A dormitory safety power remote monitoring and management system based on internet of things

Abstract

This invention discloses a remote monitoring and management system for safe electricity use in dormitories based on the Internet of Things (IoT), belonging to the field of dormitory safe electricity use monitoring technology. The system includes a current sampling module, a feature extraction module, a graph generation module, an appliance identification module, a total power calculation module, and an over-power control module. The current sampling module collects transient and steady-state current waveforms during the startup period of each power circuit. The feature extraction module extracts surge characteristic sequences and harmonic characteristic sequences. The graph generation module aligns the surge characteristic sequences and harmonic characteristic sequences of the same circuit in time to generate a load switching characteristic graph. The appliance identification module determines the type identifier of the connected appliance based on waveform distortion points. The total power calculation module calculates the total power of the appliances currently operating in the dormitory. When the total power exceeds a threshold, the over-power control module issues power-off commands sequentially according to the current current amplitude of the circuit from largest to smallest until the remaining total power is below the threshold.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) safe electricity monitoring technology, specifically to an IoT-based remote monitoring and management system for safe electricity use in dormitories. Background Technology

[0002] University dormitories have numerous electrical circuits and complex load types. The use of unauthorized electrical appliances and exceeding total power limits are the main monitoring targets for dormitory electricity safety management. Existing remote monitoring methods for dormitory electricity typically collect total current, total voltage, and total power at the main incoming line or floor distribution boxes. When the total power exceeds a set threshold, an alarm is triggered or a unified circuit breaker is tripped. Some systems use the steady-state current RMS value, steady-state harmonic components, or power factor as load identification criteria, and combine this with a preset circuit numbering sequence to disconnect overloaded loads.

[0003] In terms of load identification, existing solutions mainly rely on the current characteristics of electrical appliances after they enter stable operation, which is insufficient for utilizing the transient current changes during the startup period. The surge characteristics in the transient current waveform reflect the electrical transient process at the moment the appliance is switched on, while the harmonic characteristics in the steady-state current waveform reflect the nonlinear characteristics of the appliance during stable operation. However, existing systems typically do not correlate these two on a time axis. When identification is based solely on steady-state harmonics or the effective value of the current, it is difficult to accurately distinguish between appliances with similar startup surge processes but different steady-state harmonics. The load switching time is also easily affected by background current fluctuations, leading to misjudgments and omissions in appliance type identification.

[0004] Regarding over-power regulation, existing systems often employ a single main switch for unified power outages or sequential power outages according to a fixed circuit order, failing to prioritize load contributions based on the current amplitude of each circuit. During power reduction, circuits contributing less to the total power may be disconnected while those contributing more remain active, leading to an expanded outage area or requiring multiple switching operations to reduce the total power below the threshold. Remote monitoring of dormitory electricity usage needs to address the issues of insufficient accuracy in identifying appliance types and inappropriate selection of power-off circuits under over-power conditions. Summary of the Invention

[0005] To address the issues of insufficient accuracy in appliance type identification and unreasonable over-power cutoff ranges in existing remote monitoring systems for safe electricity use in dormitories, this paper proposes an IoT-based remote monitoring and management system for safe electricity use in dormitories. This system extracts surge and harmonic characteristic sequences from transient and steady-state current waveforms during the startup period of each power circuit, aligns these sequences to generate a load switching characteristic map, and determines the appliance type identifier based on waveform distortion points. Furthermore, it dynamically determines the power-off sequence according to the current current amplitude of each power circuit during over-power conditions, thus improving the accuracy of appliance type identification and over-power control.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The present invention provides a dormitory safe electricity remote monitoring and management system based on the Internet of Things, including a current sampling module, a feature extraction module, a spectrum generation module, an appliance identification module, a total power calculation module, and an overpower regulation module. The current sampling module is used to collect the transient and steady-state current waveforms of each power circuit in the university dormitory during the startup period; the feature extraction module is used to extract the surge feature sequence corresponding to each power circuit based on the transient current waveform, and extract the harmonic feature sequence corresponding to each power circuit based on the steady-state current waveform; the spectrum generation module is used to time-align the surge feature sequence and harmonic feature sequence of the same power circuit to generate the load switching feature spectrum of the power circuit during the startup period; the appliance identification module is used to determine the type identifier of the appliances connected in the power circuit based on the waveform distortion points in the load switching feature spectrum; the total power calculation module is used to calculate the total power of the appliances currently running in the dormitory based on the type identifier of all power circuits and the steady-state current amplitude of the corresponding circuits; the over-power control module is used to generate an over-power warning signal when the total power of the appliances exceeds the preset power threshold, and to issue power-off commands to the corresponding power circuits in descending order of the current current amplitude of each power circuit until the total power of the remaining operating appliances is lower than the power threshold. This scheme extracts load switching feature maps by combining transient and steady-state current waveforms, and identifies electrical appliance types based on waveform distortion points. This improves the accuracy of dormitory power load identification and the targeting of over-power power-off control, reducing misjudgments and missed judgments.

[0007] Preferably, the current sampling module installs a high-frequency sampling transformer on the neutral wire of each power circuit to collect the transient current signal of each power circuit during the startup period at a first sampling frequency, obtaining the transient current waveform; and installs a power frequency sampling transformer on the live wire of each power circuit to collect the steady-state current signal of each power circuit during the startup period at a second sampling frequency, obtaining the steady-state current waveform. The first sampling frequency is higher than the second sampling frequency, and the startup period starts from the moment when the current of the power circuit rises from zero and ends when the current of the power circuit enters the stable fluctuation range threshold. By combining high-frequency neutral wire sampling and power frequency live wire sampling, the rapid surge details and steady-state harmonic information during the startup process can be obtained simultaneously.

[0008] Preferably, the feature extraction module performs differential operations on the transient current waveform to obtain the current change rate waveform of the power circuit during the startup period. The peak and valley points in the current change rate waveform are used as surge feature points, and the amplitude and time coordinates of all surge feature points are combined into a surge feature sequence. A fast Fourier transform is performed on the steady-state current waveform to extract the amplitude and phase of the odd harmonic components in the steady-state current of the power circuit, and the amplitude and phase of the odd harmonic components are combined into a harmonic feature sequence. Through differential operations and Fourier transforms, the surge characteristics and steady-state harmonic characteristics at the startup instant can be highlighted, providing a stable feature basis for subsequent spectrum generation.

[0009] Preferably, the load switching feature generation module takes the start point of the startup period as time zero, converts the time coordinates of each surge feature point in the surge feature sequence and the phase angles of each harmonic component in the harmonic feature sequence into time offsets; it pairs surge feature points with the same time coordinates with harmonic components with the same time offsets to generate load switching feature vectors corresponding to multiple sampling times within the startup period; and it arranges the load switching feature vectors corresponding to multiple sampling times in chronological order to form the load switching feature map of the power circuit. Through time alignment and feature pairing, surge features and harmonic features can be correlated in the time dimension to form a map structure that reflects the changes in the load switching process.

[0010] Preferably, the appliance identification module calculates the Euclidean distance between load switching feature vectors corresponding to adjacent sampling times in the load switching feature map. The next sampling time among adjacent sampling times where the Euclidean distance is greater than a preset distortion threshold is marked as a waveform distortion point. The load switching feature vector corresponding to the waveform distortion point is extracted as the switching transient vector, and the load switching feature vector corresponding to the sampling time preceding the waveform distortion point is obtained as the pre-switching steady-state vector. The switching transient vector is compared with the switching transient reference vectors of each standard appliance in the standard appliance library for similarity, and the type identifier of the standard appliance with the highest similarity is determined as the type identifier of the appliance connected in the power circuit. By detecting waveform distortion points in the load switching feature map, the features of the appliance at the moment of connection can be matched with the standard appliance library, improving the accuracy and automation of appliance type identification.

[0011] Preferably, the total power calculation module queries the rated power value of the appliances connected in each electrical circuit from the rated power mapping table based on the type identifier of each circuit. The instantaneous power of the circuit connected to the appliances in each circuit is calculated by multiplying the steady-state current amplitude of each circuit by its corresponding rated voltage. The total instantaneous power of all circuits is then summed to obtain the total power of the appliances currently operating in the dormitory. This method, by utilizing the identified appliance types and combining measured current and rated voltage to calculate the instantaneous power of the circuits, more accurately reflects the current electrical load.

[0012] Preferably, the overpower control module subtracts a power threshold from the total power of the appliances to obtain the power to be reduced. It then calculates the instantaneous power of each circuit in descending order of current amplitude and adds the calculated instantaneous power to the cumulative reduction amount. The circuits through which the cumulative reduction reaches the power to be reduced are designated as the set of circuits to be disconnected. Power-off commands are simultaneously issued to all circuits in the set of circuits to be disconnected. This method prioritizes disconnecting circuits with larger current amplitudes, quickly reducing the total power to a safe range and minimizing the impact on normal dormitory power usage.

[0013] Preferably, when converting the phase angle of a harmonic component into a time offset, the quotient obtained by dividing the phase angle of the harmonic component by the fundamental angular frequency is used as the time offset corresponding to the harmonic component. This conversion method can unify phase information and time coordinates to the same dimension, which facilitates feature alignment and pairing.

[0014] Preferably, the preset distortion threshold is dynamically set as a percentage of the root mean square value of the steady-state current amplitude of the power circuit. By dynamically setting the distortion threshold, it is possible to adapt to the current fluctuation levels of different load circuits and reduce miscalibration caused by current fluctuations.

[0015] Preferably, the rated power value corresponding to each type identifier in the rated power mapping table is the median value of that type of appliance within the factory-specified power range. By using the median value of the factory-specified power range, the power level of the appliances can be reasonably reflected while reducing the impact of individual differences on the total power calculation.

[0016] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0017] By synchronously acquiring transient and steady-state current waveforms during the startup period of each power circuit, surge feature points are obtained through differential calculation of the transient current waveform, and the amplitude and phase of odd harmonic components are obtained by performing a fast Fourier transform on the steady-state current waveform. The time coordinates of the surge feature points are then paired with the time offsets converted from the phase angles of the harmonic components to generate load switching feature vectors corresponding to multiple sampling moments. The load switching feature map unifies the peak and valley values ​​of the current change rate at the moment of appliance switching with the steady-state odd harmonic characteristics onto the same time axis. During appliance type identification, the waveform distortion points are located by utilizing the Euclidean distance change between adjacent load switching feature vectors, and then the switching transient vectors corresponding to the waveform distortion points are matched with reference vectors in the standard appliance library for similarity. This process utilizes transient surge changes and steady-state harmonic characteristics to jointly constrain appliance type identification, which can reduce type confusion caused by relying solely on the effective value of steady-state current or a single harmonic, has a more accurate ability to distinguish appliances with similar startup characteristics, and can more accurately capture the load switching moment when there are background current fluctuations.

[0018] During overpower regulation, when the total power of electrical appliances exceeds a preset power threshold, the power to be reduced is first calculated. Then, the instantaneous power of each circuit is accumulated sequentially according to its current current amplitude, from largest to smallest, until the accumulated reduction reaches the power to be reduced. The circuits passed through during the accumulation process are then designated as the set of circuits to be disconnected, and power-off commands are simultaneously issued to these circuits. This method prioritizes disconnecting circuits with larger current amplitudes, achieving the power reduction target with fewer disconnected circuits and avoiding the impact of uniform tripping or power outages in a fixed circuit sequence on non-critical loads. Because the disconnection sequence is dynamically determined based on the real-time current amplitude of each circuit, adjustments can still be made according to the actual contribution when the load contribution changes, making overpower control more closely reflect the actual operating status of each electrical circuit in the dormitory. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0020] Figure 1 This is a schematic diagram of the dormitory safe electricity remote monitoring and management system based on the Internet of Things;

[0021] Figure 2 This is a flowchart for extracting transient and steady-state current features from an electrical circuit.

[0022] Figure 3 This is a flowchart of the load switching feature map generation method;

[0023] Figure 4 This is a schematic diagram of the Euclidean distance of the load switching characteristic vector and the waveform distortion points. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] See Figure 1This invention provides a remote monitoring and management system for safe electricity use in dormitories based on the Internet of Things (IoT), including a current sampling module, a feature extraction module, a graph generation module, an appliance identification module, a total power calculation module, and an over-power control module. The current sampling module collects transient and steady-state current waveforms of each electrical circuit in the university dormitory during the startup period. The feature extraction module extracts the surge feature sequence corresponding to each electrical circuit based on the transient current waveform and the harmonic feature sequence corresponding to each electrical circuit based on the steady-state current waveform. The graph generation module aligns the surge feature sequence and harmonic feature sequence of the same electrical circuit in time to generate a load switching feature graph of that electrical circuit during the startup period. The appliance identification module determines the type of appliance connected in the electrical circuit based on the waveform distortion points in the load switching feature graph. The total power calculation module calculates the total power of the appliances currently operating in the dormitory based on the type identification of all electrical circuits and the steady-state current amplitude of the corresponding circuits. When the total power of electrical appliances exceeds the preset power threshold, the overpower control module generates an overpower warning signal and sends power-off commands to the corresponding electrical circuits in descending order of the current amplitude of each circuit until the total power of the remaining operating electrical appliances is lower than the power threshold.

[0026] In specific implementation, please refer to Figure 2 The current sampling module installs high-frequency sampling transformers on the neutral line of each power circuit. The high-frequency sampling transformers use ferrite cores. The primary side of the high-frequency sampling transformer is connected in series with the neutral line of the power circuit, and the secondary side is connected to the analog front end of the current sampling module via a coaxial cable. The first sampling frequency is set to 100 kHz. The reason for setting the first sampling frequency is that the transient current waveform generated by load switching in each power circuit in the university dormitory contains high-frequency oscillation components. The highest frequency of the high-frequency oscillation component in the transient current waveform is 40 kHz. According to the Nyquist sampling theorem, the first sampling frequency must be greater than twice the highest frequency of the transient current signal. Setting the first sampling frequency to 100 kHz satisfies the condition for complete recording of the transient current waveform. When the current in the power circuit starts to rise from zero, the current sampling module starts acquiring the transient current waveform. It performs analog-to-digital conversion on the transient current signal output by the high-frequency sampling transformer at the first sampling frequency to obtain the transient current waveform. The starting point of the startup period is the moment when the current in the power circuit starts to rise from zero. The start-up period ends when the current in the power circuit enters the stable fluctuation range threshold. The stable fluctuation range threshold is set to 5% of the steady-state current amplitude of the power circuit. The basis for setting the stable fluctuation range threshold is that the fluctuation amplitude of the effective current value of the dormitory power circuit during stable operation is less than 5% of the steady-state current amplitude. When the change amplitude of the effective current value of the power circuit does not exceed 5% of the steady-state current amplitude within ten consecutive power frequency cycles, the current in the power circuit is determined to have entered the stable fluctuation range threshold, and transient current waveform acquisition is stopped.

[0027] In practice, power frequency sampling transformers are installed on the live wires of each power circuit. The power frequency sampling transformers use permalloy cores. The primary side of the transformer is connected in series with the live wire of the power circuit, and the secondary side is connected to the analog front end of the current sampling module via a differential amplifier circuit. The second sampling frequency is set to 4 kHz. The basis for setting the second sampling frequency is as follows: the highest order of odd harmonic components to be extracted from the steady-state current waveform is 25th; the fundamental frequency of the dormitory power supply is 50 Hz; the frequency of the 25th odd harmonic component is 1250 Hz; according to the Nyquist sampling theorem, the second sampling frequency needs to be greater than 2500 Hz. Furthermore, to facilitate sampling at integer points within one power frequency cycle, the second sampling frequency is selected as 80 times the number of sampling points within the power frequency cycle, i.e., 4 kHz. During the startup period, the current sampling module performs analog-to-digital conversion on the steady-state current signal output from the power frequency sampling transformer at the second sampling frequency to obtain the steady-state current waveform. The start and end points of the startup period are consistent with the startup period corresponding to the transient current waveform.

[0028] It is understandable that the high-frequency sampling transformer is installed on the neutral line and the power frequency sampling transformer is installed on the live line to avoid common ground interference between the transient current acquisition channel and the steady-state current acquisition channel.

[0029] In the step of performing differential calculation on the transient current waveform, the rate of change of current at the m-th sampling point of the transient current waveform is calculated using the following formula:

[0030]

[0031] in, This represents the sequence number of the sampling point in the transient current waveform. The value range is from 1 to , This represents the total number of sampling points in the transient current waveform. The first transient current waveform The transient current sampling value corresponding to each sampling point; The first transient current waveform The transient current sampling value corresponding to each sampling point; The first transient current waveform The time coordinates corresponding to each sampling point; The first transient current waveform The time coordinates corresponding to each sampling point; The transient current waveform is at the 1st The rate of change of current at each sampling point. Arrange the rate of change of current at all sampling points in chronological order to form the waveform of the rate of change of current of the power circuit during the startup period.

[0032] When identifying peak and trough points in a current rate of change waveform, local maxima where the first derivative changes from positive to negative are identified as peak points, and local minima where the first derivative changes from negative to positive are identified as trough points. All peak and trough points are considered surge feature points. Each surge feature point has two attributes: amplitude and time coordinate. The amplitude is the current rate of change value at the surge feature point in the current rate of change waveform, and the time coordinate is the sampling time corresponding to the surge feature point. The amplitude and time coordinates of all surge feature points are combined into a surge feature sequence. The surge feature points are arranged in chronological order according to their time coordinates, and each surge feature point is represented as a binary tuple containing both amplitude and time coordinates.

[0033] During the Fast Fourier Transform (FFT) of the steady-state current waveform, the waveform is segmented into analysis windows of four power frequency cycles each, with each window containing 320 sampling points and 160 overlapping sampling points between windows. The FFT is then applied to the steady-state current waveform within each analysis window to obtain the corresponding spectrum. In the spectrum corresponding to each analysis window, the components corresponding to the fundamental frequency of 50 Hz, the third harmonic frequency of 150 Hz, the fifth harmonic frequency of 250 Hz, the seventh harmonic frequency of 350 Hz, the ninth harmonic frequency of 450 Hz, the eleventh harmonic frequency of 550 Hz, the thirteenth harmonic frequency of 650 Hz, the fifteenth harmonic frequency of 750 Hz, the seventeenth harmonic frequency of 850 Hz, the nineteenth harmonic frequency of 950 Hz, the twenty-first harmonic frequency of 1050 Hz, the twenty-third harmonic frequency of 1150 Hz, and the twenty-fifth harmonic frequency of 1250 Hz are extracted. The amplitude of the odd-harmonic component is obtained by taking the modulus of the complex spectral values ​​corresponding to the listed odd-harmonic frequencies in the spectrum of each analysis window, and the phase of the odd-harmonic component is obtained by taking the argument of the complex spectral values ​​corresponding to the listed odd-harmonic frequencies in the spectrum of each analysis window. The amplitudes and phases of all odd harmonic components within the same analysis window are combined into a harmonic characteristic sequence. The harmonic characteristic sequence is arranged in order of increasing odd harmonic order. Each odd harmonic component is represented as a binary tuple containing amplitude and phase.

[0034] Optionally, when extracting the amplitude and phase of odd harmonic components, the amplitude corresponding to the same odd harmonic frequency within multiple analysis windows is averaged, and the phase corresponding to the same odd harmonic frequency within multiple analysis windows is averaged. The averaged amplitude and phase of the odd harmonic components are then combined into a harmonic feature sequence.

[0035] It is understandable that the amplitude and phase of the odd harmonic components in the steady-state current waveform can reflect the nonlinear load characteristics of the electrical appliances connected in the power circuit.

[0036] In specific implementation, please refer to Figure 3 The spectrum generation module acquires the surge characteristic sequence and harmonic characteristic sequence of the same power circuit. Each surge characteristic point in the surge characteristic sequence includes amplitude and time coordinates, and each harmonic component in the harmonic characteristic sequence includes amplitude and phase angle. The start-up period is defined as the moment when the current in the power circuit begins to rise from zero.

[0037] For each harmonic component in the harmonic characteristic sequence, the corresponding phase angle is read. The phase angle is in radians. The fundamental angular frequency is calculated based on the grid fundamental frequency, which is 50 Hz, and the fundamental angular frequency is 100π radians / second. The phase angle of the harmonic component is converted into a time offset using the following formula:

[0038]

[0039] in, The phase angle representing the harmonic component, expressed in radians; This represents the fundamental angular frequency, with a value of 100π radians / second. This represents the time offset corresponding to the harmonic component, in seconds.

[0040] The phase angles of all harmonic components are converted according to the above formula to obtain the time offset corresponding to each harmonic component.

[0041] It is understandable that the starting point of the start-up period is time zero, the time coordinate of the surge characteristic point is the sampling time measured with time zero as the reference, and the time offset corresponding to the harmonic component is also the time position with time zero as the reference.

[0042] In practice, the time matching tolerance is set to half of the first sampling period. The first sampling frequency is 100 kHz, the first sampling period is the reciprocal of the first sampling frequency, the first sampling period is 0.00001 seconds, and the time matching tolerance is 0.000005 seconds. The time matching tolerance is set based on the following: the time coordinates of surge feature points are obtained by sampling the transient current waveform, and the time resolution of the transient current waveform is the first sampling period. To avoid mispairing temporally adjacent but different surge feature points with harmonic components, the time matching tolerance does not exceed half of the first sampling period.

[0043] For each surge feature point, obtain the corresponding time coordinate. For each harmonic component, obtain the corresponding time offset. When the absolute value of the difference between the time coordinate corresponding to the surge feature point and the time offset corresponding to the harmonic component is less than the time matching tolerance, it is determined that the time coordinate corresponding to the surge feature point and the time offset corresponding to the harmonic component are the same.

[0044] When the same time offset corresponds to multiple harmonic components, the harmonic component with the largest amplitude is selected from the multiple harmonic components, and the selected harmonic component is paired with a surge feature point with the same time coordinate. When the same time offset corresponds to only one harmonic component, this harmonic component is paired with a surge feature point with the same time coordinate.

[0045] For each pair of paired surge feature points and harmonic components, a load switching feature vector is generated. The load switching feature vector contains three elements: the amplitude of the surge feature point, the amplitude of the harmonic component, and the phase angle of the harmonic component. The sampling time corresponding to the load switching feature vector is the time coordinate of the surge feature point at the time of pairing.

[0046] The generated load switching feature vectors are arranged in ascending order according to their corresponding sampling times to form the load switching feature map of the power circuit. The load switching feature map is a time series within the startup period. Each element in the load switching feature map is a load switching feature vector, and each load switching feature vector corresponds to a sampling time.

[0047] In practical implementation, the appliance identification module acquires a load switching feature map. The load switching feature map contains multiple load switching feature vectors arranged in order of sampling time. Each load switching feature vector contains three elements: surge feature point amplitude, harmonic component amplitude, and harmonic component phase angle.

[0048] The weighted Euclidean distance between the load switching feature vectors corresponding to adjacent sampling times is calculated using the following formula:

[0049]

[0050] in, , , These are the dimensionally normalized weighting coefficients, with values ​​of: , , Numerically, it can be taken as 1, and physically it means normalizing each dimension to a dimensionless quantity. This indicates the sequence number of the sampling time in the load switching feature map. The value range is from 1 to , This represents the total number of load-splitting feature vectors in the load-splitting feature map; Indicates the first The surge feature point amplitude in the load switching feature vector corresponding to each sampling time; Indicates the first The surge feature point amplitude in the load switching feature vector corresponding to each sampling time; Indicates the first The amplitude of harmonic components in the load switching feature vector corresponding to each sampling time; Indicates the first The amplitude of harmonic components in the load switching feature vector corresponding to each sampling time; Indicates the first The phase angle of the harmonic components in the load switching feature vector corresponding to each sampling time; Indicates the first The phase angle of the harmonic components in the load switching feature vector corresponding to each sampling time; Indicates the first The sampling time and the first The weighted Euclidean distance between the load switching feature vectors corresponding to each sampling time is a dimensionless quantity.

[0051] After calculating the Euclidean distance between the load switching feature vectors corresponding to every two adjacent sampling times, the appliance identification module obtains the preset distortion threshold. The preset distortion threshold is dynamically set as a percentage of the root mean square value of the steady-state current amplitude of the power circuit.

[0052] In practice, the root mean square value of the steady-state current amplitude is determined as follows: Select the steady-state current sampling value within one power frequency cycle before the end of the start-up period in the steady-state current waveform, square each of the selected steady-state current sampling values ​​and calculate the average value, then take the square root of the average value to obtain the root mean square value of the steady-state current amplitude.

[0053] The preset distortion threshold is set to 10%. This 10% percentage is based on the following: in the dormitory power circuit, during steady-state operation, the change in the load switching characteristic vector between two adjacent sampling times is less than 10% of the root mean square value of the steady-state current amplitude, while the change in the load switching characteristic vector at the instant of load switching is greater than 10% of the root mean square value of the steady-state current amplitude. The preset distortion threshold is calculated based on this percentage, and is equal to the root mean square value of the steady-state current amplitude multiplied by 0.10.

[0054] Each Euclidean distance is compared with a preset distortion threshold. When an Euclidean distance is greater than the preset distortion threshold, the latter of the two adjacent sampling times used to calculate this Euclidean distance is marked as a waveform distortion point. When an Euclidean distance is less than or equal to the preset distortion threshold, no waveform distortion point is marked.

[0055] Next, the appliance identification module extracts the load switching feature vector corresponding to the waveform distortion point, and uses the extracted load switching feature vector as the switching transient vector. The appliance identification module obtains the load switching feature vector corresponding to the sampling time before the waveform distortion point, and uses the obtained load switching feature vector as the steady-state vector before switching.

[0056] In practice, a standard electrical appliance library is pre-built. This library stores type identifiers for multiple standard electrical appliances, each corresponding to a switching transient reference vector. During the library's construction, each standard electrical appliance is individually connected to the same sampling environment. Transient and steady-state current waveforms are collected during the appliance's startup period. Surge and harmonic feature sequences are extracted in the same manner as in the aforementioned embodiments to generate a load switching feature map corresponding to the standard electrical appliance. Waveform distortion points in the load switching feature map are marked, and the load switching feature vectors corresponding to these distortion points are extracted as the standard electrical appliance's switching transient reference vectors. The type identifiers of the standard electrical appliances are then stored in correspondence with the switching transient reference vectors.

[0057] In the similarity comparison process, cosine similarity is used as the similarity metric. For each standard appliance's switching transient reference vector, the inner product is obtained by multiplying the corresponding three elements of the switching transient vector and the switching transient reference vector, and then summing the results. The norms of the switching transient vector and the switching transient reference vector are calculated separately; the norm is the square root of the sum of the squares of the three elements in the vector. The cosine similarity is the inner product divided by the product of the norms of the switching transient vector and the switching transient reference vector. The cosine similarity between each switching transient vector and the switching transient reference vector of each standard appliance in the standard appliance library is calculated, resulting in multiple cosine similarity values. The maximum value among these cosine similarity values ​​is selected, and the type identifier of the standard appliance corresponding to the maximum cosine similarity value is determined as the type identifier of the appliance connected in the power circuit.

[0058] Optionally, when the switching transient reference vectors of multiple standard electrical appliances have the same cosine similarity to the switching transient vector and are both at their maximum values, the type identifier of the standard electrical appliance ranked first in the standard electrical appliance library is selected as the type identifier of the electrical appliance connected in the power circuit.

[0059] See Figure 4 In the figure, the horizontal axis represents the sampling time sequence, and the vertical axis represents the Euclidean distance between the load switching feature vectors corresponding to adjacent sampling times. The curves are marked with blue solid lines and dots, indicating the changing trend of the Euclidean distance between the load switching feature vectors corresponding to every two adjacent sampling times. The preset distortion threshold is marked with a red dashed line in the figure, with a threshold value of approximately 1.2. Orange pentagrams mark the waveform distortion points, that is, the sampling times where the Euclidean distance exceeds the preset threshold.

[0060] The curve trend shows that the Euclidean distance values ​​at most sampling moments are below the preset distortion threshold, with small fluctuations, indicating that the load switching characteristic vector changes relatively smoothly and is in a steady-state operation phase. At several sampling moments, the curve shows significant peaks that are much higher than the threshold, with the highest peak being approximately 2.7, significantly exceeding the threshold line, reflecting drastic changes in the load switching characteristic vector. The sampling moments corresponding to these peaks are identified by this system as waveform distortion points.

[0061] The figure shows eight distinct waveform distortion points, spaced approximately at sampling time numbers of 40, 90, 140, 220, 310, 400, 480, and 560. The Euclidean distance corresponding to each distortion point is significantly higher than the preset distortion threshold, ensuring accurate detection of abnormal changes. Simultaneously, the Euclidean distance before and after the distortion points drops to a lower level, demonstrating the system's ability to effectively distinguish between transient and steady-state characteristics during load switching.

[0062] In practice, the total power calculation module receives the type identifier of each power circuit and the steady-state current amplitude of each power circuit. The type identifier of the power circuit is output by the appliance identification module, and the steady-state current amplitude of the power circuit is output by the current sampling module.

[0063] The rated power mapping table is pre-stored in the total power calculation module. The table contains multiple records, each with a type identifier and a rated power value. The type identifier is the type of appliance connected in the power circuit, and the rated power value is the median value of the appliance within its factory-rated power range. The rated power value for each type identifier in the mapping table is determined as follows: obtain the lower and upper limits of the factory-rated power range for the appliance corresponding to the type identifier, add the lower and upper limits, divide by 2, and use the quotient as the rated power value for the type identifier. The lower limit of the factory-rated power range is the minimum power value rated at the factory for the appliance corresponding to the type identifier, and the upper limit is the maximum power value rated at the factory for the appliance corresponding to the type identifier.

[0064] Based on the type identifier of each electrical circuit, compare each record in the rated power mapping table to find the record with the same type identifier as the electrical circuit, read the rated power value in the found record, and use the read rated power value as the rated power value of the electrical appliances connected in the electrical circuit.

[0065] The steady-state current amplitude is obtained as follows: Select the steady-state current sampling value within the most recent complete power frequency cycle from the steady-state current waveform of the power circuit. Square each of the selected steady-state current sampling values, calculate the average value, and then take the square root of the average value to obtain the steady-state current amplitude of the power circuit. The rated voltage corresponding to the power circuit is the power supply voltage of the university dormitory, which is 220 volts.

[0066] The instantaneous power of each electrical circuit is calculated using the following formula:

[0067]

[0068] in, Indicates the serial number of the power circuit. The value range is from 1 to , This indicates the total number of electrical circuits in the dormitory. Indicates the first The steady-state current amplitude of each power circuit, in amperes; Indicates the first The rated voltage corresponding to each electrical circuit, in volts. The value is 220 volts; Indicates the first The instantaneous power of an electrical appliance connected in a circuit, measured in watts.

[0069] The total power of all electrical circuits currently in operation in the dormitory is obtained by summing the instantaneous power of all electrical circuits.

[0070] In practice, the over-power control module receives the total power consumption of currently operating appliances in the dormitory and a preset power threshold. The preset power threshold is pre-set by the dormitory power management terminal and stored in the configuration parameter area of ​​the over-power control module. The over-power control module compares the total power consumption of currently operating appliances in the dormitory with the preset power threshold. When the total power consumption of currently operating appliances in the dormitory exceeds the preset power threshold, the over-power control module generates an over-power warning signal. The over-power warning signal is sent to the dormitory management terminal via the IoT communication module, and the dormitory management terminal displays the over-power warning signal.

[0071] In practice, the overpower regulation module calculates the amount of power to be reduced. The formula for calculating the amount of power to be reduced is:

[0072]

[0073] in, This indicates the amount of power to be reduced, expressed in watts. This indicates the total power of the electrical appliances currently operating in the dormitory, expressed in watts. This indicates the preset power threshold, in watts.

[0074] The overpower control module sorts the power circuits in descending order of their current current amplitude. The current current amplitude of each power circuit is determined as follows: the steady-state current sample value within the most recent complete power frequency cycle of the steady-state current waveform of each power circuit is obtained; the average value is calculated by squaring each of the obtained steady-state current sample values; and the square root of the average value is then taken to obtain the current current amplitude of each power circuit.

[0075] After sorting, the overpower control module establishes an ordered list of power circuits. The order of the power circuits in the ordered list is consistent with the order of the current amplitude from largest to smallest. The overpower control module initializes the cumulative power reduction to zero watts.

[0076] In practice, the overpower control module starts from the first power circuit in the ordered power circuit list and calculates the instantaneous power of each power circuit. The instantaneous power of the circuit is calculated by multiplying the current amplitude of the power circuit by the rated voltage of the power circuit. The rated voltage of the power circuit is the power supply voltage of the university dormitory, which is 220 volts.

[0077] The currently calculated instantaneous power of each circuit is added to the cumulative reduction amount. After adding the instantaneous power of each circuit, the cumulative reduction amount is compared with the power to be reduced. When the cumulative reduction amount reaches the power to be reduced, the circuits traversed by the currently added instantaneous power are designated as the set of circuits to be disconnected, and the addition of subsequent circuits is stopped. When the cumulative reduction amount has not reached the power to be reduced, the next circuit is retrieved from the ordered circuit list, and the calculation and addition of instantaneous power are repeated until the cumulative reduction amount reaches the power to be reduced.

[0078] Optionally, when the cumulative power reduction first exceeds the power to be reduced, the circuits through which the currently accumulated instantaneous power passes are designated as the set of circuits to be disconnected. When the cumulative power reduction is exactly equal to the power to be reduced, the circuits through which the currently accumulated instantaneous power passes are designated as the set of circuits to be disconnected.

[0079] It is understandable that accumulating the instantaneous power of the circuit according to the current amplitude from large to small can reduce the number of power circuits that are disconnected while meeting the power reduction requirements.

[0080] In practice, the overpower control module synchronously sends power-off commands to all electrical circuits in the set of circuits to be disconnected via the Internet of Things (IoT) communication module. The power-off command includes the circuit number and a power-off action command. Each electrical circuit is equipped with a relay; upon receiving the power-off command, the relay disconnects the circuit, thus de-energizing it. All electrical circuits in the set of circuits to be disconnected synchronously execute the power-off action, reducing the total power of the remaining operating appliances to below a preset power threshold.

[0081] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A remote monitoring and management system for safe electricity use in dormitories based on the Internet of Things, characterized in that, include: The current sampling module collects the transient and steady-state current waveforms of each electrical circuit in the university dormitory during the startup period. The feature extraction module extracts the surge feature sequence corresponding to each power circuit based on the transient current waveform, and extracts the harmonic feature sequence corresponding to each power circuit based on the steady-state current waveform. The spectrum generation module aligns the surge characteristic sequence and harmonic characteristic sequence of the same power circuit in time sequence to generate the load switching characteristic spectrum of the power circuit during the startup period. The appliance identification module determines the type identifier of the appliance connected in the power circuit based on the waveform distortion points in the load switching feature map. The total power calculation module calculates the total power of the electrical appliances currently running in the dormitory based on the type identification of all electrical circuits and the steady-state current amplitude of the corresponding circuits. The overpower control module generates an overpower warning signal when the total power of the electrical appliances exceeds a preset power threshold. Then, it sends power-off commands to the corresponding electrical circuits in descending order of the current amplitude of each circuit until the total power of the remaining operating electrical appliances is lower than the power threshold.

2. The dormitory safety electricity remote monitoring and management system based on the Internet of Things as described in claim 1, characterized in that, The collection of transient and steady-state current waveforms of each electrical circuit in the university dormitory during the startup period includes: High-frequency sampling transformers are installed on the neutral line of each power circuit to collect transient current signals of each power circuit during the start-up period at the first sampling frequency, and the transient current waveform is obtained. Install power frequency sampling transformers on the live wires of each power circuit, and collect the steady-state current signal of each power circuit during the start-up period at the second sampling frequency to obtain the steady-state current waveform. The first sampling frequency is higher than the second sampling frequency, and the start-up period begins when the current of the power circuit starts to rise from zero and ends when the current of the power circuit enters the stable fluctuation range threshold.

3. The dormitory safety electricity remote monitoring and management system based on the Internet of Things as described in claim 2, characterized in that, The step of extracting the surge feature sequence corresponding to each power circuit based on the transient current waveform and extracting the harmonic feature sequence corresponding to each power circuit based on the steady-state current waveform includes: Differential operation is performed on the transient current waveform to obtain the current change rate waveform of the power circuit during the startup period. The peak point and valley point in the current change rate waveform are used as surge feature points. The amplitude and time coordinates of all surge feature points are combined into a surge feature sequence. Perform a Fast Fourier Transform on the steady-state current waveform to extract the amplitude and phase of the odd harmonic components in the steady-state current of the power circuit, and combine the amplitude and phase of the odd harmonic components into a harmonic characteristic sequence.

4. The dormitory safety electricity remote monitoring and management system based on the Internet of Things as described in claim 3, characterized in that, The step of aligning the surge characteristic sequences and harmonic characteristic sequences of the same power circuit in time to generate a load switching characteristic map of the power circuit during the startup period includes: Taking the starting point of the start-up period as time zero, the time coordinates of each surge feature point in the surge feature sequence and the phase angles of each harmonic component in the harmonic feature sequence are converted into time offsets. Pair surge feature points with the same time coordinate with harmonic components with the same time offset to generate load switching feature vectors corresponding to multiple sampling times within the startup period. The load switching feature vectors corresponding to the multiple sampling times are arranged in chronological order to form the load switching feature map of the power circuit.

5. A dormitory safety electricity remote monitoring and management system based on the Internet of Things as described in claim 4, characterized in that, The step of determining the type identifier of the electrical appliance connected in the power circuit based on the waveform distortion points in the load switching characteristic spectrum includes: Calculate the Euclidean distance between the load switching feature vectors corresponding to adjacent sampling times in the load switching feature map, and mark the next sampling time in adjacent sampling times where the Euclidean distance is greater than a preset distortion threshold as a waveform distortion point; Extract the load switching feature vector corresponding to the waveform distortion point as the switching transient vector, and obtain the load switching feature vector corresponding to the sampling time before the waveform distortion point as the steady-state vector before switching; The switching transient vector is compared with the switching transient reference vector of each standard electrical appliance in the standard electrical appliance library one by one for similarity. The type identifier of the standard electrical appliance with the highest similarity is determined as the type identifier of the electrical appliance connected in the power circuit.

6. A dormitory safety electricity remote monitoring and management system based on the Internet of Things as described in claim 5, characterized in that, The calculation of the total power of currently operating electrical appliances in the dormitory, based on the type identification of all power circuits and the steady-state current amplitude of the corresponding circuits, includes: Based on the type identifier of each power circuit, look up the rated power value of the electrical appliances connected in that power circuit in the rated power mapping table; The product of the steady-state current amplitude of each power circuit and the rated voltage corresponding to that power circuit is taken as the instantaneous power of the electrical appliance connected in that power circuit. By summing the instantaneous power of all electrical circuits, the total power of the electrical appliances currently operating in the dormitory can be obtained.

7. A dormitory safety electricity remote monitoring and management system based on the Internet of Things as described in claim 1, characterized in that, When the total power of the electrical appliances exceeds a preset power threshold, an over-power warning signal is generated, and power-off commands are sequentially issued to the corresponding electrical circuits according to the current current amplitude of each circuit from largest to smallest, until the total power of the remaining operating electrical appliances is lower than the power threshold, including: Subtract the power threshold from the total power of the electrical appliances to obtain the amount of power to be reduced; According to the current current amplitude of each power circuit in descending order, calculate the instantaneous power of each power circuit one by one, and add the currently calculated instantaneous power of the circuit to the cumulative reduction amount. The power circuits that the cumulative reduction amount passes through when it reaches the power to be reduced amount are taken as the set of circuits to be cut off. Power-off commands are simultaneously issued to all power-consuming circuits in the set of circuits to be disconnected.

8. A dormitory safety electricity remote monitoring and management system based on the Internet of Things as described in claim 4, characterized in that, When converting the phase angle of a harmonic component into a time offset, the quotient obtained by dividing the phase angle of the harmonic component by the fundamental angular frequency is used as the time offset corresponding to the harmonic component.

9. A dormitory safety electricity remote monitoring and management system based on the Internet of Things as described in claim 5, characterized in that, The preset distortion threshold is dynamically set as a percentage of the root mean square value of the steady-state current amplitude of the power circuit.

10. A dormitory safety electricity remote monitoring and management system based on the Internet of Things as described in claim 6, characterized in that, The rated power value corresponding to each type identifier in the rated power mapping table is the median value of that type of electrical appliance within the factory-nominated power range.

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