Data fusion-based instant hot boiled water dispenser state monitoring method and system

Abstract

The present application belongs to the technical field of water dispenser state monitoring, and particularly relates to a method and system for monitoring the state of an instant heating type hot boiled water dispenser based on data fusion, which first acquires the equipment mode, key operating parameters and fault codes uploaded by the water dispenser, and generates a skeleton screen placeholder set matched with the real components; a benchmark model defining a nonlinear evolution law is constructed according to the equipment working condition, the visual characteristics of the placeholder are continuously evolved, and the virtual predicted power value is synchronously output as a calibration benchmark; the model driving step is dynamically adjusted using real-time data flow, so as to ensure that the visual performance is synchronized with the equipment state; a hierarchical display weight sequence is established, and the placeholders corresponding to the core operating parameters are preferentially replaced; when the equipment is abnormal, a sinusoidal disturbance signal is injected to generate visual artifacts, and early fault prompt is realized. The present application effectively improves the accuracy of state monitoring and the visual coherence of human-computer interaction, and is suitable for the remote state monitoring scene of intelligent instant heating type water dispensers.

Description

Technical Field

[0001] This invention relates to the field of water dispenser status monitoring technology. More specifically, this invention relates to a method and system for status monitoring of instant hot water dispensers based on data fusion. Background Technology

[0002] Instant hot water dispensers, with their features of immediate hot water, precise temperature control, energy efficiency, and environmental friendliness, have gradually become essential drinking water devices in modern homes and offices. In intelligent application scenarios such as cloud monitoring and remote control, users often view the water dispenser's operating status in real time via mobile terminals or control panels, including heating mode, inlet and outlet water temperatures, real-time flow rate, and fault alarms. However, due to limitations such as network communication latency, device sensor response time, and data reporting cycles, the terminal user interface often faces unavoidable loading delays when acquiring and rendering real-time data.

[0003] Currently, most water dispenser monitoring applications use only traditional spinning animations or maintain a blank page during data loading, resulting in a poor user experience. Existing technologies already offer solutions to the page loading wait problem. For example, Chinese patent document CN116861110B discloses a page display method, apparatus, device, storage medium, and program. This patent, when loading a landing page, first acquires skeleton screen protocol data, parses this protocol data, and displays the skeleton screen page on the user interface. After the landing page rendering data is loaded, the skeleton screen page is replaced with the actual landing page content. This skeleton screen technology, by pre-displaying the basic layout outline of the page during data loading, can alleviate the visual blankness experienced by users while waiting to some extent.

[0004] However, the existing skeleton screen technology and its application in the field of water dispenser status monitoring still have the following shortcomings: First, existing skeleton screens are merely static placeholders and lack the ability to dynamically evolve with the device's operating status. While waiting for loading, users cannot obtain any perception of the device's operating trend through the interface's visual elements. Second, there is a lack of an effective status synchronization calibration mechanism. Even if some solutions attempt to add simple dynamic effects, they cannot correct the visual presentation based on real-time device operating data, leading to a gradual deviation between the interface display and the actual device status, resulting in insufficient accuracy in status monitoring. Third, when various monitoring data arrive at the terminal concurrently, existing display technologies lack the ability to differentiate between core operating parameters and secondary status data, and cannot perform orderly and smooth interface replacement based on the actual importance and arrival sequence of the data. Users may first see updates to secondary information, while key parameters directly related to the user experience, such as inlet and outlet water temperatures, appear later due to data delays. This misalignment in display order reduces the information transmission efficiency of the monitoring interface.

[0005] In summary, there is a clear disconnect between the existing interface visual presentation and the actual status of the device. This not only disrupts the user's visual continuity during the monitoring process but also results in a lack of synchronization in the overall status feedback, making it difficult to meet the requirements of modern smart instant hot water dispensers for high-quality human-computer interaction and accurate status monitoring. Summary of the Invention

[0006] To address the issues of disconnect between visual feedback and actual physical conditions in existing water dispenser monitoring interfaces, lack of real-time calibration of state evolution, and delays in core information due to disordered concurrent data rendering, this invention provides a data fusion-based method and system for monitoring the status of instant hot water dispensers.

[0007] In a first aspect, the present invention provides a data fusion-based method for monitoring the status of an instant hot water dispenser, comprising: S1, acquiring initial status data uploaded by the dispenser, including device mode and key operating parameters, and generating a skeleton screen placeholder set corresponding to the actual data display components in the user interface; S2, determining the predicted status duration based on the device mode and the key operating parameters, and generating a benchmark model for driving the evolution of the visual characteristics of the placeholders in the skeleton screen placeholder set, wherein the benchmark model is configured to synchronously output virtual predicted power values; S3, within the predicted status duration, using the benchmark model to drive the visual characteristics of the placeholders to continuously evolve, and using the received real-time status data stream to continuously calibrate the evolution status of the benchmark model to ensure that the visual performance is synchronized with the actual status of the device; S4, during or after the status evolution, based on the arrival of real-time data, prioritizing the replacement of placeholders corresponding to preset core operating parameters in the key operating parameters with actual data display components, and replacing the remaining placeholders after receiving the corresponding data.

[0008] This invention first generates a set of skeleton screen placeholders based on initial state data, solving the problem of blank interfaces during data loading. Then, based on the device mode and key parameters, it predicts the duration of the state and constructs a benchmark model defining the nonlinear evolution law of the device's operating conditions. This model simultaneously outputs virtual predicted power values ​​to provide a benchmark for subsequent real-time calibration. Within the predicted duration, the benchmark model drives the continuous evolution of the placeholder visual characteristics, displaying the device's operating trend before the actual data fully arrives. Simultaneously, it uses real-time data streams to calibrate the model, ensuring that the visual performance remains synchronized with the actual device state. Finally, based on the data arrival sequence, placeholders corresponding to core parameters are replaced first, with other parameters replaced sequentially. This method extends the skeleton screen from static placeholders to an intelligent evolution mechanism deeply associated with the device's dynamic characteristics, improving the accuracy of state monitoring and the visual coherence of human-computer interaction.

[0009] Preferably, the method further includes an abnormal state monitoring step: when acquiring the initial state data, it also includes acquiring the device fault code uploaded by the water dispenser; when the device fault code indicates that the device is abnormal, a preset sinusoidal disturbance signal is injected into the rendering parameters of the placeholder so that the placeholder produces a periodic displacement jump visual artifact to represent the fault state.

[0010] By synchronously collecting equipment fault codes during the initial data acquisition phase and injecting corresponding disturbance signals during the baseline model generation phase, fault information is transformed into intuitive visual artifacts. This allows for early warning of faults without waiting for all data to be loaded, enabling users to perceive equipment abnormalities at the first moment, avoiding usage risks caused by delayed fault warnings, and improving the safety of equipment operation.

[0011] Preferably, the step of acquiring the initial state data uploaded by the water dispenser, including the device mode and key operating parameters, includes: constructing and configuring a background monitoring daemon service task to intercept and record the data packet queue of a specified IP node according to a 50ms communication detection cycle; extracting the first two bytes of the data packet start frame according to a preset protocol as the basic parameter value of the device mode; and sequentially extracting the real-time power value, real-time flow value, and real-time measured temperature value of the inlet and outlet water temperature sensors after specific markings in the data packet as the key operating parameters.

[0012] Preferably, the step of generating a skeleton screen placeholder set corresponding to the actual data display component in the user interface includes: parsing the interface mapping ratio in the front-end rendering engine, determining the horizontal and vertical coordinate limits and array size parameters of the actual data display component in the display panel; using the horizontal and vertical coordinate limits and array size parameters, rendering a rectangular placeholder block with a single background color in the corresponding area of ​​the user interface; configuring the rectangular placeholder block as a solid color texture mode without additional graphics, text, and border lines, and setting the color code to a preset light gray feature color, forming a skeleton screen placeholder set that replaces the actual component.

[0013] Preferably, the predicted state duration is determined based on the device mode and the key operating parameters, and a benchmark model is generated to drive the evolution of the visual characteristics of the placeholders in the skeleton screen placeholder set. This includes: inputting the category features of the device mode, the temperature difference between the outlet and inlet water temperatures calculated based on the real-time measured temperature values ​​of the inlet and outlet water temperature sensors, and the real-time flow rate value as input features into a preset regression model, and outputting the predicted state duration; constructing the benchmark model using a cubic Bézier curve function, and outputting a nonlinear easing coefficient value based on a preset time progress variable to control the continuous evolution of the placeholder visual characteristics.

[0014] By integrating multi-dimensional features such as equipment mode, temperature difference, and flow rate, the system accurately predicts the duration of the state using a regression model. By constructing a benchmark model using cubic Bézier curves, the system can accurately reproduce the nonlinear change law of water temperature during the heating process, making the visual evolution process of the placeholder highly consistent with the actual operating state of the equipment, allowing users to intuitively perceive the operating trend of the equipment during the loading phase.

[0015] Preferably, the step of generating the baseline model further includes a step of mapping the initial values ​​of the key operating parameters to the initial visual characteristics of the placeholder, specifically including: extracting the initial constant value of the inlet water temperature from the real-time measured temperature values ​​as the numerator parameter; accessing the local control table to obtain the preset maximum threshold value of the ultimate anti-dry-burning safe temperature as the denominator parameter; dividing the numerator parameter by the denominator parameter to calculate a floating-point decimal between a preset lower limit of transparency and 1 as the feature ratio parameter; and directly passing the feature ratio parameter to the underlying display rendering system and assigning it to the placeholder. The channel transparency parameter is used to adjust the visual depth of the placeholder's appearance color, completing the initial visual characteristic mapping settings.

[0016] Preferably, injecting a preset sinusoidal perturbation signal into the rendering parameters of the placeholder includes: extracting the error identification byte group at the end of the data packet uploaded by the water dispenser as the device fault code; when it is determined that the received device fault code is not equal to the preset no-fault identifier constant, identifying the specific fault code value from the device fault code; according to the identified fault code value, querying the preset fault code and amplitude mapping table to obtain the corresponding amplitude offset constant; using the amplitude offset constant as an input parameter, passing it to the preset sine generator function to generate a sinusoidal perturbation signal with a fixed period as the sinusoidal perturbation signal; superimposing the sinusoidal perturbation signal onto the horizontal or vertical display coordinates of the placeholder to make the placeholder produce a periodic displacement jump visual effect, thereby serving as an artifact representing an abnormal error.

[0017] By establishing a mapping relationship between fault codes and amplitudes, sinusoidal disturbance signals of varying intensities are generated and superimposed on placeholder coordinates to produce displacement jumps. Different faults correspond to different jitter amplitudes, allowing for a direct differentiation of fault severity. This non-textual visual warning method is highly recognizable, eliminating the need for users to interpret complex codes and significantly improving the efficiency of fault information identification.

[0018] Preferably, the step of continuously calibrating the evolution state of the reference model using the received real-time status data stream includes: calculating the difference between the real-time power value of the heating component and the virtual predicted power value output by the reference model at the same time; obtaining the driving step size coefficient currently used by the reference model to control the evolution speed, and determining the relationship between the difference and a preset fixed limit error threshold of the system; when the difference exceeds the limit, multiplying the driving step size coefficient by the corresponding preset acceleration coefficient or preset deceleration coefficient according to the positive or negative attribute of the difference to obtain a new step size coefficient; replacing the original driving step size with the new step size coefficient to adjust the evolution speed of the reference model and resynchronize it with the actual state of the device.

[0019] By comparing the difference between the real power and the virtual predicted power in real time, the driving step size of the benchmark model is dynamically adjusted using a closed-loop control method. When the model evolves too fast, the step size is reduced, and when it evolves too slowly, the step size is increased. This effectively eliminates the cumulative deviation caused by network fluctuations and sensor sampling errors, ensuring that the visual performance of the interface is always synchronized with the actual operating status of the device.

[0020] Preferably, the step of preferentially replacing the placeholders corresponding to the preset core operating parameters in the key operating parameters with actual data display components, and replacing the remaining placeholders after receiving the corresponding data, includes: establishing a display weight sequence library, and configuring the inlet and outlet water temperatures, the real-time flow rate, and the real-time power value of the heating component in the real-time measured temperature values ​​of the inlet and outlet water temperature sensors as the highest priority core operating parameters in the display weight sequence library, while treating the remaining parameters not set as core operating parameters as secondary parameters; when data corresponding to the core operating parameters is detected to be stored in the data packet queue, the skeleton screen placeholders in the corresponding area are erased first and the actual data display components are rendered; for the secondary parameters, when their corresponding data is stored in the data packet queue, the corresponding placeholders are replaced with actual data display components in sequence according to the default order in the display weight sequence library.

[0021] Secondly, the present invention provides a status monitoring system for an instant hot water dispenser based on data fusion, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the aforementioned method for monitoring the status of an instant hot water dispenser based on data fusion is implemented.

[0022] By adopting the above technical solution, the above-mentioned data fusion-based instant hot water dispenser status monitoring method is generated into a computer program and stored in a memory, so that it can be loaded and executed by the processor. In this way, a terminal device can be made based on the memory and the processor for convenient use.

[0023] The technical solution of the present invention has the following beneficial technical effects:

[0024] This invention generates placeholder skeleton screens that match the actual component positions and dimensions by acquiring initial state data of a water dispenser. A benchmark model defining the nonlinear evolution of equipment operating conditions is constructed to drive the continuous evolution of the placeholder visual characteristics. Real-time power difference closed-loop calibration of the model's evolution speed is used, and a hierarchical display weight sequence library is established to prioritize the replacement of core operating parameters. Furthermore, visual artifacts generated by injecting sinusoidal perturbation signals provide early fault indication. This solution deeply integrates visual feedback during the loading phase with the actual operation of the equipment, ensuring synchronization between the interface display and equipment operating conditions. It optimizes information display logic and effectively solves the problems of static skeleton screens lacking dynamic evolution, visual disconnect from operating conditions, disordered display of core information, and delayed fault indications in existing technologies. This improves the accuracy of status monitoring and the visual coherence of human-computer interaction. Attached Figure Description

[0025] Figure 1 A flowchart of a data fusion-based method for monitoring the status of instant hot water dispensers; Figure 2 This is a diagram illustrating the transparency mapping relationship; Figure 3 This is a schematic diagram of the step size calibration curve. Detailed Implementation

[0026] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0027] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. To facilitate understanding of the technical solution of the present invention, the key terms used herein are first uniformly defined: visual characteristics refer to the perceptible visual attributes of the skeleton screen placeholder presented on the user interface, including but not limited to the transparency, background color, size, and display position of the placeholder; real-time status data stream refers to the dynamic operating data set that the water dispenser equipment periodically uploads to the terminal during operation, which at least includes the real-time power value of the heating element, and may also include other operating parameters such as real-time flow rate and inlet / outlet water temperature.

[0028] This invention discloses a method for monitoring the status of an instant hot water dispenser based on data fusion, referring to... Figure 1 This includes the following steps: S1. Obtain initial state data to generate a skeleton screen placeholder set.

[0029] The purpose of this step is to establish a data channel between the water dispenser and the backend, and to pre-render a visual placeholder structure on the user interface that corresponds to the position and size of the actual data display components before the actual data arrives. The aim is to eliminate the visual blank during the data loading stage and provide a carrier for subsequent visual evolution.

[0030] In the specific implementation of data acquisition, this embodiment establishes a bidirectional communication link between the water dispenser device and the backend server through a message queue telemetry transmission protocol. The device acquires analog signals from the temperature sensor and flow meter through the universal asynchronous transceiver interface of the embedded controller. After being converted into digital quantities by an analog-to-digital converter, the device mode field, key operating parameter field, and device fault code field (represented by an eight-bit unsigned integer) are encapsulated into a network transmission data packet using a TLV (Type-Length-Value) message payload packaging algorithm. Each field contains a 1-byte type identifier, a 1-byte length identifier, and a corresponding length of numerical content. A 2-byte CRC checksum is added to the end of the data packet to ensure data integrity.

[0031] In one implementation, the process of acquiring initial state data further includes: building and configuring a listening daemon service task in the operating system background, unaffected by the user interface session lifecycle; binding a TCP / IP network socket to a designated IP node on the water dispenser's main control board; and using a non-blocking I / O model combined with a timer mechanism to intercept and record data packets from the network card buffer queue at a 50ms communication detection cycle. Setting a 50ms cycle helps to ensure data timeliness while avoiding excessive polling overhead. Subsequently, the data parsing engine extracts the first two bytes from the start frame of the byte array according to a preset protocol as the basic parameter values ​​for the device mode. For example, 0x00 and 0x01 are extracted to represent the instant heating mode, and 0x00 and 0x02 are extracted to represent the heat preservation mode. Then, the data message is scanned sequentially. After locating a specific two-byte hexadecimal start marker, the engine sequentially extracts the two adjacent bytes as the real-time power value of the heating element, then extracts two more bytes as the real-time flow value, then extracts four consecutive bytes as the real-time measured temperature values ​​of the inlet and outlet water temperature sensors, and extracts the remaining filter life value and TDS water quality index from the reserved bytes in the subsequent message, which together form a complete set of operating parameters. For example, the power bytes 0x08 and 0x34 can be converted to decimal 2100W, and the outlet water temperature bytes 0x00 and 0x55 can be converted to 85℃.

[0032] When acquiring initial status data, the error identification byte group reserved at the end of the data packet is also extracted as a device fault code, and it is encapsulated as a hexadecimal fault code object and pushed into the abnormal status pool based on the memory key-value database or memory queue architecture for use in the abnormal status monitoring steps.

[0033] In generating the skeleton screen placeholder set, this embodiment first parses the interface mapping ratio in the front-end rendering engine. For example, by obtaining the device pixel parameters of the display panel, the mapping ratio between logical pixels and physical pixels is calculated, for example, the mapping ratio coefficient is 2. Then, the logical bounding box parameters of the actual data display component are extracted by accessing the interface layout configuration file, thereby determining the horizontal and vertical coordinate limits and array size parameters of the actual data display component in the display panel. For example, the X-axis coordinate range is defined as 150px to 350px, the Y-axis coordinate range is defined as 400px to 480px, the component width is 200px, and the height is 80px. Subsequently, using the aforementioned coordinate range and size parameters, the underlying graphics rendering interface is invoked to render a rectangular placeholder area with a single background color in the corresponding area of ​​the user interface. During parameter configuration, the text renderer engine and border stroke attributes are disabled, the brush attribute is set to solid fill mode, and a preset light gray characteristic color is assigned, such as the hexadecimal color code #E0E0E0, equivalent to the RGBA value 224,224,224,1. This forms a set of skeleton screen placeholders with a solid color background pattern, without additional graphics, text, or border lines. Each placeholder is implemented using scalable vector graphics technology to adapt to different screen resolutions.

[0034] S2. Generate an evolutionary benchmark model based on key operating parameters.

[0035] This step calculates the expected duration of the current state based on the equipment mode and key operating parameters, and constructs a benchmark model that can drive the evolution of placeholder visual characteristics with nonlinear laws and can synchronously output virtual predicted power values. The purpose is to make the visual performance of the loading stage substantially related to the actual working process of the instant hot water dispenser.

[0036] When determining the duration of the predicted state, this embodiment processes the category features of the device mode using a one-heat encoding algorithm, and combines them with the temperature difference between the inlet and outlet water temperatures calculated from real-time temperature measurements by inlet and outlet water temperature sensors, as well as the real-time flow rate, as input feature vectors. These vectors are then fed into a regression model trained on a historical operating database. After traversing the branch conditions of tree nodes and integrating and summing the weights of leaf nodes, the output represents the predicted state duration in milliseconds required for heating. The regression model can employ a gradient boosting decision tree regression model.

[0037] In constructing the baseline model, this embodiment uses a cubic Bézier curve function as the basic mathematical expression. The time progress variable is input into the Bézier formula as the independent variable, yielding a nonlinear easing coefficient value between 0 and 1. This coefficient accurately reproduces the nonlinear upward curve of water temperature changing with heating power, thereby controlling the continuous evolution of the placeholder's visual characteristics within a state cycle. The normalized setting of the time progress variable allows the evolution process under different prediction durations to be uniformly mapped to the same value range, facilitating the reuse and calibration of the evolution logic. While the baseline model progresses according to its inherent evolution law, it can simultaneously output virtual predicted power values ​​at any point in time, providing a reference for power difference calibration in subsequent steps.

[0038] When generating the baseline model, this embodiment also maps the initial values ​​of key operating parameters to the initial visual characteristics of placeholders. This ensures that the placeholders carry visual information reflecting the true state of the equipment from the very beginning of the evolution process. Specifically, the initial constant value of the inlet water temperature is extracted from the real-time measured temperature values ​​as the numerator parameter. The preset maximum threshold value for anti-dry-burning safety temperature is obtained from the local control table as the denominator parameter. A floating-point decimal value between the preset lower limit of transparency and 1 is calculated as the feature ratio parameter. This mapping relationship can be expressed as: ; in, This is the initial constant value of the inlet water temperature; This is the preset maximum threshold temperature for preventing dry burning; The lower limit of transparency to prevent the skeleton screen placeholder from becoming completely transparent is set to 0.15 in this embodiment; The feature ratio parameter obtained from the mapping is used here. The ratio of the inlet water temperature to the safe temperature threshold is used for dimensionless processing, and a lower limit value is used to constrain the calculation. This can effectively prevent the calculated transparency from being close to zero under extremely low inlet water temperature conditions, and ensure the basic visual visibility of the skeleton screen placeholder under any initialization conditions.

[0039] Subsequently, this feature scaling parameter is directly passed to the underlying display rendering system and assigned to the placeholder. Channel transparency parameter. For example, when the inlet water temperature is 35°C and the limit threshold is configured to 105°C, a feature ratio parameter of approximately 0.333 is calculated. (Placeholder for placeholder) The parameter was changed from the default 1 to 0.333, giving the gray placeholder approximately 33.3% opacity. For example... Figure 2 As shown, the initial inlet water temperature and placeholder Transparency exhibits a direct linear relationship; the higher the influent temperature, the larger the corresponding area. The larger the parameter, the darker the grayscale display, thus completing the initial level mapping from the core temperature parameter to the visual depth of the screen.

[0040] Meanwhile, the abnormal status monitoring module deployed in the background performs bitwise comparison logic on the hexadecimal fault codes extracted from the abnormal status pool on a cycle. When it is determined that the current device fault code is not equal to the preset fault-free identifier constant, the specific fault code value is extracted through a mask. For example, parsing error code 0xE2 represents a heating tube open circuit fault. Using this fault code value as the hash index key, a matching query is performed in the pre-loaded fault code and amplitude mapping table to obtain the corresponding amplitude offset constant. For example, the amplitude offset constant corresponding to a severe fault is... Each screen pixel. Then, the amplitude offset constant is passed as an input parameter to a preset sine generator function to generate a sinusoidal perturbation signal with a fixed period. Its calculation formula can be expressed as: ; in, for The real-time displacement offset output at all times is used to create visual artifacts; The amplitude offset constant is obtained from the table and is used to control the maximum displacement deviation of the placeholder on the coordinate axis. The vibration frequency parameter is preset to a constant of 5Hz, which means 5 complete cycles per second. This represents the accumulated time in milliseconds since the abnormality was triggered. This formula describes the periodic displacement using a sine function, which can generate regular visual jitter without relying on text prompts, making the fault state visually distinct from the normal evolution process.

[0041] Subsequently, the calculated sinusoidal perturbation signal is superimposed frame by frame onto the horizontal or vertical display coordinates of the placeholder. For example, if the target placeholder was originally stationary at a horizontal coordinate of 200px, after the perturbation signal is injected, its rendering coordinates will oscillate between 188px and 212px at a frequency of 5Hz, causing the pure gray placeholder block to exhibit a horizontally jittering visual artifact, thereby indicating to the user in a deformed, non-textual way that the hardware is in an error state.

[0042] S3 drives the continuous evolution of visual characteristics and continuously calibrates the model.

[0043] The purpose of this step is to drive the visual characteristics of the placeholder to change continuously during the duration of the predicted state, and to continuously correct the deviation between the model and the actual state of the device using real-time data streams. The goal is to ensure the synchronization between the visual representation of the interface and the actual operating condition of the device.

[0044] In terms of driving evolution, within each screen refresh cycle after the startup time-driven loop event, a high-precision timestamp acquisition function is called to calculate the elapsed relative time, which is then divided by the predicted state duration to obtain the current evolution ratio. This ratio is input into a cubic Bézier curve function to obtain the corresponding visual interpolation weight. Based on this, a linear vector blending algorithm is used to calculate the background color and size change values ​​of the current frame placeholder. Specifically, the initial visual characteristic vector and the target visual characteristic vector are weighted and summed according to the current evolution ratio to obtain the visual characteristic vector of the current frame, which is then updated to the style attributes of the rendering tree node. Among these changes, the background color gradually changes from the initial light gray (#E0E0E0) to the target blue (#1E90FF), and the size scales from the initial 90% to 100%, consistent with the rising water temperature trend, achieving continuous visual evolution.

[0045] For continuous calibration, this embodiment uses the real-time power of the heating component as the calibration benchmark. During each screen rendering cycle of the placeholder evolution, the synchronization control module reads the currently measured real-time power value of the heating component from the serial port sensor buffer and extracts the virtual predicted power value output by the benchmark model at the same time, calculating the difference between the two: ; in, This represents the actual measured real-time power value of the heating component; The virtual predicted power value is the output of the baseline model at the same time; This represents the power difference.

[0046] Then With respect to the system's preset fixed limit error threshold The comparison and determination are performed, and the drive step size coefficient is adjusted according to the following rules: ; in, These are the driving step size coefficients currently used by the baseline model to control the evolution speed; This is the adjusted new step size coefficient; The preset speed reduction coefficient can be selected from 0.5 to 0.8. This is a preset acceleration factor. The reason for setting a fixed limit error threshold here is that a small fluctuation between the measured power and the predicted power is normal. The step size is only adjusted when the absolute value of the difference exceeds the threshold, which can avoid over-responding to normal noise and causing visual jitter to a certain extent.

[0047] When the difference is negative and its absolute value exceeds the limit, it indicates that the virtual heating rate predicted by the interface has outpaced the actual hardware temperature rise. Therefore, the driving step size coefficient is multiplied by a deceleration coefficient to slow down the evolution. When the difference is positive and exceeds the limit, it indicates that the actual temperature rise is faster than the prediction. Therefore, an acceleration coefficient is multiplied to speed up the evolution. For example, when If, at 500ms, the actual power is read as 1850W and the model's predicted power as 2050W, the difference is -200W, exceeding the threshold of 100W. Therefore, the default base step size of 1 is multiplied by a reduction factor of 0.75 to obtain a new step size of 0.75, thus reducing the cumulative increment of the placeholder transparency parameter in the next frame. Conversely, if the measured power is 150W higher than the prediction, the step size is multiplied by an acceleration factor of 1.25. Figure 3 As shown, as the rendering cycle changes, when the absolute value of the power deviation exceeds the preset limit error threshold, the automatic scaling of the driving step size coefficient will be triggered synchronously. If the positive deviation exceeds the limit, the evolution will be accelerated, and if the negative deviation exceeds the limit, the evolution will be slowed down, so that the visual process displayed on the interface and the actual thermodynamic state of the water dispenser return to within the synchronization threshold.

[0048] In one implementation, the front end can also maintain a long-connection network socket channel with the server to listen to subsequent real-time status data streams, and use the Kalman filter algorithm to construct a state observer model. The latest actual water temperature value and the theoretical predicted temperature value calculated by the benchmark model are used as the observation variable and the prediction variable, respectively. The Kalman gain coefficient is calculated to output the optimal state estimate. When there is a residual value exceeding the threshold between the estimated value and the theoretical water temperature corresponding to the current visual display parameter, the proportional-integral-derivative control algorithm can be used to adjust the coordinate position of the control point of the cubic Bézier curve, thereby smoothly changing the evolution rate of subsequent time as a supplement to the step size calibration.

[0049] S4. Prioritize replacing the real data display component based on core parameters.

[0050] The purpose of this step is to differentiate and orderly replace the skeleton screen placeholders with real components based on the importance of the parameters and the arrival time of the data. The goal is to ensure the priority presentation of key information and maintain the logical coherence of the interface replacement.

[0051] In this embodiment, a display weight sequence library is established and initialized based on a priority queue algorithm or a balanced tree structure during the memory space allocation phase. In the data dictionary of this sequence library, inlet water temperature, outlet water temperature, real-time flow rate, and real-time power of heating components are configured as core operating parameters with the highest priority. For example, the weight value of outlet water temperature is assigned to 100, the weight value of real-time flow rate is 99, the weight value of inlet water temperature is 98, and the weight value of real-time power is 97. Non-urgent information such as filter cartridge remaining life and TDS water quality index are marked as secondary parameters with low weight, for example, assigned weight values ​​of 50 and 10.

[0052] The main control loop spawns independent timed monitoring threads that scan the memory data cache pool written by the underlying network communication at a preset polling cycle, for example, performing a scan every 10ms to check whether the key-value space corresponding to the highest weight identifier has been filled with a non-empty data packet. Once it detects that the data corresponding to the core operating parameters has been stored, such as detecting that the character constant 95℃ has been written under the water temperature cache key name, it bypasses the regular rendering queue and immediately throws a forced replacement command to the UI thread through the event bus. After detecting the command, the main UI thread prioritizes suspending other drawing tasks, executes the canvas clearing interface of a specific coordinate system to erase the light gray rectangular skeleton screen placeholder in the corresponding area, and calls the font engine to render the real numerical characters and the set color and font size attributes to that coordinate area.

[0053] For secondary parameters, even if their data packet parsing time is earlier than that of the core data, they will not preempt priority. Instead, after their corresponding data is stored in the cache pool, they are sorted in descending order according to the weight scores of 50, 10, etc. assigned to them in the display weight sequence library. Using the idle time slices remaining after the main thread renders the core data, the corresponding gray placeholders are erased one by one and the real text value controls are drawn until all the skeleton placeholders on the interface are replaced by real data components.

[0054] Through the coordinated efforts of the above steps, this embodiment ultimately generates a status monitoring screen on the user interface that continuously evolves with the actual operating conditions of the device and can present visual artifacts in case of failure: During the data loading and status evolution phases, placeholders present visual changes according to the nonlinear laws driven by the benchmark model and are continuously calibrated by the real-time data stream, so that the visual feedback during the loading phase is synchronized with the actual state of the device; when the data is available, the placeholders corresponding to the core operating parameters are preferentially replaced with the real data display components, and the placeholders corresponding to the secondary parameters are replaced one by one according to the weight order, thereby completing the smooth transition from the skeleton screen to the real interface.

[0055] To verify the effectiveness of the above solution, this embodiment selected 200 smart instant hot water dispensers of the same model as experimental subjects. All were uniformly configured with a main control board and network environment, and the communication detection cycle was set to 50ms. The test scenarios covered cold start, water heating, and simulated heating tube open circuit failure. Three sets of comparative experiments were set up: the basic group used a traditional static loading prompt and data packet reception order rendering scheme; the ablation group used basic skeleton screen rendering and fixed step-size evolution, but removed real-time power calibration and priority replacement mechanisms; the experimental group used a complete scheme, including transparency mapping based on feature ratio parameters, step-size calibration based on power differences, and a core parameter forced replacement mechanism based on a display weight sequence library.

[0056] Test results show that the average visual presentation delay of real-time water temperature in the basic group was 1450ms, the synchronization error rate between the interface and the actual water temperature was 22%, and the fault visual perception response time was 1800ms. The corresponding indicators for the ablation group were 920ms, 15%, and 280ms, respectively. In the experimental group, the average presentation delay of real-time water temperature was reduced to 210ms, the synchronization error rate decreased to 2.1%, and the fault visual perception response time was reduced to 110ms by utilizing the sinusoidal perturbation displacement artifact triggered by the fault code. These comparative results demonstrate that the solution in this embodiment plays a positive role in shortening visual presentation delay, reducing synchronization error, and improving the timeliness of fault perception, effectively enhancing the coherence of human-computer interaction and the accuracy of status monitoring.

[0057] This invention also discloses a data fusion-based instant hot water dispenser status monitoring system, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a data fusion-based instant hot water dispenser status monitoring method according to this invention is implemented.

[0058] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for monitoring the status of an instant hot water dispenser based on data fusion, characterized in that, include: S1. Obtain the initial status data uploaded by the water dispenser, which includes the device mode and key operating parameters, and generate a set of skeleton screen placeholders corresponding to the actual data display components in the user interface. S2. Determine the duration of the prediction state based on the device mode and the key operating parameters, and generate a benchmark model for driving the evolution of the visual characteristics of the placeholders in the skeleton screen placeholder set, and the benchmark model is configured to synchronously output virtual prediction power values. S3. During the duration of the predicted state, the visual characteristics of the placeholder are continuously evolved using the benchmark model, and the evolution state of the benchmark model is continuously calibrated using the received real-time state data stream to ensure that the visual performance is synchronized with the actual state of the device. S4. During or after the state evolution process, based on the arrival of real-time data, the placeholders corresponding to the preset core operating parameters in the key operating parameters are replaced with the actual data display components first, and the remaining placeholders are replaced after the corresponding data is received.

2. The method for monitoring the status of an instant hot water dispenser based on data fusion according to claim 1, characterized in that, It also includes an abnormal state monitoring step: when acquiring the initial state data, it also includes acquiring the equipment fault code uploaded by the water dispenser; When the device fault code indicates a device malfunction, a preset sinusoidal perturbation signal is injected into the rendering parameters of the placeholder to cause the placeholder to produce periodic displacement jump visual artifacts, thereby representing the fault state.

3. The method for monitoring the status of an instant hot water dispenser based on data fusion according to claim 1, characterized in that, The process of obtaining the initial status data uploaded by the water dispenser, which includes the device mode and key operating parameters, includes: building and configuring a background monitoring daemon service task, and intercepting and recording the data packet queue of the specified IP node according to a communication detection cycle of 50ms. The first two bytes of the data packet start frame are extracted according to a preset protocol and used as the basic parameter values ​​of the device mode; the real-time power value, real-time flow value and real-time measured temperature value of the heating component after a specific mark in the data packet are extracted in sequence and used as the key operating parameters.

4. The method for monitoring the status of an instant hot water dispenser based on data fusion according to claim 1, characterized in that, The step of generating a set of skeleton screen placeholders corresponding to the actual data display components in the user interface includes: Analyze the interface mapping ratio in the front-end rendering engine to determine the limit range of the horizontal and vertical coordinates of the actual data display components in the display panel and the array size parameters; Using the horizontal and vertical coordinates to define the range and array size parameters, a rectangular placeholder area with a single background color is rendered in the corresponding area of ​​the user interface. The rectangular placeholder area is configured as a solid color background mode with no additional graphics, no text and no border lines, and the color code is set to a preset light gray feature color, forming a set of skeleton screen placeholders to replace the real components.

5. The method for monitoring the status of an instant hot water dispenser based on data fusion according to claim 3, characterized in that, The predicted state duration is determined based on the device mode and the key operating parameters, and a benchmark model is generated to drive the evolution of the visual characteristics of the placeholders in the skeleton screen placeholder set. This includes: taking the category features of the device mode, the temperature difference between the outlet and inlet water temperatures calculated based on the real-time measured temperature values ​​of the inlet and outlet water temperature sensors, and the real-time flow rate as input features, inputting them into a preset regression model, and outputting the predicted state duration. The baseline model is constructed using a cubic Bézier curve function, and a nonlinear easing coefficient value is output based on a preset time progress variable to control the continuous evolution of the placeholder's visual characteristics.

6. The method for monitoring the status of an instant hot water dispenser based on data fusion according to claim 5, characterized in that, When generating the baseline model, the method also includes a step of mapping the initial values ​​of the key operating parameters to the initial visual characteristics of the placeholders, specifically including: extracting the initial constant value of the inlet water temperature from the real-time measured temperature values ​​as a molecular parameter. Access the local control table to obtain the preset maximum threshold of the ultimate dry-burning safety temperature as the denominator parameter; divide the numerator parameter by the denominator parameter to calculate a floating-point decimal between the preset lower limit of transparency and 1 as the characteristic ratio parameter. The feature scaling parameters are directly passed to the underlying display rendering system and assigned to the placeholders. The channel transparency parameter is used to adjust the visual depth of the placeholder's appearance color, completing the initial visual characteristic mapping settings.

7. The method for monitoring the status of an instant hot water dispenser based on data fusion according to claim 2, characterized in that, Injecting a preset sinusoidal perturbation signal into the rendering parameters of the placeholder includes: extracting the error identification byte group at the end of the data packet uploaded by the water dispenser as the device fault code; when it is determined that the received device fault code is not equal to the preset no-fault identifier constant, identifying the specific fault code value from the device fault code; according to the identified fault code value, querying the preset fault code and amplitude mapping table to obtain the corresponding amplitude offset constant; using the amplitude offset constant as an input parameter, passing it to the preset sine generator function to generate a sinusoidal perturbation signal with a fixed period as the sinusoidal perturbation signal; superimposing the sinusoidal perturbation signal onto the horizontal or vertical display coordinates of the placeholder to make the placeholder produce a periodic displacement jump visual effect, which serves as an artifact representing an abnormal error.

8. The method for monitoring the status of an instant hot water dispenser based on data fusion according to claim 3, characterized in that, The step of continuously calibrating the evolution state of the benchmark model using the received real-time status data stream includes: calculating the difference between the real-time power value of the heating component and the virtual predicted power value output by the benchmark model at the same time. Obtain the driving step size coefficient currently used to control the evolution speed of the benchmark model, and determine the relationship between the difference and the system's preset fixed limit error threshold. When the difference exceeds the limit, the driving step size coefficient is multiplied by the corresponding preset acceleration coefficient or preset deceleration coefficient according to the positive or negative attribute of the difference to obtain a new step size coefficient. The original driving step size is replaced with the new step size coefficient to adjust the evolution speed of the reference model and resynchronize it with the actual state of the device.

9. The method for monitoring the status of an instant hot water dispenser based on data fusion according to claim 3, characterized in that, The process of prioritizing the replacement of placeholders corresponding to preset core operating parameters in the key operating parameters with actual data display components, and replacing the remaining placeholders after receiving the corresponding data, includes: establishing a display weight sequence library, and configuring the inlet and outlet water temperatures, the real-time flow rate, and the real-time power value of the heating component in the real-time measured temperature values ​​of the inlet and outlet water temperature sensors as the highest priority core operating parameters in the display weight sequence library, while treating the remaining parameters not set as core operating parameters as secondary parameters; when data corresponding to the core operating parameters is detected in the data packet queue, the skeleton screen placeholders in the corresponding area are erased first and the actual data display components are rendered; for the secondary parameters, after their corresponding data is stored in the data packet queue, the corresponding placeholders are replaced with actual data display components in the order of the default order in the display weight sequence library.

10. A status monitoring system for an instant hot water dispenser based on data fusion, characterized in that, include: A processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement the data fusion-based instant hot water dispenser status monitoring method according to any one of claims 1-9.

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