Intelligent apartment equipment remote control and state monitoring method and system

Abstract

The application provides a kind of intelligent apartment equipment remote control and state monitoring method and system, it is related to intelligent control technical field, method includes establishing equipment physical reaction principle model, internal mechanism digitization;Real-time acquisition and extraction equipment electric signal deep feature parameter;Based on model and feature parameter, equipment state depth diagnosis and health degree evaluation are carried out;According to the diagnosis result, self-adapting adjustment electric signal modulation transmission parameter is carried out, and the dynamic matching of communication and equipment state is realized;Based on diagnosis and modulation parameter, multi-level control decision is executed;Through system integration and closed loop feedback, model and strategy are continuously optimized;System includes physical model establishment and system integration and optimization etc. module, the application solves the problem that equipment state monitoring is superficial, communication and control rigid, realizes accurate state perception, self-adapting reliable communication and intelligent collaborative control, and improves energy efficiency, safety and reliability.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, and in particular to a method and system for remote control and status monitoring of intelligent apartment equipment. Background Technology

[0002] Remote control and status monitoring technologies for smart apartments have developed rapidly in recent years. However, in actual deployment and application, especially after long-term operation, the internal physical working mechanism of the equipment, the characteristics of the electrical signals used to transmit its status, and the upper-level control decision logic are often disconnected. This disconnect prevents the system from truly understanding the equipment, resulting in only superficial, reflexive control that cannot handle complex working scenarios. For example, in an apartment that has been running for two or three years, the scale buildup in the tank of its storage-type electric water heater is becoming increasingly severe, and the actual heat exchange efficiency of the heating element is slowly decreasing. However, the existing monitoring system is still routinely collecting and reporting surface parameters such as power-on status and water temperature of 45°C, failing to detect the scale buildup. The heating element requires longer operation time to reach the set temperature, causing energy consumption to quietly increase by more than 20%. Furthermore, localized overheating accelerates the aging of the heating element's insulation material. From the system's perspective, the device appears normal until the heating element burns out due to prolonged overheating, at which point a fault code indicating device offline or abnormal current is reported. However, by then, the fault has already occurred, disrupting the user's comfort and incurring higher repair costs. Existing technologies suffer from the following problems: First, status perception is disconnected from physical mechanisms. Monitoring is limited to collecting terminal data such as switch signals and temperature values, like reading a thermometer without understanding the underlying cause. There is a lack of modeling of internal mechanisms such as electrothermal conversion, wear, and vibration, making in-depth, principle-based health assessments impossible. Second, data transmission is disconnected from device status. The communication module uses a fixed modulation and coding scheme, and its parameter adjustments rely solely on wireless channel quality, completely ignoring changes in the device's own electrical signals. For example, the electromagnetic noise generated when a motor bearing wears out can severely pollute its data transmission environment. If a modulation method that prioritizes high speed but has weak noise immunity is still used, the critical vibration anomaly warning data packet is very likely to be lost in the chaotic electrical noise, making the control center inefficient. Finally, the control decision is disconnected from the above two points. The control logic relies on preset, rigid scenario rules and cannot dynamically optimize by integrating the real-time health score of the equipment and the reliability of the communication link. The system will not automatically adjust its operating curve to extend the life of the water pump when it detects a 5% drop in pump efficiency, nor will it intelligently downgrade the control objective from energy saving priority to ensuring safe operation when the communication link is unstable. Therefore, there is an urgent need in the market for a new method and system for remote control and status monitoring of smart apartment equipment. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for remote control and status monitoring of intelligent apartment equipment, in order to solve the problems in the existing technology, such as lack of deep perception of the internal working mechanism of the equipment, disconnect between communication transmission and the physical state of the equipment, single control strategy and lack of global coordination, and lack of adaptive optimization capability in long-term system operation. These problems together cause technical bottlenecks such as passive and lagging equipment maintenance, low energy utilization efficiency, insufficient system reliability under complex working conditions, and continuous decline in long-term operating performance. The specific technical solution is as follows: This invention provides a method for remote control and status monitoring of smart apartment equipment, comprising: S10, Establish a physical reaction principle model for smart apartment equipment, and transform the internal working mechanism of the equipment into a quantifiable mathematical model; S20: Real-time acquisition of raw electrical signal waveforms during equipment operation, and extraction of characteristic parameters that reflect the internal state of the equipment; S30, Based on the physical reaction principle model and the real-time extracted feature parameters, perform equipment status diagnosis and health assessment; S40, Based on the status diagnosis results, adaptively adjust the electrical signal modulation transmission parameters to achieve dynamic matching between communication transmission and device status; S50, based on the state diagnosis results and modulation transmission parameters, execute multi-level control logic decisions and generate optimized control commands; S60, system integration and closed-loop optimization, continuously calibrates model parameters and modulation strategies through a feedback mechanism to achieve collaborative optimization of equipment status monitoring and remote control.

[0004] Furthermore, S10 includes: S101, Establish the electrical characteristic model of the equipment. The electrical characteristic model reflects the relationship between the resistance value of the equipment and the temperature. S102, Establish a thermodynamic model of the equipment, which reflects the heat transfer process of the equipment; S103, Establish a performance degradation model for the equipment. The performance degradation model reflects the trend of equipment efficiency decaying with cumulative operating time.

[0005] Furthermore, S20 includes: S201, with a sampling frequency of not less than 10kHz and 16-bit precision, acquires the device's operating current waveform and voltage waveform for at least 10 power frequency cycles; S202, preprocess the acquired raw signal, the preprocessing includes Butterworth low-pass filtering, moving average method to eliminate power frequency interference, and wavelet threshold denoising; S203, extract feature parameters from the preprocessed signal, the feature parameters including fundamental current amplitude, third harmonic current amplitude, fifth harmonic current amplitude, total harmonic distortion rate, power factor and current waveform distortion rate.

[0006] Furthermore, S30 includes: S301, based on the physical reaction principle model, calculate the theoretical expected value according to the feature parameters extracted in real time. The theoretical expected value includes the theoretical resistance value, theoretical thermal efficiency, and theoretical harmonic content. S302, calculate the deviation between the measured value and the corresponding theoretical expected value, wherein the deviation includes resistance deviation, thermal efficiency deviation and harmonic deviation; S303, based on a preset diagnostic threshold, the device status is diagnosed as normal, warning, or fault state according to the deviation.

[0007] Furthermore, in S303, the diagnostic threshold is set as follows: When the resistance deviation is no greater than 2%, the thermal efficiency deviation is no greater than 3%, and the harmonic deviation is no greater than 5%, it is diagnosed as a normal state. When the resistance deviation is greater than 2% but not greater than 5%, or the thermal efficiency deviation is greater than 3% but not greater than 8%, or the harmonic deviation is greater than 5% but not greater than 12%, it is diagnosed as a warning state. When the resistance deviation is greater than 5%, the thermal efficiency deviation is greater than 8%, or the harmonic deviation is greater than 12%, it is diagnosed as a fault condition.

[0008] Furthermore, S40 includes: S401. Calculate the Channel Condition Index (CCI) based on the status diagnosis results, channel signal-to-noise ratio, total harmonic distortion rate of the equipment, and resistance deviation. S402, based on the numerical range of the CCI, adaptively select a high-order modulation, mid-order modulation, or low-order modulation mode; S403 dynamically determines the carrier frequency, channel bandwidth, symbol rate, coding rate, and modulation index based on the selected modulation scheme.

[0009] Furthermore, in S401, the Channel Condition Index (CCI) is a weighted sum of the reciprocal of the signal-to-noise ratio, the total harmonic distortion rate, and the resistance deviation; in S402, the modulation scheme is selected based on the CCI as follows: When CCI is less than 0.35, 64QAM is selected as the high-order modulation scheme, with a corresponding carrier frequency of 2.412GHz, a channel bandwidth of 20MHz, and a symbol rate of 5.4Mbps. When the CCI is greater than or equal to 0.35 and less than 0.65, 16QAM is selected as the intermediate-order modulation method, with a corresponding carrier frequency of 2.437GHz, a channel bandwidth of 10MHz, and a symbol rate of 2.4Mbps. When CCI is greater than or equal to 0.65, QPSK is selected as the low-order modulation method, with a corresponding carrier frequency of 2.462 GHz, a channel bandwidth of 5 MHz, and a symbol rate of 1 Mbps.

[0010] Furthermore, the S50 includes: S501 performs device-level local control with a response time of less than 100 milliseconds and adjusts the device operating parameters based on a PID control algorithm with a proportional coefficient of 0.8, an integral time of 120 seconds, and a derivative time of 20 seconds. S502 performs regional-level coordination control with a coordination cycle of 1 second, and optimizes the collaborative work of multiple devices within the same functional area based on fuzzy logic algorithms. S503 executes system-level global decisions with a decision cycle of 10 seconds. Based on model predictive control algorithms, it comprehensively considers time-based electricity prices, user habits, weather forecasts, and equipment health status to optimize the operation strategy of the entire apartment system. In S501, the device-level local control selects different control modes based on the device status diagnostic results: When the equipment is in a normal state, the normal control mode is used to maintain the current operating parameters of the equipment. When the equipment is in a warning state, the optimized control mode is adopted, and the efficiency optimization algorithm is activated to dynamically adjust the equipment power. When the equipment is in a fault state, a protection control mode is adopted to immediately reduce the equipment load.

[0011] Furthermore, the S60 includes: S601 performs periodic system self-calibration, which includes sensor calibration, model parameter updates, and modulation parameter optimization. S602, Real-time computing system overall performance index E, wherein index E is a weighted sum of system average efficiency, communication reliability, control accuracy and maintenance early warning accuracy; S603, dynamically adjust the system control parameters, modulation parameters and diagnostic thresholds according to the numerical range of the overall system performance index E.

[0012] The present invention also provides a remote control and status monitoring system for intelligent apartment equipment, used to implement the method described above, the system comprising: The physical model building module is used to build a physical response principle model for smart apartment equipment; The electrical signal acquisition and feature extraction module is used to acquire the raw electrical signal waveforms during equipment operation in real time and extract feature parameters. The status diagnosis module is used to perform equipment status diagnosis and health assessment based on the physical model and characteristic parameters. The adaptive modulation module is used to adaptively adjust the electrical signal modulation and transmission parameters based on the state diagnosis results. The hierarchical control decision module is used to execute multi-level control logic decisions based on state diagnosis results and modulation parameters; The system integration and optimization module is used to continuously calibrate model parameters and modulation strategies through a feedback mechanism.

[0013] The present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the aforementioned method for remote control and status monitoring of intelligent apartment equipment.

[0014] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method for remote control and status monitoring of intelligent apartment equipment.

[0015] The beneficial effects of this invention are as follows: First, by using a mechanistic model and state diagnosis based on characteristics such as electrical signal harmonics and waveform distortion, this invention achieves deep perception from whether the device is working to its health status. It can provide early warnings of potential faults such as heating element efficiency degradation and bearing wear weeks or even months in advance, transforming passive maintenance into proactive prevention. Second, it is the first to quantify the equipment health status into a channel condition index and directly drive the dynamic switching of modulation methods, enabling real-time matching of communication reliability with equipment operating conditions. When the equipment's own electrical noise increases, it automatically strengthens error correction and speed reduction, ensuring the efficiency of critical alarms and control commands under harsh conditions. Finally, the three-level control architecture dynamically selects control strategies from fine optimization to safety fallback based on real-time diagnosis and communication quality, achieving a unity of three goals: optimal energy efficiency, extended equipment lifespan, and system safety and stability. This invention deeply integrates equipment physical response principle modeling, deep feature extraction of electrical signals, adaptive communication modulation, and multi-level control decision-making, ultimately resulting in reduced maintenance costs, improved overall energy efficiency, and a significant increase in the system's mean time between failures (MTBF).

[0016] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram illustrating the steps of a remote control and status monitoring method for intelligent apartment equipment according to the present invention; Figure 2 This is a schematic diagram of the structure of a remote control and status monitoring system for intelligent apartment equipment according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0020] It should also be noted that when a component is referred to as "fixed to" or "set on" another component, it can be directly on the other component or may also have an intervening component present. When a component is referred to as "connected to" another component, it can be directly connected to the other component or may also have an intervening component present.

[0021] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least that feature. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed by this invention.

[0022] In an embodiment of the present invention, a method for remote control and status monitoring of smart apartment equipment is provided. Please refer to [link to relevant documentation]. Figure 1 The method includes the following steps: S10, Establish a physical reaction principle model for smart apartment equipment, and transform the internal working mechanism of the equipment into a quantifiable mathematical model; S20: Real-time acquisition of raw electrical signal waveforms during equipment operation, and extraction of characteristic parameters that reflect the internal state of the equipment; S30, Based on the physical reaction principle model and the real-time extracted feature parameters, perform equipment status diagnosis and health assessment; S40, Based on the status diagnosis results, adaptively adjust the electrical signal modulation transmission parameters to achieve dynamic matching between communication transmission and device status; S50, based on the state diagnosis results and modulation transmission parameters, execute multi-level control logic decisions and generate optimized control commands; S60, system integration and closed-loop optimization, continuously calibrates model parameters and modulation strategies through a feedback mechanism to achieve collaborative optimization of equipment status monitoring and remote control.

[0023] The principle and effect of the above technical solution are as follows: This invention realizes the digital description of the internal working mechanism of the equipment by establishing an accurate physical model, and elevates the equipment status information from surface parameters to the physical essence level by extracting deep features of electrical signals in real time. Through adaptive modulation technology, the communication transmission process can be dynamically optimized according to the equipment status. Through multi-level control logic, intelligent decision-making from local rapid response to global collaborative optimization is realized. This vertically integrated fusion method can provide early warning of potential faults, dynamically optimize equipment operating conditions, improve energy utilization efficiency and significantly reduce maintenance costs, and has excellent adaptability and scalability.

[0024] In one embodiment, S10 includes: S101, Establish the electrical characteristic model of the equipment, specifically including the resistance-temperature relationship model R(t)=R0[1+α(T(t)-T0)], where R0 is the reference resistance value of 2.4Ω, α is the resistance temperature coefficient of 0.0039 / ℃, T(t) is the real-time temperature, and T0 is the reference temperature of 20℃; S102, Establish the thermodynamic model of the equipment, specifically including the heat transfer equation Q=K·A·ΔT·t, where K is the heat transfer coefficient 8.2W / (m²). 2 ·K), where A is the effective heat transfer area of ​​0.85m². 2 ΔT is the temperature difference, and t is the time. S103, establish a performance degradation model for the equipment, specifically including the efficiency decay equation η(t)=η0·exp(-β·t), where η0 is the initial efficiency of 95.2%, β is the decay coefficient of 0.00015 / hour, and t is the cumulative running time.

[0025] The principle and effects of the above technical solution are as follows: By establishing a precise mathematical model, the complex physical processes inside the equipment are transformed into a calculable and analyzable digital expression, providing a theoretical basis for subsequent condition diagnosis. The electrical characteristic model can accurately describe the impact of temperature changes on electrical parameters, the thermodynamic model can quantify the heat transfer process, and the performance degradation model can predict the efficiency change trend of the equipment during long-term operation. The combination of these three models enables the system to comprehensively understand the working mechanism of the equipment from multiple dimensions, laying a solid foundation for in-depth condition monitoring.

[0026] In one embodiment, S20 includes: S201 acquires the device's operating current waveform I(t) and voltage waveform U(t) in real time at a sampling frequency of not less than 10kHz, with a sampling accuracy of 16 bits and a sampling duration of not less than 10 power frequency cycles. S202, preprocesses the acquired raw signal, including using a Butterworth low-pass filter to filter out noise components higher than 2kHz, using a moving average method to eliminate power frequency interference, and using a wavelet threshold denoising method to suppress random noise; S203, extract characteristic parameters from the preprocessed signal, including the fundamental current amplitude I1, the third harmonic current amplitude I3, the fifth harmonic current amplitude I5, the total harmonic distortion (THD), the power factor PF, and the current waveform distortion rate Kf, where THD = √(I3 / 2). 2 +I5 2 ) / I1×100%,Kf=I_rms / I_avg,I_rms is the effective value of the current,I_avg is the average value of the current.

[0027] The principle and effect of the above technical solution are as follows: The original electrical signals of the equipment are acquired through high-speed, high-precision sampling. After multi-stage preprocessing to eliminate various interferences, key characteristic parameters reflecting the internal state of the equipment are extracted. These characteristic parameters not only include conventional electrical parameters but also deeper information such as harmonic components and waveform distortion. This information is closely related to the internal operating state of the equipment, component aging, and load changes. For example, changes in harmonic components can reflect the saturation degree of magnetic materials, waveform distortion can reflect the performance degradation of switching devices, and changes in the power factor can reflect the reactive power compensation status. This deep feature extraction enables the system to obtain richer and more accurate status information than traditional monitoring methods.

[0028] In one embodiment, S30 includes: S301, input the real-time extracted feature parameters into the physical reaction principle model to calculate the theoretical expected values, including the theoretical resistance value R_calc, the theoretical thermal efficiency η_calc, and the theoretical harmonic content H_calc; S302, calculate the deviation between the measured value and the theoretical value, including the resistance deviation ΔR=|R_meas-R_calc| / R_calc×100%, the thermal efficiency deviation Δη=|η_meas-η_calc| / η_calc×100%, and the harmonic deviation ΔH=|THD_meas-H_calc| / H_calc×100%; S303, based on the deviation, perform state diagnosis. When ΔR≤2% and Δη≤3% and ΔH≤5%, it is diagnosed as a normal state. When 2%<ΔR≤5% or 3%<Δη≤8% or 5%<ΔH≤12%, it is diagnosed as a warning state. When ΔR>5% or Δη>8% or ΔH>12%, it is diagnosed as a fault state.

[0029] The principle and effects of the above technical solution are as follows: By comparing measured characteristic parameters with theoretical values ​​calculated based on a physical model, a deep diagnosis from surface parameters to internal state is achieved. Traditional monitoring methods can only determine whether the equipment is working, while this method can determine the health level, efficiency level, and potential risks of the equipment. The deviation calculation adopts a multi-parameter comprehensive judgment, avoiding the possibility of misjudgment based on a single parameter. The setting of three diagnostic thresholds (normal, warning, and fault) is based on a large amount of experimental data and statistical analysis, ensuring the accuracy and reliability of the diagnosis. This physical model-based diagnostic method can detect the performance degradation trend of equipment in advance and provide early warning before the fault occurs, realizing a fundamental shift from passive maintenance to proactive maintenance.

[0030] In one embodiment, S40 includes: S401, determine the current channel condition index CCI based on the state diagnosis result, CCI=w1·(1 / SNR)+w2·THD+w3·ΔR, where SNR is the signal-to-noise ratio, w1=0.4, w2=0.3, w3=0.3 are weighting coefficients; S402 selects the modulation method based on the CCI value: when CCI < 0.35, high-order modulation is selected; when 0.35 ≤ CCI < 0.65, mid-order modulation is selected; and when CCI ≥ 0.65, low-order modulation is selected. S403 adjusts the transmission parameters according to the selected modulation scheme, including carrier frequency, symbol rate, coding rate, and modulation index. The specific parameter configuration is dynamically determined according to the modulation scheme category.

[0031] The principle and effect of the above technical solution are as follows: It directly maps equipment status diagnostic results to communication modulation parameters, achieving deep coupling between communication transmission and equipment status. Traditional communication systems only consider the characteristics of the channel itself (such as signal-to-noise ratio and multipath effects), while this method innovatively introduces equipment status parameters (such as harmonic distortion rate and resistance deviation) as the basis for modulation decisions. This is because changes in equipment status directly affect the characteristics of the generated electrical signals, thereby affecting communication quality. Through this coupling, the system can automatically reduce the modulation order and increase coding redundancy when the equipment status deteriorates, ensuring reliable transmission of control commands; when the equipment status is good, it can use higher-order modulation to improve transmission efficiency and meet real-time requirements. This adaptive modulation technology significantly improves the communication reliability of the system under complex operating conditions.

[0032] In one embodiment, S50 includes: S501, device-level local control, with a response time requirement of less than 100 milliseconds, adjusts device operating parameters based on PID control algorithm, with proportional coefficient Kp=0.8, integral time Ti=120 seconds, and derivative time Td=20 seconds; S502, a regional-level coordinated control system with a coordination cycle of 1 second, optimizes the collaborative work of multiple devices within the same functional area based on fuzzy logic algorithms, including temperature field balancing control, load balancing control, and energy distribution optimization. S503 is a system-level global decision-making system with a decision cycle of 10 seconds. It optimizes the operation strategy of the entire apartment system based on model predictive control algorithms, taking into account factors such as time-based electricity prices, user habits, weather forecasts, and equipment health status.

[0033] The principle and effects of the above technical solution are as follows: A three-level linkage control logic architecture is constructed, realizing comprehensive control from micro to macro, and from rapid response to long-term optimization. Device-level local control ensures the real-time performance and accuracy of control, enabling rapid response to emergencies; regional-level coordinated control realizes collaborative optimization between devices, avoiding the problem of overall performance degradation due to optimization of a single device; system-level global decision-making considers external environmental factors and global optimization objectives, achieving comprehensive optimization of energy efficiency, economy, and comfort. This hierarchical control architecture not only ensures the real-time performance of control but also achieves global system optimization, resolving the contradiction between local and global optimization in traditional control methods.

[0034] In one embodiment, S60 includes: S601, the system self-calibrates, automatically performing a calibration procedure every 24 hours. The calibration includes sensor zero-point drift correction, adaptive update of model parameters, and optimization and adjustment of modulation parameters. S602, Performance Evaluation and Feedback, Real-time calculation of the overall system efficiency index E=0.3·η_sys+0.3·R_sys+0.2·C_sys+0.2·M_sys, where η_sys is the average system efficiency, R_sys is the communication reliability, C_sys is the control accuracy, and M_sys is the maintenance early warning accuracy. S603 features adaptive parameter adjustment, dynamically adjusting control parameters, modulation parameters, and diagnostic thresholds based on performance evaluation results. When E≥90%, the current parameters are maintained; when 80%≤E<90%, parameter fine-tuning is initiated; and when E<80%, comprehensive system optimization is triggered.

[0035] The principle and effect of the above technical solution are as follows: A closed-loop feedback mechanism enables the system to self-optimize and self-improve. Traditional monitoring systems, once deployed, often have fixed parameters, unable to adapt to dynamic factors such as equipment aging, environmental changes, and shifts in usage habits. This method, through periodic self-calibration, real-time performance evaluation, and adaptive parameter adjustment, allows the system to continuously optimize its performance over time. This self-learning and adaptive capability ensures the long-term stability and reliability of the system, significantly reducing the need for manual maintenance and truly achieving intelligent equipment management.

[0036] In one specific embodiment, in S40, if the CCI < 0.35 and the state diagnosis is normal, then 64QAM modulation is adopted, and the specific steps are as follows: SA41: The carrier frequency is set to 2.412 GHz, the channel bandwidth is 20 MHz, OFDM multi-carrier technology is adopted, and the number of subcarriers is 52, of which 48 are used for data transmission and 4 are used for pilot signals; SA42: The symbol rate is set to 5.4 Mbps, convolutional coding is used, the coding rate is 3 / 4, the constraint length is 7, and the generator polynomial is (133,171) octal. SA43: Adopts adaptive bit loading technology, which dynamically allocates the number of bits according to the signal-to-noise ratio of each subcarrier. Subcarriers with a signal-to-noise ratio higher than 25dB are loaded with 6 bits, those with a signal-to-noise ratio of 20-25dB are loaded with 4 bits, and those with a signal-to-noise ratio of 15-20dB are loaded with 2 bits. SA44: Insert a cyclic prefix with a length of 1 / 4 of the symbol period, i.e., 0.8 microseconds, to eliminate multipath interference.

[0037] The 64QAM modulation scheme can provide a data transmission rate of up to 5.4Mbps when the channel conditions are good and the equipment is in normal condition, which can meet the high bandwidth requirements of high-definition video surveillance and multi-parameter synchronous transmission. At the same time, the reliability of transmission is guaranteed by adaptive bit loading and error correction coding.

[0038] In S40, if 0.35 ≤ CCI < 0.65 and the state diagnosis is a warning state, then 16QAM modulation is adopted. The specific steps of the 16QAM modulation are as follows: SB41: The carrier frequency is set to 2.437 GHz, the channel bandwidth is 10 MHz, single-carrier modulation is used, and the roll-off factor is 0.25; SB42: The symbol rate is set to 2.4 Mbps, Turbo coding is used, the coding rate is 1 / 2, the interleaving depth is 1024 bits, and the number of iterations for decoding is 8. SB43: Employs differential coding technology to avoid phase ambiguity issues; the reference phase is updated every 10 symbols. SB44: Adds a power control mechanism to dynamically adjust the transmit power based on the received signal strength index RSSI. When RSSI < -75dBm, the transmit power is increased by 3dB, and when RSSI > -65dBm, the transmit power is decreased by 3dB.

[0039] Under moderate channel conditions and when the equipment status issues an early warning, the 16QAM modulation method significantly improves the system's anti-interference capability and robustness while maintaining a transmission rate of 2.4Mbps by reducing the modulation order, enhancing error correction coding, and adding power control, thus ensuring the reliable transmission of critical control commands.

[0040] In S40, if the CCI ≥ 0.65 or the state is diagnosed as a fault state, then QPSK modulation is used. The specific steps of QPSK modulation are as follows: SC41: The carrier frequency is set to 2.462 GHz, the channel bandwidth is 5 MHz, the spread spectrum technology is adopted, the spread spectrum code is a 63-bit Gold sequence, and the chip rate is 11 Mcps; SC42: The symbol rate is set to 1 Mbps, concatenated coding is used, the outer code is RS(255,223) code, the inner code is convolutional code (2,1,7), and the coding rate is 1 / 3; SC43: Employs diversity reception technology, including time diversity (maximum of 3 retransmissions) and frequency diversity (frequency hopping pattern includes 32 frequency points). SC44: Enable emergency transmission mode, prioritize the transmission of fault alarm signals and control commands, and reserve 12 bytes of emergency information field in the data frame.

[0041] When channel conditions are poor or equipment malfunctions, the QPSK modulation method employs multiple protection measures such as low-order modulation, strong error correction coding, spread spectrum technology, and diversity reception. Although the transmission rate is reduced to 1Mbps, it can ensure communication reliability under extreme conditions, providing a guarantee for fault handling and emergency control.

[0042] In one specific embodiment, in S50, if the thermal efficiency deviation Δη ≤ 3% and the resistance deviation ΔR ≤ 2%, then the normal control mode is adopted, and the specific steps are as follows: SA51: Maintain the current operating parameters of the equipment, including heating power at the rated value of 2.4kW, water temperature setpoint at the user-set value of 45℃, and circulation pump speed at 2800rpm; SA52: Performs conventional PID control with proportional coefficient Kp=0.8, integral time Ti=120 seconds, derivative time Td=20 seconds, and control cycle of 200 milliseconds; SA53: Monitors the fluctuation range of key parameters, with water temperature fluctuations controlled within ±0.5℃, current fluctuations controlled within ±5%, and power fluctuations controlled within ±3%. SA54: Records operating data every 30 minutes, including cumulative power consumption, average efficiency, and peak power, and stores the data in a local cache.

[0043] The normal control mode aims to maintain stable operation when the equipment is in good condition. It uses precise PID control to keep the equipment working at its design conditions, ensuring user comfort while achieving efficient energy utilization.

[0044] In S50, if 3% < Δη ≤ 8% or 2% < ΔR ≤ 5%, then an optimized control mode is adopted. The specific steps of the optimized control mode are as follows: SB51: Activate the efficiency optimization algorithm and dynamically adjust the heating power P = P_nom × [0.9 + 0.1 × (η_meas / η_nom)] based on the real-time calculated thermal efficiency η_meas, where P_nom is the rated power of 2.4kW and η_nom is the rated efficiency of 95.2%. SB52: Adjust the PID control parameters: adjust the proportional coefficient to Kp=0.6, the integral time to Ti=180 seconds, the derivative time to Td=15 seconds, and shorten the control cycle to 100 milliseconds; SB53: Enables predictive control function, predicts heat load changes in the next 15 minutes based on historical data, and adjusts heating power 1 minute in advance. The prediction model adopts ARIMA(2,1,2) time series model. SB54: Increase the frequency of status monitoring, shorten the sampling interval of key parameters from 30 seconds to 10 seconds, and increase the data upload frequency from once per minute to once every 20 seconds.

[0045] When the equipment experiences slight performance degradation, the optimized control mode, through measures such as dynamically adjusting operating parameters, optimizing control algorithms, and increasing monitoring frequency, extends the equipment's service life as much as possible while ensuring basic functions, slows down the rate of performance degradation, and provides more detailed data support for maintenance decisions.

[0046] In S50, if Δη > 8% or ΔR > 5%, a protection control mode is adopted. The specific steps of the protection control mode are as follows: SC51: Immediately reduce the equipment load, limit the heating power to 70% of the rated value, i.e., below 1.68kW, adjust the upper limit of the water temperature setting to 40℃, and limit the circulation pump speed to 2000rpm; SC52: Switch to safety control algorithm, using fuzzy PID control, with proportional coefficient range Kp∈[0.3,0.5], integral time range Ti∈[240,360] seconds, and derivative time range Td∈[10,15] seconds, dynamically adjusted according to real-time status; SC53: Activate emergency maintenance warning, simultaneously alarming via audible and visual alarms and remote notification. Alarm information includes fault type, severity, recommended measures, and estimated remaining service life. SC54: Performs safety operation monitoring, shortens the sampling interval of key parameters to 1 second, uploads data to the cloud in real time, and stores the full parameter operation records of the most recent 24 hours locally.

[0047] When the equipment experiences significant malfunctions or severe performance degradation, the protection and control mode prioritizes safety. It prevents the escalation of the malfunction and protects the safety of personnel and equipment by limiting the load, switching control algorithms, and strengthening monitoring. At the same time, it provides maintenance personnel with detailed fault information and operating data.

[0048] In one specific embodiment, in S60, if the overall system performance index E ≥ 90%, then a high-performance operation mode is adopted, and the specific steps are as follows: SA61: Enables all advanced features, including adaptive modulation, predictive control, multi-device collaborative optimization, energy efficiency analysis, and fault prediction; SA62: Set system parameters to optimal values, control cycle to 100 milliseconds, data update frequency to 10Hz, model update cycle to 1 hour, and automatic modulation mode selection range covering 64QAM, 16QAM, and QPSK; SA63: Performs comprehensive data recording and analysis, recording detailed operating data of all equipment, including electrical parameters, thermodynamic parameters, control parameters, and communication parameters, with a data retention period of 90 days; SA64: Provides a complete user interface and API interface, supporting real-time monitoring, historical query, report generation, remote configuration, and software upgrades.

[0049] When the overall system is in good condition, the high-performance operating mode fully leverages all the system's functional advantages to provide users with the best monitoring experience and control effect, while accumulating detailed operating data to provide data support for system optimization and equipment management.

[0050] In S60, if 80%≤E<90%, a balanced operation mode is adopted. The specific steps of the balanced operation mode are as follows: SB61: Enable core functions, including basic status monitoring, adaptive modulation, PID control, and fault alarms; suspend some advanced functions such as long-term trend prediction and multi-objective optimization. SB62: Optimize system resource configuration, adjust control cycle to 200 milliseconds, data update frequency to 5Hz, model update cycle to 4 hours, and limit modulation method to 16QAM and QPSK; SB63: Perform selective data logging, recording only the operating data and abnormal events of critical equipment, and shortening the data retention period to 30 days; SB64: Provides a basic user interface, supports real-time status display, parameter setting, alarm viewing, and pauses advanced analysis functions.

[0051] The balanced operation mode, when the system performance has decreased but is still acceptable, reduces the system load and extends the service life of equipment while ensuring the normal operation of core functions by optimizing resource allocation and simplifying functional modules, thus ensuring stable system operation.

[0052] In S60, if E < 80%, a safety maintenance mode is adopted. The specific steps of the safety maintenance mode are as follows: SC61: Enable only basic safety functions, including equipment status monitoring, emergency control, and fault alarms; suspend all optimization and control functions. SC62: Employs a minimal system configuration, extends the control cycle to 500 milliseconds, reduces the data update frequency to 1 Hz, pauses model updates, and uses QPSK modulation. SC63: Performs critical data logging, recording only fault events and emergency operations, with a data retention period of 7 days, and data stored in local non-volatile memory; SC64: Provides a minimized user interface that displays only device status and alarm information. All configuration functions are locked and await maintenance personnel intervention.

[0053] When system performance degrades significantly, the security maintenance mode prioritizes ensuring basic security. It maintains the system's most basic security monitoring functions by minimizing system configuration, simplifying functions, and reducing load, thereby preventing system crashes and creating conditions for maintenance and repair.

[0054] Accordingly, based on the method for remote control and status monitoring of intelligent apartment equipment according to an embodiment of the present invention, this embodiment of the present invention also provides a system for remote control and status monitoring of intelligent apartment equipment, used to implement the method, such as... Figure 2 As shown, the system includes: The physical model building module is used to establish a physical response principle model for the intelligent apartment equipment, transforming the internal working mechanism of the equipment into a quantifiable mathematical model. Specifically, it includes establishing the electrical characteristic model of the equipment: R(t) = R0[1 + α(T(t) - T0)], where R0 is the reference resistance value of 2.4Ω, α is the temperature coefficient of resistance of 0.0039 / ℃, and T0 is the reference temperature of 20℃; and establishing the thermodynamic model of the equipment: Q = K·A·ΔT·t, where K is the heat transfer coefficient of 8.2W / (m³). 2 ·K), where A is the effective heat transfer area of ​​0.85m². 2 Establish a performance degradation model for the equipment: η(t) = η0·exp(-β·t), where η0 is the initial efficiency of 95.2% and β is the degradation coefficient of 0.00015 / hour. The electrical signal acquisition and feature extraction module is used to acquire the device's operating current waveform I(t) and voltage waveform U(t) in real time at a sampling frequency of not less than 10kHz, with a sampling accuracy of 16 bits and a sampling duration of not less than 10 power frequency cycles. The module preprocesses the acquired raw signals, including using a Butterworth low-pass filter to filter out noise components higher than 2kHz, using a moving average method to eliminate power frequency interference, and using a wavelet threshold denoising method to suppress random noise. Feature parameters are extracted from the preprocessed signals, including the fundamental current amplitude I1, the 3rd harmonic current amplitude I3, the 5th harmonic current amplitude I5, the total harmonic distortion (THD), the power factor PF, and the current waveform distortion rate Kf, where THD = √(I3 / 2). 2 +I5 2 ) / I1×100%, Kf=I_rms / I_avg; The status diagnosis module is used to input the real-time extracted feature parameters into the physical reaction principle model, calculate the theoretical expected values, including the theoretical resistance value R_calc, the theoretical thermal efficiency η_calc, and the theoretical harmonic content H_calc; calculate the deviation between the measured values ​​and the theoretical values, including the resistance deviation ΔR = R_meas - R_calc / R_calc × 100%, the thermal efficiency deviation Δη = η_meas - η_calc / η_calc × 100%, and the harmonic deviation ΔH = THD_meas - H_calc / H_calc × 100%; and perform status diagnosis based on the deviation. When ΔR ≤ 2% and Δη ≤ 3% and ΔH ≤ 5%, it is diagnosed as a normal state; when 2% < ΔR ≤ 5% or 3% < Δη ≤ 8% or 5% < ΔH ≤ 12%, it is diagnosed as a warning state; and when ΔR > 5% or Δη > 8% or ΔH > 12%, it is diagnosed as a fault state. An adaptive modulation module is used to determine the current channel condition index (CCI) based on the state diagnosis result, where CCI = w1·(1 / SNR) + w2·THD + w3·ΔR, and SNR is the signal-to-noise ratio, w1 = 0.4, w2 = 0.3, and w3 = 0.3 are weighting coefficients. Based on the CCI value, a modulation scheme is selected: when CCI < 0.35, 64QAM modulation is selected with a carrier frequency of 2.412 GHz, a channel bandwidth of 20 MHz, and a symbol rate of 5.4 Mbps; when 0.35 ≤ CCI < 0.65, 16QAM modulation is selected with a carrier frequency of 2.437 GHz, a channel bandwidth of 10 MHz, and a symbol rate of 2.4 Mbps; and when CCI ≥ 0.65, QPSK modulation is selected with a carrier frequency of 2.462 GHz, a channel bandwidth of 5 MHz, and a symbol rate of 1 Mbps. The hierarchical control decision module is used to execute device-level local control with a response time requirement of less than 100 milliseconds. It adjusts device operating parameters based on PID control algorithms, with a proportional coefficient Kp=0.8, integral time Ti=120 seconds, and derivative time Td=20 seconds. It also executes area-level coordinated control with a coordination cycle of 1 second, optimizing the collaborative operation of multiple devices within the same functional area based on fuzzy logic algorithms. Finally, it executes system-level global decision-making with a decision cycle of 10 seconds, optimizing the overall operation strategy of the apartment system based on model predictive control algorithms. The system integration and optimization module is used to perform system self-calibration, automatically executing a calibration procedure every 24 hours. The calibration includes sensor zero-point drift correction, adaptive update of model parameters, and optimization adjustment of modulation parameters. It also performs performance evaluation and feedback, calculating the overall system efficiency index E=0.3·η_sys+0.3·R_sys+0.2·C_sys+0.2·M_sys in real time, where η_sys is the system average efficiency, R_sys is the communication reliability, C_sys is the control accuracy, and M_sys is the maintenance warning accuracy. Finally, it performs adaptive parameter adjustment, dynamically adjusting control parameters, modulation parameters, and diagnostic thresholds based on performance evaluation results. When E≥90%, the current parameters are maintained; when 80%≤E<90%, parameter fine-tuning is initiated; and when E<80%, comprehensive system optimization is triggered.

[0055] It should be noted that the intelligent apartment equipment remote control and status monitoring system provided in this embodiment of the invention is used to execute all the process steps of the intelligent apartment equipment remote control and status monitoring method in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0056] This invention also provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a remote control and status monitoring program for smart apartment equipment. When the processor executes the computer program, it implements the steps described in the various embodiments of the remote control and status monitoring methods for smart apartment equipment. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the various device embodiments described above, such as a remote control and status monitoring module for smart apartment equipment.

[0057] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0058] The electronic device may be a computing device such as a smart gateway, edge computing node, cloud server, or mobile terminal. The electronic device may include, but is not limited to, processors and memory. Those skilled in the art will understand that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0059] The processor can be an ARM Cortex-M7 microprocessor with a clock speed of not less than 400MHz, or other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device through various interfaces and lines.

[0060] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory, and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created according to device usage, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart memory card, secure digital card, flash memory card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0061] Wherein, if the modules / units integrated in the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0062] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for remote control and status monitoring of smart apartment equipment, characterized in that, include: S10, Establish a physical reaction principle model for smart apartment equipment, and transform the internal working mechanism of the equipment into a quantifiable mathematical model; S20: Real-time acquisition of raw electrical signal waveforms during equipment operation, and extraction of characteristic parameters that reflect the internal state of the equipment; S30 performs equipment status diagnosis and health assessment based on a physical reaction principle model and real-time extracted feature parameters. S40, based on the status diagnosis results, adaptively adjusts the electrical signal modulation transmission parameters to achieve dynamic matching between communication transmission and equipment status; S50, based on the state diagnosis results and modulation transmission parameters, executes multi-level control logic decisions and generates optimized control commands; S60, system integration and closed-loop optimization, continuously calibrates model parameters and modulation strategies through a feedback mechanism to achieve collaborative optimization of equipment status monitoring and remote control.

2. The method as described in claim 1, characterized in that, S10 includes: S101, Establish the electrical characteristic model of the equipment. The electrical characteristic model reflects the relationship between the resistance value of the equipment and the temperature. S102, Establish a thermodynamic model of the equipment, which reflects the heat transfer process of the equipment; S103, Establish a performance degradation model for the equipment. The performance degradation model reflects the trend of equipment efficiency decaying with cumulative operating time.

3. The method as described in claim 1, characterized in that, S20 includes: S201, Acquires the operating current waveform and voltage waveform of the acquisition device; S202, preprocessing the acquired raw signal, including Butterworth low-pass filtering, moving average method to eliminate power frequency interference, and wavelet threshold denoising; S203 extracts characteristic parameters from the preprocessed signal. The characteristic parameters include the fundamental current amplitude, the third harmonic current amplitude, the fifth harmonic current amplitude, the total harmonic distortion rate, the power factor, and the current waveform distortion rate.

4. The method as described in claim 1, characterized in that, S30 includes: S301, based on the physical reaction principle model, calculates the theoretical expected value according to the feature parameters extracted in real time. The theoretical expected value includes the theoretical resistance value, theoretical thermal efficiency and theoretical harmonic content. S302, calculate the deviation between the measured value and the corresponding theoretical expected value. The deviation includes resistance deviation, thermal efficiency deviation and harmonic deviation. S303, based on a preset diagnostic threshold, diagnoses the equipment status as normal, warning, or fault state according to the deviation.

5. The method as described in claim 4, characterized in that, The diagnostic threshold settings in S303 include: When the resistance deviation is no greater than 2%, the thermal efficiency deviation is no greater than 3%, and the harmonic deviation is no greater than 5%, it is diagnosed as a normal state. When the resistance deviation is greater than 2% but not greater than 5%, or the thermal efficiency deviation is greater than 3% but not greater than 8%, or the harmonic deviation is greater than 5% but not greater than 12%, it is diagnosed as a warning state. When the resistance deviation is greater than 5%, the thermal efficiency deviation is greater than 8%, or the harmonic deviation is greater than 12%, it is diagnosed as a fault condition.

6. The method as described in claim 1, characterized in that, S40 includes: S401. Calculate the Channel Condition Index (CCI) based on the status diagnosis results, channel signal-to-noise ratio, total harmonic distortion rate of the equipment, and resistance deviation. S402 adaptively selects high-order modulation, mid-order modulation, or low-order modulation based on the numerical range of the Channel Condition Index (CCI). S403 dynamically determines the carrier frequency, channel bandwidth, symbol rate, coding rate, and modulation index based on the selected modulation scheme.

7. The method as described in claim 6, characterized in that, In S401, the Channel Condition Index (CCI) is a weighted sum of the reciprocal of the signal-to-noise ratio, the total harmonic distortion (THD), and the resistance deviation. In S402, selecting the modulation scheme based on the CCI specifically includes: When CCI is less than 0.35, 64QAM is selected as the high-order modulation scheme, with a corresponding carrier frequency of 2.412GHz, a channel bandwidth of 20MHz, and a symbol rate of 5.4Mbps. When the CCI is greater than or equal to 0.35 and less than 0.65, 16QAM is selected as the intermediate-order modulation method, with a corresponding carrier frequency of 2.437GHz, a channel bandwidth of 10MHz, and a symbol rate of 2.4Mbps. When CCI is greater than or equal to 0.65, QPSK is selected as the low-order modulation method, with a corresponding carrier frequency of 2.462 GHz, a channel bandwidth of 5 MHz, and a symbol rate of 1 Mbps.

8. The method as described in claim 1, characterized in that, The S50 includes: S501 performs device-level local control with a response time of less than 100 milliseconds and adjusts the device operating parameters based on a PID control algorithm with a proportional coefficient of 0.8, an integral time of 120 seconds, and a derivative time of 20 seconds. S502 performs regional-level coordination control with a coordination cycle of 1 second, and optimizes the collaborative work of multiple devices within the same functional area based on fuzzy logic algorithms. S503 executes system-level global decisions with a decision cycle of 10 seconds. Based on model predictive control algorithms, it comprehensively considers time-based electricity prices, user habits, weather forecasts, and equipment health status to optimize the operation strategy of the entire apartment system. In S501, device-level local control selects different control modes based on device status diagnostic results, including: When the equipment is in a normal state, the normal control mode is used to maintain the current operating parameters of the equipment. When the equipment is in a warning state, the optimized control mode is adopted, and the efficiency optimization algorithm is activated to dynamically adjust the equipment power. When the equipment is in a fault state, a protection control mode is adopted to immediately reduce the equipment load.

9. The system as described in claim 1, characterized in that, The S60 includes: S601 performs periodic system self-calibration, which includes sensor calibration, model parameter updates, and modulation parameter optimization. S602, the overall performance index E of the real-time computing system, which is the weighted sum of the system's average efficiency, communication reliability, control accuracy, and maintenance early warning accuracy. S603 dynamically adjusts the system control parameters, modulation parameters, and diagnostic thresholds based on the numerical range of the overall system performance index E.

10. A remote control and status monitoring system for intelligent apartment equipment, used to implement the method according to any one of claims 1-9, characterized in that the system... include: The physical model building module is used to build a physical response principle model for smart apartment equipment; The electrical signal acquisition and feature extraction module is used to acquire the raw electrical signal waveforms during equipment operation in real time and extract feature parameters. The status diagnosis module is used to perform equipment status diagnosis and health assessment based on physical models and characteristic parameters. The adaptive modulation module is used to adaptively adjust the electrical signal modulation and transmission parameters based on the state diagnosis results. The hierarchical control decision module is used to execute multi-level control logic decisions based on state diagnosis results and modulation parameters; The system integration and optimization module is used to continuously calibrate model parameters and modulation strategies through a feedback mechanism.

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