A measurement instrument display method, system, device, and medium

Abstract

The application discloses a kind of measuring instrument display method, system, equipment and medium, by synchronous sampling and extracting electric parameter characteristic to voltage, current, active power, reactive power, power factor and total harmonic distortion;The electric parameter characteristic is associated with the corresponding event semantic label and is stored in hierarchical ring buffer after being associated, the hierarchical ring buffer includes raw data layer, second-level aggregation layer and minute-level aggregation layer;Electric parameter event semantic atlas is constructed based on the event co-occurrence frequency in sliding window;According to the event type currently detected, the electric parameter event semantic atlas is queried to determine associated events, and the user intent is inferred according to the event type and associated events;According to the user intent, the display mode is switched, and the display screen pixel resources are dynamically allocated based on the information value function to render multi-parameter visual content.The application realizes the intelligent perception of electric measuring instrument to operating state and the efficient communication of multi-parameter information.

Description

Technical Field

[0001] This invention belongs to the field of intelligent display technology for measuring instruments, and relates to a method, system, device and medium for displaying measuring instruments. Background Technology

[0002] Traditional electrical measurement instruments typically only display the current instantaneous values ​​of electrical parameters such as voltage, current, active power, reactive power, power factor, or total harmonic distortion, lacking the ability to structure historical data and perform multi-parameter correlation analysis. While some high-end instruments support simple historical curve playback, this is limited to a single electrical parameter and cannot reflect the dynamic coupling relationships between multiple parameters. When diagnosing power system anomalies, users often need to simultaneously observe whether voltage sags are accompanied by current inrushes or whether harmonic bursts cause power factor deterioration; existing instruments cannot provide such multi-dimensional fusion views. Furthermore, existing solutions generally employ fixed interface layouts, presenting data in the same way regardless of changes in operating status, making it easy to overlook critical anomaly information. Some products switch display modes via touchscreens, but the switching logic relies entirely on manual user operation; the instrument itself lacks the ability to understand operating events and cannot proactively adjust the displayed content to match current maintenance needs. On resource-constrained embedded platforms, existing technologies struggle to simultaneously handle multiple tasks such as real-time sampling, historical storage, and graphics rendering, often sacrificing information density or response speed due to insufficient memory or computing power.

[0003] Existing publicly available technologies only store historical curves for single parameters, without incorporating event semantics or correlation analysis. While they support interface switching, the switching mechanism is independent of the system's operating status and lacks context awareness. Power quality analysis methods based on machine learning proposed in academic literature typically rely on external computing units or cloud collaboration, making them unsuitable for purely locally deployed embedded measurement instruments. Furthermore, existing measurement instruments usually use digital tubes for parameter display. Traditional digital tubes are integrally packaged structures, integrating multiple LEDs in a seven-segment format with a decimal point, and soldered to the circuit board via pins. When any LED fails, the entire digital tube assembly must be replaced, resulting in high repair costs and cumbersome operation.

[0004] Therefore, there is an urgent need for an electrical measurement instrument display solution that can simultaneously achieve multi-parameter feature extraction, event semantic modeling, user intent inference, and adaptive visualization under limited hardware resources, in order to address the shortcomings of existing technologies in terms of information expression depth, interactive intelligence, and resource utilization efficiency. Summary of the Invention

[0005] To address the problems existing in the background art, the present invention proposes a measurement instrument display method, system, device, and medium.

[0006] The first aspect of this application provides a method for displaying a measuring instrument, comprising:

[0007] Voltage, current, active power, reactive power, power factor, and total harmonic distortion are sampled synchronously and electrical parameter characteristics are extracted.

[0008] The electrical parameter features are associated with the corresponding event semantic tags and stored in a hierarchical circular buffer, which includes a raw data layer, a second-level aggregation layer, and a minute-level aggregation layer.

[0009] A semantic graph of electrical parametric events is constructed based on the co-occurrence frequency of events within a sliding window; the semantic graph of electrical parametric events is queried according to the currently detected event type to determine associated events, and user intent is inferred based on the event type and associated events;

[0010] The display mode is switched according to the user's intent, and the display screen pixel resources are dynamically allocated based on the information value function to render multi-parameter visualization content.

[0011] Optionally, the event semantic tags include at least one of voltage sag, current inrush, harmonic burst, and three-phase imbalance; the event semantic tags are generated in real time by local threshold rules, wherein voltage sag is triggered when the voltage is lower than 90% of the rated voltage and the duration exceeds 100 milliseconds, current inrush is triggered when the current change rate is greater than five times the rated current per second, and harmonic burst is triggered when the total harmonic distortion is greater than 8% and the active power is lower than 50% of the rated active power.

[0012] Optionally, in the hierarchical circular buffer, the original data layer stores electrical parameter features and event semantic tags at 10-millisecond intervals, the second-level aggregation layer stores the data from the original data layer after aggregating the mean and extreme values ​​every second, and the minute-level aggregation layer stores the data from the second-level aggregation layer after a second aggregation every minute; each layer writes data using a rolling over method.

[0013] Optionally, the electrical parameter event semantic graph is a directed graph structure, where nodes represent event types and edge weights represent the co-occurrence strength of two events within a sliding window; the co-occurrence strength is updated using a recursive formula:

[0014] The current co-occurrence intensity is equal to the co-occurrence intensity at the previous moment multiplied by the forgetting factor plus the indicator value indicating whether the two events occur simultaneously within the current window; the forgetting factor is 0.95.

[0015] Optionally, the information value function is a weighted sum of the information entropy of the electrical parameter, the semantic relevance of the electrical parameter to the current context, and whether the electrical parameter is in an alarm state; the information entropy is calculated based on the numerical distribution of the electrical parameter within a preset time window; the semantic relevance is taken from the edge weights between the corresponding event and the current event in the electrical parameter event semantic graph.

[0016] Optionally, calculate the information value function value of each electrical parameter; and allocate the vertical pixel height of each electrical parameter on the display screen according to the proportion of the information value function value of each electrical parameter to the sum of the information value function values ​​of all electrical parameters.

[0017] When the assigned height is less than 8 pixels, the curve is drawn using symbol encoding; when the electrical parameter is in alarm state, its assigned height is not less than 12 pixels.

[0018] A second aspect of this application provides a measuring instrument display system, comprising:

[0019] The data acquisition module is used to synchronously sample and extract electrical parameter characteristics of voltage, current, active power, reactive power, power factor and total harmonic distortion;

[0020] A semantic storage module is used to associate the electrical parameter features with the corresponding event semantic tags and store them in a hierarchical circular buffer. The hierarchical circular buffer includes a raw data layer, a second-level aggregation layer, and a minute-level aggregation layer.

[0021] The event graph construction module is used to construct an electrical parameter event semantic graph based on the co-occurrence frequency of events within a sliding window;

[0022] The intent inference module is used to query the electrical parameter event semantic graph based on the currently detected event type to determine associated events, and infer the user intent based on the event type and associated events;

[0023] The adaptive rendering module is used to switch display modes according to the user's intent and dynamically allocate display screen pixel resources based on the information value function to render multi-parameter visualization content.

[0024] Optionally, when dynamically allocating display screen pixel resources, the adaptive rendering module calculates the information value function value of each electrical parameter; based on the proportion of the information value function value of each electrical parameter to the sum of the information value function values ​​of all electrical parameters, it allocates the vertical pixel height of each electrical parameter on the display screen; when the allocated height is less than 8 pixels, it replaces curve drawing with symbol encoding; when the electrical parameter is in an alarm state, its allocated height is not less than 12 pixels.

[0025] A third aspect of this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described measuring instrument display method.

[0026] A fourth aspect of this application provides a computer-readable medium storing a computer program that, when executed by a processor, implements the above-described measuring instrument display method.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] This invention provides a measurement instrument display method, system, device, and medium. By associating and storing electrical parameter features with event semantic tags in a hierarchical circular buffer, it achieves efficient fusion of operational data and state semantics across multiple time scales, significantly improving the contextual interpretability of historical backtracking. An electrical parameter event semantic graph constructed based on the event co-occurrence frequency within a sliding window enables the instrument to autonomously learn the correlation patterns between events in a local embedded environment, without relying on external models or cloud computing. The graph is queried based on the current event type to determine associated events, and user intent is inferred accordingly, transforming the display content from passive presentation to active adaptation, effectively improving user interaction efficiency. Display pixel resources are dynamically allocated based on the information value function, ensuring that highly fluctuating, strongly correlated, or alarm-state electrical parameters receive priority display space, achieving clear parallel display of multi-parameter information. When the allocated height is insufficient, symbol encoding is used instead of curve drawing, balancing information density and readability; a minimum display height is enforced for alarm electrical parameters, improving the visual salience of abnormal states. The entire solution completes the entire process of synchronous sampling, semantic modeling, intent inference, and adaptive rendering under limited processor performance and memory resources, and is applicable to common embedded electrical measurement instruments in industrial settings. Therefore, without increasing hardware costs, this invention significantly enhances the intelligence level, human-computer interaction efficiency, and operational status perception capabilities of electrical measuring instruments. Attached Figure Description

[0029] Figure 1 This is a flowchart of a measuring instrument display method according to an embodiment of the present invention;

[0030] Figure 2 This is a schematic diagram of a measuring instrument display system according to an embodiment of the present invention;

[0031] Figure 3 This is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0032] 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] In one embodiment, such as Figure 1 As shown, a method for displaying a measuring instrument is provided, which is applied to... Figure 1 Taking China as an example, the following specific steps will be used:

[0034] S10: Simultaneously sample and extract electrical parameter characteristics of voltage, current, active power, reactive power, power factor, and total harmonic distortion.

[0035] Specifically, in this invention, synchronous sampling and extraction of electrical parameter characteristics for voltage, current, active power, reactive power, power factor, and total harmonic distortion (THD) refers to high-speed digital acquisition of three-phase voltage and current signals using an analog-to-digital converter (ADC) circuit with a unified time reference, and real-time calculation of multiple key electrical parameters based on the acquired raw data. Specifically, the ADC circuit synchronously acquires the instantaneous values ​​of voltage and current for each phase at a sampling frequency of 10000 Hz, ensuring strict time alignment of data from all channels and avoiding phase errors or power calculation deviations caused by asynchronous sampling. Within one power frequency cycle, i.e., a 20-millisecond time window, a total of 200 sampling points are collected. Based on this sampling sequence, the fundamental component is first extracted using discrete Fourier transform, and then the effective values ​​of voltage, current, active power, reactive power, and power factor for each phase are calculated. Active power is obtained by averaging the product of instantaneous voltage and current values ​​over one cycle; reactive power is obtained through Hilbert transform or orthogonal component method; power factor is defined as the ratio of active power to apparent power. Simultaneously, harmonic analysis is performed on the voltage and current signals, calculating the effective values ​​of each harmonic from the 2nd to the 50th order. Based on the definition of total harmonic distortion (THD), the square root of the sum of the squares of the effective values ​​of each harmonic is divided by the fundamental effective value, and then multiplied by 100% to obtain the THD percentage. All these calculations are performed within the embedded processor, forming a set of timestamped multidimensional electrical parameter feature vectors. This vector contains six core parameters: voltage, current, active power, reactive power, power factor, and THD. This process not only ensures the synchronicity of each parameter in the time dimension but also guarantees their physical consistency, providing an accurate data foundation for subsequent event semantic label generation and multi-parameter fusion visualization. For example, during the startup monitoring of a variable frequency drive motor, the system detects at t=0 seconds that the A-phase current rapidly increases from 5 amps to 60 amps, while the A-phase voltage drops from 220 volts to 190 volts, the active power jumps from 3 kW to 8 kW, the total harmonic distortion increases from 2% to 11%, and the power factor decreases from 0.92 to 0.65. These parameters are synchronously captured within the same sampling window and recorded as overall features, enabling the system to accurately identify the composite event of "current inrush accompanied by voltage dip and harmonic burst," thereby triggering corresponding semantic tags and display strategies. This synchronous sampling and feature extraction mechanism significantly improves the spatiotemporal consistency of electrical parameter data and the accuracy of event discrimination, which is a prerequisite for achieving context-aware display.

[0036] S20: After associating the electrical parameter features with the corresponding event semantic tags, store them in a hierarchical circular buffer, which includes a raw data layer, a second-level aggregation layer, and a minute-level aggregation layer.

[0037] Specifically, storing the electrical parameter features associated with their corresponding event semantic tags in a hierarchical circular buffer means that, based on the extraction of electrical parameter features, the system performs real-time judgment on the current operating state according to preset local threshold rules, assigns one or more event semantic tags to the data record at that moment, and then writes the data structure containing electrical parameter features and event semantic tags into a hierarchical circular buffer with three levels of time granularity. This hierarchical circular buffer consists of a raw data layer, a second-level aggregation layer, and a minute-level aggregation layer, which are interconnected through time aggregation relationships. The raw data layer stores the complete electrical parameter feature vector and its associated event semantic tags at 10-millisecond intervals. Each record contains six parameters: voltage, current, active power, reactive power, power factor, and total harmonic distortion, as well as zero or more event semantic tags. This layer uses a fixed-capacity circular queue structure with a capacity of 600 records, covering the raw data of the most recent 6 seconds. When new data is written, the oldest data is overwritten. The second-level aggregation layer aggregates every 100 consecutive records (i.e., data within 1 second) from the original data layer, calculating the average, maximum, and minimum values ​​of each electrical parameter, and statistically analyzing the semantic tag types of all events occurring within that second, forming a second-level aggregated record. This layer also uses a circular queue with a capacity of 60 records, covering the data from the most recent 60 seconds. The minute-level aggregation layer further aggregates every 60 consecutive records from the second-level aggregation layer, generating a minute-by-minute statistical summary, including the mean range, extreme values, and dominant event types of each electrical parameter. This summary has a capacity of 1440 records, fully preserving a 24-hour historical summary. All three layers of data are organized chronologically, supporting automatic selection of the optimal layer for reading based on the user's query time span, avoiding redundant calculations. For example, when a user swipes on the instrument interface to view the voltage change trend over the past 5 minutes, the system first reads 5 records from the minute-level aggregation layer to quickly draw a coarse curve. If the user double-clicks a minute interval, it automatically switches to the second-level aggregation layer to obtain 60 more granular data points. If the user further long-presses to enter detailed analysis, it loads the high-resolution sampling features corresponding to 6 seconds from the original data layer. Throughout this process, event semantic tags remain bound to the aggregated data at each level, ensuring that historical backtracking includes not only numerical information but also semantic interpretations of the operational status. This storage mechanism, occupying only 48 kilobytes of random access memory, achieves efficient association storage of electrical parameters and event semantics across multiple time scales, significantly improving the speed of historical data retrieval and contextual interpretability, and providing reliable data support for subsequent intent inference and adaptive rendering based on event semantic graphs.

[0038] S30: Construct a semantic graph of electrical parametric events based on the co-occurrence frequency of events within a sliding window.

[0039] Specifically, constructing an electrical parameter event semantic graph based on the co-occurrence frequency of events within a sliding window involves the system continuously monitoring the event semantic tags recorded in a hierarchical circular buffer during instrument operation. It then statistically analyzes the event occurrences using a sliding window of fixed time length, dynamically establishing a directed graph structure reflecting the correlation strength between different event types. Nodes in this electrical parameter event semantic graph represent predefined event types, including voltage sags, current inrushes, harmonic bursts, and three-phase imbalances. Directed edges in the graph represent the tendency for two events to co-occur in time, and the edge weights quantify the strength of this co-occurrence relationship. The sliding window duration is set to 10 seconds, and the system slides forward once every second, checking the event semantic tags contained in all records within the current window. If both voltage sags and current inrushes are detected simultaneously within the same sliding window, these two events are considered to co-occur. The system updates the co-occurrence strength between any two events in real time using a recursive formula: the co-occurrence strength at the current moment equals the co-occurrence strength at the previous moment multiplied by a forgetting factor of 0.95, plus an indicator value for whether the two events occur simultaneously within the current sliding window; this indicator value is 1 when co-occurrence occurs, and 0 otherwise. Through this recursive mechanism, the graph can retain long-term statistical regularities while remaining sensitive to recent changes in operating patterns. When the electrical parameter event semantic graph is initialized, all edge weights are 0. As the instrument continues to operate, the weight values ​​gradually converge to a stable range. For example, when monitoring the startup process of a large motor in an industrial site, the system repeatedly records current inrush and voltage sag events simultaneously at the moment of startup. After several startups, the edge weight pointing from the current inrush node to the voltage sag node rises to 0.72. When a current inrush event is subsequently detected again, the system queries the electrical parameter event semantic graph and finds a high-weight association between it and the voltage sag, inferring that the user may be interested in the voltage response. Therefore, the system preloads the historical voltage curve and highlights phase change information in the display interface. This graph is built and maintained entirely in a local embedded environment, requiring no external training or cloud support, and occupies less than 2 kilobytes of memory. Through this mechanism, the instrument possesses the lightweight modeling capability for potential causal or co-occurring relationships between running events, enabling the visualization content to proactively adjust based on the event context, significantly improving the intelligence and foresight of human-computer interaction.

[0040] S40: Query the electrical parameter event semantic graph based on the currently detected event type to determine the associated events, and infer the user intent based on the event type and associated events.

[0041] Specifically, querying the electrical parameter event semantic graph based on the currently detected event type to determine associated events, and inferring user intent based on the event type and associated events, means that after the system identifies one or more event semantic tags in real time, it actively accesses the constructed electrical parameter event semantic graph, retrieves other event types that are strongly associated with the current event, and maps them to a preset user intent category based on the combination relationship, thereby providing a decision basis for subsequent display mode switching.

[0042] Specifically, when the instrument detects a certain event type, such as a harmonic burst, the system immediately traverses all directed edges in the electrical parameter event semantic graph starting from that event and filters out associated events with edge weights greater than 0.6. If the edge weight between the harmonic burst and three-phase imbalance is found to be 0.68, then the three-phase imbalance is marked as a highly correlated event. Subsequently, the system combines the current event with all highly correlated events to form an event combination and matches it against a built-in intent rule base. This intent rule base stores the mapping relationship between several event combinations and user intents. For example, when the event combination includes both harmonic bursts and three-phase imbalance, the corresponding user intent is to assess power quality; when the event combination includes both current inrush and voltage sag, the corresponding user intent is to diagnose equipment startup impact; when the event combination only includes a single voltage sag and no strongly correlated events, the corresponding user intent is to monitor power supply stability. Each user intent is bound to a default display mode.

[0043] For example, when the system infers that the user's intention is to assess power quality, it automatically activates the harmonic focusing mode, displaying the superimposed waveform of the fundamental wave and major harmonics in the main display area, and showing a histogram of the amplitude of each harmonic in the auxiliary display area. When the system infers that the user's intention is to diagnose equipment startup impact, it switches to startup analysis mode, simultaneously displaying the current and voltage curves and annotating the phase difference changes. This inference process is performed entirely locally, with a response time of less than 50 milliseconds. In one embodiment, during the operation of a frequency converter load, the system detects that the total harmonic distortion has risen to 12%, triggering a harmonic burst event. After querying the semantic graph of electrical parameter events, it finds that the co-occurrence intensity with three-phase imbalance is 0.71. Based on this, the system infers that the user is concerned about power quality issues and automatically switches the display from compact mode to harmonic focusing mode, without requiring manual operation from the user. This mechanism enables the instrument to shift from passive display to proactive understanding of the operating scenario, significantly reducing user interaction steps, improving fault identification efficiency, and ensuring intelligent human-machine collaboration on resource-constrained embedded platforms.

[0044] S50: Switch the display mode according to the user's intention, and dynamically allocate display screen pixel resources based on the information value function to render multi-parameter visualization content.

[0045] Specifically, switching display modes based on the user's intent and dynamically allocating display screen pixel resources based on the information value function to render multi-parameter visualization content means that after the system infers the user's intent, it first selects a mode matching the intent from a preset set of display modes, and then initiates a pixel-level resource scheduling mechanism to re-plan the layout and graphic representation of each electrical parameter on the display screen according to the importance of each electrical parameter in the current context. The preset display modes include compact mode, startup analysis mode, harmonic focus mode, and three-phase comparison mode. Each mode defines a default combination of electrical parameters of interest and an initial rendering strategy. After selecting a display mode, the system calculates the information value function value of all electrical parameters to be displayed. The information value function is a weighted sum of three components. The first component is the information entropy of the electrical parameter within the most recent 10-second window, used to measure the uncertainty of its numerical fluctuation. The second component is the semantic relevance between the event type corresponding to the electrical parameter and the current user intent context; this value is directly taken from the weight of the corresponding edge in the electrical parameter event semantic graph. The third component is the alarm status indication value; it is 1 if the electrical parameter is currently in an over-limit alarm state, and 0 otherwise. The weights of each component are 0.4, 0.4, and 0.2, respectively. The system sums the information value function values ​​of each electrical parameter to obtain the total value and allocates the available vertical pixel height of the display screen proportionally. Assuming the display screen resolution is 128 pixels wide by 64 pixels high, and the status bar occupies a fixed height of 6 pixels, the available height is 58 pixels. If the information value function values ​​of voltage, current, and total harmonic distortion are 1.2, 1.5, and 0.9, respectively, with a sum of 3.6, then the allocated heights are 19 pixels, 24 pixels, and 15 pixels, respectively. After allocation, the system performs differentiated rendering for each electrical parameter: if the allocated height is greater than or equal to 8 pixels, a mini curve or bar chart is drawn; if the allocated height is less than 8 pixels, a symbolic encoding method is used, for example, an upward arrow to represent an upward trend, a downward arrow to represent a downward trend, and a horizontal arrow to represent stability; if an electrical parameter is in an alarm state, even if its original allocated height is less than 12 pixels, it is forcibly increased to 12 pixels to ensure that key information is visible. In one embodiment, when the system infers that the user's intention is to diagnose the device's startup impact, it switches to startup analysis mode. At this time, the current has high information entropy due to drastic changes, and its event is highly correlated with the current intention, resulting in the highest information value function value. It is allocated a height of 24 pixels and a complete curve is drawn; voltage is second, allocated 20 pixels; the power factor changes smoothly and has no alarm, only 7 pixels are allocated, so it is displayed using symbolic encoding. This mechanism achieves an optimal balance between information density and readability within a limited screen space, allowing users to obtain multiple dimensions of key operating status simultaneously without switching interfaces, significantly improving the information transmission efficiency and operational intuitiveness of small-sized embedded instruments.

[0046] In one embodiment, in step S20, the event semantic label includes at least one of voltage sag, current inrush, harmonic burst, and three-phase imbalance; the event semantic label is generated in real time by local threshold rules, wherein the voltage sag is triggered when the voltage is lower than 90% of the rated voltage and the duration exceeds 100 milliseconds, the current inrush is triggered when the current change rate is greater than five times the rated current per second, and the harmonic burst is triggered when the total harmonic distortion is greater than 8% and the active power is lower than 50% of the rated active power.

[0047] Specifically, the event semantic tags include at least one of voltage sag, current inrush, harmonic burst, and three-phase imbalance. These tags are generated in real time during operation by the instrument's internal local threshold rules, without relying on external systems or offline training models. The criteria for a voltage sag event are: the effective value of any phase voltage is continuously lower than 90% of the rated voltage of that phase, and this state is maintained for more than 100 milliseconds. The system continuously monitors the voltage sampling values ​​through a sliding window, and the event semantic tag is triggered only when the voltage values ​​at all 10-millisecond intervals within the window meet the threshold condition. The determination of a current inrush event is based on the current change rate, specifically: the ratio of the change in current of any phase within a 100-millisecond time window to the rated current is greater than 5, which is equivalent to the current change rate exceeding 5 times the rated current per second. This change rate is calculated by differentiating adjacent sampling points and then smoothing and filtering to avoid false triggering due to instantaneous noise. The determination of a harmonic burst event requires the simultaneous fulfillment of two conditions: total harmonic distortion (THD) greater than 8%, and active power less than 50% of the rated active power. This combined condition is used to distinguish between harmonic pollution under normal high load and abnormal harmonic pollution caused by light-load nonlinear load. Three-phase imbalance events are identified by calculating the ratio of the negative-sequence to the positive-sequence components of the three-phase voltage or current; a value exceeding 2% is flagged. All event semantic tags are generated within the embedded processor, with the determination logic embedded in the program code, resulting in a response latency of less than 10 milliseconds. In one embodiment, a factory lighting circuit is connected to a large number of LED drivers. During nighttime light-load operation, the system detects that the THD of phase A is 11%, and the active power is only 30% of the rated value, meeting the dual threshold conditions for a harmonic burst. A harmonic burst event semantic tag is then generated and associated with the current data record. In another embodiment, at the moment the large water pump starts, the B-phase current rises from 4 amps to 55 amps within 80 milliseconds. The rated current is 10 amps, and the calculated rate of change is 51 amps per second, equivalent to 5.1 times the rated current per second. This exceeds the threshold, and the system immediately marks a current inrush event. The aforementioned local threshold rule design balances sensitivity and interference resistance, ensuring that the event semantic labels accurately reflect the actual operational anomalies. This provides reliable input for subsequent event correlation analysis, user intent inference, and adaptive display, effectively avoiding the information overload or omission of critical states caused by the lack of semantic understanding in traditional instruments.

[0048] In one embodiment, in step S20, in the hierarchical circular buffer, the original data layer stores electrical parameter features and event semantic tags at 10-millisecond intervals, the second-level aggregation layer stores the data from the original data layer after aggregating the mean and extreme values ​​every second, and the minute-level aggregation layer stores the data from the second-level aggregation layer after a second aggregation every minute; each layer writes data using a rolling over method.

[0049] Specifically, in the hierarchical circular buffer, the raw data layer stores electrical parameter features and event semantic tags at 10-millisecond intervals. This means that after each 10-millisecond cycle of synchronous sampling and feature extraction, the system immediately writes the electrical parameter feature vector, containing six parameters—voltage, current, active power, reactive power, power factor, and total harmonic distortion—along with zero or more event semantic tags determined at that moment, as a complete record into the raw data layer. The raw data layer adopts a fixed-length circular queue structure with a capacity of 600 records, corresponding to the high-resolution running data of the most recent 6 seconds. When the 601st record is written, it automatically overwrites the earliest written record, achieving rolling over. The second-level aggregation layer stores the data from the raw data layer after aggregating the mean and extreme values ​​every second. Specifically, the system triggers an aggregation operation every 100 raw records received. For each of the six electrical parameters, the arithmetic mean, maximum, and minimum values ​​of 100 values ​​are calculated. The semantic tag types of all events occurring within that second are also counted, forming a second-level aggregated record containing 18 numerical fields and several tag fields. This second-level aggregation layer also uses a circular queue with a capacity of 60 records, covering the medium-granularity data of the most recent 60 seconds. When full, a rolling over strategy is employed. The minute-level aggregation layer performs a secondary aggregation of the data from the second-level aggregation layer. This means that after collecting 60 second-level aggregated records, the average value of each electrical parameter is averaged again, the maximum value is taken as the overall maximum, and the minimum value is taken as the overall minimum. The semantic tag of the event occurring most frequently within that minute is extracted as the dominant event. This generates a minute-level aggregated record representing the running status of the entire minute. This layer has a capacity of 1440 records and can continuously store 24 hours of historical summaries. It also uses a rolling over strategy to manage storage space. The three-layer structure is physically contiguously allocated in random access memory and logically uses pointer indexes for fast access. In one embodiment, when a user views the power changes over the past 3 minutes at 10:05 AM, the system directly reads 3 records from the minute-level aggregation layer to quickly draw a trend overview. When the user clicks on the 10:03 AM interval, the system switches to the second-level aggregation layer to load 60 records, displaying more detailed fluctuations. If the user further long-presses to enter in-depth analysis, 600 high-precision records corresponding to 6 seconds are extracted from the original data layer for waveform reconstruction.

[0050] This hierarchical rolling storage mechanism enables full-scale data support, from millisecond-level transient capture to hourly-level long-term backtracking. It significantly reduces the computational overhead of historical queries while ensuring that the context of key events is not lost, providing a solid data foundation for embedded electrical measurement instruments to achieve efficient multi-granularity visualization under resource-constrained conditions.

[0051] In one embodiment, in step S30, the electrical parameter event semantic graph is a directed graph structure, where each node of the directed graph structure represents a predefined event category, and the edge weight represents the co-occurrence intensity of two events within a sliding window; wherein, the co-occurrence intensity is updated by a recursive formula: the current co-occurrence intensity is equal to the co-occurrence intensity at the previous moment multiplied by the forgetting factor plus the indication value of whether the two events occur simultaneously within the current window; the forgetting factor is 0.95.

[0052] Specifically, the electrical parameter event semantic graph is a directed graph structure. Each node in this directed graph structure represents a predefined event category, including voltage sag, current inrush, harmonic burst, and three-phase imbalance. A directed edge can exist between any two nodes in the graph, indicating that the event category corresponding to the source node and the event category corresponding to the target node tend to occur concurrently or sequentially during instrument operation. Each directed edge is associated with a numerical weight, called co-occurrence strength, which quantifies the statistical significance of the co-occurrence of two event categories within a sliding window. The co-occurrence strength is updated in real time using a recursive formula: the co-occurrence strength at the current moment equals the co-occurrence strength at the previous moment multiplied by a forgetting factor, plus an indication value for whether the two events occur simultaneously within the current sliding window. The forgetting factor is fixed at 0.95, used to assign higher weight to recent observations while retaining historical statistical trends. The indication value is a binary variable; if two event categories are detected simultaneously within the current 10-second sliding window, the indication value is 1; otherwise, it is 0. The system slides the window every second and performs an update operation on all event category combinations. Initially, the co-occurrence strength of all edges is set to 0. As the instrument continues to operate, the weights of frequently co-occurring event pairs will gradually increase and stabilize. In one embodiment, a large induction motor on a production line starts multiple times daily, with each start accompanied by a current surge and a voltage dip. After 20 starts, the co-occurrence strength of the current surge node pointing to the voltage dip node gradually accumulates from 0 to 0.72. When the system detects a current surge event again, it queries the edge weight and finds that it is greater than a preset threshold of 0.6, thus determining that there is a strong correlation between the two and triggering the start analysis mode. This recursive update mechanism requires minimal computational overhead, with each update involving only one multiplication and one addition, and can be executed efficiently on an embedded processor; the entire graph occupies less than 2 kilobytes of memory. Through this directed graph structure and its dynamic update mechanism, the instrument can autonomously learn and memorize the coupling patterns of typical events on-site without external model support, providing a reliable basis for subsequent user intent inference and display content generation, significantly improving local intelligent decision-making capabilities and the contextual adaptability of human-computer interaction.

[0053] In one embodiment, in step S50, the information value function is a weighted sum of the information entropy of the electrical parameter, the semantic relevance of the electrical parameter to the current context, and whether the electrical parameter is in an alarm state; the information entropy is calculated based on the numerical distribution of the electrical parameter within a preset time window; the semantic relevance is taken from the edge weights between the corresponding event and the current event in the semantic graph of the electrical parameter event.

[0054] Specifically, the information value function is a weighted sum of the information entropy of the electrical parameter, the semantic relevance of the electrical parameter to the current context, and whether the electrical parameter is in an alarm state. This function quantifies the information importance of each electrical parameter in the current display scenario to guide the dynamic allocation of pixel resources. The information entropy reflects the uncertainty of the electrical parameter's numerical changes. Its calculation is based on the distribution of the electrical parameter's sampled values ​​within a preset 10-second time window. Specifically, the range of electrical parameter values ​​is divided into eight equally wide intervals, and the frequency of sampling points in each interval is statistically analyzed, denoted as p1 to p8. If there is no data in a certain interval, the corresponding frequency is 0. The information entropy H is calculated by multiplying pᵢ by the logarithm of pᵢ (base 2) from i equal to 1 to 8 using the formula H = negative. When pᵢ is 0, this term is considered 0. The larger the information entropy value, the more drastic the fluctuation of the electrical parameter and the higher its information content. Semantic relevance indicates the degree of association between the event category associated with the electrical parameter and the current user intent context. The system first determines the dominant event type, and then, based on the event category corresponding to the electrical parameter, queries the directed edge weight from the dominant event node to the event category node in the electrical parameter event semantic graph. If the edge exists, the semantic relevance is the edge weight value; if it does not exist, the semantic relevance is 0. For example, when the dominant event is harmonic burst, and a certain electrical parameter is total harmonic distortion, its corresponding event category is harmonic burst, then the semantic relevance is taken from the loop edge from the harmonic burst node to itself or is set to 1 by default. The alarm status is a binary indicator. If the electrical parameter currently exceeds the preset threshold and triggers an over-limit alarm, the alarm status value is 1; otherwise, it is 0. The final value of the information value function V is obtained by weighted summation of the three factors, with weights of 0.4, 0.4, and 0.2, respectively. That is, V equals 0.4 multiplied by information entropy plus 0.4 multiplied by semantic relevance plus 0.2 multiplied by alarm status. In one embodiment, the instrument detects a current inrush event and infers that the user's intention is to diagnose a diagnostic initiation surge. Due to the rapid rise, the information entropy of the current is 1.35, corresponding to the current inrush event category, consistent with the dominant event, with a semantic relevance of 1.0. Since the current does not exceed the limit, the alarm status is 0. Therefore, the information value function of the current is 0.4 × 1.35 + 0.4 × 1.0 + 0.2 × 0 = 0.94. Meanwhile, the power factor changes smoothly during this period, with an information entropy of 0.2 and a semantic relevance of 0.3 (due to the co-occurrence weight of current inrush and power factor decrease in the graph being 0.3), and no alarm. Its information value function is 0.4 × 0.2 + 0.4 × 0.3 + 0.2 × 0 = 0.2. The ratio of these two values ​​determines the pixel allocation ratio. This mechanism ensures that highly fluctuating, strongly correlated, or abnormal electrical parameters receive higher display priority, achieving prominent presentation of key information within limited screen space. This effectively avoids the problem of important states being ignored due to traditional fixed layouts, significantly improving the information transmission efficiency and operational intuitiveness of embedded instruments under complex operating conditions.

[0055] In one embodiment, step S50, the dynamic allocation of display screen pixel resources, specifically includes the following steps:

[0056] S51: Calculate the information value function value of each electrical parameter;

[0057] S52: Allocate the vertical pixel height of each electrical parameter on the display screen according to the proportion of the information value function value of each electrical parameter to the sum of the information value function values ​​of all electrical parameters.

[0058] S53: When the allocated height is less than 8 pixels, the curve is drawn using symbol encoding; when the electrical parameter is in alarm state, its allocated height is not less than 12 pixels.

[0059] Specifically, the dynamic allocation of display screen pixel resources includes the following steps: First, after determining the current display mode, the system calculates the information value function value of each electrical parameter to be displayed; then, the information value function values ​​of each electrical parameter are summed to obtain the total value; next, for each electrical parameter, its information value function value is divided by the total value, and then multiplied by the vertical pixel height available for data visualization on the display screen to obtain the initial allocation height of that electrical parameter; wherein, the total resolution of the display screen is 128 pixels wide multiplied by 64 pixels high, and the status bar occupies a fixed 6 pixels high, therefore the available vertical pixel height is 58 pixels; if If the initial height of an electrical parameter is less than 8 pixels, the system will not draw a curve graph, but will instead use symbol encoding to represent its trend. The symbol encoding includes an upward arrow to indicate an increasing value, a downward arrow to indicate a decreasing value, and a horizontal arrow to indicate a stable value. If an electrical parameter is currently in an alarm state, the system will force its height to be set to no less than 12 pixels, regardless of its initial height, to ensure that the alarm information has sufficient visual salience on the screen. After the allocation is completed, the system will render the visualization content of each electrical parameter in order from top to bottom, with the main display area prioritizing the display of the electrical parameter with the largest allocated height. In one embodiment, the user's intent is inferred to be to assess power quality. The electrical parameters to be displayed include voltage, current, total harmonic distortion (THD), and power factor. Their calculated information value function values ​​are 0.8, 0.6, 1.2, and 0.3, respectively, with a total value of 2.9. Therefore, the initial height allocations are 16 pixels, 12 pixels, 24 pixels, and 6 pixels, respectively. The power factor is allocated a height of 6 pixels, which is less than 8 pixels, so it is displayed as a horizontal arrow. THD, due to its highest information value and being in an alarm state, is allocated a height of 24 pixels and plotted as a bar chart. Voltage and current are plotted as mini-curves. The entire interface clearly presents the four dimensions of information on a 128×64 pixel screen without overlap or obstruction. This dynamic allocation mechanism, under strictly limited display resources, achieves precise matching between information importance and visual expression, avoiding the problems of high-value information being compressed or low-value information occupying too much space caused by traditional fixed partitioning. This significantly improves the usability and human-computer interaction efficiency of small-sized embedded instruments in multi-parameter monitoring scenarios.

[0060] In one embodiment, such as Figure 2 As shown, a measurement instrument display system is provided, which corresponds one-to-one with the measurement instrument display methods in the above embodiments. The measurement instrument display system includes: a data acquisition module, a semantic storage module, an event graph construction module, an intent inference module, and an adaptive rendering module. The detailed descriptions of each functional module are as follows:

[0061] The data acquisition module is used to synchronously sample and extract electrical parameter characteristics of voltage, current, active power, reactive power, power factor and total harmonic distortion;

[0062] A semantic storage module is used to associate the electrical parameter features with the corresponding event semantic tags and store them in a hierarchical circular buffer. The hierarchical circular buffer includes a raw data layer, a second-level aggregation layer, and a minute-level aggregation layer.

[0063] The event graph construction module is used to construct an electrical parameter event semantic graph based on the co-occurrence frequency of events within a sliding window;

[0064] The intent inference module is used to query the electrical parameter event semantic graph based on the currently detected event type to determine associated events, and infer the user intent based on the event type and associated events;

[0065] The adaptive rendering module is used to switch display modes according to the user's intent and dynamically allocate display screen pixel resources based on the information value function to render multi-parameter visualization content.

[0066] Furthermore, when dynamically allocating display screen pixel resources, the adaptive rendering module calculates the information value function value of each electrical parameter; based on the proportion of the information value function value of each electrical parameter to the sum of the information value function values ​​of all electrical parameters, it allocates the vertical pixel height of each electrical parameter on the display screen; when the allocated height is less than 8 pixels, it replaces curve drawing with symbol encoding; when the electrical parameter is in an alarm state, its allocated height is not less than 12 pixels.

[0067] Specific limitations regarding the measurement instrument display system can be found in the limitations on the measurement instrument display method described above, and will not be repeated here. Each module in the aforementioned measurement instrument display system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0068] In one embodiment, such as Figure 3 As shown, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the measurement instrument display method.

[0069] For specific limitations on electronic devices, please refer to the limitations on the display methods of measuring instruments mentioned above, which will not be repeated here.

[0070] In one embodiment, a computer-readable medium is provided that stores a computer program, which, when executed by a processor, performs the following steps:

[0071] Voltage, current, active power, reactive power, power factor, and total harmonic distortion are sampled synchronously and electrical parameter characteristics are extracted.

[0072] The electrical parameter features are associated with the corresponding event semantic tags and stored in a hierarchical circular buffer, which includes a raw data layer, a second-level aggregation layer, and a minute-level aggregation layer.

[0073] Constructing an electrical parametric event semantic graph based on the co-occurrence frequency of events within a sliding window;

[0074] Based on the currently detected event type, the electrical parameter event semantic graph is queried to determine associated events, and user intent is inferred based on the event type and associated events;

[0075] The display mode is switched according to the user's intent, and the display screen pixel resources are dynamically allocated based on the information value function to render multi-parameter visualization content.

[0076] For specific limitations on computer-readable media, please refer to the limitations on the display methods of measuring instruments mentioned above, which will not be repeated here.

[0077] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0078] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for displaying a measuring instrument, characterized in that, include: Voltage, current, active power, reactive power, power factor, and total harmonic distortion are sampled synchronously and electrical parameter characteristics are extracted. The electrical parameter features are associated with the corresponding event semantic tags and stored in a hierarchical circular buffer, which includes a raw data layer, a second-level aggregation layer, and a minute-level aggregation layer. Constructing an electrical parametric event semantic graph based on the co-occurrence frequency of events within a sliding window; Based on the currently detected event type, the electrical parameter event semantic graph is queried to determine associated events, and user intent is inferred based on the event type and associated events; The display mode is switched according to the user's intent, and the display screen pixel resources are dynamically allocated based on the information value function to render multi-parameter visualization content; the information value function is a weighted sum of the information entropy of the electrical parameter, the semantic relevance of the electrical parameter to the current context, and whether the electrical parameter is in an alarm state; the information entropy is calculated based on the numerical distribution of the electrical parameter within a preset time window; the semantic relevance is taken from the edge weight between the corresponding event and the current event in the semantic graph of electrical parameter events.

2. The measuring instrument display method according to claim 1, characterized in that, The event semantic tags include at least one of voltage sag, current inrush, harmonic burst, and three-phase imbalance; the event semantic tags are generated in real time by local threshold rules, wherein voltage sag is triggered when the voltage is lower than 90% of the rated voltage and the duration exceeds 100 milliseconds, current inrush is triggered when the current change rate is greater than five times the rated current per second, and harmonic burst is triggered when the total harmonic distortion is greater than 8% and the active power is lower than 50% of the rated active power.

3. The measuring instrument display method according to claim 1, characterized in that, In the hierarchical circular buffer, the original data layer stores electrical parameter features and event semantic tags at 10-millisecond intervals, the second-level aggregation layer stores the data from the original data layer after aggregating the mean and extreme values ​​every second, and the minute-level aggregation layer stores the data from the second-level aggregation layer after a second aggregation every minute; each layer writes data using a rolling over method.

4. The measuring instrument display method according to claim 1, characterized in that, The electrical parameter event semantic graph is a directed graph structure. Each node in the directed graph structure represents a predefined event category, and the edge weight represents the co-occurrence intensity of two events within a sliding window. The co-occurrence intensity is updated by a recursive formula: the current co-occurrence intensity equals the co-occurrence intensity at the previous moment multiplied by the forgetting factor plus the indicator value indicating whether the two events occur simultaneously within the current window. The forgetting factor is 0.

95.

5. The measuring instrument display method according to claim 1, characterized in that, The dynamically allocated display screen pixel resources include: Calculate the information value function value of each electrical parameter; Based on the proportion of the information value function value of each electrical parameter to the sum of the information value function values ​​of all electrical parameters, the vertical pixel height of each electrical parameter on the display screen is allocated. When the allocated height is less than 8 pixels, curve drawing is replaced by symbolic encoding. When the electrical parameter is in alarm state, its assigned height shall not be less than 12 pixels.

6. A measuring instrument display system, characterized in that, include: The data acquisition module is used to synchronously sample and extract electrical parameter characteristics of voltage, current, active power, reactive power, power factor and total harmonic distortion; A semantic storage module is used to associate the electrical parameter features with the corresponding event semantic tags and store them in a hierarchical circular buffer. The hierarchical circular buffer includes a raw data layer, a second-level aggregation layer, and a minute-level aggregation layer. The event graph construction module is used to construct an electrical parameter event semantic graph based on the co-occurrence frequency of events within a sliding window; The intent inference module is used to query the electrical parameter event semantic graph based on the currently detected event type to determine associated events, and infer the user intent based on the event type and associated events; An adaptive rendering module is used to switch display modes according to the user's intent and dynamically allocate display screen pixel resources to render multi-parameter visualization content based on an information value function. The information value function is a weighted sum of the information entropy of the electrical parameter, the semantic relevance of the electrical parameter to the current context, and whether the electrical parameter is in an alarm state. The information entropy is calculated based on the numerical distribution of the electrical parameter within a preset time window. The semantic relevance is taken from the edge weights between the corresponding event and the current event in the electrical parameter event semantic graph.

7. The measuring instrument display system according to claim 6, characterized in that, When dynamically allocating display screen pixel resources, the adaptive rendering module calculates the information value function value of each electrical parameter; based on the proportion of the information value function value of each electrical parameter to the sum of the information value function values ​​of all electrical parameters, it allocates the vertical pixel height of each electrical parameter on the display screen; when the allocated height is less than 8 pixels, it replaces curve drawing with symbol encoding; when the electrical parameter is in an alarm state, its allocated height is not less than 12 pixels.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the measuring instrument display method according to any one of claims 1 to 5.

9. A computer-readable medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the measuring instrument display method according to any one of claims 1 to 5.

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