A fusion augmented reality micro-grid standardized operation auxiliary method and system

Abstract

The application discloses a micro-grid standardization operation auxiliary method and system fusing augmented reality, and belongs to the technical field of data processing, and comprises the following steps: S1, performing augmented reality initialization configuration, and performing operation detection after the configuration is completed, obtaining an auxiliary operation label and performing virtual display through the augmented reality technology; S2, obtaining an augmented reality adjustment determination instruction; S3, based on the augmented reality adjustment determination instruction analysis, if the augmented reality adjustment determination instruction is a first execution instruction, no parameter adjustment is performed, otherwise corresponding parameter adjustment is performed, thereby completing the micro-grid standardization operation processing, and solving the problem that the augmented reality label display appears flickering and other unstable phenomena caused by the sudden change of the brightness of the collected image due to the light and shadow change in the prior art.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a standardized operation assistance method and system for microgrids that integrates augmented reality. Background Technology

[0002] Existing standardized operation assistance systems for microgrids utilize augmented reality technology to capture environmental images through visual sensors and match the captured two-dimensional objects with preset equipment information or three-dimensional environment models. After processing by computing devices, the identified operation objects are associated with corresponding task information to generate operation assistance information, which is then presented on the display interface of the augmented reality device.

[0003] For example, the Chinese invention patent with announcement number CN106920071B discloses a substation on-site operation assistance method and system, which includes: upon receiving an operation assistance information acquisition instruction, calling an augmented reality device to acquire relevant information of the operation object in the substation work site; matching the relevant information of the operation object with the relevant information of each object included in the substation in a preset manner to identify the operation object; obtaining the operation task corresponding to the operation object; obtaining operation assistance information based on the operation object and the operation task; and displaying the operation assistance information in the window of the augmented reality device.

[0004] For example, Chinese invention patent CN110310175B discloses a system and method for mobile augmented reality, comprising: a visual sensor configured to capture images of an environment and a computing device communicating with the visual sensor. The computing device has a processor and a storage device storing computer-executable code. When executed at the processor, the computer-executable code is configured to: identify two-dimensional (2D) objects in the captured images; construct a three-dimensional (3D) map of the environment using the captured images; define 3D objects in the 3D map by mapping the identified 2D objects in the captured images to corresponding points in the 3D map; and render a 3D model on the 3D map based on the 3D objects defined in the 3D map.

[0005] The above-mentioned technology has at least the following technical problems:

[0006] Existing technologies primarily focus on object recognition and information display, lacking effective adaptation to environmental influences such as changes in lighting conditions in complex environments. Particularly in indoor microgrid scenarios with dense energy storage devices or highly reflective electrical equipment, changes in light and shadow can cause sudden shifts in the brightness of the acquired images, leading to instability phenomena such as flickering, jittering, or positional shifts when displaying augmented reality tags. This issue severely impacts the visualization of operational information and operational safety, thus presenting a technical problem of flickering and other instability phenomena in augmented reality tag display caused by sudden changes in the brightness of acquired images due to changes in light and shadow. Summary of the Invention

[0007] To address the technical problem of flickering and other instability issues in augmented reality tag display caused by sudden changes in image brightness due to variations in light and shadow in existing technologies, this invention provides a microgrid standardized operation assistance method and system that integrates augmented reality. The technical solution is as follows:

[0008] On the one hand, a method for assisting standardized microgrid operations by integrating augmented reality is provided. This method includes: S1. After a visual sensor receives a standardized microgrid operation signal, it acquires the light intensity of the standardized microgrid operation environment, performs augmented reality initialization configuration based on the light intensity, and performs operation detection after configuration to obtain an auxiliary operation label, which is then virtually displayed using augmented reality technology; S2. It acquires stable display parameters for the auxiliary operation label, analyzes and obtains the label display stability value, acquires environmental change impact parameters for the operation environment, analyzes and obtains the environmental change impact value, and analyzes and obtains an augmented reality adjustment judgment instruction based on the label display stability value and the environmental change impact value. The label display stability value represents the stability of the auxiliary operation label display, and the environmental change impact value represents the degree of influence of dynamic environmental changes on the identification and analysis of the standardized microgrid operation environment; S3. Based on the analysis of the augmented reality adjustment judgment instruction, if the augmented reality adjustment judgment instruction is the first execution instruction, no parameter adjustment is performed; otherwise, corresponding parameter adjustments are performed, thereby completing the standardized microgrid operation processing.

[0009] Furthermore, cloud archiving is also included. The specific method is as follows: Based on the analysis of stable display and environmental judgment states, if there is a period where the stable display judgment state is unqualified and / or the environmental judgment state is unqualified, then that period is marked as the archiving execution period; the network bandwidth after the microgrid operation ends is obtained and compared with a preset network bandwidth threshold. If the network bandwidth is above the network bandwidth threshold, then a portion of key frames from the video data of the archiving execution period are selected and uploaded sequentially to the cloud for archiving; the specific method for selecting a portion of key frames from the video data of the archiving execution period is as follows: if the archiving execution period only has stable display unqualified, then the stable execution difference is marked as the archiving value; if the archiving execution period only has environmental unqualified, then the first difference in environmental change is marked as the archiving value; otherwise, the sum of the stable execution difference and the first difference in environmental change is marked as the archiving value; based on the archiving value, the database is matched to obtain the archiving key frame upload ratio, and a portion of key frames are selected for archiving based on the archiving key frame upload ratio.

[0010] On the other hand, a microgrid standardized operation assistance system integrating augmented reality is provided. This system includes: an initial configuration module, an adjustment analysis module, and an intelligent adjustment module. The initial configuration module, upon receiving a microgrid standardized operation signal from a visual sensor, acquires the light intensity of the microgrid standardized operation environment, performs augmented reality initialization configuration based on the light intensity, and performs operation detection after configuration to obtain an auxiliary operation label, which is then virtually displayed using augmented reality technology. The adjustment analysis module acquires stable display parameters of the auxiliary operation label, analyzes to obtain the label display stability value, acquires environmental change impact parameters of the operation environment, analyzes to obtain the environmental change impact value, and obtains an augmented reality adjustment judgment command based on the label display stability value and the environmental change impact value. The intelligent adjustment module analyzes the augmented reality adjustment judgment command; if the augmented reality adjustment judgment command is the first execution command, no parameter adjustment is performed; otherwise, corresponding parameter adjustments are performed, thereby completing the microgrid standardized operation processing.

[0011] The beneficial effects of the technical solutions provided by the embodiments of the present invention include at least the following:

[0012] 1. The microgrid standardized operation assistance method integrating augmented reality provided by this invention obtains the stable display parameters of the auxiliary operation tag and the environmental change impact parameters of the operation environment, and analyzes the stable display value of the tag and the impact value of environmental change to obtain the augmented reality adjustment judgment command. Based on the augmented reality adjustment judgment command, the augmented reality parameters are intelligently adjusted, thereby realizing the stable display of the augmented reality tag. This effectively solves the problem of unstable phenomena such as flickering when displaying augmented reality tags caused by sudden changes in the brightness of the acquired image due to changes in light and shadow in the prior art.

[0013] 2. This invention performs tag recognition and rendering adjustment, including calculating the stable execution difference, and obtaining the frame voting window adjustment ratio based on the stable execution difference and matching it with the database. The frame voting window is then increased, and the adjustment value of the first tag display is statistically analyzed. If the stable threshold is not reached, the short-term label retention time is further adjusted. This achieves the gradual optimization of the display of augmented reality tags in dynamic environments, ensuring clear, continuous and stable display of tags under different operating conditions.

[0014] 3. This invention analyzes the first difference in environmental changes and matches it with a database to obtain the line readout rate adjustment value, the key frame extraction interval adjustment duration, or the short and long exposure sequence ratio adjustment value. It gradually optimizes each parameter through alternating adjustments in rounds, and at the same time, it statistically analyzes the stable improvement value of the label display in each round and compares it with a preset threshold. This enables the adaptive optimization of augmented reality-assisted operations under dynamic environmental changes, ensuring the stability of label display and the accuracy of operation detection.

[0015] 4. This invention achieves efficient and complete video data archiving by using cloud-based archiving processing, including determining the upload ratio of archived keyframes based on the archiving processing value and selecting some keyframes for upload through content analysis. This ensures the integrity and traceability of job information while avoiding the problems of wasted network bandwidth and duplicate data uploads. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating a standardized operation assistance method for microgrids integrating augmented reality, provided in an embodiment of this application.

[0018] Figure 2 A flowchart illustrating the steps of the microgrid standardized operation assistance method integrating augmented reality provided in this application embodiment;

[0019] Figure 3 A flowchart illustrating the label recognition, rendering, and adjustment process of the microgrid standardized operation assistance method integrating augmented reality provided in this application embodiment;

[0020] Figure 4 A schematic diagram of the structure of the microgrid standardized operation assistance system integrating augmented reality provided in the embodiments of this application;

[0021] Figure 5 The first part is a schematic diagram of the auxiliary operation of the microgrid standardized operation auxiliary system integrating augmented reality provided in the embodiments of this application;

[0022] Figure 6 Part 2 of the schematic diagram of the auxiliary operation of the microgrid standardized operation auxiliary system that integrates augmented reality provided in the embodiments of this application. Detailed Implementation

[0023] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0024] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0025] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0026] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0027] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0028] like Figure 1 The diagram shows a flowchart of a microgrid standardized operation assistance method integrating augmented reality provided in this application embodiment. The method includes the following steps: S1. After the visual sensor receives the microgrid standardized operation signal, it acquires the light intensity of the microgrid standardized operation environment, performs augmented reality initialization configuration based on the light intensity, and performs operation detection after configuration to obtain an auxiliary operation label and virtually display it using augmented reality technology; S2. It acquires stable display parameters of the auxiliary operation label, analyzes and obtains the label display stability value, acquires environmental change impact parameters of the operation environment, analyzes and obtains the environmental change impact value, and obtains an augmented reality adjustment judgment instruction based on the label display stability value and the environmental change impact value. The label display stability value is used to represent the stability of the auxiliary operation label display, and the environmental change impact value is used to represent the degree of impact of dynamic environmental changes on the identification and analysis of the microgrid standardized operation environment; S3. Based on the analysis of the augmented reality adjustment judgment instruction, if the augmented reality adjustment judgment instruction is the first execution instruction, no parameter adjustment is performed; otherwise, the corresponding parameter adjustment is performed, thereby completing the microgrid standardized operation processing.

[0029] In this embodiment, in the microgrid operation and maintenance process, mechanical intelligent inspection is usually used as a preliminary step. Its function is to quickly screen the operating status of equipment and locate risk aggregation nodes (i.e., abnormal points where equipment failures occur). The risk aggregation node is then transmitted to the microgrid operation and maintenance control center. After receiving the risk aggregation node signal, the microgrid operation and maintenance control center issues an alert and reminds the operation and maintenance personnel to maintain the risk aggregation node. At this time, the operation and maintenance personnel can use smart wearable devices combined with augmented reality technology to perform microgrid maintenance work. The microgrid maintenance work is based on the on-site images and ambient lighting information collected by visual sensors. First, augmented reality initialization configuration is performed, and then auxiliary work labels (i.e., augmented reality labels) are superimposed and presented on-site in real time. At the same time, the stable parameters of the auxiliary work label display and the environmental change impact parameters of the working environment are acquired and analyzed to generate augmented reality judgment instructions and execution strategies for dynamic adjustment. This ensures that the work prompts at the risk aggregation node are clear, continuous, and identifiable, thereby improving the safety, standardization, and efficiency of microgrid on-site operations.

[0030] The light intensity of the microgrid's operating environment can be obtained by continuously capturing images of the operating environment using a vision sensor (such as an industrial camera) and then acquiring the light intensity from the images.

[0031] Augmented reality initialization configuration based on illumination intensity is achieved through the following method: Real-time acquisition of raw illumination intensity data of the working environment is performed using a visual sensor. The acquired raw illumination signals are then converted from analog to digital (ADC) to obtain quantifiable illumination intensity values. These values ​​are used as initialization parameters for the augmented reality system and input into the rendering pipeline to configure the brightness, contrast, and shadow rendering effects of virtual objects. This ensures that the augmented reality overlay content matches the real-world lighting, guaranteeing stable display of auxiliary operation labels. It should be noted that auxiliary operation labels refer to tags that use augmented reality technology to annotate and explain microgrid equipment, operating procedures, and safety prompts in the real-world scene using virtual information (such as text, graphics, and symbols) during microgrid construction work. These labels are displayed in real-time in the user's field of vision, helping workers complete standardized microgrid operations more efficiently and safely. The specific demonstration method is as follows: the operator identifies the equipment in the microgrid by wearing smart wearable devices (such as smart glasses, smart helmets, etc.) and overlays virtual tags on the devices. For example, when the operator points the AR device at the transformer, the device will display information such as "Transformer model is XXX, rated power is XXX kW".

[0032] Furthermore, the stable value of the label display is obtained by the following method: obtaining the stable display parameters of the auxiliary operation labels within a preset time period, including the flicker frequency, label refresh delay, and label pixel jitter amplitude; obtaining the preset stable display calibration set in the database and performing dimensionless processing on it with the stable display parameters to obtain the dimensionless stable display result; and introducing the corresponding weighting factor based on the dimensionless stable display result for coupling processing to obtain the stable value of the label display; the stable display calibration set includes the flicker frequency calibration value, the label refresh delay calibration value, and the label pixel jitter amplitude calibration value.

[0033] In this embodiment, the flicker frequency refers to the number of times the brightness of the auxiliary work label changes during the display process within a preset time period, i.e., the speed at which the label's brightness fluctuates. An excessively high flicker frequency can cause visual interference. The label refresh delay represents the time required from when the auxiliary work label updates its label information to when the label is actually displayed, and the label pixel jitter amplitude represents the random jitter amplitude at the pixel level during the label's display process.

[0034] The specific method to obtain stable values ​​for label display is as follows:

[0035] ;

[0036] In the formula, WT represents the stable value of the label display, WP represents the blink frequency, TP represents the blink frequency calibration value, WC represents the label refresh delay, TC represents the label refresh delay calibration value, WF represents the label pixel jitter amplitude, TF represents the label pixel jitter amplitude calibration value, σ1 represents the blink frequency weighting factor, σ2 represents the label refresh delay weighting factor, and σ3 represents the label pixel jitter amplitude weighting factor.

[0037] The flicker frequency weighting factor, label refresh delay weighting factor, and label pixel jitter amplitude weighting factor can be obtained through historical data clustering analysis. Specifically, the method involves extracting historical datasets containing historical flicker frequencies, historical label refresh delays, and historical label pixel jitter amplitudes from a database. Feature extraction is performed on each historical dataset to form a multi-dimensional feature vector set with flicker frequency, label refresh delay, and label pixel jitter amplitude as dimensions. Clustering analysis is then performed on this feature vector set to obtain multiple cluster centers. Each cluster center represents a typical stable label display pattern. For each cluster center, the feature variance, mean deviation, and normalization sensitivity of each dimension within that cluster center are calculated and used as quantitative indicators of the degree of influence of each dimension on label display stability. Finally, the flicker frequency weighting factor, label refresh delay weighting factor, and label pixel jitter amplitude weighting factor are obtained by weighted averaging of the above indicators for all cluster centers.

[0038] By analyzing stable display parameters including flicker frequency, label refresh delay, and label pixel jitter amplitude, a stable label display value is obtained. This analysis takes into account the interrelationships between these parameters. For example, an increased label refresh delay can lead to untimely label updates, causing inconsistencies in label display between consecutive frames, thus increasing the flicker frequency. It may also cause the label to appear visually out of position. Furthermore, an excessively high flicker frequency not only affects visual comfort but also indirectly impacts the user's perception of label position, making the negative effects more pronounced. In addition, excessive label pixel jitter amplitude, even with a small refresh delay and positional offset, can make the overall label display appear unstable.

[0039] By acquiring stable display parameters of augmented reality (AR) tag displays in microgrid environments, including flicker frequency, tag refresh delay, and pixel jitter amplitude, the stability of tag displays can be analyzed, thereby determining the level of stability and achieving a quantitative assessment of the AR tag display status. Analyzing stable display parameters allows for the timely detection of unstable factors in tag displays, such as flickering, positional shifts, or pixel jitter, preventing errors in information retrieval or visual fatigue for operators due to abnormal tag displays. Secondly, quantifying the stability value enables the implementation of AR adjustment strategies based on stability assessments, ensuring that tag displays remain readable, continuous, and reliable even in complex environments such as varying lighting conditions, glare, or low light. Thirdly, stable displays improve the efficiency and safety of microgrid operations, reducing the risk of misoperation caused by AR information confusion or visual interference. Conversely, unstable displays can lead to tag flickering, positional drift, or delayed updates, affecting operators' judgment of environmental conditions and equipment status, increasing operational risks and errors. Therefore, this approach enables refined and quantifiable management of AR-assisted operations, significantly improving operational safety, efficiency, and reliability.

[0040] like Figure 2 As shown, Figure 2 The flowchart illustrates the steps of the microgrid standardized operation assistance method integrating augmented reality provided in this application embodiment. It involves receiving microgrid standardized operation signals via a visual sensor, acquiring real-time illumination intensity of the operating environment, and entering the augmented reality initialization configuration phase. This phase includes setting basic parameters, detecting the operation process and extracting auxiliary operation tags, and simultaneously acquiring stable tag display parameters and environmental change impact parameters. Based on the analysis results, an augmented reality adjustment judgment instruction is generated. If the judgment result is the first execution instruction, no parameter adjustment is performed, and the current operation data is directly archived to the cloud. If the judgment result is the second, third, or fourth execution instruction, tag recognition rendering or environmental parameter adjustment is performed, and the processing results are then archived to the cloud, completing the process.

[0041] Furthermore, the environmental change impact value is obtained through the following method: Environmental change impact parameters of the working environment within a preset time period are acquired, including the proportion of overexposed pixels, the proportion of underexposed pixels, the rate of change of light intensity, and the brightness range; a preset environmental change benchmark set is obtained from the database, and compared and weighted with the environmental change impact parameters to obtain each weighted value, including the weighted values ​​for the proportion of overexposed pixels, the proportion of underexposed pixels, the rate of change of light intensity, and the brightness range; based on comparing the impact values ​​of the overexposed pixel proportion and underexposed pixel proportion, the direction with the larger impact of the weighted value is taken as the direction of environmental change impact, and then combined with the weighted values ​​of the rate of change of light intensity and the brightness range to obtain the environmental change impact value; the environmental change benchmark set includes the benchmark values ​​for the proportion of overexposed pixels, the proportion of underexposed pixels, the rate of change of light intensity, and the brightness range.

[0042] In this embodiment, it should be noted that the light intensity change rate represents the rate of change of ambient light intensity within a preset time period, i.e., the amount of change in light intensity per unit time, reflecting the dynamic stability of the current ambient light. A larger change rate indicates more significant light fluctuations. The overexposed pixel ratio represents the proportion of pixels in an image frame whose brightness exceeds the sensor's saturation threshold within the preset time period. A higher overexposed pixel ratio results in poorer contrast and readability of the augmented reality overlay. The underexposed pixel ratio represents the proportion of pixels in an image frame whose brightness is below the sensor's effective light-sensing threshold within the preset time period. A higher underexposed pixel ratio indicates an overly dark image, making augmented reality labels difficult to distinguish or reducing recognition rates. The brightness range represents the difference between the brightest and lowest brightness pixels in an image frame within the preset time period, used to measure the overall uniformity of brightness distribution in the image. A larger brightness range indicates a strong contrast between light and dark areas in the environment, leading to poor integration of virtual content with the background.

[0043] The specific method for obtaining the impact values ​​of environmental change is as follows:

[0044] ;

[0045] In the formula, HX represents the impact value of environmental change, HG represents the overexposed pixel ratio, XG represents the baseline value of the overexposed pixel ratio, HQ represents the underexposed pixel ratio, XQ represents the baseline value of the underexposed pixel ratio, HL represents the light intensity change rate, XL represents the baseline value of the light intensity change rate, HC represents the brightness range, XC represents the baseline value of the brightness range, ω1 represents the weighting factor for the overexposed pixel ratio, ω2 represents the weighting factor for the underexposed pixel ratio, ω3 represents the weighting factor for the light intensity change rate, and ω4 represents the weighting factor for the brightness range.

[0046] It should be noted that when analyzing the impact of environmental changes, the environmental change weight impact value of the overexposed pixel ratio is obtained by multiplying the weighting factor of the overexposed pixel ratio with the standardized overexposed pixel ratio, and the environmental change weight impact value of the underexposed pixel ratio is obtained by multiplying the weighting factor of the underexposed pixel ratio with the standardized underexposed pixel ratio. The environmental change weight impact values ​​of the overexposed pixel ratio and the underexposed pixel ratio are then superimposed to obtain the superimposed value. It is determined whether the superimposed value is greater than zero (i.e., whether the overexposed or underexposed effect is more serious). If it is greater than zero, it means that the overexposed effect is more serious than the underexposed effect; if it is equal to zero, it means that the effects are the same; if it is less than zero, it means that the underexposed effect is more serious. Among them, the weighting factors of the overexposed pixel ratio weighting factor, the light intensity change rate weighting factor, and the brightness range weighting factor are positive numbers, indicating a positive effect (i.e., the larger the value, the more serious the effect). The weighting factor of the underexposed pixel ratio weighting factor is negative, indicating a negative effect (i.e., the effect of the underexposed angle; the larger the negative value, the more serious the effect).

[0047] The impact of environmental changes is divided into overexposure and underexposure impact angles. When the impact of overexposure is greater than that of underexposure, overexposure is adjusted first, and vice versa. This allows for faster device optimization because the causes and adjustment methods for overexposure and underexposure are significantly different. For example, overexposure requires reducing the line readout rate and increasing the keyframe extraction interval, while underexposure requires reducing the proportion of short and long exposure sequences and the keyframe extraction interval. Therefore, distinguishing between the two helps to more accurately match the corresponding adjustment methods, thereby avoiding mutual interference between the two adjustments, shortening the environmental adaptation time, improving the stability of image display, and enhancing the accuracy of augmented reality guidance.

[0048] The weighting factors for overexposed pixel ratio, underexposed pixel ratio, illumination intensity change rate, and brightness range can be obtained through historical data clustering analysis. Specifically, the method involves extracting historical datasets containing historical overexposed pixel ratios, underexposed pixel ratios, illumination intensity change rates, and brightness ranges from a database. Feature extraction is performed on each historical dataset to form a multi-dimensional feature vector set with overexposed pixel ratio, underexposed pixel ratio, illumination intensity change rate, and brightness range as dimensions. Clustering analysis is then performed on this feature vector set to obtain multiple cluster centers, each representing a typical illumination and exposure state pattern. Further, for each cluster center, the feature variance, mean deviation, and normalization sensitivity of each dimension within that cluster center are calculated, serving as quantitative indicators of the influence of each dimension on image exposure stability. Finally, by weighted averaging of these indicators across all cluster centers, the weighting factors for overexposed pixel ratio, underexposed pixel ratio, illumination intensity change rate, and brightness range are obtained.

[0049] The impact of environmental changes is analyzed by considering the interrelationships between these parameters, including the rate of change of light intensity, the proportion of overexposed pixels, the proportion of underexposed pixels, and the brightness difference. For example, an increase in the rate of change of light intensity usually leads to overexposed or underexposed pixel areas in the image, especially when the light intensity fluctuates rapidly. The brightness difference also increases, thus exacerbating local overexposure or underexposure. At the same time, the brightness difference reflects the overall brightness contrast of the image. An excessively large or small brightness difference will aggravate the changes in the proportion of overexposed or underexposed pixels, reducing the stability of image recognition and augmented reality tag rendering.

[0050] Furthermore, the augmented reality adjustment judgment instruction is obtained, specifically through the following methods: First, the preset label display stability threshold and environmental change impact threshold are retrieved from the database. Second, the label display stability threshold and the label display stability value are compared to obtain a stable display judgment status. If the label display stability value is above the label display stability threshold, the stable display judgment status is considered satisfactory; otherwise, it is considered unsatisfactory. Third, the environmental change impact threshold and the absolute value of the environmental change impact value are compared to obtain an environmental judgment status. If the absolute value of the environmental change impact value is less than the environmental change impact threshold, the environmental judgment status is considered satisfactory; otherwise, it is considered unsatisfactory. Finally, based on the stable display judgment status and the environmental judgment status analysis, the augmented reality adjustment judgment instruction is obtained. If the stable display judgment status is "stable display qualified" and the environment judgment status is "environment qualified", then the augmented reality adjustment judgment instruction is the first execution instruction, and the adjustment process is not executed; if the stable display judgment status is "stable display unqualified" and the environment judgment status is "environment qualified", then the augmented reality adjustment judgment instruction is the second execution instruction, and the label recognition rendering adjustment is executed; if the stable display judgment status is "stable display qualified" and the environment judgment status is "environment unqualified", then the augmented reality adjustment judgment instruction is the third execution instruction, and the environment acquisition parameter adjustment is executed; if the stable display judgment status is "stable display unqualified" and the environment judgment status is "environment unqualified", then the augmented reality adjustment judgment instruction is the fourth execution instruction, and the environment acquisition parameter adjustment is executed first, followed by the label recognition rendering adjustment.

[0051] In this embodiment, augmented reality adjustment judgment instructions are divided into four types of execution instructions. This is to adopt the most effective optimization strategy for different label display stability and dynamic environmental changes in microgrid standardized operations, ensuring the display stability of auxiliary operation labels and the accuracy of environmental recognition, while improving the reliability of data collection and analysis. Specifically, the first execution instruction is applicable when the label display is stable and the environmental change impact value is acceptable. In this case, no adjustment is required; the current augmented reality auxiliary operation state can be maintained to ensure operation efficiency. The second execution instruction is for cases where the label display is unstable but the environmental impact is acceptable. It focuses on processing from an augmented reality perspective, that is, performing label recognition and rendering adjustments. By increasing the frame voting window and adjusting the short-term label holding time, the rendering and stability of the labels are optimized to ensure the continuity and recognizability of the auxiliary operation labels in visual presentation. The third execution instruction is for cases where the labels are stable but the environment is unacceptable. It focuses on adjusting the environmental acquisition parameters. By adjusting parameters such as the line readout rate, keyframe extraction interval, or the proportion of short and long exposure sequences, the quality of the acquired operation environment information is improved from the source, so that the subsequently generated auxiliary operation labels can more accurately reflect the microgrid operation site. The fourth execution instruction is applicable to situations where the tags are unstable and the environment is unqualified. In this case, the environmental acquisition parameters are adjusted first to optimize the acquisition of environmental data from the source, and then the tag recognition and rendering are adjusted. This provides a reliable foundation for obtaining the correct tags and achieving standardized operations. Therefore, it enables precise adjustment for different abnormal situations and improves the stability, reliability and accuracy of augmented reality-assisted operations and microgrid operation environment recognition.

[0052] Furthermore, the label recognition and rendering adjustment is performed, specifically as follows: A difference is calculated between the stable label display value and the stable label display threshold (the absolute value is taken after difference processing) to obtain a stable execution difference; this stable execution difference is matched with the database to obtain the frame voting window adjustment ratio; the frame voting window is increased based on this ratio, and the stable label display value after the increase is calculated and recorded as the first label display adjustment value; the first label display adjustment value is compared with the stable label display threshold. If the first label display adjustment value is above the stable label display threshold… If the label recognition and rendering adjustment is completed, then the label short-term retention duration adjustment is performed. The label short-term retention duration adjustment is performed as follows: the difference between the first label display adjustment value and the label display stable threshold is processed (the absolute value is taken after the difference processing) to obtain a stable adjustment difference value. The stable adjustment difference value is matched with the database to obtain the label short-term retention duration adjustment value. Then, the label short-term retention duration is increased based on the label short-term retention duration adjustment value (that is, the current label short-term retention duration is added to the label short-term retention duration adjustment value to obtain the adjusted label short-term retention duration).

[0053] In this embodiment, as Figure 3 As shown, Figure 3 The flowchart for the tag recognition rendering adjustment process of the microgrid standardized operation assistance method integrating augmented reality provided in this application embodiment first analyzes the augmented reality adjustment judgment instruction in depth, dividing it into two branches: tag recognition rendering adjustment and environmental acquisition parameter adjustment. In the tag recognition rendering adjustment branch, the frame voting window adjustment ratio is calculated and increased. Then, the adjustment value of the first tag display is statistically analyzed, and it is determined whether the value is greater than the tag display stability threshold. If the condition is met, the tag recognition rendering adjustment is considered complete, and the process ends directly; if the condition is not met, the short-term holding time adjustment of the tag is further executed. The process ends after the holding time adjustment value is calculated and increased. In the environmental acquisition parameter adjustment branch, based on the positive or negative of the judgment result, the line readout rate or the proportion of short and long exposure sequences is gradually reduced, and the key frame extraction interval is increased simultaneously. The statistical tag display stability improvement value is adjusted alternately in rounds until the threshold requirement is met, and the adjustment is completed and then the process ends.

[0054] The frame voting window adjustment ratio is obtained by matching the stable execution difference with the database. The specific method is as follows: obtain each historical stable execution difference stored in the database and compare it with the stable execution difference. Select the two historical stable execution differences that are closest to the stable execution difference as the historical reference stable execution difference. Obtain the historical frame voting window adjustment ratio corresponding to the historical reference stable execution difference and perform average processing on it to obtain the average historical frame voting window adjustment ratio. Use the average historical frame voting window adjustment ratio as the frame voting window adjustment ratio.

[0055] The short-term retention duration adjustment value of the annotation is obtained by matching the stable adjustment difference with the database. The specific method is as follows: obtain each historical stable adjustment difference stored in the database and compare it with the stable adjustment difference. Select the two historical stable adjustment differences that are closest to the stable adjustment difference as the historical control stable adjustment difference. Obtain the historical annotation short-term retention duration adjustment value corresponding to the historical control stable adjustment difference and perform mean processing on it to obtain the mean of the historical annotation short-term retention duration adjustment value. Use the mean of the historical annotation short-term retention duration adjustment value as the annotation short-term retention duration adjustment value.

[0056] By analyzing the difference between the stable value of the label display and the stable threshold of the label display, a stability adjustment difference is obtained, which can accurately quantify the stability of the current auxiliary operation label in augmented reality display. This difference is a key basis for judging the adjustment range, which can avoid the frame voting window being adjusted too large or too small, thereby achieving fine-grained adjustment and ensuring the rendering stability and continuity of the auxiliary operation label. Secondly, based on the stability adjustment difference, the frame voting window adjustment ratio is determined, and the frame voting window is increased. This can effectively smooth the flickering or position drift caused by inter-frame fluctuations during label rendering, improving the visual stability and recognizability of the label display. When the first label display adjustment value reaches or exceeds the threshold, it means that the adjustment of the frame voting window can meet the stability requirements, and no further adjustment is needed, avoiding unnecessary parameter adjustments and ensuring system efficiency. However, if the first label display adjustment value still does not reach the threshold, it means that the adjustment of the frame voting window alone is not enough to improve stability. At this time, the short-term holding time of the label is adjusted. By extending the display of the label for a short period of time, the label jitter and flickering problems are further alleviated. The benefit of increasing this parameter is that it enhances the continuity and reliability of the label's visual presentation, enabling the auxiliary operation label to maintain a stable display even under short-term environmental fluctuations. This provides more reliable augmented reality assistance for standardized microgrid operations, improving operational efficiency and safety.

[0057] Furthermore, the frame voting window is increased and adjusted. Specifically, based on the analysis of the current frame voting window and its adjustment ratio, the adjustment amount of the frame voting window ratio is obtained (specifically, by multiplying the current frame voting window by its adjustment ratio), and this is taken as the theoretically optimal adjustment amount. The preset adjustment step size of the frame voting window in the database is obtained, and the frame voting window is iteratively increased multiple times based on this step size until the adjustment value of the frame voting window reaches the theoretically optimal adjustment amount. The frame voting window at the end of each iteration round is obtained in real time and marked as the execution value of the frame voting window in each round. The execution value of the frame voting window in each round is simultaneously statistically analyzed. The corresponding label display stability value is marked as the label display stability improvement value for each round; the preset label display stability improvement threshold in the database is obtained and compared with the label display stability improvement value for each round. If the label display stability improvement value for a certain round is below the label display stability improvement threshold, the frame voting window enlargement process is stopped in advance (keeping the current frame voting window size running continuously). Otherwise, the frame voting window enlargement process continues until the label display stability improvement value for a certain round is above the label display stability improvement threshold, and the label display stability improvement value at this time is marked as the first label display adjustment value.

[0058] In this embodiment, if the augmented reality adjustment judgment instruction is the fourth execution instruction, it is determined that the environmental acquisition parameter adjustment will be executed first, followed by the label recognition rendering adjustment. If the label display stability improvement value after adjustment is consistently less than the label display stability improvement threshold due to severe environmental influence, an early warning will be issued, indicating that the environment cannot achieve the display of auxiliary operation labels. For example, during a thunderstorm night with a power outage, when construction workers are performing maintenance using auxiliary lighting equipment, outdoor lightning and other flashes will severely affect augmented reality recognition. At this time, the display of auxiliary operation labels will be subject to certain interference. In this case, the augmented reality terminal (such as augmented reality glasses and augmented reality helmets and other smart wearable devices) will issue an early warning reminder that the current environmental quality is too poor.

[0059] By analyzing the stable execution difference and matching it with the database, the frame voting window adjustment ratio is obtained. This allows for the precise derivation of the theoretically optimal adjustment amount, rather than blindly adjusting arbitrarily. Directly and randomly increasing or decreasing the frame voting window size can lead to over-adjustment or under-adjustment, causing persistent label jitter or wasting computational resources. Iteratively increasing the size over multiple rounds, rather than making a large adjustment all at once, is to achieve gradual optimization and avoid new instability issues in label display caused by a single large adjustment. Increasing the frame voting window parameter is chosen because in augmented reality scenarios, a larger frame voting window helps smooth inter-frame fluctuations, improves the robustness and anti-interference ability of label recognition, and thus improves the continuous display effect of labels.

[0060] Obtaining the statistical improvement value of label display stability after each iteration and comparing it with the label display stability improvement threshold is to dynamically evaluate the actual effect of the adjustment. If the improvement value in a certain round is lower than the threshold, it means that simply relying on increasing the frame voting window can no longer significantly improve stability. At this time, iterative processing is stopped to avoid wasting computing resources. In this case, it is necessary to adjust the short-term persistence duration of the labels to improve the stability value of the label display. Adjusting the short-term persistence duration of the labels, as a subsequent supplementary adjustment method, can prolong the short-term stable display of the labels in the field of view, enabling augmented reality rendering to maintain better continuity and reliability in complex environments.

[0061] Furthermore, environmental acquisition parameters are adjusted. Specifically, the following method is used: A difference is calculated between the environmental change impact value and the environmental change impact threshold to obtain the first environmental change difference (the absolute value of the environmental change impact value is subtracted from the environmental change impact threshold). Based on the environmental change impact value analysis, if the environmental change impact value is positive, the first environmental change difference is matched with the database to obtain the line readout rate adjustment value and the keyframe extraction interval adjustment duration. The line readout rate is then gradually reduced in rounds, and the keyframe extraction interval duration is increased. If the environmental change impact value is negative, the first environmental change difference is matched with the database to obtain the short-long exposure sequence ratio adjustment value and the keyframe extraction interval adjustment duration. The short-long exposure sequence ratio and the keyframe extraction interval duration are then gradually reduced in rounds.

[0062] In this embodiment, the row readout rate adjustment value and the keyframe extraction interval adjustment duration are obtained by matching the first difference of environmental changes with the database. Specifically, the method is as follows: First differences of historical environmental changes stored in the database are obtained and compared with the first difference of environmental changes. The two historical first differences of environmental changes closest to the first difference of environmental changes are selected as historical reference first differences of environmental changes. The historical row readout rate adjustment values ​​corresponding to the historical reference first differences of environmental changes are obtained and averaged to obtain the average historical row readout rate adjustment value. This average historical row readout rate adjustment value is then used as the row readout rate adjustment value. The method for obtaining the keyframe extraction interval adjustment duration is similar to that for obtaining the row readout rate adjustment value; both involve matching historical values ​​in the database and averaging the historical values.

[0063] Based on the first difference of environmental changes, the adjustment values ​​for the proportion of short and long exposure sequences and the adjustment duration of keyframe extraction intervals are obtained by matching them with the database. Specifically, the method involves extracting features of the current environmental changes from multiple dimensions, including changes in light intensity and ambient light color distribution, forming a multi-dimensional environmental change feature vector. This feature vector is then matched with pre-clustered environmental change feature vectors in the database. Historical data in the database has been divided into multiple cluster centers through cluster analysis. Each cluster center represents a typical environmental change pattern and stores the corresponding historical adjustment values ​​for the proportion of short and long exposure sequences and the adjustment duration of keyframe extraction intervals. Once the cluster center closest to the current environmental change feature vector is found, all relevant samples are extracted from the historical data of that cluster center. The mean of the adjustment values ​​for the proportion of short and long exposure sequences and the adjustment duration of keyframe extraction intervals for these samples is calculated, and these two means are used as the adjustment values ​​for the proportion of short and long exposure sequences and the adjustment duration of keyframe extraction intervals under the current environment.

[0064] The proportion of short and long exposure sequences and the keyframe extraction interval are gradually reduced in rounds. Specifically, the proportion of short and long exposure sequences is reduced according to the preset adjustment step size, and the keyframe extraction interval is reduced according to the preset adjustment step size. Each round of reducing the proportion of short and long exposure sequences corresponds to a reduction in the keyframe extraction interval.

[0065] It should be noted that the proportion of short and long exposure sequences in this scheme refers to the proportion of short exposure frames in the augmented reality imaging process. This can be obtained by statistically analyzing the continuously acquired image frames. For example, if the proportion of short and long exposure sequences is 0.7 and its value range is [0,1], it means that the proportion of short frames is 70%, and the proportion of long and short frames is 30%.

[0066] By performing difference processing based on the environmental change impact value and the environmental change impact threshold, the actual degree of interference of the environment on image acquisition quality can be quantitatively assessed, ensuring that subsequent adjustments have a clear objective rather than blind adjustments. The reason for choosing to adjust the row readout rate is that the row readout rate directly determines the speed at which the sensor acquires data line by line. Reducing this rate can effectively extend the exposure time of a single frame, thereby improving the image signal-to-noise ratio and detail fidelity under low light or uneven brightness conditions, and mitigating image jitter and brightness flicker caused by environmental changes. Adjusting the keyframe extraction interval is chosen because the keyframe extraction interval directly affects the time base for subsequent augmented reality tag generation. Increasing this interval can reduce the frequent triggering of tag updates due to inter-frame differences caused by environmental fluctuations, thereby improving the stability and continuity of tag display.

[0067] By jointly adjusting the row readout rate and keyframe extraction interval, the acquisition process can be optimized from the source. Adjusting the row readout rate ensures the quality of the original image, while adjusting the keyframe extraction interval ensures the rationality of the timing. The two work together to achieve synchronous stability between the acquisition end and the processing end, avoiding the additional computational overhead and lag caused by relying solely on backend software algorithms. Employing a phased, step-by-step adjustment strategy ensures the correct adjustment direction while avoiding sudden changes in image refresh rate or keyframe loss due to large-scale adjustments at once. Through multiple iterations, the system gradually approaches the optimal acquisition state, enabling smooth transitions in dynamic environments. This improves environmental adaptability in augmented reality scenarios, making the tag recognition input data more stable and reliable. It provides high-quality image input for subsequent database matching and correct tag generation, thereby enhancing the accuracy and robustness of the entire microgrid standardized operation assistance method.

[0068] Furthermore, the line readout rate is gradually reduced in rounds, and the keyframe extraction interval is increased. Specifically, the method is as follows: Obtain the preset line readout rate adjustment step size and keyframe extraction interval adjustment step size from the database; based on the line readout rate adjustment step size, gradually reduce the line readout rate in rounds, and after each round of reducing the line readout rate, gradually increase the keyframe extraction interval duration based on the keyframe extraction interval adjustment step size, thus performing round-by-round alternating adjustments to obtain stable label display values ​​after each round of alternating adjustments; based on the analysis of the stable label display values ​​after each round of alternating adjustments, obtain the stable improvement value of the label display after each round of alternating adjustments, and mark it as the alternating improvement value for each round; obtain the preset alternating improvement threshold from the database and compare it with the alternating improvement value for each round. If the alternating improvement value for a certain alternating round is below the alternating improvement threshold, the adjustment is completed; otherwise, the adjustment continues.

[0069] In this embodiment, based on the analysis of the stable values ​​of the label display after each round of alternating adjustments, the stability improvement value of the label display after each round of alternating adjustments is obtained. The specific method is as follows: subtract the stable value of the label display in the previous round from the stable value of the label display after each round of alternating adjustments to obtain the stability difference of alternating adjustments in each round. Divide the stability difference of alternating adjustments in each round by the stable value of the label display in the previous round to obtain the stability improvement value of the label display after each round of alternating adjustments.

[0070] By adjusting the execution step size based on the preset row readout rate and keyframe extraction interval in the database, the adjustment process becomes controllable and traceable, avoiding instability in auxiliary label display caused by a single large adjustment. Employing a round-by-round alternating adjustment strategy, each round first reduces the row readout rate to improve single-frame exposure quality, then extends the keyframe extraction interval to enhance inter-frame stability. This achieves simultaneous optimization of temporal and spatial resolution at the acquisition end, ensuring that the stable values ​​of the displayed labels gradually approach the optimal state.

[0071] By comparing the alternating improvement values ​​of each round with the alternating improvement threshold, it is possible to determine whether the adjustment has ended. This allows for dynamic monitoring of the contribution of each round of adjustment to the actual effect of the system. When the alternating improvement value is detected to be lower than the alternating improvement threshold, the adjustment is stopped in advance to avoid over-adjustment that could lead to system response delays or increased energy consumption, thereby improving the stability of label display.

[0072] like Figure 5 and Figure 6 As shown, Figure 5 The first part is a schematic diagram of the auxiliary operation of the microgrid standardized operation auxiliary system integrating augmented reality provided in the embodiments of this application. Figure 6The second part of the schematic diagram of the auxiliary operation of the microgrid standardized operation assistance system integrating augmented reality provided in the embodiments of this application shows the basic information of the current microgrid auxiliary operation, such as the task number of the microgrid operation being performed, the start time of the operation, the operation progress, the personnel performing the operation, and the estimated completion time. It can also query the real-time status of the operation environment, such as light intensity and ambient temperature, and can obtain the system's reminder information in real time to assist the operators in completing the operation tasks reasonably and in a standardized manner.

[0073] Furthermore, cloud archiving is also included. The specific method is as follows: Based on the analysis of stable display and environmental judgment states, if there is a period where the stable display judgment state is unqualified and / or the environmental judgment state is unqualified, then that period is marked as the archiving execution period; the network bandwidth after the microgrid operation ends (which can be obtained through network performance testing tools such as iPerf) is obtained and compared with a preset network bandwidth threshold. If the network bandwidth is above the network bandwidth threshold, then a portion of key frames from the video data of the archiving execution period are selected and uploaded sequentially to the cloud for archiving; the specific method for selecting a portion of key frames from the video data of the archiving execution period is as follows: if the archiving execution period only has stable display unqualified, then the stable execution difference is marked as the archiving value; if the archiving execution period only has environmental unqualified, then the first difference in environmental change is marked as the archiving value; otherwise, the sum of the stable execution difference and the first difference in environmental change is marked as the archiving value; based on the archiving value, the database is matched to obtain the archiving key frame upload ratio, and a portion of key frames are selected for archiving based on the archiving key frame upload ratio.

[0074] In this embodiment, the archived keyframe upload ratio is obtained by matching the archived processing value with the database. The specific method is as follows: obtain each historical archived processing value stored in the database, compare it with the archived processing value, select the two historical archived processing values ​​that are closest to the archived processing value as historical reference archived processing values, obtain the historical archived keyframe upload ratio corresponding to the historical reference archived processing value, and perform average processing on it to obtain the average historical archived keyframe upload ratio, and use the average historical archived keyframe upload ratio as the archived keyframe upload ratio.

[0075] The method for selecting keyframes for archiving based on the keyframe upload ratio is as follows: During the archiving execution period, the total number of frames to be uploaded is determined according to the keyframe upload ratio obtained from database matching. Then, the video frames are analyzed in chronological order to extract key feature information of each frame (such as the specific content, location, and status of auxiliary operation tags). Subsequently, adjacent frames are compared for similarity. For frames with similar or repeated content, only one frame is selected as the upload frame, ensuring that the content of the entire video is complete and non-redundant. This achieves strict control over the number of uploaded frames while ensuring that the archived video frames cover changes in the work environment and tag information, thus realizing efficient and effective cloud archiving.

[0076] like Figure 4 The diagram shows a schematic of the microgrid standardized operation assistance system integrating augmented reality provided in this application embodiment. The system includes an initial configuration module, an adjustment analysis module, and an intelligent adjustment module. The initial configuration module, upon receiving a microgrid standardized operation signal from a visual sensor, acquires the light intensity of the microgrid standardized operation environment, performs augmented reality initialization configuration based on the light intensity, and performs operation detection after configuration to obtain an auxiliary operation label and virtually display it using augmented reality technology. The adjustment analysis module acquires stable display parameters of the auxiliary operation label, analyzes to obtain a stable label display value, acquires environmental change impact parameters of the operation environment, analyzes to obtain an environmental change impact value, and obtains an augmented reality adjustment judgment instruction based on the stable label display value and the environmental change impact value. The intelligent adjustment module analyzes the augmented reality adjustment judgment instruction; if the instruction is the first execution instruction, no parameter adjustment is performed; otherwise, corresponding parameter adjustments are performed, thereby completing the microgrid standardized operation processing.

[0077] In summary, this embodiment obtains stable display parameters of auxiliary operation tags and environmental change impact parameters of the operation environment, and analyzes the stable display value of the tags and the impact value of environmental changes to obtain augmented reality adjustment judgment instructions. Based on the augmented reality adjustment judgment instructions, the augmented reality parameters are intelligently adjusted, thereby achieving stable display of augmented reality tags. This effectively solves the problem of unstable phenomena such as flickering when displaying augmented reality tags caused by sudden changes in the brightness of the acquired image due to changes in light and shadow in the prior art.

[0078] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0079] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0080] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0081] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0082] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0083] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A standardized operation assistance method for microgrids integrating augmented reality, characterized in that, Includes the following steps: S1. When the vision sensor receives the microgrid standardized operation signal, it obtains the light intensity of the microgrid standardized operation environment, performs augmented reality initialization configuration based on the light intensity, performs operation detection after configuration, obtains auxiliary operation labels, and performs virtual display through augmented reality technology. S2. Obtain stable display parameters of auxiliary operation tags, analyze and obtain stable tag display values, obtain environmental change impact parameters of the operation environment, analyze and obtain environmental change impact values, and obtain augmented reality adjustment judgment instructions based on stable tag display values ​​and environmental change impact values. The stable tag display value is used to represent the stability of auxiliary operation tag display, and the environmental change impact value is used to represent the degree of impact of dynamic environmental changes on microgrid standardized operation environment identification and analysis. S3. Based on the analysis of augmented reality adjustment judgment instructions, if the augmented reality adjustment judgment instruction is the first execution instruction, no parameter adjustment will be performed; otherwise, the corresponding parameter adjustment will be performed, thereby completing the microgrid standardized operation processing. The method for obtaining stable label display values ​​is as follows: The stable display parameters include flicker frequency, label refresh delay, and label pixel jitter amplitude; Obtain the preset stable display calibration set from the database and perform dimensionless processing on it with the stable display parameters to obtain the dimensionless stable display result. Based on the dimensionless stable display result, introduce the corresponding weighting factor for coupling processing to obtain the stable value of the label display. The stable display calibration set includes flicker frequency calibration values, annotation refresh delay calibration values, and annotation pixel jitter amplitude calibration values; The specific method for obtaining the environmental change impact value is as follows: The environmental change impact parameters include the percentage of overexposed pixels, the percentage of underexposed pixels, the rate of change in light intensity, and the brightness difference. Obtain a preset environmental change benchmark set from the database, and compare it with the environmental change impact parameters to obtain various comparison weighting values. These comparison weighting values ​​include the overexposed pixel ratio weighting value, the underexposed pixel ratio weighting value, the light intensity change rate weighting value, and the brightness range weighting value. Based on comparing the changes in the weighted values ​​of the overexposed pixel ratio and the underexposed pixel ratio, the direction with the greater change in the weighted value is taken as the direction of environmental change influence. Then, the environmental change influence value is obtained by combining the weighted values ​​of the light intensity change rate and the brightness range. The environmental change benchmark set includes the overexposed pixel ratio benchmark value, the underexposed pixel ratio benchmark value, the light intensity change rate benchmark value, and the brightness range benchmark value.

2. The microgrid standardized operation assistance method integrating augmented reality as described in claim 1, characterized in that: The specific method for obtaining the augmented reality adjustment determination instruction is as follows: Obtain the preset label display stability threshold and environmental change impact threshold from the database; The stability of the label display is determined by comparing the stability threshold and the stability value of the label display. If the stability value of the label display is above the stability threshold, the stability of the label display is qualified; otherwise, the stability of the label display is unqualified. The environmental assessment status is determined by comparing the threshold value of environmental change impact with the absolute value of environmental change impact. If the absolute value of environmental change impact is less than the threshold value of environmental change impact, the environmental assessment status is qualified; otherwise, the environmental assessment status is unqualified. Based on the analysis of the stable display judgment state and the environment judgment state, the augmented reality adjustment judgment instruction is obtained. If the stable display judgment state is stable display qualified and the environment judgment state is environment qualified, the augmented reality adjustment judgment instruction is the first execution instruction, and the adjustment process is not executed. If the stable display judgment status is "unstable display unqualified" and the environment judgment status is "environment qualified", then the augmented reality adjustment judgment instruction is the second execution instruction, and the label recognition rendering adjustment is executed. If the stable display judgment status is "stable display qualified" and the environment judgment status is "environment unqualified", then the augmented reality adjustment judgment instruction is the third execution instruction, which determines the adjustment of the environment acquisition parameters. If the stable display judgment status is "unstable display unqualified" and the environment judgment status is "environment unqualified", then the augmented reality adjustment judgment instruction is the fourth execution instruction, which determines to first execute the environment acquisition parameter adjustment and then execute the label recognition rendering adjustment.

3. The microgrid standardized operation assistance method integrating augmented reality as described in claim 2, characterized in that: The specific method for performing tag recognition rendering adjustment is as follows: The stable execution difference is obtained by performing difference processing between the stable value of tag display and the stable threshold of tag display. The frame voting window adjustment ratio is obtained by matching the stable execution difference with the database. The frame voting window is then increased based on the frame voting window adjustment ratio. The stable value of the label display after the increase process is calculated and recorded as the first label display adjustment value. Based on the comparison between the first label display adjustment value and the label display stability threshold, if the first label display adjustment value is above the label display stability threshold, the label recognition and rendering adjustment is completed; otherwise, the label short-term retention time adjustment is performed. The adjustment of the short-term retention time for the execution annotation is as follows: The difference between the first label display adjustment value and the label display stable threshold is processed to obtain a stable adjustment difference. The stable adjustment difference is then matched with the database to obtain the label short-term retention duration adjustment value. Based on this adjustment value, the label short-term retention duration is increased.

4. The microgrid standardized operation assistance method integrating augmented reality as described in claim 3, characterized in that: The specific method for increasing and adjusting the frame voting window is as follows: Based on the analysis of the current frame voting window and frame voting window adjustment ratio, the adjustment amount of the frame voting window adjustment ratio is obtained and taken as the theoretical optimal adjustment amount. Obtain the preset adjustment step size of the frame voting window in the database, and perform multiple rounds of iterative increase processing on the frame voting window based on the adjustment step size until the adjustment value of the frame voting window reaches the theoretical optimal adjustment amount. In real time, obtain the frame voting window after the end of each iteration round, mark it as the execution value of the frame voting window in each round, and synchronously count the stable value of the label display corresponding to the execution value of the frame voting window in each round, and mark it as the stable improvement value of the label display in each round. Obtain the preset label display stability improvement threshold from the database and compare it with the label display stability improvement value of each round. If the label display stability improvement value of a certain round is below the label display stability improvement threshold, stop the frame voting window enlargement process in advance; otherwise, continue the frame voting window enlargement process until the label display stability improvement value of a certain round is above the label display stability improvement threshold, and mark the label display stability improvement value at this time as the first label display adjustment value.

5. The microgrid standardized operation assistance method integrating augmented reality as described in claim 2, characterized in that: The specific method for adjusting the execution environment acquisition parameters is as follows: The first difference in environmental change is obtained by performing difference processing between the environmental change impact value and the environmental change impact threshold. Based on the analysis of the impact value of environmental changes, if the impact value of environmental changes is positive, the first difference of environmental changes is matched with the database to obtain the row readout rate adjustment value and the key frame extraction interval adjustment time. In this way, the row readout rate is gradually reduced in rounds and the key frame extraction interval time is increased. If the impact value of environmental change is negative, the first difference of environmental change is matched with the database to obtain the adjustment value of the proportion of short and long exposure sequences and the adjustment time of key frame extraction interval. In this way, the proportion of short and long exposure sequences and the adjustment time of key frame extraction interval are gradually reduced in rounds.

6. The microgrid standardized operation assistance method integrating augmented reality as described in claim 5, characterized in that: The method for gradually reducing the line readout rate in rounds and increasing the keyframe extraction interval is as follows: Obtain the preset row read rate from the database and adjust the execution step size and keyframe extraction interval; The line readout rate is gradually reduced in rounds based on the step size adjustment. After each round of reducing the line readout rate, the keyframe extraction interval is gradually increased in rounds based on the step size adjustment. This is done by alternating adjustments in rounds to obtain stable label display values ​​after each round of alternating adjustments. Based on the analysis of the stable values ​​of the label display after each round of alternating adjustments, the stable improvement values ​​of the label display after each round of alternating adjustments are obtained and marked as the alternating improvement values ​​of each round; Obtain the preset alternating improvement threshold from the database and compare it with the alternating improvement value of each round. If the alternating improvement value of a certain alternating round is below the alternating improvement threshold, the adjustment is completed; otherwise, continue the adjustment process.

7. The microgrid standardized operation assistance method integrating augmented reality as described in claim 2, characterized in that: This also includes cloud archiving, the specific method of which is as follows: Based on the analysis of stable display judgment status and environment judgment status, if there is a stable display judgment status of unqualified and / or an environment judgment status of unqualified within a certain time period, then that time period is marked as the archiving execution period. The network bandwidth after the microgrid operation is completed is obtained and compared with the preset network bandwidth threshold. If the network bandwidth is above the network bandwidth threshold, a portion of the key frames of the video data during the archive execution period are selected and uploaded to the cloud for archiving processing. The specific method for selecting a portion of key frames from the video data of the archive execution period is as follows: If the only issue during the archiving execution period is stable display failure, then the stable execution difference will be marked as the archiving processing value. If the only issue during the archiving execution period is environmental failure, then the first difference in environmental change will be marked as the archiving processing value. Otherwise, the sum of the stable execution difference and the first difference in environmental change will be marked as the archiving processing value. The archived values ​​are matched with the database to obtain the archived keyframe upload ratio. Based on the archived keyframe upload ratio, a portion of keyframes are selected for archived processing.

8. A system applying the microgrid standardized operation assistance method integrating augmented reality as described in any one of claims 1-7, characterized in that, include: Initial configuration module, adjustment analysis module, and intelligent adjustment module; The initial configuration module is used to obtain the light intensity of the microgrid standardized operation environment after the visual sensor receives the microgrid standardized operation signal, perform augmented reality initialization configuration based on the light intensity, perform operation detection after the configuration is completed, obtain auxiliary operation labels and perform virtual display through augmented reality technology. The adjustment and analysis module is used to obtain stable display parameters of auxiliary operation labels, analyze and obtain stable label display values, obtain environmental change impact parameters of the operation environment, analyze and obtain environmental change impact values, and obtain augmented reality adjustment judgment instructions based on the stable label display values ​​and environmental change impact values. The intelligent adjustment module is used to analyze the augmented reality adjustment judgment instruction. If the augmented reality adjustment judgment instruction is the first execution instruction, no parameter adjustment will be performed; otherwise, the corresponding parameter adjustment will be performed, thereby completing the standardized operation processing of the microgrid.

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