A wiring-free air conditioning data acquisition method, device and equipment

Abstract

The present invention discloses a wiring-free air-conditioning data acquisition method, device and equipment, which collect air-conditioning vibration signals and electromagnetic field data, perform multi-scale decomposition on the vibration signals to generate characteristic frequency bands, cross-validate the characteristic frequency bands with power frequency interference components to generate operation characteristic codes; separate periodic vibration components based on the operation characteristic codes, trace the mechanical energy propagation path to construct an energy flow topology map; extract the thermal gradient field to identify the heat dissipation bottleneck position, and compare with the operation characteristic code to identify the fault symptom point; generate a dynamic sampling interval based on the fault symptom point, form an adaptive sampling sequence and perform sparse processing to generate a data compression table; separate low-frequency energy from the vibration signal to perform electrical energy conversion, resonate and match with the data compression table to generate a transmission time slot, and construct an energy convergence area; use the energy convergence area to trigger a burst transmission window, convert the data into a modulated signal to complete data acquisition, and realize intelligent matching of vibration energy collection and data transmission.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioning equipment monitoring, and in particular to a wiring-free air conditioning data acquisition method, device and equipment. Background Art

[0002] As buildings become increasingly intelligent, air conditioning systems, a major component of building energy consumption, face an increasingly urgent need for operational status monitoring and fault warning. Traditional air conditioning monitoring systems rely primarily on wired sensor networks for data collection. However, during the renovation of existing buildings, wiring installation is not only costly but also detrimental to the building structure, severely hindering the widespread adoption of monitoring systems.

[0003] While existing wireless monitoring technologies have addressed wiring issues to some extent, they often suffer from technical bottlenecks such as high power dependency, frequent battery replacements, and poor data transmission reliability. In the complex electromagnetic environment of air conditioning systems, traditional wireless transmission is particularly susceptible to interference and has high data loss rates, making it difficult to meet the practical needs of long-term, stable monitoring. Furthermore, existing monitoring methods lack the ability to comprehensively analyze the multi-physics field information of air conditioning systems, making it impossible to accurately identify cooling bottlenecks and fault signs. This results in inaccurate early warnings and low maintenance efficiency. Summary of the Invention

[0004] The present invention provides a wiring-free air-conditioning data collection method, device and equipment, aiming to solve technical problems such as complex wiring, power supply dependence, and unstable data transmission in traditional air-conditioning monitoring systems. Through multi-scale fusion analysis of vibration signals and electromagnetic field data, accurate identification of heat dissipation bottlenecks and intelligent early warning of fault signs are achieved. Combined with vibration energy collection and resonance matching transmission technology, a self-powered, wiring-free air-conditioning operation status monitoring system is constructed to achieve intelligent operation and maintenance and energy-saving optimization of the air-conditioning system.

[0005] A first aspect of the present invention provides a wiring-free air conditioning data collection method, comprising the following steps:

[0006] Collect vibration signals and electromagnetic field disturbance data from the air conditioner indoor unit, perform multi-scale decomposition on the vibration signals to generate characteristic frequency bands, extract power frequency interference components based on the electromagnetic field disturbance data, and cross-validate the characteristic frequency bands with the power frequency interference components to generate an operation signature code;

[0007] Separating a periodic vibration component based on the operation characteristic code, tracing a mechanical energy propagation path through the periodic vibration component, sensing temperature changes along the mechanical energy propagation path to form thermal distribution data, and constructing an energy flow topology map based on the thermal distribution data;

[0008] Performing node analysis on the energy flow topology diagram to extract high temperature difference areas, extracting thermal gradient fields from the high temperature difference areas, identifying heat dissipation bottleneck locations based on the thermal gradient fields, and comparing the heat dissipation bottleneck locations with the operating characteristic codes to identify fault symptom points;

[0009] generating a dynamic sampling interval based on the fault symptom point, forming an adaptive sampling sequence using the dynamic sampling interval, performing a sparse processing on the adaptive sampling sequence to extract a sparse feature set, and generating a data compression table based on the sparse feature set;

[0010] Separating low-frequency energy components from the vibration signal and converting them into electrical energy to form energy storage, resonating and matching the energy storage with the data compression table to generate transmission time slots, formulating an intermittent transmission sequence based on the transmission time slots, and constructing an energy convergence area based on the intermittent transmission sequence;

[0011] The energy convergence area is used to trigger a burst transmission window, and the data compression table is converted into a modulated signal based on the burst transmission window, thereby completing wiring-free air-conditioning data collection.

[0012] A second aspect of the present invention provides a wiring-free air conditioning data acquisition device, comprising:

[0013] a signal analysis module for collecting vibration signals and electromagnetic field disturbance data from the air conditioner indoor unit, performing multi-scale decomposition on the vibration signals to generate characteristic frequency bands, extracting power frequency interference components based on the electromagnetic field disturbance data, and cross-validating the characteristic frequency bands with the power frequency interference components to generate an operation signature code;

[0014] a flow field modeling module, configured to separate a periodic vibration component based on the operation characteristic code, trace a mechanical energy propagation path through the periodic vibration component, sense temperature changes along the mechanical energy propagation path to form thermal distribution data, and construct an energy flow topology map based on the thermal distribution data;

[0015] a bottleneck locating module, configured to perform node analysis on the energy flow topology diagram to extract high temperature difference areas, extract a thermal gradient field from the high temperature difference areas, identify a heat dissipation bottleneck location based on the thermal gradient field, and compare the heat dissipation bottleneck location with the operation feature code to identify a fault sign point;

[0016] a data reduction module, configured to generate a dynamic sampling interval based on the fault symptom point, form an adaptive sampling sequence using the dynamic sampling interval, perform a sparse processing on the adaptive sampling sequence to extract a sparse feature set, and generate a data compression table based on the sparse feature set;

[0017] an energy coordination module, configured to separate low-frequency energy components from the vibration signal, convert them into electrical energy, generate energy storage capacity, resonate and match the energy storage capacity with the data compression table to generate transmission time slots, formulate an intermittent transmission sequence according to the transmission time slots, and construct an energy convergence area based on the intermittent transmission sequence;

[0018] The modulation transmission module is used to use the energy convergence area to trigger a burst transmission window, convert the data compression table into a modulation signal based on the burst transmission window, and complete the wiring-free air-conditioning data collection.

[0019] A third aspect of the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of a wiring-free air-conditioning data acquisition method disclosed in the first aspect are implemented.

[0020] The beneficial effects of the present invention are reflected in the following points: 1. Through the multi-scale fusion analysis of vibration signals and electromagnetic field data, combined with the mechanical energy propagation path tracking and energy flow topology map construction, a full-link monitoring system for the operation status of the air conditioner is established, and the complete energy flow monitoring from the vibration source to the heat dissipation terminal is realized, and the fault signs and heat dissipation bottleneck positions of key components such as compressors, fans, and heat exchangers are accurately identified. 2. An intelligent data processing mechanism driven by fault signs is constructed. Through adaptive sampling and sparse processing technology, combined with the resonance matching algorithm of vibration energy collection and data transmission, the dynamic matching of data acquisition frequency and energy supply status is realized, and the energy utilization efficiency of the system is greatly improved while ensuring the monitoring accuracy. 3. A burst transmission strategy triggered by the energy convergence area is established, which transforms the traditional timed data transmission into opportunistic transmission based on energy status. Through the efficient collection of vibration energy and intelligent modulation technology, autonomous operation completely independent of external power supply and communication lines is realized, solving the deployment problem of air conditioning monitoring systems in existing buildings.

[0021] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings herein illustrate specific examples of the technical solutions described in the present invention, and together with the specific implementation methods constitute a part of the specification, and are used to explain the technical solutions, principles and effects of the present invention.

[0023] Unless otherwise specified or defined, the same reference numerals in different drawings represent the same or similar technical features, and the same or similar technical features may also be represented by different reference numerals.

[0024] Figure 1The present invention is a flowchart of a wiring-free air-conditioning data acquisition method.

[0025] Figure 2 This is a structural block diagram of a wiring-free air-conditioning data acquisition device of the present invention.

[0026] Figure 3 It is a structural schematic diagram of a computer device of the present invention. DETAILED DESCRIPTION

[0027] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0028] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0029] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0030] The technical solutions of the embodiments of this application are introduced below.

[0031] like Figure 1 As shown, an embodiment of the present invention provides a wiring-free air conditioning data collection method, including the following steps S110 to S160:

[0032] Step S110 , collecting vibration signals and electromagnetic field disturbance data of the air conditioner indoor unit, performing multi-scale decomposition on the vibration signal to generate characteristic frequency bands, extracting power frequency interference components based on the electromagnetic field disturbance data, and cross-validating the characteristic frequency bands with the power frequency interference components to generate an operation feature code.

[0033] Specifically, vibration signals and electromagnetic field disturbance data are collected from the air conditioner indoor unit. Triaxial vibration sensors are deployed at key locations within the indoor unit, including the compressor housing, fan motor base, refrigerant pipe connection, and indoor unit housing. These sensors are MEMS accelerometers with a measurement range of ±16g and a sampling frequency of 2000Hz. Vibration signal acquisition covers acceleration data in the X, Y, and Z directions, with the data format being [timestamp, sensor number, X-axis acceleration, Y-axis acceleration, Z-axis acceleration]. Electromagnetic field disturbance data is collected using broadband electromagnetic field probes with a measurement frequency range of 1Hz-10kHz. These probes are deployed at three locations: the indoor unit electrical control box, the compressor motor side, and the fan motor side. Electromagnetic field intensity measurements include two components: electric field intensity (E) and magnetic field intensity (H), with data acquisition accuracy of 0.1V / m and 0.1A / m. The acquisition system uses a synchronized trigger mechanism to ensure accurate temporal correspondence between vibration signals and electromagnetic field data. Data preprocessing includes DC component removal, anti-aliasing filtering, and data format standardization, with a filter cutoff frequency set to 1000Hz.

[0034] In some embodiments, the multi-scale decomposition of the vibration signal to generate a characteristic frequency band includes: performing energy distribution analysis on the vibration signal to identify compressor vibration, fan vibration and pipeline vibration; establishing a main frequency template based on the compressor vibration; using the main frequency template to perform frequency calibration on the fan vibration and the pipeline vibration to generate a calibration spectrum; and fusing the main frequency template with the calibration spectrum to generate a characteristic frequency band.

[0035] Energy distribution analysis of vibration signals is performed to identify compressor vibration, fan vibration, and pipeline vibration. The collected triaxial vibration data is used to calculate the vibration energy density distribution at each measurement point. Compressor vibration signatures are identified by analyzing low-frequency components at the compressor casing measurement point. The primary frequency range is 20-120 Hz, corresponding to the compressor rotational frequency and its multiples. Fan vibration signatures are identified based on data from the fan motor base. The characteristic frequency range is 200-800 Hz, encompassing the fan blade pass frequency and the motor pole-pair frequency. Pipeline vibration signatures are identified at the refrigerant pipeline connection point. The frequency range is 80-300 Hz, primarily due to refrigerant flow pulsation and pipeline resonance. Vibration source separation is performed using independent component analysis, with the separation matrix W being derived by maximizing non-Gaussianity. Energy contribution is used to calculate the relative strength of each vibration source. Compressor vibration typically contributes 40%-60%, fan vibration 25%-35%, and pipeline vibration 15%-25%. Vibration amplitude statistics, including root mean square (RMS), peak value, and peak-to-peak value, are used to quantify the intensity characteristics of different vibration sources.

[0036] Exemplarily, the establishing of a main frequency template based on the compressor vibration includes: performing frequency hierarchical decomposition of the compressor vibration to generate a basic frequency layer and a harmonic frequency layer; establishing a frequency anchor point based on the basic frequency layer; using the frequency anchor point to perform frequency alignment on the harmonic frequency layer to generate an aligned frequency; and synthesizing the basic frequency layer and the aligned frequency to establish a main frequency template.

[0037] Compressor vibration is subjected to a hierarchical frequency decomposition process to generate a fundamental frequency layer and a harmonic frequency layer. The separated compressor vibration signal is then subjected to the empirical mode decomposition method for frequency segmentation. The fundamental frequency layer corresponds to the compressor's fundamental frequency component, with a frequency range of 20-60 Hz, including the compressor rotational frequency f_rot and the motor pole-pole frequency f_pole. The harmonic frequency layer contains integer multiples of the fundamental frequency, with the primary harmonics being the 2nd, 3rd, and 4th harmonics, ranging from 60-240 Hz. This hierarchical decomposition is achieved using intrinsic mode functions (IMFs), each corresponding to a specific frequency scale. The amplitude characteristics of the fundamental frequency layer are obtained through envelope detection, and the envelope signal reflects the variation of the compressor load. The phase relationship of the harmonic frequency layer is calculated using the Hilbert transform: the phase difference Δφ = φ_harmonic - n × φ_fundamental, where n is the harmonic order. The stability of the frequency layer is assessed by frequency variance, and frequency components with a variance less than 5% are considered stable.

[0038] Frequency anchors are established based on the base frequency layer. The frequency anchors serve as the benchmark for frequency alignment. The frequency components with the largest amplitude and most stability within the base frequency layer are selected, prioritizing frequency components with a signal-to-noise ratio greater than 20dB to ensure anchor reliability. The anchor frequency f_anchor is determined by power spectral density peak detection, using parabolic interpolation to improve frequency resolution. It typically corresponds to the compressor speed or its doubled frequency, and the frequency range is concentrated between 30 and 80 Hz. The anchor strength A_anchor, defined as the power spectral density value at the anchor frequency, is used for weighting in subsequent frequency calibration. The strength is calculated using a three-point averaging method to minimize noise impact. The anchor bandwidth B_anchor is set to ±5% of the anchor frequency, covering the frequency range adjacent to the anchor frequency. Frequency components within the bandwidth are uniformly attributed to the anchor's domain of influence. Anchor stability is assessed using time-domain sliding window analysis, with a window length of 5 seconds and a 50% overlap ratio. The stability coefficient is defined as the inverse of the anchor frequency variance. Anchors with a coefficient greater than 10 are considered highly stable. The anchor point parameter records include characteristic parameters such as anchor point frequency, strength, bandwidth, stability coefficient, etc., and also record the anchor point confidence and effective duration information.

[0039] Frequency anchor points are used to align the harmonic frequency layer to produce aligned frequencies. Based on the proportional relationship between the anchor frequency and the harmonic frequency, the harmonic frequencies are adjusted to maintain a strict frequency-multiplication relationship. The alignment process uses an iterative algorithm to gradually approach the ideal frequency position. The alignment algorithm first identifies candidate harmonic components in the harmonic frequency layer and scans the neighborhood of integer multiples of the anchor frequency using a frequency search window with a search window width set to ±3 Hz. The algorithm calculates the deviation between the measured harmonic frequency and the ideal harmonic frequency: Δf = f_measured - n × f_anchor, where f_measured is the measured harmonic frequency and n is the theoretical harmonic number, ranging from 2 to 8 harmonics. Frequency correction is performed using a linear interpolation method combined with a weighted averaging strategy. The corrected frequency, f_aligned, is calculated as f_measured - k × Δf, where k is the correction factor, set to 0.8. When the deviation exceeds 5 Hz, the correction factor is adjusted to 0.6 to avoid overcorrection. Alignment accuracy requires that harmonic frequency deviations be less than 2% of the fundamental frequency, typically within ±1 Hz. Harmonic components exceeding these accuracy requirements are marked as abnormal frequencies and excluded from the alignment process. Alignment weights are statistically determined based on anchor point strength, harmonic amplitude, and source amplitude. The weight calculation formula is w = α × A_anchor + β × A_harmonic + γ × A_source, where α, β, and γ are weight coefficients. Frequency components with large peaks and high RMS values ​​receive higher weights. The amplitudes of the aligned frequencies remain unchanged; only the frequency positions are adjusted to satisfy the harmonic relationship. This results in an aligned frequency spectrum, F_aligned(f), containing complete harmonic frequency component information.

[0040] The base frequency layer and the aligned frequency layer are combined to create a primary frequency template. The original frequency components of the base frequency layer are synthesized with the aligned harmonic frequency components in the frequency domain. A phase-preserving strategy is used during the synthesis process to ensure that the phase relationship between the frequency components is not disrupted. The synthesis method uses frequency domain superposition. The template spectrum T(f) = F_base(f) + F_aligned(f), where F_base(f) is the base frequency layer spectrum and F_aligned(f) is the aligned frequency spectrum. During the superposition process, a maximum amplitude selection strategy is used in the overlapping frequency regions to avoid spectral contamination. The template resolution is set to 0.5 Hz, the frequency range covers 20-300 Hz, and the total number of frequency points is 560. Zero padding is used to improve frequency domain interpolation accuracy. Template feature parameters extracted include key features such as center frequency, effective bandwidth, peak frequency, and valley frequency. The center frequency is calculated by calculating the spectral centroid. The effective bandwidth is defined as the frequency range containing 90% of the energy. The peak frequency is the location of the global maximum of the template spectrum. The template data structure contains fields such as frequency coordinates, amplitude coordinates, phase information, and feature parameters.

[0041] A master frequency template is used to calibrate fan and piping vibration frequencies to produce a calibration spectrum. The master frequency template provides a frequency reference for adjusting the frequency coordinates of fan and piping vibrations. First, the frequency components of fan and piping vibrations related to the master frequency template are identified, and then proportionally adjusted based on the template frequency. Fan vibration calibration focuses on the relationship between the fan rotational frequency and its harmonics and the compressor frequency. The rotational frequency ratio is typically 3-8 times the compressor rotational frequency. Piping vibration calibration is based on the coupling relationship between the pipeline resonant frequency and the compressor excitation frequency. The calibrated pipeline frequency should correspond to the compressor's frequency harmonic components. Calibration accuracy controls frequency deviation within a ±1Hz range; frequency components exceeding this accuracy remain unchanged at their original frequencies. The calibration spectrum retains the original amplitude information, adjusting only the frequency axis coordinates to achieve frequency alignment. The calibrated spectra are labeled as the fan calibration spectrum and the piping calibration spectrum, respectively, for ease of subsequent processing.

[0042] The main frequency template and the calibration spectrum are fused to generate characteristic frequency bands. This characteristic band fusion utilizes a frequency domain synthesis method, superimposing the main frequency template, fan calibration spectrum, and pipeline calibration spectrum according to weights. Fusion weights are dynamically assigned based on the measured vibration source energy proportions: a compressor weight of 0.5, a fan weight of 0.3, and a pipeline weight of 0.2. The fused spectrum F_fusion(f) = w1 × T(f) + w2 × F_fan(f) + w3 × F_pipe(f), where w1, w2, and w3 are normalized weight coefficients, T(f) is the main frequency template, F_fan(f) is the fan calibration spectrum, and F_pipe(f) is the pipeline calibration spectrum. Characteristic frequency band partitioning divides the fused spectrum into three frequency bands: low (20-100Hz), mid (100-400Hz), and high (400-1000Hz). Feature parameters extracted for each frequency band include in-band energy, peak frequency, bandwidth, and number of peaks. The temporal characteristics of the characteristic frequency bands were analyzed by short-time Fourier transform with a time window length of 1 second and an overlap rate of 75%.

[0043] Power frequency interference components are extracted based on electromagnetic field disturbance data. Using the collected electromagnetic field strength data, the 50Hz power frequency and its harmonic interference components are identified and extracted. The power frequency fundamental wave is identified using a 50Hz bandpass filter with a bandwidth set to ±2Hz. Power frequency harmonic components, including those at multiples of 100Hz, 150Hz, and 200Hz, are extracted using a comb filter bank. The electric field power frequency component E_50Hz and the magnetic field power frequency component H_50Hz are calculated separately, and their intensity is calculated using the RMS method. The power frequency interference intensity is assessed using total harmonic distortion (THD), which reflects the severity of the power frequency interference. The time-varying characteristics of the interference components are analyzed using a sliding RMS analysis with a 10-second sliding window and a 1-second step size. Interference spectrum characteristics include parameters such as interference frequency, interference intensity, interference bandwidth, and spectral purity. Interference phase information is obtained through phase-locked detection, and phase stability is used to assess the stability of the interference source.

[0044] The characteristic frequency band is cross-validated with the power frequency interference component to generate an operating signature code. The presence and intensity consistency of the 50Hz and its harmonic components within the characteristic frequency band are calculated. A frequency matching test confirms the presence of frequency components corresponding to the power frequency within the characteristic frequency band, with a matching accuracy of ±1Hz. Amplitude correlation analysis calculates the correlation coefficient between the vibration characteristic frequency band and the electromagnetic interference intensity. A correlation coefficient greater than 0.7 and a THD value greater than 5% are considered strongly correlated anomalies. A phase consistency test compares the phase difference between the vibration signal and the electromagnetic signal at the power frequency. The stability of the phase difference reflects the degree of signal coupling. The operating signature code is encoded in binary format, with 8 bits representing the characteristic status of different frequency bands and power frequency components. The encoding rules are as follows: low-frequency anomalies are assigned the first bit, mid-frequency anomalies the second bit, high-frequency anomalies the third bit, power frequency interference the fourth to sixth bits, stability coefficients less than 0.9 the seventh bit, and correlation anomalies the eighth bit. The signature code is updated every minute to reflect changes in the equipment's operating status in real time. A signature code database establishes signature codes corresponding to different operating states for fault diagnosis and status identification.

[0045] Step S120 , separating the periodic vibration component based on the operation characteristic code, tracing the mechanical energy propagation path through the periodic vibration component, sensing the temperature change along the mechanical energy propagation path to form thermal distribution data, and constructing an energy flow topology diagram based on the thermal distribution data.

[0046] Specifically, periodic vibration components are isolated based on the operating signature code. Bits 1-3 of the signature code correspond to the low-frequency, mid-frequency, and high-frequency bands. A value of 1 indicates the presence of periodic vibration, while a value of 0 indicates random vibration or noise. Periodic vibration components are extracted using autocorrelation analysis. The autocorrelation function R(τ) = E[x(t) × x(t+τ)], where x(t) is the vibration signal, τ is the time delay, and E is the mathematical expectation. The periodicity threshold is set to an autocorrelation peak value greater than 0.6, and the corresponding delay time τ_peak is the vibration period. Vibration component separation utilizes a bandpass filter bank, with filter parameters set according to the frequency band range indicated by the signature code. The low-frequency filter has a passband of 20-100 Hz, the mid-frequency filter has a passband of 100-400 Hz, and the high-frequency filter has a passband of 400-1000 Hz. The amplitude of the periodic vibration component is obtained using envelope detection, and the envelope signal A_env(t) reflects the time-varying characteristics of the vibration intensity. The phase information is extracted through Hilbert transform, phase φ(t)=arctan(Im[H(x(t))] / Re[H(x(t))]), where H(x(t)) is the analytical signal.

[0047] The propagation path of mechanical energy is tracked through periodic vibration components. Based on the frequency and phase information of the separated periodic vibration components, the propagation trajectory of the vibration energy in the air conditioning system is calculated. The mechanical energy propagation speed is determined by the medium: the elastic wave velocity in metal pipes is approximately 5000 m / s, in plastic pipes is approximately 2000 m / s, and in air is approximately 340 m / s. The propagation speed is dynamically adjusted along the path. Propagation path tracking uses a wavefront tracking algorithm. The starting point is set at the vibration source location, the propagation direction is determined by the phase gradient, and the propagation intensity is determined by the envelope amplitude A_env(t). The formula for calculating the path node position is r_n=r_0+v×t_n, where r_n is the nth node position, r_0 is the starting position, v is the propagation velocity, and t_n is the propagation time. Propagation attenuation takes into account distance attenuation and dielectric loss. The attenuation coefficient α_total=α_distance+α_material, where the distance attenuation coefficient α_distance=1 / r², and the material loss coefficient α_material is determined by the dielectric type. Propagation path branching takes energy distribution into account, with the branch energy ratio determined by cross-sectional area ratio and impedance matching. Path tracing is terminated when the energy decays to less than 5% of the initial value or the propagation distance exceeds 5 meters.

[0048] Temperature changes along the mechanical energy propagation path are sensed to generate thermal distribution data. Temperature sensors are placed along the defined mechanical energy propagation path, with sensor spacing set at 1 / 10 of the path length, with a minimum spacing of 20 cm. Temperature sensors are thermocouples or infrared temperature measurement devices, with a measurement accuracy of ±0.5°C and a response time of less than 10 seconds. Temperature data is collected synchronously with the vibration signal, sampling at a 10Hz frequency. The acquisition time is determined based on the propagation time sequence, ensuring a precise temporal correspondence between temperature changes and mechanical energy propagation. The temperature gradient is calculated using the temperature difference between adjacent measuring points: dT / dr = (T_i+1-T_i) / (ri_i+1-ri_i), where T_i is the temperature at the i-th measuring point and ri is the measuring point location. Heat flux is estimated using Fourier's law q = -k × (dT / dr), corrected by the energy decay sequence. The corrected heat flux q' = q × (1-α_total), where α_total is the decay coefficient at the corresponding location. Thermal distribution features extracted include the location of peak temperature, the location of maximum temperature gradient, and heat flow convergence and divergence points. Spatial interpolation of thermal distribution data uses radial basis functions to generate a continuous temperature field distribution. The temporal evolution of the thermal distribution is analyzed using a sliding window with a window length of 60 seconds and a 50% overlap.

[0049] In some embodiments, constructing an energy flow topology diagram based on the heat distribution data includes: identifying an evaporator heat source and a condenser heat source based on the heat distribution data; performing heat flow diffusion detection from the evaporator heat source and the condenser heat source outward to form a diffusion range; establishing a heat flow node connection relationship within the diffusion range; and constructing an energy flow topology diagram based on the node connection relationship.

[0050] Evaporator and condenser heat sources are identified based on heat distribution data. Peak temperature and heat flux information from the heat distribution data is used to identify the primary heat source locations. Evaporator heat sources are identified by locating them in the low-temperature region, with a temperature threshold set at ambient temperature minus 5°C, corresponding to the evaporation temperature under cooling conditions. Condenser heat sources are identified by locating them in the high-temperature region, with a temperature threshold set at ambient temperature plus 10°C, corresponding to the condensation temperature. Heat source intensity is calculated by integrating the temperature gradient around the heat source: intensity Q = ∮(k × dT / dr) × dA, where k is the thermal conductivity and dA is the area element. Heat source location accuracy is determined by calculating the temperature centroid, whose coordinates are the weighted average of the temperatures at each measurement point. Evaporator heat sources are typically located near the indoor heat exchanger and are characterized by a negative heat source (heat absorption). Condenser heat sources are located near the outdoor heat exchanger and are characterized by a positive heat source (heat release). Heat source stability is assessed through time series analysis, with heat sources with temperature fluctuations of less than 2°C considered stable. Heat source parameter records include heat source location coordinates, heat source intensity, heat source type, stability coefficient and other information.

[0051] The diffusion range is formed by performing heat flow diffusion detection from the evaporator heat source and the condenser heat source. The diffusion detection takes the heat source position as the starting point and tracks the heat flow propagation trajectory along the heat flux density gradient direction. The diffusion algorithm uses the particle tracking method, and the heat flow particles move along the negative gradient direction of the heat flux density field. Diffusion speed , where D is the thermal diffusivity, is the heat flux gradient. The diffusion boundary is determined as the position where the heat flux decays to 1 / e at the heat source, corresponding to the effective diffusion range. The diffusion range of the evaporator is usually distributed in an elliptical shape, with the major axis corresponding to the length direction of the heat exchanger and the minor axis corresponding to the thickness direction. The diffusion range of the condenser is affected by the fan and is distributed in a fan shape, and the fan angle is determined by the wind direction and wind speed. The temperature field within the diffusion range is approximated by the Gaussian distribution, and the distribution parameters are determined by the heat source intensity and diffusion coefficient. The overlapping area of ​​multi-heat source diffusion is processed by the superposition principle, and the heat flux in the overlapping area is the vector sum of the contributions of each heat source. The diffusion range data includes parameters such as boundary coordinates, diffusion intensity distribution, and main diffusion direction.

[0052] Exemplarily, establishing a heat flux node connection relationship within the diffusion range includes: performing a heat flux density scan on the diffusion range to identify heat flux-rich areas and heat flux-sparse areas; establishing a heat flux attraction effect based on the heat flux-rich areas; utilizing the heat flux attraction effect to attract and aggregate the heat flux-sparse areas to generate aggregated heat flux; and establishing a node connection relationship between the heat flux-rich areas and the aggregated heat flux.

[0053] Heat flux scanning was performed within the diffusion range to identify heat-rich and heat-sparse regions. A grid-based heat flux scan was performed within the defined diffusion range, with a grid resolution of 5 cm × 5 cm. Heat flux thresholds were set as follows: the threshold for enriched regions was set at least 1.5 times the average heat flux, the threshold for sparse regions was set at less than 0.5 times the average heat flux, and intermediate regions fell between the two thresholds. Connected domain analysis was used to identify enriched regions, merging adjacent high-heat flux grids into continuous regions. Heat flux convergence points were preferentially selected as the centers of enriched regions. Sparse regions were identified using the same method, marking continuous regions with low heat flux. Areas surrounding heat flux divergence points generally corresponded to sparse regions. Characteristic parameters for enriched regions included area, average heat flux, peak heat flux, and coordinates of the region's centroid. Characteristic parameters for sparse regions included area, minimum heat flux, shape coefficient, and distance from the enriched region. Heat flux distribution was visualized using pseudo-color images, with red indicating enriched regions, blue indicating sparse regions, and green indicating intermediate regions.

[0054] A heat attraction effect is established based on heat flux enrichment zones. This effect simulates the convergence of heat flux by a heat flux enrichment zone, similar to the universal gravitational force in a gravitational field. The attraction intensity F is related to the heat flux intensity Q and distance r of the enrichment zone as F = k × Q / r², where k is the attraction coefficient. The attraction range is defined as a circular region extending outward from the enrichment zone boundary, with an extension radius twice the equivalent radius of the enrichment zone. The attraction effect is directed toward the center of gravity of the enrichment zone, forming a centripetal heat flux convergence pattern. The attraction intensity is graded based on the heat flux density of the enrichment zone: k = 2.0 for strongly enriched zones, k = 1.5 for moderately enriched zones, and k = 1.0 for weakly enriched zones. The temporal characteristics of the attraction effect account for thermal inertia, and the response time constant τ is determined based on heat capacity. The attraction effect for multiple coexisting enrichment zones adopts the superposition principle, with the total attraction being the vector sum of the attractions of each zone.

[0055] The attraction effect of heat flow is used to attract and aggregate sparse heat flow areas, generating agglomerated heat flow. The attraction and aggregation process simulates the movement of heat flow from sparse areas toward enriched areas under the influence of attraction. The aggregation algorithm utilizes particle swarm optimization principles. Heat flow particles in sparse areas are attracted to enriched areas and move toward them. The particle motion equation is dv / dt=F / m, where v is the particle velocity, F is the force of attraction, and m is the heat capacity equivalent mass. The aggregation path calculation considers the combined attraction effect of multiple enriched areas, with particles moving in the direction of the resultant force. The aggregation velocity is affected by the damping of the medium, and the damping coefficient is determined based on the heat transfer medium. Heat flow loss during the aggregation process is described by the attenuation coefficient. Heat flow aggregated over long distances experiences some loss. Aggregation is terminated when the heat flow particles reach the boundary of the enriched area or when the aggregation time exceeds a set threshold. The intensity of the aggregated heat flow is the sum of the heat flow intensities of the participating sparse areas, minus propagation losses.

[0056] A node connection relationship is established between the heat flow enrichment area and the aggregate heat flow. The node connection relationship establishes a directed connection graph based on the propagation path and intensity of the heat flow. The enrichment area serves as the sink node, the starting position of the aggregate heat flow serves as the source node, and the connection edge represents the heat flow propagation path. The connection strength weight is determined according to the intensity of the aggregate heat flow, and the connection weight with a large intensity is high. The connection direction points from the source node to the sink node, indicating the propagation direction of the heat flow. The connection length is the geometric length of the aggregate path and is used to calculate the propagation time and loss. When multiple aggregate heat flows converge into the same enrichment area, a many-to-one connection relationship is established. The stability of the connection relationship is evaluated through time series analysis, and the strength fluctuation of stable connections is less than 20%. The connection weight normalization process ensures that the sum of all connection weights is 1.0.

[0057] Heat flow node connections are established within the diffusion range. Node connection relationships are constructed based on the physical laws of heat flow propagation and diffusion test results. Key nodes include heat source nodes (evaporators, condensers), propagation nodes (critical points on the propagation path), and sink nodes (heat flow convergence points). Connection relationships are represented using graph theory, with nodes as graph vertices and heat flow propagation paths as graph edges. Edge weights are determined based on the heat flow intensity and propagation impedance: weight w = q / R, where q is the heat flow intensity and R is the thermal conduction impedance. The connection topology considers both parallel and series heat flow relationships: weights for parallel paths are summed, and impedances for series paths are summed. Node degree represents the number of edges connected to that node; nodes with high degrees are key heat transfer nodes. The dynamic characteristics of the connection relationships are represented using a time-varying graph, where edge weights change over time. Connection relationships are verified through energy conservation checks, ensuring that the input heat flow at each node is equal to the output heat flow.

[0058] An energy flow topology map is constructed based on node connectivity. The energy flow topology map uses a directed graph structure, with nodes representing heat sources, key points in heat transfer paths, and heat sinks, and edges representing energy flow propagation paths. The graph data structure consists of a node list, an edge list, an adjacency matrix, and a weight matrix. Node attributes record parameters such as node location, node type, heat capacity, and temperature. Edge attributes record parameters such as propagation distance, propagation time, thermal resistance, and heat flux intensity. The topology map is hierarchically structured into three layers: the source layer (heat source), the propagation layer (intermediate nodes), and the sink layer (heat sink). Graph connectivity analysis identifies connected components and isolated nodes, with regions of strong connectivity identified as primary energy flow pathways. The topology map is visualized using a force-directed layout algorithm, with node size representing heat capacity and edge thickness representing heat flux intensity. A topology map update mechanism dynamically adjusts the graph structure based on changes in heat distribution data. Graph property analysis includes network characteristic parameters such as node centrality, edge betweenness, and clustering coefficient. Through systematic analysis of heat distribution data and the establishment of node connectivity relationships, an energy flow topology map is ultimately constructed.

[0059] Step S130 , performing node analysis on the energy flow topology diagram to extract high temperature difference areas, extracting the thermal gradient field of the high temperature difference areas, identifying the heat dissipation bottleneck location based on the thermal gradient field, and comparing the heat dissipation bottleneck location with the operation feature code to identify the fault sign point.

[0060] Specifically, node analysis of the energy flow topology is performed to extract high-temperature difference regions. The temperature difference between adjacent nodes is calculated as ΔT_ij = |T_i - T_j|, where T_i and T_j represent the temperature values ​​of adjacent nodes i and j. A 5°C temperature difference threshold is set for high-temperature difference region identification. Regions with temperature differences exceeding the threshold are marked as high-temperature difference regions. Temperature difference distribution statistics include characteristic parameters such as mean temperature difference, maximum temperature difference, and standard deviation. The spatial connectivity of high-temperature difference regions is analyzed using a connected domain algorithm, merging adjacent high-temperature difference nodes into continuous regions. Region boundaries are defined as the intersection of high-temperature difference nodes and nodes with normal temperature differences. High-temperature difference intensity is assessed using a weighted average of the temperature differences within the region, with weights assigned based on the heat capacity of the nodes. Region shape features include geometric parameters such as area, perimeter, compactness, and aspect ratio. High-temperature difference regions are classified according to the degree of temperature difference: strong high-temperature difference regions (ΔT > 10°C), moderate high-temperature difference regions (5°C < ΔT < 10°C), and weak high-temperature difference regions (3°C < ΔT < 5°C).

[0061] Extract the thermal gradient field of the high temperature difference area. Calculate the temperature gradient vector field of each point in the space within the determined high temperature difference area. The thermal gradient calculation uses the finite difference method, and the gradient component , , where Δx and Δy are spatial step sizes set to 2 cm. Gradient amplitude , indicating the severity of temperature change. Gradient direction angle , indicating the direction of the fastest temperature increase. The spatial resolution of the thermal gradient field is dynamically set according to the regional compactness: a fine resolution of 1 cm × 1 cm is adopted for regions with high compactness, and a standard resolution of 2 cm × 2 cm is adopted for regions with low compactness. The gradient field interpolation process uses bicubic interpolation to ensure the spatial continuity of the gradient field. The extraction of gradient field features includes key feature points such as the positions of gradient peaks, gradient zeros, and gradient saddle points. The extraction density is adjusted according to the classification of high-temperature difference regions: high density extraction is adopted for strong high-temperature difference regions, medium density for medium high-temperature difference regions, and sparse extraction for weak high-temperature difference regions. The streamline calculation of the gradient field uses the fourth-order Runge-Kutta method, and the streamline equation .

[0062] In some embodiments, identifying the heat dissipation bottleneck position based on the thermal gradient field includes: evaluating the heat dissipation impedance strength based on the thermal gradient field to determine the bottleneck identification accuracy, where the heat dissipation impedance strength includes gradient steepness, heat resistance concentration, and heat dissipation path curvature; setting an impedance threshold according to the bottleneck identification accuracy; using the impedance threshold to screen high-impedance regions in the thermal gradient field; and marking the peak points of the high-impedance regions as the heat dissipation bottleneck positions.

[0063] Evaluating the heat dissipation impedance strength based on the thermal gradient field to determine the bottleneck identification accuracy. The heat dissipation impedance strength is evaluated according to three key characteristic parameters of the thermal gradient field. Gradient steepness , where s is the distance along the streamline direction, indicating the sharpness of the gradient change. The heat resistance concentration C is calculated through the variance of the gradient field, , where is the standard deviation of the gradient amplitude, is the mean value of the gradient amplitude, reflecting the spatial concentration degree of the heat resistance. The heat dissipation path curvature κ is calculated through the streamline curvature, κ = |dθ / ds|, where θ is the angle of the streamline tangent direction and s is the arc length parameter. The comprehensive index R of the heat dissipation impedance strength is R = α × S + β × C + γ × κ, where α, β, and γ are weight coefficients with values of 0.4, 0.4, and 0.2 respectively. The bottleneck identification accuracy P is determined according to the impedance strength grading, and the accuracy grading algorithm is based on the correspondence between the impedance strength R value and the spatial characteristics of the heat dissipation bottleneck. Strong impedance regions (R > 2.0) correspond to highly concentrated heat dissipation bottlenecks, with clear bottleneck boundaries and limited spatial ranges, requiring high-precision positioning, and the accuracy is set to P = ±1 cm. Medium impedance regions (1.0 < R < 2.0) correspond to medium-level heat dissipation obstacles, with relatively clear bottleneck features but slightly blurred boundaries, and the accuracy is set to P = ±2 cm. Weak impedance regions (R < 1.0) correspond to slight heat dissipation non-uniformity, with insufficiently significant features and being easily affected by environmental noise, and the accuracy is set to P = ±5 cm. The value of the accuracy level P directly affects the coefficient selection in subsequent threshold setting and the parameter configuration of the screening algorithm. The temporal stability of the impedance strength is analyzed through a sliding window, with a window length of 30 seconds, to evaluate the persistence characteristics of the impedance.

[0064] The impedance threshold is set based on bottleneck detection accuracy. The threshold calculation formula is Th = μ_R + k × σ_R, where μ_R is the regional average impedance strength, σ_R is the standard deviation of the impedance strength, and k is adjusted based on the regional average temperature difference: k is increased by 20% when the average temperature difference exceeds 8°C. The value of the threshold coefficient k is determined entirely by the bottleneck detection accuracy P. High-precision areas (P = ±1 cm) require strict bottleneck detection standards and use a threshold coefficient k = 1.5. A smaller coefficient value improves detection sensitivity and ensures that even small impedance changes within the high-precision area are detected. Medium-precision areas (P = ±2 cm) allow for a moderate detection tolerance and use a threshold coefficient k = 2.0. This moderate coefficient value strikes a balance between detection sensitivity and stability. Low-precision areas (P = ±5 cm) prioritize avoiding false positives and use a threshold coefficient k = 2.5. A larger coefficient value reduces detection sensitivity and avoids overly sensitive false positives in areas with less distinct features. The accuracy-driven coefficient selection mechanism ensures that the threshold setting is consistent with spatial positioning requirements. Dynamic threshold adjustment takes into account ambient temperature fluctuations, increasing the threshold by 10%-15% as the ambient temperature rises. Multi-level threshold settings include a primary threshold (Th_primary) and a secondary threshold (Th_secondary) equal to 0.8 × Th_primary. The primary threshold is used to identify candidate bottleneck areas, while the secondary threshold is used to refine the boundary. Threshold parameter records include the threshold value, accuracy level, validity period, and adjustment coefficient.

[0065] An impedance threshold is used to screen high-impedance regions in the thermal gradient field. Using the impedance threshold Th, a threshold segmentation algorithm is used to identify regions with impedance strength greater than the set threshold as candidate bottleneck regions. Since different precision regions use different threshold coefficients, the threshold is selected based on the precision region to which the grid point belongs during the screening process: Th_high = μ_R + 1.5 × σ_R for high-precision regions, Th_mid = μ_R + 2.0 × σ_R for medium-precision regions, and Th_low = μ_R + 2.5 × σ_R for low-precision regions. The screening process first calculates the impedance strength of the thermal gradient field and then binarizes it using a threshold condition. Connected domain analysis merges adjacent high-impedance pixels into continuous regions, prioritizing the gradient peak location as the connected domain seed point. The minimum connected domain area is set to 4 cm². Regional morphological processing includes opening and closing operations to eliminate isolated noise points and fill internal voids. Geometric features of high-impedance regions are calculated, including parameters such as the region's centroid, equivalent ellipse, and principal axis direction. Regional intensity features include average impedance, peak impedance, and impedance gradient within the region. After screening, high-impedance areas are sorted by intensity, with the top five areas of highest intensity being selected as key areas of focus. Regional boundary refinement is adjusted based on the level of recognition accuracy: high-precision areas use sub-pixel boundary positioning, medium-precision areas use standard boundary tracking, and low-precision areas use simplified boundary detection.

[0066] Mark the peak points in the high impedance regions as the heat dissipation bottleneck positions. Utilize the high impedance region information to detect peak points within the boundary contours of each high impedance region. The search range is limited within the region boundary, and the centroid coordinates of the region are used as the starting point for the search. The heat dissipation bottleneck position is determined as the local maximum point of the impedance intensity within each high impedance region. Peak detection uses a local extreme value search algorithm to find the pixel point with the maximum impedance intensity within a 3×3 neighborhood. The optimization of peak point accuracy is processed according to the corresponding recognition accuracy level of the high impedance region: for high-precision regions (±1 cm), sub-pixel interpolation and quadratic surface fitting are used, and the positioning accuracy reaches 0.1 cm; for medium-precision regions (±2 cm), bilinear interpolation is used, and the positioning accuracy is 0.5 cm; for low-precision regions (±5 cm), the grid point coordinates are directly used, and the positioning accuracy is 1 cm. The bottleneck position coordinates (x_peak, y_peak) are determined by the extreme points of the fitted surface, and the coordinate accuracy is consistent with the recognition accuracy level of the所属区域. Multiple peak processing considers that there may be multiple local peaks within a high impedance region, and the main peak is selected according to the peak intensity ranking. The bottleneck intensity level is divided into severe bottleneck (R_peak > 3.0), medium bottleneck (2.0 < R_peak < 3.0), and mild bottleneck (I.5 < R_peak < 2.0) according to the peak impedance intensity. Analyze the air conditioning components corresponding to the determined bottleneck points, such as heat exchanger fins, pipe joints, and duct bends. The stability evaluation of the bottleneck position uses a drift threshold matching the recognition accuracy: within the high-precision region, a drift less than 1 cm is considered stable; within the medium-precision region, a drift less than 2 cm is considered stable; within the low-precision region, a drift less than 5 cm is considered stable.

[0067] It should be noted that there is an unclear expression "所属区域" in the original text which is translated as "所属区域" in the above translation. It may need to be further clarified and corrected according to the actual context.The heat dissipation bottleneck location is compared with the operating signature code to identify fault symptom points. Based on the previously determined heat dissipation bottleneck location data structure, the bottleneck's precise coordinates are used to determine the nearest vibration measurement point. Spatial distance calculation selects measurement points within 50 cm as associated measurement points. The corresponding S110 operating signature code status is then extracted for abnormality determination. The signature code abnormality determination rule combines the bottleneck severity level and corresponding component information: when any of bits 1-3 is 1 and the bottleneck's corresponding component is a compressor or fan, it is determined to be a mechanical fault symptom; when bits 4-6 are abnormal and the bottleneck's corresponding component is the electronic control system, it is determined to be an electrical fault symptom; when bits 7-8 are abnormal and the bottleneck's severity level is severe, it is determined to be a systemic fault symptom. The symptom point severity is assessed directly using the bottleneck's peak intensity R_peak and the severity of the signature code abnormality. The evaluation formula is Score = w1 × R_peak + w2 × Code_abnormal, where w1 and w2 are weight coefficients set to 0.6 and 0.4, respectively, and Code_abnormal is the severity of the signature code abnormality. Symptom point classification is automatically categorized based on the corresponding component fields of the bottleneck. Symptom point reliability assessment utilizes bottleneck stability information. Only bottlenecks marked as "stable" are considered reliable fault symptom points. Temporal correlation analysis checks the consistency between the bottleneck occurrence time and the signature code anomaly time. A time difference of less than 5 minutes is considered a strong correlation. Symptom point development trend prediction predicts the likely time of fault occurrence by analyzing the time series of bottleneck intensity and signature code changes. A comprehensive comparison and analysis of the cooling bottleneck location and operational signature codes ultimately identifies potential fault symptom points in the air conditioning system.

[0068] Step S140 , generating a dynamic sampling interval based on the fault symptom point, forming an adaptive sampling sequence using the dynamic sampling interval, performing sparse processing on the adaptive sampling sequence to extract a sparse feature set, and generating a data compression table based on the sparse feature set.

[0069] Specifically, a dynamic sampling interval is generated based on the fault symptom points. Using the fault symptom point information, the data sampling interval is adjusted according to the severity of the symptom points and the fault type. The severity level of the symptom points is classified using an evaluation score Score. The sampling interval corresponding to severe symptom points (Score > 2.5) is set to 0.1 seconds, the sampling interval for medium symptom points (1.5 < Score < 2.5) is 0.5 seconds, and the sampling interval for mild symptom points (Score < 1.5) is 2 seconds. The fault type affects the sampling strategy. For compressor fault symptoms, the vibration signal is monitored intensively, and the sampling interval is shortened to 0.05 seconds; for fan fault symptoms, the change in electromagnetic field is concerned, and the sampling interval is 0.2 seconds; for heat exchanger fault symptoms, temperature monitoring is emphasized, and the sampling interval is 1 second. The dynamic adjustment mechanism adjusts the sampling interval in real time according to the development trend of the symptom points. When the symptom is strengthened, the sampling interval is halved; when the symptom is weakened, the sampling interval is doubled. The upper limit of the sampling interval is set to 10 seconds, and the lower limit is set to 0.01 seconds to prevent over-sampling or under-sampling. The time window sliding strategy uses an overlapping window with a window length of 10 times the sampling interval and an overlapping rate of 50%.

[0070] An adaptive sampling sequence is formed using the dynamic sampling interval. The original data stream is re-sampled through the dynamic sampling interval. The sampling sequence includes three types of information: vibration signal, electromagnetic field data, and temperature data. Each type of data is collected according to the corresponding sampling interval. The sequence synchronization mechanism ensures that data with different sampling frequencies are aligned on the time axis. The time window sliding strategy is used for data alignment with a window length of 10 times the sampling interval, an overlapping rate of 50%, and the reference time unit is the greatest common divisor. Data interpolation processing upsamples the low-frequency sampled data and uses cubic spline interpolation to maintain data continuity. The sequence marking system adds identification information such as timestamp, data type, sampling interval, and symptom point association to each sampling point. Adaptive weight allocation adjusts the data weight according to the importance of the symptom points, and the weight of the relevant data for important symptom points is increased by 2 - 3 times. The sequence cache management uses a circular buffer, and the buffer size is determined according to the highest sampling frequency and storage capacity. Data preprocessing includes outlier detection, noise filtering, and amplitude normalization to ensure the quality of the sequence data.

[0071] In some embodiments, the sparse feature set is extracted by sparsifying the adaptive sampling sequence, including: performing data density analysis on the adaptive sampling sequence to generate dense sampling areas and sparse sampling areas; identifying key operating state data based on the dense sampling areas; constructing a data retention rule using the key operating state data; and screening the adaptive sampling sequence through the data retention rule to form a sparse feature set.

[0072] Data density analysis of the adaptive sampling sequence generates dense and sparse sampling regions. Based on the number of sampling points per unit time, the density formula is ρ = N / Δt, where N is the number of sampling points within the time interval Δt. Density thresholds are set as follows: dense sampling regions have a density ρ > 20 points / second, sparse sampling regions have a density ρ < 5 points / second, and medium density regions fall between the two thresholds. Density analysis uses a sliding window approach with a window length of 30 seconds and a step size of 5 seconds. The average sampling density for each time window is calculated. Dense sampling regions are identified by merging consecutive high-density windows; three or more consecutive high-density windows are merged into a dense region. Sparse sampling regions are identified using the same principle, marking consecutive low-density time periods. Density gradient analysis is used to precisely locate region boundaries, with the boundary location being the time point with the greatest rate of density change. Dense sampling regions typically correspond to periods of active fault symptoms or equipment state transitions, while sparse sampling regions correspond to periods of stable operation. Region duration statistics include characteristic parameters such as the average duration of dense regions, the average duration of sparse regions, and the frequency of region transitions.

[0073] Key operating status data is identified based on dense sampling areas. Multidimensional feature analysis is employed, encompassing data amplitude, rate of change, spectral characteristics, and correlation. Amplitude features are extracted through statistical analysis of data within dense areas, including parameters such as mean, variance, peak value, and peak-to-peak value. Rate of change features calculate the difference between adjacent sampling points, with the rate of change r = |xi+1-xi| / Δt, where xi represents the value at the i-th sampling point. Spectral features analyze the frequency domain characteristics of dense areas using a short-time Fourier transform, extracting parameters such as the dominant frequency, band energy, and spectral center of gravity. Data correlation analyzes the degree of association between different data types, using the correlation coefficient ρxy = Cov(X,Y) / (σX×σY), where Cov represents the covariance and σ represents the standard deviation. Criteria for determining critical status include: an amplitude change exceeding 50%, a rate of change exceeding three times the threshold, significant spectral feature shifts, and abnormal multidimensional correlation. The determination process incorporates data type and sampling interval information from the sequence tagging system to enhance accuracy. The status marking system marks the identified key status data as important data, and the marking weight is assigned according to the criticality.

[0074] Data retention rules are constructed using critical operational status data. Screening criteria are developed based on the characteristic patterns of critical operational status data, and retention priorities are adjusted based on adaptive weighting. Data with higher weightings are retained first. Retention rules are categorized into three levels: mandatory retention, conditional retention, and deletable. Mandatory retention rules target the most critical data, including the activation of fault symptoms, status mutation points, and abnormal peaks, with a 100% retention rate. Conditional retention rules are dynamically adjusted based on data importance and storage capacity. The importance assessment considers criticality, temporal correlation, and spatial correlation. Deletable rules target redundant and low-value data, with deletion criteria including high repetition, small fluctuations, and low correlation. Rule parameterization uses a decision tree structure, with tree nodes representing decision conditions and leaf nodes representing retention decisions. Rule prioritization ensures that important rules are executed first and avoids rule conflicts. Adaptive rule adjustment dynamically modifies rule parameters based on data compression requirements and storage constraints. Rule validity verification ensures that critical information is not lost by checking the information integrity of retained data.

[0075] The adaptive sampling sequence is filtered through data retention rules to form a sparse feature set. Rules are executed in order of priority, with the mandatory retention rule applied first to mark the data points that must be retained. The execution of the conditional retention rule takes into account the current compression ratio requirement: compression ratio R = N_original / N_target, where N_original is the amount of original data and N_target is the target amount of data. Dynamic threshold adjustment adjusts the threshold for conditional retention based on the amount of retained data and the remaining capacity. The screening process maintains the continuity of the time series, and inserts time markers when deleting data points to maintain sequence integrity. The sparsification effect is evaluated using three indicators: compression ratio, information retention rate, and reconstruction error. The information retention rate I = H_sparse / H_original, where H is the information entropy. The reconstruction error is calculated by the mean squared error of the original sequence reconstructed from the sparse data. The sparse feature set optimization uses an iterative screening method, with multiple rounds of screening gradually approaching the target compression ratio.

[0076] Generate a data compression table based on a sparse feature set. The compression table adopts a hierarchical structure, including three levels: an index layer, a feature layer, and a data layer. The index layer records the time positions and data types of sparse features and uses a B+ tree structure to improve query efficiency. The feature layer stores feature parameters and importance weights and adopts a fixed-length record format. The data layer stores the original data values and uses variable-length coding to compress the storage space. The compression algorithm adopts a hybrid coding strategy, using variable-length integer coding for integer data and quantization coding for floating-point data. The quantization precision is adjusted according to the data importance. Important data maintains the original precision, and less important data reduces the precision to save space. The access interface of the compression table provides functions such as time range query, feature type query, importance screening, etc. Table structure optimization includes techniques such as data pre-sorting, index optimization, and cache strategy. Compression efficiency statistics include performance metrics such as compression ratio, query response time, and storage overhead. Data integrity protection adopts a checksum mechanism to ensure the accuracy of the compressed data. The metadata record of the compression table includes creation time, data range, compression parameters, version information, etc. Through the systematic organization and optimized coding of the sparse feature set, an efficient data compression table is finally generated.

[0077] In step S150, separate the low-frequency energy component from the vibration signal for power conversion to form the stored energy电量, generate a transmission time slot through resonance matching based on the stored energy电量 and the data compression table, formulate an intermittent transmission sequence according to the transmission time slot, and construct an energy convergence area based on the intermittent transmission sequence.

[0078] Specifically, separate the low-frequency energy component from the vibration signal for power conversion to form the stored energy电量. Based on the three-axis vibration signal collected in S110, use a low-pass filter to separate the low-frequency vibration component with a frequency range of 20 - 100 Hz. The low-frequency energy separation uses a Butterworth filter with a cut-off frequency set to 100 Hz and a filter order of 4 to ensure that the high-frequency component attenuation is greater than 40 dB. The energy conversion uses a piezoelectric energy harvester to convert mechanical vibration energy into electrical energy. The piezoelectric material selects a PZT ceramic sheet with a size of 40 mm × 20 mm × 0.5 mm. The conversion efficiency is calculated by the ratio of electrical energy output to mechanical energy input, and the conversion efficiency is usually 15% - 25%. The energy storage circuit uses a rectifier bridge and an energy storage capacitor. The rectifier bridge uses Schottky diodes to reduce voltage drop losses, and the capacitance of the energy storage capacitor is set to 1000 μF. The stored energy电量 is calculated using the standard capacitor energy storage formula, which is half of the product of the capacitance value and the square of the voltage. The charge accumulation uses a sliding integration method with an integration time window of 60 seconds. The energy storage state grading is divided into a full-load state (E > 0.8E_max), a medium-load state (0.3E_max < E < 0.8E_max), and a low-load state (E < 0.3E_max), where E_max is the maximum energy storage capacity.

[0079] In some embodiments, the generation of transmission time slots based on the resonance matching of the energy storage capacity and the data compression table includes: extracting the energy pulse frequency and the data frequency of the data compression table from the energy storage capacity; performing frequency synchronization adjustment based on the energy pulse frequency and the data frequency to generate a synchronization frequency; establishing a resonance transmission condition using the synchronization frequency; and dividing the transmission time slots according to the resonance transmission condition.

[0080] The energy pulse frequency and the data frequency of the data compression table are extracted from the energy storage capacity. The energy pulse frequency is obtained through time-domain analysis of the energy storage capacity. The pulse frequency corresponds to the dominant frequency of the periodic variation of the energy storage capacity. Pulse frequency extraction uses power spectral density analysis to calculate the spectral characteristics of the energy storage capacity time series. The dominant pulse frequency f_pulse is determined by power spectrum peak detection and is typically within a range of 2-4 times the compressor speed. The pulse amplitude A_pulse is defined as the peak-to-peak value of the energy storage capacity and reflects the intensity of the energy pulse. The pulse period is the inverse of the pulse frequency and represents the time interval between adjacent pulses. The data frequency is extracted from the data compression table generated in S140. The data frequency f_data corresponds to the inverse of the average time interval between data points in the compression table. The data frequency is calculated as the ratio of the total number of data points to the data time span. The data bandwidth is determined by the range of the data frequency variation and is the difference between the maximum and minimum frequencies. Frequency stability is assessed by calculating the frequency variance, which reflects the degree of frequency stability.

[0081] Exemplarily, the use of the synchronization frequency to establish the resonant transmission condition includes: constructing an energy-data coupling oscillator based on the synchronization frequency; establishing a resonant cavity effect through the coupling oscillator; using the resonant cavity effect to amplify energy to generate amplified transmission power; and determining the resonant transmission condition based on the amplified transmission power.

[0082] An energy-data coupled oscillator is constructed based on a synchronous frequency. The coupled oscillator establishes a coordinated oscillation relationship between the energy pulse and the data stream based on the synchronous frequency f_sync. The oscillator is modeled as a second-order oscillator system, with the system equation d²x / dt²+2ζω0dx / dt+ω0²x=F(t), where x is the oscillation amplitude, ζ is the damping coefficient, set to 0.1, ω0=2πf_sync is the natural angular frequency, and F(t) is the driving force function. The energy oscillation component represents the oscillation portion of the stored energy and contains information about the oscillation amplitude and initial phase. The data oscillation component represents the periodic characteristics of the data stream and contains information about the data oscillation strength and phase. The coupling strength coefficient is calculated using the energy-data correlation formula, with stronger correlation indicating better coupling. The quality factor of the coupled oscillator is the ratio of the natural frequency to the damping frequency; a higher quality factor corresponds to a stronger resonance effect. The steady-state response amplitude of the oscillator is calculated using the standard second-order system frequency response function, which depends on the driving force amplitude and driving frequency. The coupling phase relationship determines the synchronization characteristics of energy and data, and the optimal coupling condition is when the phase difference is 0 or π.

[0083] A resonant cavity effect is established by coupling oscillators. The resonant cavity effect uses the metal casing and piping structure of the air conditioning system to create an electromagnetic resonance environment. The cavity geometry parameters include the dimensions of the air conditioning indoor unit (L×W×H), with typical dimensions of 800mm×300mm×250mm. The resonant frequency is calculated using a rectangular cavity mode, with the resonant frequency f_mnp=(c / 2)√[(m / L)²+(n / W)²+(p / H)²], where c is the speed of light and m, n, and p are the mode numbers. The dominant resonant mode is TE. 101 mode, corresponding to a frequency of approximately 300MHz. The cavity quality factor is calculated by the ratio of stored energy to lost power, reflecting the energy storage capacity of the cavity. The cavity coupling coefficient describes the coupling strength between external energy and the cavity, which is the ratio of external impedance to the characteristic impedance of the cavity. The resonance enhancement effect is characterized by the field enhancement factor, which is a function of the quality factor and the coupling coefficient. Cavity tuning uses variable capacitors or variable inductors to achieve frequency fine-tuning with a tuning range of ±5%. Multimode resonance takes into account the synergistic effect of multiple resonant frequencies to improve transmission bandwidth and robustness. Cavity Q value optimization is achieved by increasing the conductivity of the cavity surface and reducing dielectric loss, and surface silver plating improves conductivity.

[0084] Energy amplification utilizes the resonant cavity effect to generate amplified transmission power. Energy amplification is based on the field enhancement effect of the resonant cavity, amplifying weak input energy signals to a usable transmission power level. The amplification mechanism employs the principle of parametric amplification, using nonlinear elements to achieve energy transfer and amplification. The amplification factor G_amp = (Q_cavity × β) / (1-|Γ|²), where Q_cavity is the cavity quality factor, β is the cavity coupling coefficient, and Γ is the reflection coefficient. Input power P_in is provided by a piezoelectric harvester, with a typical power range of 1-10 mW. The amplified power is the product of the amplification factor, the input power, and the amplification efficiency, with the amplification efficiency ranging from 0.6 to 0.8. The frequency response of the power amplifier exhibits a bell-shaped distribution, with the amplification factor being maximum at the center frequency and decreasing away from the center frequency. Amplification stability is maintained through closed-loop control, which monitors the output power and adjusts the input coupling strength. Power limiting protection activates when the output power exceeds a set threshold to prevent system overload. The amplifier's noise figure is the ratio of the input signal-to-noise ratio to the output signal-to-noise ratio. A low noise figure ensures signal quality. Thermal management uses a heat sink and air cooling to maintain the amplifier's operating temperature below 60°C. Nonlinear distortion is minimized through pre-distortion compensation and feedback control, keeping total harmonic distortion (THD) below -40dB.

[0085] The resonant transmission conditions are determined based on the amplified transmission power. The resonant transmission conditions determine the optimal operating parameters based on the amplified transmission power level and system transmission requirements. A power threshold, P_threshold = 5mW, is set as the minimum transmission power requirement. If the power falls below the threshold, transmission is delayed until power accumulates. The transmission distance is estimated using the Friis transmission equation, which considers the relationship between transmit power, antenna gain, wavelength, and transmission distance. The maximum transmission distance is determined through a power budget calculation, which considers transmit power, antenna gain, and receiver sensitivity. The resonant bandwidth is the ratio of the resonant frequency to the system's overall quality factor, determining the frequency domain range of the transmitted signal. The transmission delay is determined by the cavity energy storage time, reflecting the signal's residence time in the cavity. The resonance mismatch tolerance is the ratio of the resonant frequency to the quality factor. Frequency deviations exceeding the tolerance require retuning. Temperature stability compensation accounts for the impact of ambient temperature changes on the resonant frequency. The temperature coefficient is -25ppm / °C, and the compensation algorithm utilizes temperature sensor feedback control.

[0086] Frequency synchronization is performed based on the energy pulse frequency and data frequency to generate the synchronization frequency. Frequency synchronization aims to establish an integer multiple relationship between the energy pulse frequency and the data frequency, achieving time alignment between energy and data transmission. Synchronization utilizes a phase-locked loop (PLL) principle, using the data frequency as a reference signal to adjust the energy pulse frequency. The frequency relationship is established using the least common multiple method: the synchronization frequency f_sync = LCM(f_pulse, f_data) / GCD(f_pulse, f_data), where LCM is the least common multiple and GCD is the greatest common divisor. The frequency adjustment range is limited to ±10% to prevent overshoot that could affect system stability. The adjustment algorithm uses proportional-integral control. The controller output is the sum of the proportional and integral terms of the frequency error, with a proportional coefficient of 2.0 and an integral coefficient of 0.5. Synchronization quality is assessed by measuring phase difference stability; the phase difference should remain within ±π / 4. The adjustment process records parameters such as the initial frequency, target frequency, adjustment time, and convergence error.

[0087] Transmission slots are allocated based on resonant transmission conditions. The slot length is determined by the resonance period, which is the ratio of the synchronization period to the number of slots. The number of slots is set to 8. Time slot allocation utilizes an energy-priority strategy, dynamically adjusting based on energy storage status. Transmission slots are increased during full load, allocated normally during medium load, and reduced during low load. High-energy periods are allocated for critical data transmission. Time slots are numbered chronologically, using the numbering scheme Slot_i, where i ranges from 0 to N_slot-1. Energy slot matching determines the available energy in each time slot based on the time distribution of energy storage capacity. Available energy is calculated by integrating the energy over time. Transmission priority is assigned based on the importance weight of the data in the S140 data compression table, with high-priority data being allocated to high-energy time slots. A slot protection mechanism reserves 10% of the time slots as emergency transmission channels to handle sudden data transmission requests. The time slot scheduling algorithm utilizes a dynamic adjustment strategy, adjusting time slot allocation based on real-time energy status and transmission demand.

[0088] An intermittent transmission sequence is established based on the transmission time slot. A time slot rotation mechanism is used, with each time slot corresponding to a transmission time slot. Transmit power is dynamically adjusted based on the available energy in the time slot. High-energy time slots use maximum transmit power, while low-energy time slots reduce power to extend transmission time. Sequence encoding utilizes time division multiplexing, assigning different types of data to different time slots. Important data is transmitted redundantly across multiple time slots to improve transmission reliability. The transmission interval is the sum of the time slot duration and the guard interval, which is set to 10% of the time slot length. Sequence synchronization utilizes a frame synchronization mechanism, with a synchronization header sent at the beginning of each transmission cycle. Transmit queue management utilizes a priority queue, with high-priority data being sent first. When the queue overflow exceeds the transmission capacity, low-priority data is discarded based on its importance. The intermittent ratio is defined as the ratio of transmit time to total time, with a typical value of 0.2-0.4.

[0089] Energy convergence zones are constructed based on intermittent transmission sequences. A convergence zone is defined as a spatial region where energy collection and consumption are balanced. The zone's extent is determined by energy propagation distance and attenuation characteristics. The center of the convergence zone is set at the installation location of the piezoelectric energy harvester, and the convergence radius is calculated using an energy attenuation model. Energy density distribution is simulated using a Gaussian distribution, with the density function including center density and distribution width parameters. Convergence efficiency is evaluated by calculating energy utilization, which is the ratio of actual energy used to total collected energy. Zones are classified according to energy density into core convergence zones (ρ > 0.8ρ_0), main convergence zones (0.5ρ_0 < ρ < 0.8ρ_0), and edge convergence zones (ρ < 0.5ρ_0). Multi-zone coordination considers the energy convergence of multiple piezoelectric harvesters, balancing the load across zones using an energy distribution algorithm. When excessive load in a zone triggers queue overflow, a load redistribution strategy is implemented to transfer some data to other convergence zones. Dynamic adjustment of the convergence zone's extent and density distribution is performed based on changes in vibration sources and transmission demand. Zone monitoring includes indicators such as energy collection, consumption, balance status, and adjustment parameters. By analyzing the energy characteristics of intermittent transmission sequences and optimizing the spatial energy distribution, an efficient energy convergence area was finally constructed.

[0090] Step S160 : using the energy convergence area to trigger a burst transmission window, converting the data compression table into a modulated signal based on the burst transmission window, and completing wiring-free air conditioning data collection.

[0091] Specifically, energy convergence zones are used to trigger burst transmission windows. Based on the energy density ρ_core in the core convergence zone, a burst transmission window is initiated when ρ_core exceeds a set threshold ρ_threshold = 0.9ρ_0, where ρ_0 is the maximum energy density at the center of the convergence zone. Trigger detection utilizes a sliding window monitoring mechanism, with a detection window length set to 5 seconds. A trigger is considered valid when three consecutive detection windows meet the trigger condition. The duration of the burst transmission window is calculated based on the available energy and transmission power in the convergence zone. The transmission power is dynamically adjusted within a range of 5-15mW based on the energy status, with a transmission efficiency of 0.7. Window priority is assigned based on the convergence zone level: windows triggered by the core convergence zone receive the highest priority, followed by the main convergence zone, and the edge convergence zone has the lowest priority. Multi-convergence zone coordination: When multiple convergence zones meet the trigger condition simultaneously, a time-division multiplexing strategy is used to allocate transmission windows, with each zone allocated time slices based on its energy status. Window status monitoring includes parameters such as window opening time, duration, energy consumption, and transmission success rate.

[0092] Based on burst transmission windows, the data compression table is converted into a modulated signal, enabling wiring-free air conditioning data collection. Building on the foundations of preamble energy harvesting and signal processing, a wireless transmission module is integrated to enable remote data collection. Leveraging the temporal characteristics of the burst transmission window, the data compression table is digitally modulated using OFDM modulation, with a carrier frequency set to 2.4 GHz. Subcarrier allocation utilizes a time-division multiplexing strategy, with windows triggered by different convergence zones using different subcarrier groups. Subcarrier resources are allocated based on energy status, for a total of 64 subcarriers. The hierarchical structure of the data compression table is mapped to frequency domain resources, with the mapping strategy adjusted based on window priority: high-priority windows prioritize the index layer and important feature layer, while low-priority windows transmit secondary data layers. Modulation parameters are dynamically adjusted based on window duration and energy budget, using 16QAM modulation when energy is sufficient and QPSK modulation when energy is limited. Signal power is allocated using a water-filling algorithm. The frame structure consists of a synchronization header, control information, and a data payload. Forward error correction uses a rate 1 / 2 convolutional code to improve interference immunity. The receiving end uses the RF front-end and digital signal processing to achieve signal demodulation and data recovery. The transmission distance is determined by the actual power budget. At a power of 5-15mW, the effective transmission distance is 3-10 meters, and the receiving sensitivity reaches -85dBm. Data integrity is ensured by CRC-16 checksum. Transmission quality monitoring includes indicators such as RSSI, bit error rate, and packet loss rate. Window status monitoring data is used to evaluate and optimize transmission performance. The collection cycle adaptively adjusts based on the air conditioner's operating status, from 5 minutes during normal operation to 1 minute during abnormal conditions. The collected content includes processed operating signature codes, fault signs, and analysis results from the data compression table. Through wireless transmission technology and efficient modulation of the data compression table, the collection of air conditioner operating status information is achieved without wiring.

[0093] In order to implement a wiring-free air conditioning data collection method corresponding to the above method embodiment, to achieve the corresponding functions and technical effects. Figure 2 , Figure 2 The following is a block diagram of a wiring-free air conditioning data acquisition device 200 provided in an embodiment of the present application. For ease of explanation, only the parts related to this embodiment are shown. The wiring-free air conditioning data acquisition device 200 provided in an embodiment of the present application includes:

[0094] The signal analysis module 201 is configured to collect vibration signals and electromagnetic field disturbance data from the air conditioner indoor unit, perform multi-scale decomposition on the vibration signals to generate characteristic frequency bands, extract power frequency interference components based on the electromagnetic field disturbance data, and cross-validate the characteristic frequency bands with the power frequency interference components to generate an operation signature code.

[0095] a flow field modeling module 202 for separating a periodic vibration component based on the operation characteristic code, tracing a mechanical energy propagation path through the periodic vibration component, sensing temperature changes along the mechanical energy propagation path to form thermal distribution data, and constructing an energy flow topology map based on the thermal distribution data;

[0096] The bottleneck location module 203 is configured to perform node analysis on the energy flow topology diagram to extract high temperature difference areas, extract a thermal gradient field from the high temperature difference areas, identify a heat dissipation bottleneck location based on the thermal gradient field, and compare the heat dissipation bottleneck location with the operation feature code to identify a fault sign point;

[0097] A data reduction module 204 is configured to generate a dynamic sampling interval based on the fault symptom point, form an adaptive sampling sequence using the dynamic sampling interval, perform a sparse processing on the adaptive sampling sequence to extract a sparse feature set, and generate a data compression table based on the sparse feature set;

[0098] An energy coordination module 205 is configured to separate low-frequency energy components from the vibration signal, convert them into electrical energy, generate energy storage capacity, resonate and match the energy storage capacity with the data compression table to generate transmission time slots, formulate an intermittent transmission sequence based on the transmission time slots, and construct an energy convergence area based on the intermittent transmission sequence;

[0099] The modulation transmission module 206 is configured to use the energy convergence area to trigger a burst transmission window, convert the data compression table into a modulation signal based on the burst transmission window, and complete wiring-free air conditioning data collection.

[0100] The aforementioned wiring-free air conditioning data acquisition device 200 can implement the wiring-free air conditioning data acquisition method of the aforementioned method embodiment. The optional options in the aforementioned method embodiment also apply to this embodiment and are not further described here. The remaining contents of the present application embodiment can be referenced to the contents of the aforementioned method embodiment and are not further described in this embodiment.

[0101] like Figure 3 As shown, the third embodiment of the present invention further provides a computer device, including a memory 301, a processor 302, and a computer program stored in the memory 301 and executable on the processor 302, characterized in that when the processor 302 executes the program, the steps of the wiring-free air-conditioning data acquisition method described in the first embodiment of the present invention are implemented.

[0102] The purpose of the above embodiments is to exemplify and deduce the technical solution of the present invention, and to fully describe the technical solution, purpose and effect of the present invention. Its purpose is to enable the public to have a more thorough and comprehensive understanding of the disclosed content of the present invention, and it does not limit the scope of protection of the present invention.

[0103] The above embodiments are not exhaustive and may include many other embodiments not listed above. Any replacements and improvements made without violating the concept of the present invention are within the scope of protection of the present invention.

Claims

1. A wiring-free air conditioning data acquisition method, characterized in that: include: Collect vibration signals and electromagnetic field disturbance data from the air conditioner indoor unit, perform multi-scale decomposition on the vibration signals to generate characteristic frequency bands, extract power frequency interference components based on the electromagnetic field disturbance data, and cross-validate the characteristic frequency bands with the power frequency interference components to generate an operation signature code; Separating a periodic vibration component based on the operation characteristic code, tracing a mechanical energy propagation path through the periodic vibration component, sensing temperature changes along the mechanical energy propagation path to form thermal distribution data, and constructing an energy flow topology map based on the thermal distribution data; Performing node analysis on the energy flow topology diagram to extract high temperature difference areas, extracting thermal gradient fields from the high temperature difference areas, identifying heat dissipation bottleneck locations based on the thermal gradient fields, and comparing the heat dissipation bottleneck locations with the operating characteristic codes to identify fault symptom points; generating a dynamic sampling interval based on the fault symptom point, forming an adaptive sampling sequence using the dynamic sampling interval, performing a sparse processing on the adaptive sampling sequence to extract a sparse feature set, and generating a data compression table based on the sparse feature set; Separating low-frequency energy components from the vibration signal and converting them into electrical energy to form energy storage, resonating and matching the energy storage with the data compression table to generate transmission time slots, formulating an intermittent transmission sequence based on the transmission time slots, and constructing an energy convergence area based on the intermittent transmission sequence; The energy convergence area is used to trigger a burst transmission window, and the data compression table is converted into a modulated signal based on the burst transmission window, thereby completing wiring-free air-conditioning data collection.

2. The method according to claim 1, characterized in that The performing multi-scale decomposition on the vibration signal to generate characteristic frequency bands includes: Performing energy distribution analysis on the vibration signal to identify compressor vibration, fan vibration, and pipeline vibration; Establishing a main frequency template based on the compressor vibration; Performing frequency calibration on the fan vibration and the pipeline vibration using the main frequency template to generate a calibration spectrum; The main frequency template and the calibration spectrum are fused to generate a characteristic frequency band.

3. The method according to claim 1, characterized in that The step of constructing an energy flow topology diagram according to the heat distribution data includes: identifying an evaporator heat source and a condenser heat source based on the heat distribution data; Conducting heat flow diffusion detection outward from the evaporator heat source and the condenser heat source to form a diffusion range; Establishing a heat flow node connection relationship within the diffusion range; An energy flow topology diagram is constructed based on the node connection relationship.

4. The method according to claim 1, wherein The identifying the heat dissipation bottleneck position based on the thermal gradient field includes: Determining bottleneck identification accuracy by evaluating heat dissipation impedance strength based on the thermal gradient field, wherein the heat dissipation impedance strength includes gradient steepness, thermal resistance concentration, and heat dissipation path curvature; setting an impedance threshold according to the bottleneck identification accuracy; screening a high impedance region in the thermal gradient field using the impedance threshold; The peak point of the high impedance region is marked as the heat dissipation bottleneck position.

5. The method according to claim 1, wherein The step of performing sparse processing on the adaptive sampling sequence to extract a sparse feature set includes: Performing data density analysis on the adaptive sampling sequence to generate a dense sampling area and a sparse sampling area; identifying key operating status data based on the dense sampling area; constructing data retention rules using the key operational status data; The adaptive sampling sequence is filtered according to the data retention rule to form a sparse feature set.

6. The method according to claim 1, characterized in that The generating of the transmission time slot based on the resonance matching of the energy storage quantity and the data compression table includes: Extracting the energy pulse frequency and the data frequency of the data compression table from the energy storage quantity; Performing frequency synchronization adjustment based on the energy pulse frequency and the data frequency to generate a synchronization frequency; establishing a resonant transmission condition using the synchronization frequency; The transmission time slots are divided according to the resonance transmission condition.

7. The method according to claim 2, characterized in that The establishing of a main frequency template based on the compressor vibration includes: Performing frequency hierarchical decomposition on the compressor vibration to generate a fundamental frequency layer and a harmonic frequency layer; Establishing a frequency anchor point based on the base frequency layer; Performing frequency alignment on the harmonic frequency layer using the frequency anchor point to generate an aligned frequency; The basic frequency layer and the aligned frequency are synthesized to establish a main frequency template.

8. The method according to claim 3, characterized in that The establishing of a heat flow node connection relationship within the diffusion range includes: Performing heat flux density scanning on the diffusion range to identify heat flux enriched areas and heat flux sparse areas; Establishing a heat flow attraction effect based on the heat flow enrichment zone; Utilizing the heat flow attraction effect to attract and aggregate the heat flow sparse area to generate aggregated heat flow; A node connection relationship is established between the heat flux enrichment zone and the aggregated heat flux.

9. A wiring-free air conditioning data acquisition device, characterized in that: include: a signal analysis module for collecting vibration signals and electromagnetic field disturbance data from the air conditioner indoor unit, performing multi-scale decomposition on the vibration signals to generate characteristic frequency bands, extracting power frequency interference components based on the electromagnetic field disturbance data, and cross-validating the characteristic frequency bands with the power frequency interference components to generate an operation signature code; a flow field modeling module, configured to separate a periodic vibration component based on the operation characteristic code, trace a mechanical energy propagation path through the periodic vibration component, sense temperature changes along the mechanical energy propagation path to form thermal distribution data, and construct an energy flow topology map based on the thermal distribution data; a bottleneck locating module, configured to perform node analysis on the energy flow topology diagram to extract high temperature difference areas, extract a thermal gradient field from the high temperature difference areas, identify a heat dissipation bottleneck location based on the thermal gradient field, and compare the heat dissipation bottleneck location with the operation feature code to identify a fault sign point; a data reduction module, configured to generate a dynamic sampling interval based on the fault symptom point, form an adaptive sampling sequence using the dynamic sampling interval, perform a sparse processing on the adaptive sampling sequence to extract a sparse feature set, and generate a data compression table based on the sparse feature set; an energy coordination module, configured to separate low-frequency energy components from the vibration signal, convert them into electrical energy, generate energy storage capacity, resonate and match the energy storage capacity with the data compression table to generate transmission time slots, formulate an intermittent transmission sequence according to the transmission time slots, and construct an energy convergence area based on the intermittent transmission sequence; The modulation transmission module is used to use the energy convergence area to trigger a burst transmission window, convert the data compression table into a modulation signal based on the burst transmission window, and complete the wiring-free air-conditioning data collection.

10. A computer device, characterized in that: The method comprises a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and implement the method according to any one of claims 1 to 8 when executing the computer program.

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