Signal processing method, controller and building control system

Abstract

The invention provides a signal processing method, a controller and a building control system, and relates to the technical field of building control. The method comprises the following steps: receiving a to-be-processed signal of a sensor, wherein the to-be-processed signal comprises a target signal and an interference signal collected by the sensor; analyzing the to-be-processed signal to determine an interference signal feature in the to-be-processed signal, the interference signal feature in the to-be-processed signal including at least one of a peak fluctuation feature and a waveform similarity feature; judging whether the to-be-processed signal contains a periodic interference signal or not according to the interference signal characteristics in the to-be-processed signal; and under the condition that the to-be-processed signal contains the periodic interference signal, the filter circuit is regulated and controlled according to the characteristics of the periodic interference signal. Through the method, the interference signal of the sensor signal can be accurately identified in real time, and filtering is performed in a targeted manner, so that the problems of sensor signal loss and non-ideal filtering effect caused by passive filtering can be solved.

Description

Technical Field

[0001] This disclosure relates to the field of building control technology, and in particular to a signal processing method, a controller, and a building control system. Background Technology

[0002] As the number and types of sensors in building control systems increase, the frequency of sensor signals received by the controller also increases. Due to the excessive length of field transmission lines, signals are easily affected by environmental interference during transmission, which severely impacts signal transmission accuracy and stability, thereby affecting the overall sensitivity of the system.

[0003] Sensor signals are typically analog signals. During transmission, they are easily affected by periodic interference signals generated by sources such as air conditioner motors, frequency converters, and power supplies, leading to signal distortion. Taking building control systems as an example, the transmission of analog quantities such as 4-20 mA current loops and 0-10 V voltage signals is highly susceptible to periodic interference signals. For instance, interference sources such as the power frequency ripple (50 Hz) generated by air conditioner frequency converters and the carrier wave (4-20 kHz frequency range) from motors can cause abrupt changes in critical sensor signal parameters such as temperature and pressure, potentially triggering malfunctions in the equipment.

[0004] In related technologies, passive filtering schemes are commonly used. These schemes typically employ filters with fixed parameters to filter the sensor signal. However, filters with fixed parameters struggle to handle carrier drift from interference sources (such as frequency converters), leading to sensor signal loss and unsatisfactory filtering results. Summary of the Invention

[0005] To address the problems of sensor signal loss and unsatisfactory filtering effects caused by passive filtering in related technologies, this disclosure provides a signal processing method, a controller, and a building control system.

[0006] According to a first aspect of this disclosure, a signal processing method is provided, comprising: receiving a signal to be processed from a sensor, the signal to be processed including a target signal and an interference signal acquired by the sensor; analyzing the signal to be processed to determine interference signal characteristics in the signal to be processed, wherein the interference signal characteristics in the signal to be processed include at least one of peak fluctuation characteristics and waveform similarity characteristics of the interference signal; determining whether the signal to be processed contains periodic interference signals based on the interference signal characteristics in the signal to be processed; and, if the signal to be processed contains periodic interference signals, adjusting a filtering circuit according to the characteristics of the periodic interference signals.

[0007] In some embodiments, analyzing the signal to be processed to determine the characteristics of interference signals in the signal to be processed includes: sampling the signal to be processed to obtain sampled data; extracting data segments from the sampled data through a sliding window to obtain data segments within each of multiple windows; determining the peak signal point of the interference signal in the data segment within each window; calculating the fluctuation value between the peak signal points of two adjacent interference signals to obtain multiple fluctuation values; and determining the peak fluctuation characteristics of the interference signal based on the multiple fluctuation values.

[0008] In some embodiments, the step of analyzing the signal to be processed to determine the interference signal features in the signal to be processed further includes: extracting waveform segment data of interference signals for multiple periods from the sampled data; calculating the correlation coefficient between the waveform segment data of interference signals for two adjacent periods in the multiple periods to obtain multiple correlation coefficients; and using the multiple correlation coefficients as waveform similarity features of the interference signal.

[0009] In some embodiments, the peak fluctuation feature includes the average of the plurality of fluctuation values, or the proportion of fluctuation values ​​less than a fluctuation threshold among the plurality of fluctuation values. Determining whether the signal to be processed contains a periodic interference signal includes: if the average of the plurality of fluctuation values ​​is less than the fluctuation threshold or the proportion of fluctuation values ​​less than the fluctuation threshold among the plurality of fluctuation values ​​is greater than a first threshold, and the plurality of correlation coefficients are all greater than a correlation coefficient threshold, then the signal to be processed contains a periodic interference signal; otherwise, the signal to be processed does not contain a periodic interference signal.

[0010] In some embodiments, the signal processing method further includes: determining the spectral characteristics of the periodic interference signal before adjusting the filter circuit according to the characteristics of the periodic interference signal, and determining the periodic interference signal as an effective interference signal based on the spectral characteristics of the periodic interference signal.

[0011] In some embodiments, determining the spectral characteristics of the periodic interference signal includes: performing a Fourier transform on the signal to be processed to obtain a signal spectrum; determining, based on the signal spectrum, the energy ratio of the fundamental frequency component of the periodic interference signal and the energy ratio of the harmonic components to the noise floor of the periodic interference signal; and using the energy ratio of the fundamental frequency component of the periodic interference signal and the energy ratio of the harmonic components to the noise floor of the periodic interference signal as the spectral characteristics of the periodic interference signal.

[0012] In some embodiments, if the energy ratio of the fundamental frequency component of the periodic interference signal is greater than a second threshold and the energy ratio of the harmonic component of the periodic interference signal to the noise floor is greater than a third threshold, the periodic interference signal is determined to be an effective interference signal.

[0013] In some embodiments, the filtering circuit includes multiple filtering modules connected in parallel, and the step of regulating the filtering circuit according to the characteristics of the periodic interference signal includes: determining, from the multiple filtering modules, a filtering module that matches the characteristics of the periodic interference signal, wherein the characteristics of the periodic interference signal include the period or frequency of the periodic interference signal; and controlling the matching filtering module to work to filter the signal to be processed.

[0014] In some embodiments, the plurality of filter modules are plurality of resistor-capacitor (RC) filter modules. Determining the filter module that matches the characteristics of the periodic interference signal from the plurality of filter modules includes: determining the capacitance value and time constant of the RC filter based on the period or frequency of the periodic interference signal; adjusting the resistance value of the RC filter module with the capacitance value among the plurality of RC filter modules based on the time constant; and using the adjusted RC filter module as the matched filter module.

[0015] In some embodiments, the signal processing method further includes: filtering out high-frequency noise in the signal to be processed based on a Butterworth filter before analyzing the signal to be processed.

[0016] According to a second aspect of this disclosure, a controller is provided, comprising: a memory; and a processor coupled to the memory, the processor being configured to perform the signal processing method as described above based on instructions stored in the memory.

[0017] According to a third aspect of this disclosure, a building control system is provided, comprising: a controller as described above; and a filtering circuit including a plurality of filtering modules connected in parallel, configured to: under the control of the controller, operate a filtering module among the plurality of filtering modules whose characteristics match those of a periodic interference signal contained in the signal to be processed, so as to filter the signal to be processed.

[0018] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement the signal processing method as described above.

[0019] According to a fifth aspect of this disclosure, a computer program product is provided having computer program instructions stored thereon, which, when executed by a processor, implement the signal processing method as described above.

[0020] Other features and advantages of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0021] The accompanying drawings, which form part of this specification, illustrate embodiments of this disclosure and, together with the specification, serve to explain the principles of this disclosure.

[0022] Figure 1 This is a schematic flowchart of a signal processing method according to some embodiments of the present disclosure;

[0023] Figure 2 This is a flowchart illustrating the steps of regulating a filter circuit in a signal processing method according to some embodiments of the present disclosure;

[0024] Figure 3 This is a schematic diagram of the structure of a filter circuit according to some embodiments of the present disclosure;

[0025] Figure 4 This is a schematic flowchart of a signal processing method according to other embodiments of the present disclosure;

[0026] Figure 5 This is a schematic diagram of the structure of a controller according to some embodiments of the present disclosure;

[0027] Figure 6 This is a schematic diagram of the structure of a controller according to other embodiments of this disclosure;

[0028] Figure 7 This is a schematic diagram of the structure of a building control system according to some embodiments of the present disclosure.

[0029] This disclosure can be more clearly understood with reference to the accompanying drawings and the following detailed description. Detailed Implementation

[0030] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present disclosure.

[0031] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0032] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use.

[0033] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0034] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0035] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0036] To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0037] To address the problems of sensor signal loss and unsatisfactory filtering effects caused by passive filtering in related technologies, this disclosure proposes a signal processing method, a controller, and a building control system.

[0038] Figure 1 This is a schematic flowchart of a signal processing method according to some embodiments of the present disclosure. Figure 1 As shown, the signal processing method includes steps S11 to S14.

[0039] In step S11, the sensor receives the signal to be processed.

[0040] The signals to be processed include the target signal and interference signals acquired by the sensor. The signals to be processed can be either current or voltage signals. For example, the signals to be processed can be current signals of 4–20 mA or voltage signals of 0–10 V.

[0041] In some examples, the target signal acquired by the sensor can be an analog signal representing temperature acquired by a temperature sensor, an analog signal representing pressure acquired by a pressure sensor, or an analog signal representing humidity acquired by a humidity sensor, etc. Interference signals can be generated by devices such as air conditioner motors, inverters, or power supplies.

[0042] In step S12, the signal to be processed is analyzed to determine the characteristics of interference signals in the signal to be processed. The characteristics of interference signals in the signal to be processed include at least one of the peak fluctuation characteristics and waveform similarity characteristics of the interference signals.

[0043] Step S12 can be implemented in various ways. The following describes two examples of implementation methods.

[0044] In a first embodiment, the interference signal characteristics in the signal to be processed include the peak fluctuation characteristics of the interference signal. In this embodiment, the peak fluctuation characteristics are determined according to steps A1 to A5.

[0045] Step A1 involves sampling the signal to be processed to obtain sampled data. For example, an analog-to-digital converter can be used to convert the signal to be processed from an analog signal into a discrete numerical signal, and this numerical signal can be used as the sampled data. Alternatively, other methods can be used to implement signal sampling in specific implementations.

[0046] Step A2 involves extracting data segments from the sampled data using a sliding window to obtain data segments within each of multiple windows.

[0047] In step A2, the window parameters (e.g., window length and window step size) are first determined, and then data segments are extracted according to these parameters. The window length can be set based on the period of potential interference signals. For example, when the window length is expressed in terms of time, if the estimated period of a potential interference signal is 0.02 seconds and the sampling rate is 1000Hz, the window length can be set to 0.04 seconds (two estimated periods), with each window containing 400 sampling points. This ensures that each window contains data from an interference signal covering as much as possible within a complete period. In practice, if the period of a potential interference signal is unknown, it can be estimated using prior knowledge or autocorrelation analysis. The window step size can be flexibly set. For example, when the window step size is expressed in terms of time, it can be half the window length, which helps reduce computation. Furthermore, in practice, the window length and window step size can also be characterized by the number of sampling points.

[0048] Step A3: Determine the peak signal point of the interference signal in the data segment within each window. For example, the peak signal point of the interference signal is the sampling point of the interference signal with the largest amplitude in the data segment within each window.

[0049] Step A4: Calculate the fluctuation value between the peak signal points of two adjacent interference signals to obtain multiple fluctuation values.

[0050] In step A4, the fluctuation value can be calculated in a variety of ways, which will be illustrated below with two examples.

[0051] In the first example, the absolute value of the difference between the amplitudes of the peak signal points of two adjacent interference signals is calculated. The ratio of this absolute value to the amplitude of the previous peak signal point among the two adjacent interference signals is taken as the fluctuation value between the peak signal points of the two adjacent interference signals. For example, if the amplitude Pk of the Kth peak signal point of the interference signal is 100, and the amplitude Pk+1 of the (K+1)th peak signal point of the interference signal is 95, then the fluctuation value is |(95-100) / 100|×100%=5%.

[0052] In the second example, the absolute value of the difference between the amplitudes of the peak signal points of two adjacent interference signals is calculated, and the absolute value of the difference is used as the fluctuation value between the peak signal points of the two adjacent interference signals.

[0053] Step A5: Determine the peak fluctuation characteristics of the interference signal in the signal to be processed based on multiple fluctuation values.

[0054] In step A5, the peak fluctuation characteristics of the interference signal can be determined based on various methods. Two examples are provided below for illustration.

[0055] In the first example, the average of multiple fluctuation values ​​is used as the peak fluctuation characteristic of the interference signal.

[0056] In the second example, the proportion of fluctuation values ​​below the fluctuation threshold among multiple fluctuation values ​​is used as the peak fluctuation characteristic of the interference signal. Furthermore, in practical implementation, the average value of multiple fluctuation values, as well as the proportion of fluctuation values ​​below the fluctuation threshold among multiple fluctuation values, can also be used as the peak fluctuation characteristic of the interference signal.

[0057] In this embodiment of the disclosure, steps A1 to A5 enable a faster and more accurate determination of the peak fluctuation characteristics of the interference signal in the signal to be processed. This facilitates subsequent real-time and accurate determination of whether the signal to be processed contains periodic interference based on these characteristics, thereby improving the real-time performance and reliability of the filtering control.

[0058] In the second embodiment, the interference signal characteristics in the signal to be processed include peak fluctuation characteristics and waveform similarity characteristics. In this embodiment, in addition to determining the peak fluctuation characteristics of the interference signal according to steps A1 to A4, the waveform similarity characteristics of the interference signal are also determined according to steps B1 to B3.

[0059] In step B1, waveform segments of the interference signal for multiple cycles are extracted from the sampled data. For example, waveform segments of the interference signal for one cycle are extracted, starting from the first peak signal point and ending at the next peak signal point; then, waveform segments of the interference signal for one cycle are extracted, starting from the second peak signal point and ending at the third peak signal point, and so on, to obtain waveform segments of the interference signal for multiple cycles.

[0060] In step B2, the correlation coefficient between waveform segments of interference signals in two adjacent cycles is calculated to obtain multiple correlation coefficients.

[0061] For example, the correlation coefficient between waveform segments of two interfering signals can be calculated using the following formula.

[0062]

[0063] Where, r i,j S represents the correlation coefficient; i(n) S represents the data of the i-th waveform segment; j(n) This represents the j-th waveform segment data; This represents the average value of the i-th waveform segment data; This represents the average value of the j-th waveform segment data.

[0064] In step B3, multiple correlation coefficients are used as waveform similarity features of the interference signal.

[0065] In this embodiment, on the one hand, steps B1 to B3 enable a faster and more accurate determination of the waveform similarity characteristics of the interference signal in the signal to be processed. This facilitates subsequent real-time and accurate determination of whether the signal to be processed contains periodic interference based on the waveform similarity characteristics, thereby improving the real-time performance and reliability of the filtering control. On the other hand, combining the waveform similarity characteristics of the interference signal with the peak fluctuation characteristics helps to better determine whether the signal to be processed contains periodic interference, further improving the reliability of the filtering control. In step S13, the presence or absence of periodic interference signals in the signal to be processed is determined based on the characteristics of the interference signal in the signal to be processed.

[0066] Step S13 can be implemented in several ways. Two examples are described below.

[0067] In the first embodiment, it is determined whether the signal to be processed contains a periodic interference signal based on the peak fluctuation characteristics of the interference signal. Specifically, in this embodiment, when the peak fluctuation characteristics of the interference signal include the average of multiple fluctuation values ​​or the proportion of fluctuation values ​​less than a fluctuation threshold, it can be determined that the signal to be processed contains a periodic interference signal if the average of the multiple fluctuation values ​​is less than the fluctuation threshold or the proportion of fluctuation values ​​less than the fluctuation threshold is greater than a first threshold; otherwise, it is determined that the signal to be processed does not contain a periodic interference signal.

[0068] In the second embodiment, whether the signal to be processed contains periodic interference signals is determined based on the peak fluctuation characteristics and waveform similarity characteristics of the interference signal. Specifically, in this embodiment, when the peak fluctuation characteristics of the interference signal include the average of multiple fluctuation values ​​or the percentage of fluctuation values ​​less than a fluctuation threshold, and the waveform similarity characteristics of the interference signal include multiple correlation coefficients, the signal to be processed is determined to contain periodic interference signals if the average of the multiple fluctuation values ​​is less than the fluctuation threshold or the percentage of fluctuation values ​​less than the fluctuation threshold is greater than a first threshold, and all correlation coefficients are greater than a correlation coefficient threshold; otherwise, the signal to be processed is determined not to contain periodic interference signals. The first threshold, fluctuation threshold, and correlation coefficient threshold can be flexibly set. For example, the first threshold can be set to 0.9, the fluctuation threshold to 10%, and the correlation coefficient threshold to 0.8.

[0069] In this embodiment, when determining whether a signal to be processed contains periodic interference signals, not only the peak fluctuation characteristics of the interference signals in the signal to be processed are considered, but also the waveform similarity characteristics of the interference signals in the signal to be processed. This achieves a comprehensive evaluation of whether the interference signal is a periodic interference signal from multiple feature dimensions, which helps to improve the reliability of the judgment result. Furthermore, by characterizing the peak fluctuation characteristics of the interference signal by the average value of multiple fluctuation values ​​or the proportion of fluctuation values ​​less than a fluctuation threshold among multiple fluctuation values, and by characterizing the waveform similarity of the interference signal by the correlation coefficient of multiple waveform segment data, the reliability and accuracy of the above judgment result are further improved. In this way, the reliability of filtering and control can be further improved.

[0070] In step S14, when the signal to be processed contains periodic interference signals, the filter circuit is adjusted according to the characteristics of the periodic interference signals.

[0071] In some embodiments, the filtering circuit includes multiple filtering modules connected in parallel. In these embodiments, it can be based on... Figure 2 The exemplary process shown controls the filtering circuit, including: step S141, determining a filtering module from multiple filtering modules that matches the characteristics of the periodic interference signal; step S142, controlling the matched filtering module to work in order to filter the signal to be processed.

[0072] In some examples, multiple filter modules are resistive-capacitive (RC) filter modules. In these examples, in step S141, a filter module matching the characteristics of the periodic interference signal can be determined according to steps C1 and C2.

[0073] Step C1: Determine the capacitance value and time constant of the RC filter based on the period or frequency of the periodic interference signal.

[0074] In step C1, the capacitance value of the RC filter can be determined as follows: based on the frequency of the periodic interference signal, the correspondence between the interference frequency and the capacitance value of the RC filter is looked up to determine the capacitance value of the RC filter corresponding to the frequency of the periodic interference signal. Then, an RC filter module with the above-mentioned capacitance value is selected from a plurality of existing RC filter modules. In specific implementation, before step C1, a correspondence between the frequencies of various periodic interference signals and the capacitance values ​​of filters used to remove the interference signals is preset.

[0075] For example, for a 50 Hz power frequency interference signal with periodic interference, the corresponding capacitance value is determined to be 1 μF by referring to the above correspondence. Next, an RC filter module with a capacitance value of 1 μF is selected from RC filter modules with capacitance values ​​of 100 pF, 470 pF, and 1 μF.

[0076] In step C1, the time constant of the RC filter can be determined as follows: the cutoff frequency is calculated based on the period of the periodic interference signal; the time constant of the RC filter is then calculated based on the cutoff frequency. For example, for a 50 Hz power frequency interference signal, the time constant can be determined using formula f. C The cutoff frequency f is calculated as 3 / T. C The Hz value is 150 Hz, and then according to the formula RC = 1 / 2πf c Calculate the time constant RC. Here, the time constant RC represents the product of the resistance value and the capacitance value.

[0077] Step C2: Based on the time constant, adjust the resistance value of the RC filter module with the above-mentioned capacitance value among the multiple RC filter modules, and use the adjusted RC filter module as the matching filter module.

[0078] In step C2, the quotient of the time constant and the capacitance value can be used as the target resistance value. Then, the resistance value of the RC filter module with the above capacitance value is adjusted to the target resistance value, and the RC filter module with the target resistance value and capacitance value is used as the matching filter module.

[0079] In this embodiment, by designing multiple filter modules connected in parallel and dynamically determining the operating RC filter module from among the multiple filter modules based on the characteristics of the detected periodic interference signal, more accurate "active" filtering is achieved. This better addresses the problem of sensor signal loss and unsatisfactory filtering effect caused by carrier drift from the interference source (e.g., frequency converter). Furthermore, by determining the RC filter module matching the periodic interference signal according to steps C1 to C2, not only is the compatibility between the characteristics of the periodic interference signal and the capacitance value of the RC filter module considered, but also the compatibility between the characteristics of the periodic interference signal and the resistance value of the RC filter module. This allows for the determination of the RC filter module that better matches the characteristics of the detected periodic interference signal from among the multiple RC filter modules, further improving the filtering effect. In some examples, the filter circuit may employ, for example... Figure 3 The structure shown. (As illustrated) Figure 3 As shown, the filter circuit includes a first RC filter module 31, a second RC filter module 32, a third RC filter module 33, ..., an nth RC filter module 34 connected in parallel. The controller 40 is connected to the first RC filter module 31 via switch K1, to the second RC filter module 32 via switch K2, to the third RC filter module 33 via switch K3, ..., and to the nth RC filter module 34 via switch Kn. The switches can be field-effect transistors or other types of switches.

[0080] The controller 40 adjusts the filtering circuit based on the characteristics of the periodic interference signal. For example, if the controller 40 determines that the third RC filter module 33 matches the characteristics of the periodic interference signal, it controls the switches K1 to Kn to activate the third RC filter module 33 and deactivate the other RC filter modules. This allows the optimal RC filter module to be selected from multiple available RC filter modules based on the period or frequency of the periodic interference signal. Furthermore, by detecting the periodic interference in the signal to be processed in real time, the controller dynamically switches the operating RC filter modules based on the detection results. This enables dynamic filtering of the filtering circuit at different frequencies and in different time domains. Consequently, it alleviates the loss caused by passive filtering to the sensor signal itself and improves the filtering effect.

[0081] Figure 4 This is a schematic flowchart of a signal processing method according to other embodiments of this disclosure. For example... Figure 4 As shown, the signal processing method includes steps S41 to S46.

[0082] In step S41, the sensor receives the signal to be processed.

[0083] In step S42, high-frequency noise in the signal to be processed is filtered out using a Butterworth filter. For example, a second-order Butterworth filter is used to filter out high-frequency noise in the signal to be processed. This improves signal quality and facilitates subsequent analysis of the signal to be processed.

[0084] In step S43, the signal to be processed is analyzed to determine the characteristics of interference signals in the signal to be processed.

[0085] For details on how to implement step S43, please refer to the relevant descriptions in the foregoing embodiments.

[0086] In step S44, it is determined whether there is a periodic interference signal.

[0087] For details on how to implement step S44, please refer to the relevant descriptions in the foregoing embodiments.

[0088] If step S44 determines that a periodic interference signal exists, proceed to step S45. If step S44 determines that no periodic interference signal exists, end the signal processing flow.

[0089] In step S45, it is determined whether the periodic interference signal is a valid interference signal.

[0090] In some embodiments, whether a periodic interference signal is a valid interference signal is determined by: determining the spectral characteristics of the periodic interference signal; and determining whether the periodic interference signal is a valid interference signal based on the spectral characteristics of the periodic interference signal.

[0091] In some examples, the spectral characteristics of a periodic interference signal are determined as follows: a Fourier transform is performed on the signal to be processed to obtain the signal spectrum; based on the signal spectrum, the energy ratio of the fundamental frequency component of the periodic interference signal and the energy ratio of the harmonic components of the periodic interference signal to the noise floor are determined; the energy ratio of the fundamental frequency component of the periodic interference signal and the energy ratio of the harmonic components of the periodic interference signal to the noise floor are used as the spectral characteristics of the periodic interference signal.

[0092] In some examples, the validity of a periodic interference signal is determined as follows: if the energy proportion of the fundamental frequency component of the periodic interference signal is greater than a second threshold and the energy ratio of the harmonic components to the noise floor is greater than a third threshold, the periodic interference signal is determined to be a valid interference signal; otherwise, the periodic interference signal is determined not to be a valid interference signal. For example, based on the spectrum of the signal to be processed, the energy proportion of the fundamental frequency of the periodic interference signal, and the energy ratio of the sum of the third and fifth harmonics to the noise floor are determined. When the energy proportion of the fundamental frequency of the periodic interference signal is greater than 70%, and the sum of the harmonics is 10 dB higher than the noise floor, the interference signal is determined to be valid interference; when the energy proportion of the fundamental frequency is less than or equal to 70%, or the sum of the harmonics is no more than 10 dB higher than the noise floor, the interference signal is determined not to be a valid interference signal.

[0093] In this embodiment, the validity of a periodic interference signal is determined based on its spectral characteristics. Filtering is only performed after confirming its validity, thus reducing unnecessary filtering and minimizing the impact of frequent filtering on system performance. Furthermore, by using the energy proportion of the fundamental frequency component and the energy ratio of harmonic components to noise floor to determine the validity of the interference signal, the truly influential interference signals can be more accurately identified, further reducing unnecessary filtering and minimizing the impact of frequent filtering on system performance. Moreover, determining the validity of an interference signal based on whether the energy proportion of the fundamental frequency component exceeds a second threshold and whether the energy ratio of the harmonic components to noise floor of the periodic interference signal exceeds a third threshold takes into account multiple signal components in the interference signal, allowing for a better assessment of its validity. This further improves the accuracy and reliability of interference signal selection.

[0094] If step S45 determines that the periodic interference signal is a valid interference signal, proceed to step S46. If step S45 determines that the periodic interference signal is not a valid interference signal, end the signal processing flow.

[0095] In this embodiment, step S44 is executed first, followed by step S45. In other embodiments, the following two judgment logics can be executed in parallel: determining whether the signal to be processed is a periodic interference signal; and determining whether the signal to be processed is a valid interference signal. The judgment results of the two logics are then combined to determine whether the signal to be processed is a valid periodic interference signal. In specific implementation, a microcontroller can be used to process the time-domain data (e.g., peak fluctuation feature analysis and waveform similarity feature analysis) and frequency-domain data (e.g., spectral feature analysis) of the signal to be processed in parallel. Furthermore, a Fourier transform accelerator can be used to quickly complete the Fourier transform calculation, thereby achieving the conversion of the signal from the time domain to the frequency domain.

[0096] In some embodiments, after step S45, the signal processing method further includes: updating the feature data of the interference signal stored in the database according to the characteristics of the currently detected periodic interference signal.

[0097] In step S46, the filter circuit is adjusted according to the characteristics of the periodic interference signal.

[0098] For details on how to implement step S46, please refer to the relevant descriptions in the foregoing embodiments.

[0099] In some embodiments, after step S46, the signal processing method further includes: after adjusting the filtering circuit, acquiring the signal filtered by the filtering circuit, and using the signal as the signal to be processed; for the signal to be processed, performing interference analysis and filtering adjustment again according to steps S41 to S46 until no periodic interference signal can be detected in the signal to be processed. In this way, the interference detection effect can be further improved, thereby improving the filtering effectiveness.

[0100] In some embodiments, it can be executed periodically. Figure 4 The method is illustrated. For example, within each cycle (e.g., 50 ms), interference signal detection is performed according to steps S41 to S46, and the filtering circuit is adjusted in real time based on the interference signal detection results. Another example is that interference signal detection is performed in the current cycle, and the filtering circuit is adjusted again in the next cycle based on the interference signal detection results from the previous cycle. Yet another example is that different signal detection cycles and filtering adjustment cycles are used, alternating between signal detection and filtering adjustment. For instance, interference signal detection is performed in the first signal detection cycle (e.g., 0~50 ms); filtering adjustment is performed in the first filtering adjustment cycle (e.g., 50 ms~500 ms); interference signal detection is performed in the second signal detection cycle (e.g., 500~550 ms); filtering adjustment is performed in the second filtering adjustment cycle (e.g., 550 ms~1000 ms).

[0101] In this embodiment, the above method can flexibly detect periodic interference factors experienced by the sensor under different environments and perform filtering and control accordingly. This reduces the impact of various environmental interferences on the sensor signal quality during signal transmission and improves the overall accuracy of the system. Moreover, compared with passive filtering schemes in related technologies, the method of this embodiment can effectively address the problem of unsatisfactory filtering effects caused by carrier drift of interference sources.

[0102] Figure 5 This is a schematic diagram of the controller according to some embodiments of this disclosure. For example... Figure 5 As shown, the controller includes a receiving module 51, an analysis module 52, a judgment module 53, and a control module 54.

[0103] The receiving module 51 is configured to receive signals to be processed from the sensor. These signals include target signals and interference signals acquired by the sensor.

[0104] Analysis module 52 is configured to analyze the signal to be processed to determine the characteristics of interference signals in the signal to be processed. The characteristics of interference signals in the signal to be processed include at least one of the peak fluctuation characteristics and waveform similarity characteristics of the interference signals.

[0105] The judgment module 53 is configured to determine whether the signal to be processed contains periodic interference signals based on the characteristics of the interference signals in the signal to be processed.

[0106] The control module 54 is configured to control the filter circuit according to the characteristics of the periodic interference signal when the signal to be processed contains a periodic interference signal.

[0107] In some embodiments, the controller further includes modules for performing additional steps of the signal processing method as described above.

[0108] In this embodiment of the disclosure, the above controller can identify interference signals of sensor signals in real time and accurately and filter them accordingly, thereby solving the problems of sensor signal loss and unsatisfactory filtering effect caused by passive filtering.

[0109] Figure 6 This is a schematic diagram of the controller according to other embodiments of this disclosure. For example... Figure 6 As shown, the controller includes a memory 61 and a processor 62 coupled to the memory 61. The memory 61 is used to store instructions corresponding to embodiments of the signal processing methods. The processor 62 is configured to execute the signal processing methods in any of the embodiments of this disclosure based on the instructions stored in the memory 61.

[0110] Figure 7This is a schematic diagram of the structure of a building control system according to some embodiments of the present disclosure. For example... Figure 7 As shown, the building control system 70 includes a controller 40 and a filter circuit 71.

[0111] The controller 40 is used to perform the signal processing method as described above.

[0112] The filter circuit 71 includes multiple filter modules connected in parallel and is configured to, under the control of the controller 40, operate the filter module among the multiple filter modules that matches the characteristics of the periodic interference signal contained in the signal to be processed, so as to filter the signal to be processed.

[0113] In some embodiments, the filter circuit 71 employs Figure 3 The structure shown is illustrated. However, in specific implementations, the filter circuit 71 can also employ other structures. For example, the filter circuit 71 may include multiple inductor-capacitor (LC) filter modules.

[0114] In the embodiments of this disclosure, the above system can identify interference signals of sensor signals in real time and accurately and filter them accordingly, thereby solving the problems of sensor signal loss and unsatisfactory filtering effect caused by passive filtering.

[0115] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of signal processing methods, apparatuses, building control systems, and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations thereof, can be implemented by computer-readable program instructions.

[0116] These computer-readable program instructions are provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable device to produce a machine, such that execution of the instructions by the processor produces means for implementing the functions specified in one or more boxes of the flowchart and / or block diagram.

[0117] These computer-readable program instructions may also be stored in a computer-readable storage medium. These instructions cause a computer to work in a particular manner to produce an article of manufacture, including instructions that implement the functions specified in one or more boxes in a flowchart and / or block diagram.

[0118] This disclosure may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects.

[0119] The signal processing method, controller, and building control system according to this disclosure have now been described in detail. To avoid obscuring the concept of this disclosure, some details known in the art have not been described. Those skilled in the art will fully understand how to implement the technical solutions disclosed herein based on the above description.

Claims

1. A signal processing method, comprising: Receive the signal to be processed from the sensor, the signal to be processed including the target signal and interference signal collected by the sensor; The signal to be processed is analyzed to determine the interference signal characteristics in the signal to be processed, wherein the interference signal characteristics in the signal to be processed include at least one of the peak fluctuation characteristics of the interference signal and the waveform similarity characteristics of the interference signal; Based on the characteristics of the interference signal in the signal to be processed, determine whether the signal to be processed contains a periodic interference signal; When the signal to be processed contains periodic interference signals, the filter circuit is adjusted according to the characteristics of the periodic interference signals.

2. The signal processing method according to claim 1, wherein, The analysis of the signal to be processed to determine the characteristics of interference signals in the signal to be processed includes: The signal to be processed is sampled to obtain sampled data; Data segments are extracted from the sampled data by sliding windows to obtain data segments within each of multiple windows; Identify the peak signal point of the interference signal in the data segment within each window; Calculate the fluctuation value between the peak signal points of two adjacent interference signals to obtain multiple fluctuation values; Based on the multiple fluctuation values, the peak fluctuation characteristics of the interference signal are determined.

3. The signal processing method according to claim 2, wherein, The step of analyzing the signal to be processed to determine the characteristics of interference signals in the signal to be processed further includes: Waveform segment data of interference signals from multiple cycles are extracted from the sampled data; Calculate the correlation coefficient between waveform segments of interference signals in two adjacent cycles of the multiple cycles to obtain multiple correlation coefficients; The multiple correlation coefficients are used as waveform similarity features of the interference signal.

4. The signal processing method according to claim 3, wherein, The peak fluctuation characteristic includes the average of the plurality of fluctuation values, or the proportion of fluctuation values ​​less than the fluctuation threshold among the plurality of fluctuation values, and the determination of whether the signal to be processed contains periodic interference signals includes: If the average of the plurality of fluctuation values ​​is less than the fluctuation threshold, or if the proportion of fluctuation values ​​less than the fluctuation threshold is greater than a first threshold, and if the plurality of correlation coefficients are all greater than the correlation coefficient threshold, then the signal to be processed is determined to contain a periodic interference signal; otherwise, the signal to be processed is determined not to contain a periodic interference signal.

5. The signal processing method according to any one of claims 1 to 4, further comprising: Before adjusting the filter circuit according to the characteristics of the periodic interference signal, the spectral characteristics of the periodic interference signal are determined, and the periodic interference signal is determined to be an effective interference signal based on the spectral characteristics of the periodic interference signal.

6. The signal processing method according to claim 5, wherein, The determination of the spectral characteristics of the periodic interference signal includes: Perform a Fourier transform on the signal to be processed to obtain the signal spectrum; Based on the signal spectrum, determine the energy ratio of the fundamental frequency component of the periodic interference signal and the energy ratio of the harmonic components to the noise floor of the periodic interference signal; The energy ratio of the fundamental frequency component of the periodic interference signal and the energy ratio of the harmonic components to the noise floor of the periodic interference signal are used as the spectral characteristics of the periodic interference signal.

7. The signal processing method according to claim 6, wherein, If the energy ratio of the fundamental frequency component of the periodic interference signal is greater than a second threshold and the energy ratio of the harmonic component of the periodic interference signal to the noise floor is greater than a third threshold, the periodic interference signal is determined to be an effective interference signal.

8. The signal processing method according to claim 1, wherein, The filtering circuit includes multiple filtering modules connected in parallel, and the step of adjusting the filtering circuit according to the characteristics of the periodic interference signal includes: From the plurality of filter modules, a filter module that matches the characteristics of the periodic interference signal is determined, wherein the characteristics of the periodic interference signal include the period or frequency of the periodic interference signal; The matched filtering module is controlled to operate in order to filter the signal to be processed.

9. The signal processing method according to claim 8, wherein, The plurality of filter modules are plurality of resistor-capacitor (RC) filter modules, and determining the filter module that matches the characteristics of the periodic interference signal from the plurality of filter modules includes: Determine the capacitance value and time constant of the RC filter based on the period or frequency of the periodic interference signal; Based on the time constant, the resistance value of the RC filter module with the capacitance value among the plurality of RC filter modules is adjusted, and the adjusted RC filter module is used as the matched filter module.

10. The signal processing method according to claim 1, further comprising: Before analyzing the signal to be processed, high-frequency noise in the signal to be processed is filtered out based on a Butterworth filter.

11. A controller, comprising: Memory; as well as A processor coupled to the memory, the processor being configured to perform the signal processing method as described in any one of claims 1 to 10 based on instructions stored in the memory.

12. A building control system, comprising: The controller as described in claim 11; The filtering circuit, comprising multiple filtering modules connected in parallel, is configured to, under the control of the controller, operate the filtering module among the multiple filtering modules whose characteristics match those of the periodic interference signal contained in the signal to be processed, so as to filter the signal to be processed.

13. A computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the signal processing method as described in any one of claims 1 to 10.

14. A computer program product having stored computer program instructions thereon, which, when executed by a processor, implement the signal processing method as described in any one of claims 1 to 10.

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