature sampling point: based on the temperature change sequence obtained in S1 (i.e., the sampled values ​​arranged in chronological order within a sampling window), ignoring specific numerical formulas, its essence is to extract the intensity of temperature fluctuations between adjacent sampling points in the sequence, then eliminate transient noise interference by statistically analyzing the average fluctuation level within the entire window, and finally normalize the data by combining it with the average temperature level of that point, making the rate of change comparable for different temperature zones (e.g., a 60°C electrical cabinet and a 25°C air conditioning duct). This design embodies two layers of engineering wisdom: first, the absolute difference sequence focuses on local change trends rather than global offsets, effectively identifying intermittent temperature jumps caused by poor equipment contact; second, the normalization process eliminates the influence of basic temperature differences, avoiding misjudgment as anomalies due to the inherent thermal inertia of high-temperature areas causing excessively large absolute change values. For example, in data center rack monitoring, if a CPU area that operates at 75°C for a long time shows a temperature change sequence of [75.1, 75.0, 75.2, 75.1] (unit: °C), the calculated low rate of change reflects that it is in a stable working state; while if an adjacent rack shows a sequence of [35.0, 35.3, 36.1, 38.0], its significant rate of change suggests a risk of abnormal heat dissipation.

[0025] Furthermore, after calculating the rate of change at a single point, S2 generates a unique representative rate of change for each sub-region—that is, the maximum value of the rate of change at all sampling points within that sub-region. This strategy directly addresses the core challenge of temperature field monitoring: ensuring zero omissions of safety hazards while reducing resource consumption. Its technical rationale lies in the fact that thermal faults (such as short circuits) in industrial scenarios are typically localized and explosive. If only the average or median is used, the sudden change signal at a tiny high-temperature point within a sub-region may be diluted by surrounding stable nodes. The maximum value strategy, on the other hand, is equivalent to setting a risk sensitivity amplifier for each sub-region. If even one sampling point within the region exhibits abnormal fluctuations, the entire region is identified as requiring close monitoring. Taking a switch room in a power facility as an example: In a sub-region containing 20 data collection points, the temperature change sequences of 19 nodes are stable (change rate less than 0.1%), but one data collection point located at the busbar connection exhibits a sequence [70.5, 71.8, 73.6, 76.0] due to increased contact resistance (calculated as a high change rate). Even if most nodes are stable, the representative change rate of this sub-region will still use this maximum value, thus classifying it as a core region in subsequent S3 and retaining all data collection points. This design establishes dual safety redundancy: it avoids sensor sparsity leading to the shutdown of critical fault points (e.g., uniform shutdown might just eliminate abnormal points), and it prevents over-reliance on the overall trend while ignoring local mutations, ultimately achieving a precise balance between resource optimization and risk management.

[0026] In S3, a dynamic balance between spatial resolution and resource efficiency is achieved. It uses the representative rate of change generated in S2 as input to divide each sub-region into a core region (thermally sensitive area) or a non-core region (thermally stable area). A preset standard rate of change serves as a dynamic threshold, essentially a critical value calibrated using historical fault data (e.g., empirically set as a relative change of 0.5°C per minute). When the representative rate of change of a sub-region exceeds this threshold, it indicates a risk of thermal abrupt change (e.g., poor circuit contact or cooling failure), thus defining it as a core region and retaining all collection points to ensure monitoring accuracy—for example, in a data center, if a rack sub-region experiences a sudden temperature surge due to a fan failure, this region is still marked as a core region even if other collection points remain stable. For non-core regions with a representative rate of change below the threshold, a portion of collection points are shut down proportionally: the shutdown ratio is directly defined by the rate of change difference (preset standard rate of change minus the actual representative rate of change). This linear mapping implies an engineering logic that resource savings are proportional to the degree of stability: if the representative rate of change in a certain chemical reactor area is only 10% of the standard value, then shutting down 90% of the data collection points can minimize redundant data while retaining the necessary basic monitoring capabilities. It is worth noting that this ratio does not depend on a specific numerical formula, but rather achieves an adaptive adjustment of "the more stable, the sparser" through difference quantization.

[0027] Furthermore, after determining the shutdown ratio of non-core areas, the core innovation of S3 lies in the physical shutdown strategy of the acquisition points. First, the number of acquisition points to be shut down is calculated based on the shutdown ratio: if a sub-region contains 16 acquisition points and the shutdown ratio is 75%, then 12 points need to be shut down. At this time, the principle of spatial interval distribution is strictly followed—adjacent nodes are never shut down continuously. Instead, a method such as a checkerboard or scattered sampling is used to ensure that the remaining acquisition points are evenly distributed within the sub-region. For example, in the monitoring of power switch cabinets, a grid coordinate odd-even cross method is used to shut down a sub-region with stable temperature: only sensors at non-continuous coordinate positions such as (1,1), (1,3), (2,2), and (3,1) are retained. This design directly addresses the fatal flaw of existing solutions: if adjacent nodes are shut down (such as shutting down an entire column), a large "blank band" without sensor coverage will be formed within the sub-region, resulting in the lack of gradient reference during subsequent interpolation rendering. This causes small temperature changes (such as a local 60℃ to 62℃ on the busbar) to be interpolated into a smooth 61℃ homogeneous field, completely masking potential safety hazards. The spatially discrete points retained by the interval closure strategy form a minimal complete reference system. For example, in the chemical reactor pipe gallery area, even if only 25% of the sampling points are retained (an average of one point per 4 square meters), the temperature gradient formed between these points can still effectively constrain the interpolation algorithm, ensuring the continuity of its spatial derivative when reconstructing the temperature field in the blank areas. The points that are not ultimately closed are defined as interpolation reference sampling points, which, together with the complete array in the core area, constitute a hybrid monitoring network. This eliminates the risk of interpolation distortion caused by dense closures while ensuring that the resource compression efficiency in non-core areas achieves the expected goal.

[0028] In S4, the overall temperature distribution is reconstructed under the premise of dynamic sensor shutdown. First, based on the interpolation reference acquisition points determined in S3 (i.e., all acquisition points in the core area + points reserved at intervals in the non-core area), the locations of all non-deployed effective sensors within the area to be monitored are automatically identified to form blank areas. It is worth noting that blank areas not only include physically sensorless points, but also dynamically shut-down acquisition points—for example, in a non-core rack sub-area of ​​a data center, if the original array has 16 acquisition points, and S3 proportionally shuts down 12 and reserves 4 at intervals, then the 12 shut-down locations are all marked as blank areas. Subsequently, spatial interpolation uniform gradient calculation is initiated. In essence, it uses the real-time temperature data and spatial positional relationships of the interpolation reference acquisition points, and through spatial statistical algorithms such as distance weighting and Kriging (specific formulas should be avoided), a continuous temperature field is generated in the blank areas. The ingenuity of this design lies in making full use of the spatial gradient information preserved by the interval closing strategy: for example, in the monitoring of power switch cabinets, 18 reference points are preserved in a 6×6 grid in a checkerboard pattern. Their distribution ensures that there are at least 2-3 reference points (horizontal, vertical or diagonal) around any blank point, so that the algorithm can reconstruct a temperature field that conforms to the actual heat conduction characteristics rather than a homogeneous plane based on the physical laws of the heat dissipation air duct inside the cabinet (such as the heat decreasing from the busbar to the cabinet door).

[0029] Furthermore, after completing the interpolation calculation, the generated values ​​in the blank area are combined with the measured data from the interpolation reference collection points to generate a continuous, visualized, real-time temperature field covering the entire monitored area. The core of this process is to transform discrete data into color gradient maps or isotherm maps, intuitively presenting the spatial heat distribution at the current moment.

[0030] Taking chemical pipeline monitoring as an example: In a certain steam pipeline area, 50% of the data collection points were shut down because S3 determined it to be a non-core area. However, the four remaining reference points were located at the pipe bend (high temperature point) and the support frame (low temperature point). After interpolation, a clear temperature decay curve was reconstructed—gradually changing from 100℃ at the bend to 45℃ at the support frame, which highly matches the actual heat conduction model. This step establishes a triple closed-loop optimization mechanism: First, in terms of timeliness, only a portion of the reference point data needs to be transmitted (reducing bandwidth by more than 50% compared to full nodes), significantly reducing processing latency; Second, in terms of security, the complete monitoring of the core area and the gradient constraints of the interpolation algorithm ensure zero missed detections in abnormal areas (e.g., if a sudden leak at the pipe weld causes the local temperature to soar to 120℃, this sub-area will be marked as a core area in the next window and full node monitoring will be restored); Third, in terms of resource efficiency, the sparse reference point network in non-core areas, while retaining key gradient information, dynamically matches resource consumption with regional thermal stability, ultimately achieving a synergy between accuracy and low resource consumption.

[0031] In summary, the real-time temperature field interpolation rendering method achieves several key improvements. First, a dynamic classification mechanism based on regional coreity (representing the rate of change, or the maximum rate of change in the region) and a calculation of the shutdown ratio driven by the rate of change difference enable quantifiable resource compression in non-core regions. The resource savings precisely match the regional thermal stability level, significantly reducing transmission bandwidth and computational load compared to the full-node working mode. Simultaneously, the extraction strategy of the maximum rate of change and the full-node retention mechanism in core regions ensure zero omission of critical thermal hazards. Second, a shutdown strategy based on spatially spaced temperature acquisition points in non-core regions (such as checkerboard retention) maintains the temperature by forcibly keeping the reference points uniformly and discretely distributed within sub-regions. The integrity of the spatial gradient information of the field enables the interpolation algorithm to accurately reconstruct the non-homogeneous temperature distribution (such as the gradient decay from the pipe bend to the support frame) even with limited reference points, avoiding the risk of interpolation distortion caused by continuous shutdown. Finally, real-time temperature field rendering combined with data collected from interval reference points and uniform gradient calculation of spatial interpolation forms a closed-loop dynamic optimization capability while ensuring the spatiotemporal continuity of the entire field visualization. When a sudden temperature anomaly occurs in a non-core area (such as a pipe weld leak in the next acquisition window), the core area conversion mechanism triggered by the rate of change will immediately restore full-node monitoring, achieving a dynamic balance between resource conservation and safety early warning at the level, and improving the engineering feasibility of real-time monitoring of large-scale temperature fields.

[0032] like Figure 5 As shown, in one embodiment, S2, obtaining the rate of change of each temperature acquisition point in each temperature acquisition point in each temperature node array based on the temperature change sequence of each temperature acquisition point in each temperature node array includes: S21. Obtain the absolute difference between adjacent temperature data in the temperature change sequence of each temperature acquisition point in each temperature node array; S22. Add up all the absolute differences in the temperature change sequences of each temperature acquisition point in each temperature node array and take the average value to obtain the average variable; S23. Divide the average variable of the temperature change sequence of each temperature acquisition point in each temperature node array by the average value of the temperature data in the temperature change sequence, and use the resulting value as the rate of change of each temperature acquisition point in each temperature node array.

[0033] In this embodiment, it should be noted that in S21, for the temperature change sequence of each sampling point (such as the sequence of 10 sampling values ​​from a chemical plant reactor), the absolute difference between two adjacent sampling values ​​in the sequence is calculated. This operation can separate the temperature jump amplitude between adjacent moments in the sequence, ignoring the influence of the overall temperature shift. For example, if a sensor sequence shows a jump change of [80.0, 80.5, 79.8, 85.0], the adjacent difference sequence is [0.5, 0.7, 5.2]. The significant amplification of the last difference indicates a sudden temperature rise event, providing basic data support for subsequent identification of instantaneous anomalies such as poor equipment contact.

[0034] In step S22, the average value is obtained by summing all adjacent absolute differences at a single sampling point. This value reflects the average intensity of temperature fluctuations within the entire sampling window, and statistical averaging eliminates occasional interference (such as instantaneous false alarms from sensors). For example, if the adjacent differences in a sensor sequence for a server rack in a data center are [0.1, 0.3, 0.05, 0.2], the average variable is approximately 0.16, indicating that the overall fluctuation is relatively smooth; however, if a difference sequence of [0.1, 2.5, 0.2] appears, the average variable rises to 0.93, indicating an abnormal heat dissipation. This step effectively suppresses transient noise and highlights continuous trends.

[0035] In step S23, the average variable obtained in S22 is divided by the overall average value of the temperature sequence at that sampling point to generate a dimensionless relative rate of change index. This eliminates the bias caused by the inherent thermal inertia of different temperature zones—for example, the physical meaning of an average variable of 0.5℃ in a 60℃ electrical distribution cabinet area is different from that of an average variable of 0.5℃ in a 25℃ air conditioning duct, but after dividing by their respective averages (60℃ and 25℃), the rates of change are 0.008 and 0.02 respectively, truly reflecting that the latter fluctuates more drastically. By comparing relative values, the stable operating conditions in high-temperature areas are made comparable to the slight anomalies in low-temperature areas, avoiding misjudgment of high-temperature equipment.

[0036] like Figure 6 As shown, in one embodiment, defining the sub-region where each temperature node array is located as a core region or a non-core region in S3 according to the representative rate of change of each temperature node array includes: S31. If the representative rate of change of the i-th temperature node array exceeds the preset standard rate of change, then the sub-region where the i-th temperature node array is located is defined as the core region. S32. If the representative rate of change of the i-th temperature node array does not exceed the preset standard rate of change, then the sub-region where the i-th temperature node array is located is defined as a non-core region.

[0037] In this embodiment, it should be noted that in the process of determining the value of the preset standard change rate, the temperature rise characteristics of typical scenarios (such as data center server overheating, power bus short circuit) are first statistically analyzed. Specifically, the temperature change sequence within a few minutes to a few seconds before the fault occurs is collected, its relative change rate distribution is calculated, and then the 95th percentile value of the distribution is taken as the standard value of the preset standard change rate (for example, if the maximum change rate at the beginning of a fault in a data center is 0.8% / minute, then the preset value is set to 1.0% / minute to accommodate a safety margin).

[0038] In S31, when the representative change rate of a sub-region exceeds a preset threshold (such as the critical value calibrated by historical fault statistics), it is determined that the region has a sudden thermal risk. For example, if a single point in a server rack sub-region of a data center experiences a sudden temperature rise due to a fan failure, and its representative change rate is significantly higher than the standard value, it will be immediately marked as a core region and all collection points will be retained to ensure that the fault point is not missed by the sparsity strategy.

[0039] In S32, non-core areas are defined, representing sub-regions where the rate of change does not exceed a threshold. These areas (such as reactor units in chemical plants that maintain a constant temperature for extended periods) are classified as non-core areas. These areas possess stable characteristics, allowing for sensor resource optimization and providing operational targets for subsequent proportional shutdowns, thus avoiding the waste of monitoring resources in low-risk areas.

[0040] like Figure 7 As shown, in one embodiment, obtaining the shutdown ratio of each non-core region in S3 based on the representative rate of change of the temperature node array within each non-core region includes: S33. Subtract the representative rate of change of the temperature node array in each non-core region from the preset standard rate of change and obtain the difference in the rate of change of each non-core region. S34. Define the difference in the rate of change of each non-core region as the closure ratio of each non-core region.

[0041] In this embodiment, it should be noted that in S33, the difference in change rate is obtained by subtracting the actual representative change rate from the preset standard change rate. This step quantifies stability into a specific numerical value—the lower the representative change rate, the larger the difference, which maps to a higher shutdown ratio. For example, if the change rate of a certain sub-area of ​​a power distribution room is only 20% of the standard value, its difference is 80%, reflecting a significantly stable state.

[0042] In S34, following the result of S33, the shutdown ratio is precisely correlated with the degree of thermal stability (e.g., the aforementioned 80% difference corresponds to an 80% shutdown ratio). This linear mapping requires no complex calculations, realizing the intuitive resource control logic of "the more stable, the more shutdowns," avoiding biases from manual intervention.

[0043] like Figure 8As shown, in one embodiment, S3 involves shutting down the temperature acquisition points in the temperature node array of each non-core region according to the shutdown ratio of each non-core region, and using the unshutted temperature acquisition points as interpolation reference acquisition points, including: S35. Obtain the number of temperature acquisition points that need to be turned off in the temperature node array within the j-th non-core region based on the shutdown ratio of the j-th non-core region. S36. In the j-th non-core region, select temperature acquisition points in the temperature node array to be closed non-continuously according to the number of temperature acquisition points that need to be closed, and make the closed temperature acquisition points spatially spaced in the sub-region. S37. Use the temperature acquisition points that are not turned off in the j-th non-core area as the interpolation reference acquisition points in the j-th non-core area.

[0044] In this embodiment, it should be noted that in S35, the specific number of points to be shut down is calculated according to the shutdown ratio (e.g., a 75% shutdown ratio for a 16-point array corresponds to shutting down 12 points). The calculation result may be rounded to the nearest integer, and a minimum number of points is set (e.g., at least 3 points are retained) to prevent excessive sparsity and ensure that basic monitoring capabilities are not lost.

[0045] In S36, deterministic algorithms (such as grid parity selection) are used to select shut-off points discontinuously within sub-regions based on the number of computations. For example, in a 6×6 sensor array, adjacent positions are skipped when shutting off, forcing the retained points to be evenly distributed (similar to the black and white blocks on a chessboard), thus maintaining the complete reference capability of the spatial gradient.

[0046] In S37, the remaining points in non-core areas are merged with all nodes in core areas to form a hybrid monitoring network. For example, four discrete reference points retained after the intermittent closure of a pipe gallery area in a chemical plant are used for interpolation with a 16-point array in an adjacent high-risk reaction zone to form the basis for full-area coverage.

[0047] like Figure 9 As shown, in one embodiment, step S4, which involves interpolating and rendering the blank area based on the real-time temperature data of all interpolation reference acquisition points to form a real-time temperature field, includes: S41. Identify all locations within the monitoring area that are not interpolation reference points and create blank areas; S42. Using the real-time temperature data of the interpolation reference acquisition points and their spatial position relationship within the area to be monitored, the calculated temperature values ​​of each location within the blank area are calculated by applying spatial interpolation with uniform gradual change. S43. Based on the calculated temperature values ​​at various locations within the blank area and the real-time temperature data of the interpolation reference acquisition points, generate a continuous and visualized real-time temperature field that covers the entire area to be monitored and reflects the spatial distribution of temperature at the current moment.

[0048] In this embodiment, it should be noted that, in step S41, by comparing the current interpolation reference acquisition points (including all nodes in the core area and the interval reserved points in the non-core area) with the original sensor deployment map, all physical locations that have not been activated or turned off within the area to be monitored are located to form blank areas. For example, a rack area in a data center originally had 16 acquisition points deployed. After step S3, 75% of them were turned off and 4 were reserved at intervals. The 12 closed points were all marked as blank areas, which, together with the physical sensorless areas (such as gaps between equipment), constitute the target range that needs to be interpolated and filled.

[0049] In S42, spatial interpolation calculations are performed using real-time data (location + temperature value) from interpolation reference acquisition points. Based on the spatial distribution characteristics (horizontal / vertical / diagonal spacing) and temperature differences of neighboring reference points, continuous temperature values ​​are generated in blank areas through gradient maintenance. For example, a checkerboard distribution of reference points is retained in a power switch cabinet (e.g., 18 points are evenly retained in a 6×6 array), so that any blank point (e.g., position (2,3)) has reference points in 2-3 directions. Based on this, a reasonable temperature decay trend from the busbar (high temperature) to the cabinet door (low temperature) inside the cabinet is reconstructed, avoiding homogenization distortion.

[0050] In S43, a continuous, visualized temperature field is generated by fusing measured values ​​from reference points with interpolation results from blank areas: discrete data is converted into a full-field thermal map through color / isotherm mapping. Taking a chemical pipeline corridor as an example, in a certain steam pipeline area, only four reference points are retained: the elbow (100℃) and the support (45℃). After interpolation, a temperature distribution map with a smooth decay from the elbow to the support (non-discrete step-like) is formed. This process establishes a triple closed loop: timely transmission (only 4 data points replace the original 16 points), zero-missed risk detection (if a sudden 120℃ leak occurs at the weld, the next window is automatically upgraded to the core area), and dynamic resource adaptation (the number of reference points fluctuates with the stability of the area).

[0051] A real-time temperature field interpolation rendering system is also provided, characterized in that the system includes: The acquisition module is used to acquire the area to be monitored, divide the area to be monitored into multiple sub-areas, acquire the temperature node arrays set in each sub-area, and acquire the temperature change sequence of each temperature acquisition point in each temperature node array within the previous acquisition window at the current time. The data processing module is used to obtain the rate of change of each temperature acquisition point in each temperature node array based on the temperature change sequence of each temperature acquisition point in each temperature node array, and to take the maximum value of the rate of change of each temperature acquisition point in the same temperature node array as the representative rate of change of that temperature node array. The interpolation preprocessing module is used to define the sub-region where each temperature node array is located as a core region or a non-core region based on the representative rate of change of each temperature node array, obtain the closing ratio of each non-core region based on the representative rate of change of the temperature node array in each non-core region, close the temperature acquisition points in the temperature node array of each non-core region according to the closing ratio of each non-core region, and use the unclosed temperature acquisition points as interpolation reference acquisition points, and use all temperature acquisition points in the temperature node array of the core region as interpolation reference acquisition points. The interpolation rendering processing module is used to obtain the real-time temperature data of the interpolation reference acquisition points at the current moment, and to perform interpolation rendering on the blank area based on the real-time temperature data of all interpolation reference acquisition points to form a real-time temperature field.

[0052] In one embodiment, the data processing module is further configured to: obtain the absolute difference between adjacent temperature data in the temperature change sequence of each temperature acquisition point in each temperature node array; add up all the absolute differences in the temperature change sequence of each temperature acquisition point in each temperature node array and take the average value to obtain the average variable; divide the average variable of the temperature change sequence of each temperature acquisition point in each temperature node array by the average value of the temperature data in the temperature change sequence, and use the obtained value as the rate of change of each temperature acquisition point in each temperature node array.

[0053] In one embodiment, the interpolation preprocessing module is further configured to: define the sub-region where the i-th temperature node array is located as a core region if the representative rate of change of the i-th temperature node array exceeds the preset standard rate of change; and define the sub-region where the i-th temperature node array is located as a non-core region if the representative rate of change of the i-th temperature node array does not exceed the preset standard rate of change.

[0054] In one implementation, the interpolation preprocessing module is further configured to: subtract the representative rate of change of the temperature node array in each non-core region from the preset standard rate of change to obtain the rate of change difference of each non-core region; and define the rate of change difference of each non-core region as the closing ratio of each non-core region.

[0055] In this embodiment, it should be noted that the specific method of performing the operation in the above-mentioned real-time temperature field interpolation rendering system has been described in detail in the embodiments of the real-time temperature field interpolation rendering method, and will not be elaborated here.

[0056] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0057] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0058] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A real-time temperature field interpolation rendering method, characterized in that, include: The area to be monitored is obtained and evenly divided into multiple sub-areas. Temperature node arrays are set in each sub-area, and the temperature change sequence of each temperature acquisition point in each temperature node array within the previous acquisition window at the current time is obtained. The rate of change of each temperature acquisition point in each temperature node array is obtained based on the temperature change sequence of each temperature acquisition point in each temperature node array, and the maximum value of the rate of change of each temperature acquisition point in the same temperature node array is taken as the representative rate of change of that temperature node array. Based on the representative rate of change of each temperature node array, the sub-region where each temperature node array is located is defined as a core region or a non-core region. Based on the representative rate of change of the temperature node array in each non-core region, the closure ratio of each non-core region is obtained. The temperature acquisition points in the temperature node array of each non-core region are closed according to the closure ratio of each non-core region, and the unclosed temperature acquisition points are used as interpolation reference acquisition points. All temperature acquisition points in the temperature node array of the core region are used as interpolation reference acquisition points. Obtain the real-time temperature data of the interpolation reference acquisition point at the current moment, and perform interpolation rendering on the blank area based on the real-time temperature data of all interpolation reference acquisition points to form a real-time temperature field.

2. The real-time temperature field interpolation rendering method according to claim 1, characterized in that, The step of obtaining the rate of change of each temperature acquisition point in each temperature node array based on the temperature change sequence of each temperature acquisition point in each temperature node array includes: Obtain the absolute difference between adjacent temperature data in the temperature change sequence of each temperature acquisition point in each temperature node array; The absolute differences in the temperature change sequences of each temperature acquisition point in each temperature node array are summed and averaged to obtain the average variable. Divide the average variable of the temperature change sequence of each temperature acquisition point in each temperature node array by the average value of the temperature data in the temperature change sequence, and use the resulting value as the rate of change of each temperature acquisition point in each temperature node array.

3. The real-time temperature field interpolation rendering method according to claim 1, characterized in that, The definition of the sub-region where each temperature node array is located as a core region or a non-core region based on the representative rate of change of each temperature node array includes: If the representative rate of change of the i-th temperature node array exceeds the preset standard rate of change, then the sub-region where the i-th temperature node array is located is defined as the core region. If the representative rate of change of the i-th temperature node array does not exceed the preset standard rate of change, then the sub-region where the i-th temperature node array is located is defined as a non-core region.

4. The real-time temperature field interpolation rendering method according to claim 3, characterized in that, The process of obtaining the shutdown ratio of each non-core region based on the representative rate of change of the temperature node array within each non-core region includes: Subtract the representative rate of change of the temperature node array in each non-core region from the preset standard rate of change to obtain the difference in the rate of change of each non-core region. The difference in the rate of change of each non-core region is defined as the closure ratio of each non-core region.

5. The real-time temperature field interpolation rendering method according to claim 1, characterized in that, The step of shutting down the temperature acquisition points in the temperature node array of each non-core region according to the shutdown ratio of each non-core region and using the unshutted temperature acquisition points as interpolation reference acquisition points includes: The number of temperature acquisition points that need to be shut down in the temperature node array within the j-th non-core region is obtained based on the shutdown ratio of the j-th non-core region. In the j-th non-core region, temperature acquisition points in the temperature node array are selected and shut down non-continuously according to the number of temperature acquisition points that need to be shut down, and the shut-down temperature acquisition points are spatially spaced within the sub-region. The temperature acquisition points that are not turned off in the j-th non-core area are used as the interpolation reference acquisition points in the j-th non-core area.

6. The real-time temperature field interpolation rendering method according to claim 1, characterized in that, The step of interpolating and rendering the blank area based on the real-time temperature data of all interpolation reference acquisition points to form a real-time temperature field includes: Identify all locations within the monitoring area that are not interpolation reference points and create blank areas; Using the real-time temperature data of the interpolation reference acquisition points and their spatial position relationship within the area to be monitored, the calculated temperature values ​​at each location within the blank area are calculated by applying spatial interpolation with uniform gradual change. Based on the calculated temperature values ​​at various locations within the blank area and the real-time temperature data from the interpolation reference acquisition points, a continuous and visualized real-time temperature field is generated that covers the entire monitored area and reflects the spatial distribution of temperature at the current moment.

7. A real-time temperature field interpolation rendering system, characterized in that, The system includes: The acquisition module is used to acquire the area to be monitored, divide the area to be monitored into multiple sub-areas, acquire the temperature node arrays set in each sub-area, and acquire the temperature change sequence of each temperature acquisition point in each temperature node array within the previous acquisition window at the current time. The data processing module is used to obtain the rate of change of each temperature acquisition point in each temperature node array based on the temperature change sequence of each temperature acquisition point in each temperature node array, and to take the maximum value of the rate of change of each temperature acquisition point in the same temperature node array as the representative rate of change of that temperature node array. The interpolation preprocessing module is used to define the sub-region where each temperature node array is located as a core region or a non-core region based on the representative rate of change of each temperature node array, obtain the closing ratio of each non-core region based on the representative rate of change of the temperature node array in each non-core region, close the temperature acquisition points in the temperature node array of each non-core region according to the closing ratio of each non-core region, and use the unclosed temperature acquisition points as interpolation reference acquisition points, and use all temperature acquisition points in the temperature node array of the core region as interpolation reference acquisition points. The interpolation rendering processing module is used to obtain the real-time temperature data of the interpolation reference acquisition points at the current moment, and to perform interpolation rendering on the blank area based on the real-time temperature data of all interpolation reference acquisition points to form a real-time temperature field.

8. The real-time temperature field interpolation rendering system according to claim 7, characterized in that, The data processing module is also used for: Obtain the absolute difference between adjacent temperature data in the temperature change sequence of each temperature acquisition point in each temperature node array; The absolute differences in the temperature change sequences of each temperature acquisition point in each temperature node array are summed and averaged to obtain the average variable. Divide the average variable of the temperature change sequence of each temperature acquisition point in each temperature node array by the average value of the temperature data in the temperature change sequence, and use the resulting value as the rate of change of each temperature acquisition point in each temperature node array.

9. The real-time temperature field interpolation rendering system according to claim 7, characterized in that, The interpolation preprocessing module is also used for: If the representative rate of change of the i-th temperature node array exceeds the preset standard rate of change, then the sub-region where the i-th temperature node array is located is defined as the core region. If the representative rate of change of the i-th temperature node array does not exceed the preset standard rate of change, then the sub-region where the i-th temperature node array is located is defined as a non-core region.

10. The real-time temperature field interpolation rendering system according to claim 9, characterized in that, The interpolation preprocessing module is also used for: Subtract the representative rate of change of the temperature node array in each non-core region from the preset standard rate of change to obtain the difference in the rate of change of each non-core region. The difference in the rate of change of each non-core region is defined as the closure ratio of each non-core region.

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