A dynamic display method and system of a smart meter based on a power price signal

Abstract

The application discloses a kind of based on the dynamic display method and system of intelligent electric meter of electricity price signal, it is related to electric efficiency management technical field, comprising the following steps: S1, receiving and analyzing electricity price signal;S2, dynamically generating visual feature strategy;S3, generating scene energy-saving suggestion text;S4, control display screen carries out composite information rendering and display;S5, monitor condition changes and update display;The present application can quickly, clearly understand the electricity price state and know specific operation suggestion by dynamic, strong visual impact and direct text guidance, so as to effectively promote its adjustment power consumption behavior, improve demand side response speed and effect.

Description

Technical Field

[0001] This invention relates to the field of electricity efficiency management technology, specifically to a method and system for dynamic display of smart meters based on electricity price signals. Background Technology

[0002] With the advancement of smart grid and electricity market reforms, dynamic pricing mechanisms such as time-of-use pricing and real-time pricing are widely used, aiming to guide users to smooth peak and valley loads and optimize grid load. As the terminal connecting the grid and users, smart meters serve as a crucial interface for conveying electricity price information and guiding energy-saving behaviors.

[0003] Currently, most smart meters can only statically display the current electricity price period through numbers or simple icons (such as "peak," "flat," and "valley"). For example, a smart meter entitled "Smart Meter with Digital Wireless Broadcast Receiver" (document number CN103245811A) discloses a smart meter that receives electricity price information via digital wireless broadcast and changes the display module status according to the electricity price information. Another example is a smart meter entitled "Intelligent Display Control Device for Electricity Meters and its Control Method" (document number CN102074078B), which discloses a method of switching the screen background color according to the electricity price period. These technical solutions present information in a simplistic and unintuitive way, failing to effectively attract users' attention and motivate them to actively adjust their electricity consumption behavior. Therefore, how to utilize the meter display screen to more effectively and intelligently convey electricity price information to users and provide behavioral guidance has become an important technical direction for improving demand-side response. Summary of the Invention

[0004] This invention provides a method and system for dynamic display of smart meters based on electricity price signals. Through the content displayed on the smart meter's display screen, it effectively conveys electricity price information to users and provides behavioral guidance.

[0005] This invention provides the following technical solution: a method for dynamic display of smart meters based on electricity price signals, characterized by comprising the following steps:

[0006] S1. The electricity price signal receiving module receives the electricity price signal, and the data processing module parses the electricity price signal to obtain the current electricity price status information;

[0007] S2. The display control module calls or dynamically generates corresponding visual feature strategies from the preset strategy library based on the parsed current electricity price status information.

[0008] S3. The display control module combines the current electricity price status information with the real-time total active power data obtained from the electricity metering module to generate energy-saving suggestion text for specific loads.

[0009] S4. The display control module controls the display screen to synchronously execute the visual feature strategy determined in S2 and the energy-saving suggestion text generated in S3.

[0010] S5. Continuously monitor the electricity price signal or local electricity load. When the electricity price period changes or the real-time active power changes and the visual feature strategy and energy-saving suggestion text need to be adjusted, return to step S2, update the visual feature strategy and energy-saving suggestion text, and realize the dynamic adjustment of the display.

[0011] As a further improvement of the present invention, in S2, the strategy for generating visual features specifically includes the following steps:

[0012] Collect and integrate multi-source data, which includes at least current electricity price status information, historical electricity load curves, and user interaction response data to historical display suggestions;

[0013] Based on the multi-source data, a machine learning model is used to generate and dynamically update user profiles, which include the user's sensitivity to changes in electricity prices and quantitative features of response efficiency to specific visual features.

[0014] Based on the current electricity price status information and the dynamic user profile, corresponding visual feature strategies are matched or dynamically generated from a pre-set visual strategy library.

[0015] As a further improvement of the present invention, generating scenario-based suggestion text specifically includes the following steps:

[0016] Based on non-intrusive load monitoring technology, the total electrical load is decomposed in real time, and the type and operating status of at least one specific electrical device currently in operation are identified.

[0017] Obtain short-term electricity price forecast data, wherein the forecast data includes at least the predicted electricity price value and its trend for a future preset period;

[0018] By combining the current electricity price status information, the identified specific electrical equipment information, the short-term electricity price forecast data, and the preset user profile, a personalized energy-saving suggestion text is generated through a pre-set rule base and machine learning model. This text includes specific equipment operation instructions, quantified savings amount, and forward-looking suggestions.

[0019] As a further improvement of the present invention, the real-time decomposition of total electrical load based on non-intrusive load monitoring technology specifically includes:

[0020] Monitor changes in total power data, and determine that a load switching event has occurred when the change exceeds a preset threshold;

[0021] Extract the load characteristics corresponding to the switching event. The characteristics include at least active power characteristics, reactive power characteristics, and equipment feature vectors extracted from the power change curve from equipment startup to stable operation.

[0022] Based on the extracted load characteristics, a preliminary identification is first performed using a pre-set decision tree model. If uncertain results are generated, the VI trajectory feature map of the load is further constructed and analyzed to determine the type and operating status of the specific electrical equipment.

[0023] As a further improvement of the present invention, the generation of personalized energy-saving suggestion text containing specific device operation instructions further includes:

[0024] Establish and maintain a device knowledge graph, which stores at least the typical power curves of the electrical equipment, adjustable operating modes, linkage relationships with other equipment, and users' historical adjustment preferences.

[0025] Once a specific electrical device is identified, the display control module queries the device knowledge graph. If it determines that the currently operating device has a delayed, interruptible, or adjustable power operating mode, the generated energy-saving suggestion text embeds device control instruction code that can be directly parsed and executed by the user's smart home central control system.

[0026] When displaying the suggested text, the screen provides a one-click confirmation control. After the user operates, the control command code is sent to the corresponding device through the local IoT communication module, realizing a closed-loop execution from suggested text to device control.

[0027] As a further improvement to the present invention, it also includes steps for evaluating display effects and optimizing strategies:

[0028] By using a camera or infrared sensor integrated into the display bezel, the system continuously collects data on the user's focus area and duration of attention when looking at the display screen.

[0029] The data processing module performs correlation analysis with the currently executed visual feature strategy and the displayed energy-saving suggestion text to evaluate the actual attention effect of each strategy and text item.

[0030] Based on the evaluation results, the learning model is used to dynamically optimize the strategy weights and text generation logic in the pre-set strategy and rule bases to improve the targeting of subsequent display strategies and the efficiency of user interaction.

[0031] As a further improvement of the present invention, in S1, the electricity price signal includes at least the current electricity price period identifier and the real-time electricity price value.

[0032] As a further improvement of the present invention, in S2, the visual feature strategy includes: adjusting the background color of the screen as a whole / partition, adjusting the flashing mode and color of the border or indicator light, adjusting the dynamic effect of the displayed content, and adjusting the screen brightness and contrast.

[0033] As a further improvement of the present invention, in S3, the energy-saving suggestion text is divided into basic suggestion text that directly adjusts electricity consumption behavior based on electricity prices, scenario-based suggestion text for specific electricity consumption scenarios, and quantitative suggestion text.

[0034] A smart meter dynamic display system based on electricity price signals, applicable to the aforementioned smart meter dynamic display method based on electricity price signals, comprising:

[0035] The electricity price signal receiving module is used to receive electricity price signals from the power grid dispatch center or electricity sales company in real time or periodically.

[0036] The data processing module is used to parse the electricity price signal and obtain the current electricity price status information;

[0037] The display control module is used to generate visual feature strategies and energy-saving suggestion texts, and to control the display screen to execute the visual feature strategies and energy-saving suggestion texts.

[0038] The monitoring and update module is used to continuously monitor significant changes in electricity price signals or local electricity load, and update visual features and energy-saving suggestion text.

[0039] The present invention has the following beneficial effects:

[0040] 1. In this invention, through dynamic and strong visual impact and direct text guidance, users can quickly and clearly understand the electricity price status and obtain specific operational suggestions, thereby effectively promoting their adjustment of electricity consumption behavior and improving the speed and effectiveness of demand-side response.

[0041] 2. In this invention, the interactive and service capabilities of smart meters are enhanced through the coordinated work of the electricity price signal receiving module, data processing module, display control module, and monitoring and updating module. This transforms smart meters from passive data display devices into proactive energy management assistants, thereby improving user experience and product added value. Attached Figure Description

[0042] Figure 1 This is a flowchart of the smart meter dynamic display method based on electricity price signals in this invention.

[0043] Figure 2 This is a logic block diagram of the display system in this invention.

[0044] Figure 3 This is an example of the display interface under different electricity price periods in this invention.

[0045] Figure 4 This is a flowchart of the visual feature strategy generation method in this invention.

[0046] Figure 5 This is a flowchart of the method for generating scenario-based suggestion text in this invention. Detailed Implementation

[0047] The technical solutions of the embodiments of this specification will be explained and described below with reference to the accompanying drawings. However, the following embodiments are only preferred embodiments of this specification and not all of them. Other embodiments obtained by those skilled in the art based on the embodiments in the implementation methods without creative effort are all within the protection scope of this specification.

[0048] The terms "first," "second," "third," etc., in the description, claims, and accompanying drawings are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0049] All data involved in this application are information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0050] Before introducing the technical solutions described in this manual, the application scenarios and related technologies of the technical solutions will be introduced.

[0051] Currently, most smart meters can only statically display the current electricity price period using numbers or simple icons, which has the following drawbacks:

[0052] 1. Low information perceptibility and weak guidance: Simple text or color changes are passive, and users may ignore them or fail to immediately understand their meaning, especially for users unfamiliar with electricity pricing policies. The displayed information is disconnected from specific energy-saving action suggestions; even if users know they are in a peak electricity price period, they may not know which appliances to turn off to effectively save on electricity bills.

[0053] 2. Poor interactivity and lack of scenario-based suggestions: Existing solutions provide "one-way broadcast" information and cannot offer personalized, scenario-based suggestions based on the user's real-time electricity load. For example, during peak pricing periods, if the electricity meter detects that the air conditioner is running at high power, current technology cannot proactively prompt the user to adjust the air conditioner temperature.

[0054] 3. The visual presentation is monotonous and the warning effect is insufficient: the fixed color switching may fail due to ambient light or user habits, and there is a lack of dynamic and multi-layered visual stimulation to strengthen the signal of electricity price changes, especially when the electricity price rises sharply (such as peak electricity price), which is not enough to attract users' attention.

[0055] Example 1

[0056] like Figure 1-5 As shown, this invention provides a method for dynamic display of smart meters based on electricity price signals, comprising the following steps:

[0057] S1. The electricity price signal receiving module receives the electricity price signal, and the data processing module parses the electricity price signal to obtain the current electricity price status information.

[0058] Electricity price signals originate from the power grid dispatch center or electricity sales companies; the electricity price signal receiving module includes a power line carrier communication module, a low-power wireless module, or a 5G / NB-IoT module; the electricity price signal at least includes the current electricity price time period identifier (e.g., peak, flat, valley, peak) and real-time electricity price value.

[0059] S2. The display control module calls or dynamically generates corresponding visual feature strategies from the preset strategy library based on the parsed current electricity price status information.

[0060] Visual feature strategies include at least one of the following:

[0061] Adjust the background color of the entire screen / section: for example, a smooth gradient from affordable white to peak-priced deep red.

[0062] Adjust the flashing mode and color of the border or indicator light. For example, during peak electricity prices, the red indicator light flashes at a rapid frequency.

[0063] Adjust the dynamic effects of the displayed content; for example, present electricity price values ​​as "bouncing" or "highlighting" animations.

[0064] Adjust screen brightness and contrast; for example, automatically increase screen brightness during periods of high electricity prices to enhance alerts.

[0065] The strategy for generating visual features specifically includes the following steps:

[0066] S21. Collect and integrate multi-source data, which includes at least current electricity price status information, historical electricity load curves, and user interaction response data to historical display suggestions.

[0067] The multi-source data includes electricity price and consumption data (current electricity price, historical electricity price curves, real-time / historical power, load curves); user interaction data (such as user response time to meter display suggestions, whether suggestions are followed, and preference settings made via meter buttons or associated apps); and optional environmental awareness data (such as ambient light intensity obtained through the meter's built-in light sensor, used to dynamically adjust display brightness). This data constitutes the original "user behavior dataset."

[0068] S22. Based on the multi-source data, a user profile is generated and dynamically updated using a machine learning model. The user profile includes the user's sensitivity to changes in electricity prices and quantitative features of response efficiency to specific visual features.

[0069] The profile should not only include tags such as "electricity price sensitive" and "strong energy-saving awareness," but also quantify behavioral characteristics such as "average response time to red flashing warnings is 3 seconds" and "70% probability of turning off the air conditioner when the electricity price exceeds a threshold X." The key is dynamic updating: the system should, like the USER-LLMR1 framework, adjust the profile in real time based on user feedback (such as actual changes in electricity consumption behavior) after each interaction, avoiding rigid adherence to outdated methods. This forms a closed loop from perception to feedback.

[0070] S23. Based on the current electricity price status information and the dynamic user profile, match or dynamically generate corresponding visual feature strategies from a preset visual strategy library.

[0071] The construction of the visual strategy library includes:

[0072] A. Adopting a structured visual description model: Drawing on the "eight-element description model" of the Guineration system, the visual presentation of the electricity meter display screen is decomposed into quantifiable and programmable dimensions.

[0073] For example, color: defines the core color space and smooth gradient algorithm corresponding to different electricity price levels (valley, flat, peak, and peak).

[0074] Animation: Defines the animation type (such as bouncing, pulsating, blinking), frequency, amplitude, and triggering conditions.

[0075] Structure: Define the layout templates and priority display rules for different information (electricity price values, suggested text, energy-saving charts).

[0076] Font: Defines the font, size, color, and appearance (scrolling, pop-up) of the suggested text.

[0077] Degree / Shape / Texture: Defines screen brightness, contrast, icon shape, material effects, etc.

[0078] B. Constructing Declarative Policy Rules: Associate the above eight elements with user profiles, current electricity price status information, and real-time load scenarios to form a series of "IF-THEN" declarative rules. These rules exist as structured data (such as JSON), rather than being hard-coded. For example: IF User Electricity Price Sensitivity > 0.8 AND Current Electricity Price = Peak AND Real-time Power > Threshold THEN Apply Visual Strategy {Color: Dark Red Gradient, Animation: Border Flashes Rapidly, Text: Pop-up "Please Pause Charging Immediately!"}

[0079] S24. When generating the visual feature strategy, short-term electricity price forecast data and load forecast data are also integrated; when it is predicted that the electricity price will jump in a specific period in the future and the current load is high, a forward-looking visual early warning strategy is generated and applied, and the visual feature intensity of the early warning strategy gradually increases as the predicted event approaches.

[0080] Visual strategies also have the capability to move beyond simply "reacting to the present" to "predicting and guiding the future": the system should not only receive current electricity prices but also integrate short-term (e.g., next hour) electricity price forecasting models. Combined with load forecasting, visual strategies can be forward-looking. For example, if a peak electricity price is predicted to occur in 30 minutes and the current load is high, a gentle visual cue can be activated in advance (e.g., the screen border slowly turns orange) along with a predictive suggestion: "Electricity prices are expected to rise sharply in 30 minutes; it is recommended to start reducing electricity consumption in advance."

[0081] Furthermore, in complex environments (such as when strong light shines on the meter screen), a single visual cue may fail. In such cases, the system should be able to dynamically switch or enhance the cue modality based on environmental perception data (light sensor). For example, under strong midday sunlight, the screen brightness could be automatically adjusted to the maximum and supplemented with strong color contrast; when it is detected that the user may not be paying attention to the screen (e.g., due to prolonged periods without load changes), a soft audio cue can be triggered (if the hardware supports it). This cross-modal (visual and auditory) collaboration borrows the idea of ​​adaptively allocating weights based on the environment from multimodal fusion algorithms, ensuring that guidance information is reliably conveyed under various conditions.

[0082] S3. The display control module combines the current electricity price status information with the real-time total active power data obtained from the electricity metering module to generate energy-saving suggestion text for specific loads.

[0083] The energy conservation recommendations are divided into three categories: basic recommendations that directly adjust electricity consumption behavior based on electricity prices, scenario-based recommendations for specific electricity consumption scenarios, and quantitative recommendations.

[0084] Basic suggestion text: Directly associate with electricity price periods, such as "Currently it is peak electricity price, please reduce electricity consumption".

[0085] Contextualized suggestion text: If the real-time power exceeds the preset threshold, more targeted suggestions will be generated, such as "High-power equipment is detected, it is recommended to use it during off-peak hours" or "The current air conditioning load is high, it is recommended to raise the temperature by 1℃".

[0086] Quantitative recommendation text: The amount of savings can be estimated by combining electricity price and power consumption, such as "If the current standby device is turned off, it is estimated that X yuan can be saved per month".

[0087] Generating contextualized suggestion text specifically includes the following steps:

[0088] S31. Based on non-intrusive load monitoring technology, the total electrical load is decomposed in real time, and the type and operating status of at least one specific electrical device currently in operation are identified.

[0089] Based on non-intrusive load monitoring technology, the real-time breakdown of total electrical load specifically includes:

[0090] S311. Monitor changes in total power data. When the change exceeds a preset threshold, determine that a load switching event has occurred.

[0091] S312. Extract the load features corresponding to the switching event. The features include at least active power features, reactive power features, and equipment feature vectors extracted from the power change curve from equipment startup to stable operation.

[0092] S313. Based on the extracted load characteristics, a preliminary identification is first performed using a preset decision tree model. If uncertain results are generated, the VI trajectory feature map of the load is further constructed and analyzed to determine the type and operating status of the specific electrical equipment.

[0093] In the above process, a computationally efficient decision tree model is first used for rapid screening, and only for difficult cases is a more complex, accurate, but computationally intensive secondary identification (VI trajectory analysis) initiated. This dynamic and adaptive identification process improves accuracy in complex scenarios while ensuring real-time performance.

[0094] S32. Obtain short-term electricity price forecast data, wherein the forecast data includes at least the electricity price forecast value and trend for a future preset period.

[0095] Methods for obtaining short-term electricity price forecast data include: building or connecting a short-term electricity price forecast model, which uses variational mode decomposition (VMD) algorithm to process the original electricity price sequence and constructs a CNN-BiLSTM-Attention hybrid neural network for prediction, and uses optimization algorithms (such as the dung beetle algorithm) to globally optimize the model hyperparameters; or using a forecast model that combines kernel principal component analysis (KPCA), convolutional neural network (CNN) and bidirectional long short-term memory network (BiLSTM).

[0096] S33. Combining the current electricity price status information, the identified specific electrical equipment information, the short-term electricity price forecast data, and the preset user profile, a personalized energy-saving suggestion text is generated through a pre-set expert rule base and machine learning model. This text includes specific equipment operation instructions, quantified savings amount, and forward-looking suggestions.

[0097] The generation of personalized energy-saving suggestion text containing specific device operation instructions further includes:

[0098] S331. Establish and maintain a device knowledge graph, wherein the knowledge graph stores at least the typical power curves of the electrical equipment, the adjustable operating modes, the linkage relationships with other equipment, and the user's historical adjustment preferences.

[0099] S332. After identifying a specific electrical device, the display control module queries the device knowledge graph. If it is determined that the currently operating device has an operating mode that can be delayed, interrupted, or has adjustable power, the generated energy-saving suggestion text embeds device control instruction codes that can be directly parsed and executed by the user's smart home central control system.

[0100] S333. When the display screen shows the suggested text, it provides a one-click confirmation control. After the user operates, the control command code is sent to the corresponding device through the local IoT communication module, realizing the closed-loop execution from suggested text to device control.

[0101] Through the methods described above, the system goes beyond simply generating text-based "suggestions." It provides decision support via knowledge graphs and ultimately generates executable control commands, achieving a leap from "information prompts" to "direct intervention." This allows energy-saving suggestions to be seamlessly integrated into the smart home ecosystem, forming a complete automated chain of "perception-analysis-decision-control."

[0102] The generated personalized energy-saving suggestion text includes at least one of the following types:

[0103] (1) Real-time actionable suggestions: Based on the currently identified high-energy-consuming devices and the current high electricity price, generate instructions such as "Air conditioner is detected to be cooling, it is recommended to increase the set temperature from 24℃ to 26℃";

[0104] (2) Forward-looking planning recommendations: Based on short-term electricity price forecast data, generate planning recommendations such as "It is predicted that the electricity price will continue to rise in the next hour, so it is recommended to delay the start of the washing program until 21:00";

[0105] (3) Quantitative incentive recommendations: Combine equipment power, running time and electricity price difference to estimate and display the expected amount of electricity cost savings after implementing the recommendations, such as "Immediately turn off standby equipment, and it is estimated that 15 yuan can be saved per month".

[0106] S4. The display control module controls the display screen, synchronously executing the visual feature strategy determined in S2 and the energy-saving suggestion text generated in S3. Specifically, this includes:

[0107] According to the visual feature strategy determined in step S2, change the visual appearance of the display screen (such as switching the background color or starting the border flashing).

[0108] The energy-saving suggestion text generated in step S3 is displayed on the screen along with traditional electricity consumption and price information in the form of overlay, carousel, or pop-up window.

[0109] Ensure that dynamic visual features and suggested text are semantically consistent and coordinated to reinforce the guidance message. For example, while displaying a flashing red warning for "peak electricity prices," the text "Please immediately suspend the use of charging stations" can also be displayed.

[0110] S5. Continuously monitor the electricity price signal or local electricity load. When the electricity price period changes or the real-time active power changes and the visual feature strategy and energy-saving suggestion text need to be adjusted, return to step S2, update the visual feature strategy and energy-saving suggestion text, and realize the dynamic adjustment of the display.

[0111] First, a three-pronged linkage mechanism of "signal-visual-semantic" was adopted: the abstract electricity price signal was made concrete and personalized through two methods: "dynamic visual features" (such as color and animation) and "scenario-based text suggestions", forming a multimodal and strongly guided human-computer interaction interface.

[0112] Secondly, it adopts scenario-based suggestion generation based on power load: it innovatively introduces real-time power consumption data into the display decision logic, upgrading the generated energy-saving suggestions from "universal reminders" to "personalized guidance", which greatly improves the relevance and effectiveness of the suggestions.

[0113] Finally, a dynamic adaptive display strategy library was adopted: the pre-built strategy library can match different visual warning intensities and text suggestion content for different electricity price levels (flat, valley, peak, and peak) and electricity consumption scenarios, so as to achieve hierarchical and precise guidance.

[0114] Furthermore, the generation steps of the visual feature strategy were optimized:

[0115] Example 2

[0116] This invention also discloses a smart meter dynamic display system based on electricity price signals. This system is applicable to the aforementioned smart meter dynamic display method based on electricity price signals, and includes:

[0117] The electricity price signal receiving module is used to receive electricity price signals from the power grid dispatch center or electricity sales company in real time or periodically.

[0118] The data processing module is used to parse the electricity price signal and obtain the current electricity price status information;

[0119] The display control module is used to generate visual feature strategies and energy-saving suggestion texts, and to control the display screen to execute the visual feature strategies and energy-saving suggestion texts.

[0120] The monitoring and update module is used to continuously monitor significant changes in electricity price signals or local electricity load, and update visual features and energy-saving suggestion text.

[0121] The embodiments described above are merely preferred embodiments of this specification and are not intended to limit the scope of this specification. Any modifications and improvements made by those skilled in the art to the technical solutions of this specification without departing from the spirit of this specification should fall within the protection scope defined by the claims of this specification.

Claims

1. A method for dynamic display of smart meters based on electricity price signals, characterized in that, Includes the following steps: S1. The electricity price signal receiving module receives the electricity price signal, and the data processing module parses the electricity price signal to obtain the current electricity price status information; S2. The display control module calls or dynamically generates corresponding visual feature strategies from the preset strategy library based on the parsed current electricity price status information. S3. The display control module combines the current electricity price status information with the real-time total active power data obtained from the electricity metering module to generate energy-saving suggestion text for specific loads. S4. The display control module controls the display screen to synchronously execute the visual feature strategy determined in S2 and the energy-saving suggestion text generated in S3. S5. Continuously monitor the electricity price signal or local electricity load. When the electricity price period changes or the real-time active power changes and the visual feature strategy and energy-saving suggestion text need to be adjusted, return to step S2, update the visual feature strategy and energy-saving suggestion text, and realize the dynamic adjustment of the display.

2. The method for dynamic display of smart meters based on electricity price signals according to claim 1, characterized in that, In S2, the strategy for generating visual features specifically includes the following steps: Collect and integrate multi-source data, which includes at least current electricity price status information, historical electricity load curves, and user interaction response data to historical display suggestions; Based on the multi-source data, a machine learning model is used to generate and dynamically update user profiles, which include the user's sensitivity to changes in electricity prices and quantitative features of response efficiency to specific visual features. Based on the current electricity price status information and the dynamic user profile, corresponding visual feature strategies are matched or dynamically generated from a pre-set visual strategy library.

3. The method for dynamic display of smart meters based on electricity price signals according to claim 1, characterized in that, Generating contextualized suggestion text specifically includes the following steps: Based on non-intrusive load monitoring technology, the total electrical load is decomposed in real time, and the type and operating status of at least one specific electrical device currently in operation are identified. Obtain short-term electricity price forecast data, wherein the forecast data includes at least the predicted electricity price value and its trend for a future preset period; By combining the current electricity price status information, the identified specific electrical equipment information, the short-term electricity price forecast data, and the preset user profile, a personalized energy-saving suggestion text is generated through a pre-set rule base and machine learning model. This text includes specific equipment operation instructions, quantified savings amount, and forward-looking suggestions.

4. The method for dynamic display of smart meters based on electricity price signals according to claim 3, characterized in that, Based on non-intrusive load monitoring technology, the real-time breakdown of total electrical load specifically includes: Monitor changes in total power data, and determine that a load switching event has occurred when the change exceeds a preset threshold; Extract the load characteristics corresponding to the switching event. The characteristics include at least active power characteristics, reactive power characteristics, and equipment feature vectors extracted from the power change curve from equipment startup to stable operation. Based on the extracted load characteristics, a preliminary identification is first performed using a pre-set decision tree model. If uncertain results are generated, the VI trajectory feature map of the load is further constructed and analyzed to determine the type and operating status of the specific electrical equipment.

5. The method for dynamic display of smart meters based on electricity price signals according to claim 3, characterized in that, The generation of personalized energy-saving suggestion text containing specific device operation instructions further includes: Establish and maintain a device knowledge graph, which stores at least the typical power curves of the electrical equipment, adjustable operating modes, linkage relationships with other equipment, and users' historical adjustment preferences. Once a specific electrical device is identified, the display control module queries the device knowledge graph. If it determines that the currently operating device has a delayed, interruptible, or adjustable power operating mode, the generated energy-saving suggestion text embeds device control instruction code that can be directly parsed and executed by the user's smart home central control system. When displaying the suggested text, the screen provides a one-click confirmation control. After the user operates, the control command code is sent to the corresponding device through the local IoT communication module, realizing a closed-loop execution from suggested text to device control.

6. The method for dynamic display of smart meters based on electricity price signals according to any one of claims 1-5, characterized in that, It also includes steps for evaluating display effects and optimizing strategies: By using a camera or infrared sensor integrated into the display bezel, the system continuously collects data on the user's focus area and duration of attention when looking at the display screen. The data processing module performs correlation analysis with the currently executed visual feature strategy and the displayed energy-saving suggestion text to evaluate the actual attention effect of each strategy and text item. Based on the evaluation results, the learning model is used to dynamically optimize the strategy weights and text generation logic in the pre-set strategy and rule bases to improve the targeting of subsequent display strategies and the efficiency of user interaction.

7. The method for dynamic display of smart meters based on electricity price signals according to claim 1, characterized in that, In S1, the electricity price signal includes at least the current electricity price period identifier and the real-time electricity price value.

8. The method for dynamic display of smart meters based on electricity price signals according to claim 1, characterized in that, In S2, visual feature strategies include: adjusting the background color of the entire screen / partitions, adjusting the flashing mode and color of borders or indicator lights, adjusting the dynamic effects of the displayed content, and adjusting screen brightness and contrast.

9. The method for dynamic display of smart meters based on electricity price signals according to claim 1, characterized in that, In S3, energy-saving recommendation texts are divided into basic recommendation texts that directly adjust electricity consumption behavior based on electricity prices, scenario-based recommendation texts that target specific electricity consumption scenarios, and quantitative recommendation texts.

10. A smart meter dynamic display system based on electricity price signals, the system being applicable to the smart meter dynamic display method based on electricity price signals as described in any one of claims 1-9, characterized in that, include: The electricity price signal receiving module is used to receive electricity price signals from the power grid dispatch center or electricity sales company in real time or periodically. The data processing module is used to parse the electricity price signal and obtain the current electricity price status information; The display control module is used to generate visual feature strategies and energy-saving suggestion texts, and to control the display screen to execute the visual feature strategies and energy-saving suggestion texts. The monitoring and update module is used to continuously monitor significant changes in electricity price signals or local electricity load, and update visual features and energy-saving suggestion text.

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