Building intelligent expandable power distribution cabinet and energy-saving control method

Abstract

The invention relates to the technical field of intelligent building power distribution energy-saving control, and discloses a building intelligent expandable power distribution cabinet and an energy-saving control method. The method comprises the steps of synchronously collecting energy consumption data and environment signals, constructing a time domain-energy efficiency association network after fusion processing to identify abnormal nodes, generating a thermal disturbance feature set, and marking a safety unit needing to be regulated and controlled through space matching. Analyzing the dynamic covariant relationship between the current waveform and the thermal imaging characteristic of the unit, and quantifying the electrothermal coupling distortion level; an interaction mode of power factor change and insulation parameter fluctuation is analyzed, and the insulation power collaborative degradation trend is quantified. And finally, two evaluation results are fused to generate a comprehensive regulation and control instruction. According to the method, multi-dimensional deep correlation diagnosis and early trend prediction of the state of the power distribution cabinet are realized, so that energy-saving regulation and control and safety intervention are more accurate and active.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent building power distribution energy-saving control, in particular to a building intelligent expandable power distribution cabinet and an energy-saving control method. BACKGROUND

[0002] The existing building power distribution cabinet energy-saving and safety monitoring generally adopts a technical scheme of independent parameter threshold alarm and simple logic linkage. This kind of scheme usually sets fixed safety thresholds for key parameters such as current, temperature and power factor, and triggers an alarm or performs a preset switching action when a single or multiple parameters exceed the thresholds. Some improved schemes introduce a data acquisition and monitoring control system to record and preliminarily analyze energy consumption data.

[0003] The existing technical scheme has defects. Independent threshold monitoring cannot reveal the internal cause and effect and space-time correlation between current anomalies and local overheating, and is easy to misjudge normal transient processes as faults or ignore potential risks caused by coupling deterioration. Simple logic linkage is also difficult to depict the complex interactive influence between power factor changes and insulation performance degradation, and cannot identify early signs from the trend of operating parameters before insulation significantly deteriorates and faults occur. This leads to lagging control actions and weak targeting, which may cause misoperation or refusal to operate, and is difficult to achieve real preventive performance optimization and safety control.

[0004] The present application needs to solve the problem of deep correlation diagnosis of multiple physical quantities. That is, how to break through the limitations of independent parameter analysis and accurately analyze the dynamic covariation relationship between current waveform distortion and thermal distribution anomalies, so as to locate the real risk source. The present application also needs to solve the problem of implicit degradation trend prediction. That is, how to find the internal relationship between the measurable electrical performance parameters and the fluctuation of implicit parameters such as equipment insulation state, and realize early and quantitative evaluation of insulation-power collaborative degradation trend. SUMMARY

[0005] The present application aims to provide a building intelligent expandable power distribution cabinet and an energy-saving control method to solve the problems raised in the background.

[0006] To achieve the above-mentioned purpose, the present application provides a building intelligent expandable power distribution cabinet and an energy-saving control method, which comprises: Collecting energy consumption data streams and environmental state signals of the power distribution cabinet, and performing time sequence alignment and signal fusion to form a synchronous data source; Performing time domain slicing and frequency domain conversion on the synchronous data source, constructing a time domain-energy efficiency correlation network, and identifying energy consumption abnormal nodes in the network; Extracting multi-dimensional environmental variables in the synchronous data source, generating a thermal disturbance feature set by constructing a dynamic evolution model of environmental parameters; Spatial matching of energy consumption anomaly nodes in the time-domain-energy efficiency correlation network with thermal disturbance feature sets identifies energy consumption safety units that require regulation. For energy safety units, we analyze the covariation mode of their current waveform and thermal imaging characteristics to quantitatively assess the distortion level of current-thermal coupling; For energy-safe units, the interaction between power factor changes and insulation parameter fluctuations is analyzed to quantitatively assess the degradation trend of insulation-power ratio. By integrating the distortion level of current-thermal coupling with the degradation trend of insulation-power, comprehensive control commands are generated to drive the actuators in the distribution cabinet.

[0007] Preferably, the energy consumption data stream and environmental status signals of the power distribution cabinet are collected, and time-series alignment and signal fusion are performed to form a synchronous data source, specifically: The voltage waveform, current waveform and power factor of each circuit in the distribution cabinet are continuously collected by the multi-functional energy meter to form the original monitoring data stream; Through the digital and analog input interfaces of the multi-functional energy meter, temperature distribution signals, air quality concentration signals and fire alarm status signals inside and around the power distribution cabinet are collected to form a composite environmental status stream. Add a unique timestamp identifier to each data point in the original monitoring data stream and the composite environmental status stream; Based on the timestamp identifier, the original monitoring data stream and the composite environmental status stream are aligned and interpolated to generate a synchronous data source with a unified time series reference.

[0008] Preferably, the synchronous data source is sliced ​​in the time domain and transformed in the frequency domain to construct a time-domain-energy efficiency correlation network, and abnormal energy consumption nodes in the network are identified, specifically as follows: The energy consumption data in the synchronous data source is divided into multiple time-domain segments according to a preset period; Fast Fourier transform is performed on the current and voltage waveforms in each time domain segment to extract the amplitude and phase information of the fundamental component and each harmonic component. Based on the correlation of power consumption and the similarity of harmonic spectrum among different time domain segments, a time-domain-energy efficiency correlation network is constructed in which nodes represent time domain segments and edges represent correlation strength. Calculate the local clustering coefficient and edge weight standard deviation of each node in the time-domain-energy efficiency correlation network, and identify nodes with clustering coefficients below the threshold and edge weight standard deviations above the threshold as nodes with abnormal energy consumption.

[0009] Preferably, multi-dimensional environmental variables are extracted from the synchronous data source, and a set of thermal disturbance features is generated by constructing a dynamic evolution model of environmental parameters, specifically: Separate multi-dimensional environmental variable sequences of temperature, humidity, and particulate matter concentration from synchronous data sources; For each environmental variable sequence, a dynamic evolution model is constructed with time as the independent variable and environmental parameter values ​​as the dependent variable. The parameters of the dynamic evolution model are fitted using a sliding window regression method. The dynamic evolution model is used to predict the short-term environmental parameter values ​​in the future, and the residual between the predicted values ​​and the actual monitored values ​​in the synchronous data source is calculated. When the residual continuously exceeds a preset threshold, the starting time point of the continuous exceeding of the preset threshold and the corresponding environmental variable type are recorded to form a thermal disturbance feature. All thermal disturbance features constitute a thermal disturbance feature set.

[0010] Preferably, the abnormal energy consumption nodes in the time-domain-energy efficiency correlation network are spatially matched with the thermal disturbance feature set to identify the energy consumption safety units that need to be regulated, specifically: Extract the physical power distribution circuit identifier and time information corresponding to each energy consumption anomaly node in the time-domain-energy efficiency correlation network; Extract the monitoring point location identifier and time information corresponding to each feature in the thermal disturbance feature set; Align energy consumption anomaly nodes with thermal disturbance characteristics within the time window, and compare the physical adjacency relationship between power distribution circuit identifiers and monitoring point location identifiers in terms of spatial location; The power distribution circuits corresponding to energy-abnormal nodes that meet time alignment and have physical adjacency are marked as energy-safe units that need to be regulated.

[0011] Preferably, for energy safety units, the co-variation mode of their current waveform and thermal imaging characteristics is analyzed to quantitatively assess the distortion level of current-thermal coupling, specifically: The current effective value sequence of the energy safety unit at multiple consecutive time points is acquired simultaneously, as well as the temperature sequence of the cabinet connection point collected by the infrared sensor at the corresponding time points; Calculate the cross-correlation function between the effective current value sequence and the temperature sequence at the cabinet connection point, and find the time delay that makes the cross-correlation function reach its peak value; Based on the time delay, the temperature sequence of the cabinet connection point is translated along the time axis so that the starting point of the translated temperature sequence corresponds in time to the starting point of the current effective value sequence, thus completing the sequence alignment; and the ratio of the current-temperature change rate after alignment is calculated. The distribution variance of the ratio within the observation period is statistically analyzed, and the distribution variance is quantified as the distortion level of current-thermal coupling.

[0012] Preferably, for energy-safe units, the interaction between power factor changes and insulation parameter fluctuations is analyzed to quantitatively assess the insulation-power degradation trend, specifically: Continuously monitor the real-time power factor of the energy safety unit and collect periodic sampling values ​​of the insulation resistance to ground; Draw a scatter plot with power factor on the horizontal axis and insulation resistance on the vertical axis; An ellipse is fitted to the scatter plot to obtain the slope of the major axis and the length of the minor axis of the fitted ellipse. The product of the absolute value of the major axis slope and the minor axis length is defined as a quantitative indicator of the degradation trend of insulation-power, where the major axis slope reflects the interaction sensitivity and the minor axis length reflects the fluctuation range.

[0013] Preferably, the distortion level of current-thermal coupling and the degradation trend of insulation-power are integrated to generate comprehensive control commands, specifically: The quantitative indices of distortion level of current-thermal coupling and degradation trend of insulation-power are normalized to obtain normalized distortion index and normalized degradation index. The normalized distortion index and the normalized degradation index are input into a preset instruction decision matrix, which defines the control instruction type and control parameters corresponding to different index combinations. The query instruction decision matrix outputs a comprehensive control instruction that includes the instruction type and control parameters.

[0014] Preferably, the integrated control command is sent to the intelligent circuit breaker in the power distribution cabinet through the building automation system integration platform. The intelligent circuit breaker executes the power outage, current limiting or alarm operation defined by the command type, and adjusts the intensity or threshold of the operation according to the control parameters.

[0015] Preferably, the present invention further includes a building intelligent expandable power distribution cabinet, the power distribution cabinet comprising: The data acquisition module is used to collect energy consumption data streams and environmental status signals from the power distribution cabinet. The data processing and communication module includes a processor and a memory, wherein the memory stores a computer program; The control execution module is connected to the data processing and communication module and is used to drive the actuator in the power distribution cabinet. The processor is configured to execute a computer program in the memory to implement the energy-saving control method for the building intelligent scalable power distribution cabinet as described above.

[0016] Compared with the prior art, the beneficial effects of the present invention are: By analyzing the covariance patterns between current waveform characteristics and thermal imaging characteristics, the distortion level of current-thermal coupling is quantitatively assessed, and electrical signal anomalies are accurately correlated and matched with physical thermal disturbances in the spatiotemporal dimensions. This technology can distinguish between temperature rises caused by normal load changes and abnormal thermal couplings caused by poor contact, harmonics, etc., thereby accurately locating the specific units and locations within the distribution cabinet that require intervention. This gives subsequent control commands clear spatial direction and physical basis, avoiding the blindness of overall control based on a single overheating or overcurrent signal.

[0017] By analyzing the interaction between power factor changes and insulation parameter fluctuations, this technology quantifies and assesses the degradation trend of insulation-power ratio, establishing a dynamic correlation model between operating performance parameters and equipment material condition parameters. This technology can detect subtle changes in power characteristics caused by a slow decline in insulation performance, before reaching alarm thresholds, thereby identifying potential, gradual performance degradation processes. This allows the system to issue early warnings before complete insulation failure or severe power quality degradation occurs, providing trend-based decision-making for preventative maintenance and energy efficiency optimization, rather than reactive alarms. Attached Figure Description

[0018] Figure 1 This is a schematic diagram illustrating the working principle of the energy-saving control method for the intelligent expandable power distribution cabinet in buildings as described in this invention.

[0019] Figure 2 A flowchart generated for synchronizing data sources.

[0020] Figure 3 This is a flowchart for the construction and anomaly identification of the time-domain-energy efficiency correlation network.

[0021] Figure 4 This is a biaxial line graph representing the data acquisition and synchronization phase in the energy-saving control of the power distribution cabinet.

[0022] Figure 5 This is a composite diagram of multi-dimensional analysis for the stage of identifying abnormal energy consumption nodes in the power distribution cabinet. Detailed Implementation

[0023] 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, and 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.

[0024] Please see Figure 1This invention provides a building intelligent expandable power distribution cabinet and an energy-saving control method. The method includes: collecting energy consumption data streams and environmental status signals from the power distribution cabinet, performing time-series alignment and signal fusion to form a synchronous data source; constructing a time-domain-energy efficiency correlation network by performing time-domain slicing and frequency-domain conversion on the synchronous data source and identifying abnormal energy consumption nodes in the network; extracting multi-dimensional environmental variables from the synchronous data source and generating a thermal disturbance feature set by constructing a dynamic evolution model of environmental parameters; spatially matching and marking the energy consumption safety units that need to be regulated by matching the abnormal energy consumption nodes in the time-domain-energy efficiency correlation network with the thermal disturbance feature set; analyzing the covariance mode of the current waveform and thermal imaging characteristics of the energy safety units to quantitatively assess the distortion level of current-thermal coupling; analyzing the interaction relationship between the power factor change and insulation parameter fluctuation of the energy safety units to quantitatively assess the degradation trend of insulation-power; and fusing the distortion level of current-thermal coupling and the degradation trend of insulation-power to generate a comprehensive control command to drive the actuator in the power distribution cabinet.

[0025] Example 1: See Figure 2 The system continuously collects voltage waveforms, current waveforms, and power factors of each circuit within the distribution cabinet using a multi-functional energy meter to form the original monitoring data stream. It also collects temperature distribution signals, air quality concentration signals, and fire alarm status signals inside and around the distribution cabinet through the digital and analog input interfaces of the multi-functional energy meter to form a composite environmental status stream. A unified timestamp is added to each data point in the original monitoring data stream and the composite environmental status stream. Based on the timestamp, the original monitoring data stream and the composite environmental status stream are aligned and interpolated to generate a synchronous data source with a unified timing reference.

[0026] In practical implementation, energy consumption data streams and environmental status signals from the distribution cabinet are collected, time-aligned, and fused to form a synchronous data source. Continuous data acquisition is achieved through multi-functional energy meters deployed in each circuit of the distribution cabinet. Specifically, the multi-functional energy meters continuously collect the voltage waveform of phase A distribution circuit at a sampling rate of 100 points per second, with a measured value of 230.5V. Simultaneously, the current waveform of the same circuit is collected, with a measured value of 150.3A, and the instantaneous power factor is recorded as 0.92. These data constitute a continuous sequence of the raw monitoring data stream. Furthermore, the digital input interface integrated into the multi-functional energy meters receives digital signals from a temperature sensor inside the cabinet, with a measured temperature of 42 degrees Celsius. The analog input interface collects analog voltage signals from particulate matter concentration sensors installed around the distribution cabinet, with a corresponding concentration measurement of 35 μg / m³. 3 It also receives the dry contact status signals of the fire alarm control panel, and these signals together constitute a composite environmental status stream reflecting the environmental status.

[0027] In some embodiments, a unified timestamp is attached to each data point in the original monitoring data stream and the composite environmental state stream, with the timestamp accurate to the millisecond level. Specifically, when the energy meter collects a current waveform data point with a measured value of 151.1A, a timestamp "2023-10-27 14:30:05.123" obtained from the system clock is attached to this data point; almost simultaneously, a temperature sensor uploads a temperature data point with a measured value of 42.5 degrees Celsius, and a timestamp "2023-10-27 14:30:05.124" is attached to this data point. Although physically from different sensors, all data points are marked with a timestamp generated by a unified clock source.

[0028] In some embodiments, the original monitoring data stream and the composite environmental state stream are aligned and interpolated based on timestamp identifiers. In a specific implementation, a unified time reference point sequence is set, with a time interval of 10 milliseconds. In a specific implementation, it was found that the original monitoring data stream has current values ​​I1, I2, and I3 at time points T1, T2, and T3, while the temperature data in the composite environmental state stream has values ​​Temp1 and Temp2 at time points T1.5 and T2.5, indicating that the two time series are not completely consistent. In a specific implementation, interpolation calculations are performed on the temperature data stream to generate an estimated temperature value at time point T2, thereby achieving alignment with the current data at time point T2. The interpolation method used is Lagrange interpolation, and the formula is:

[0029] in: Indicates the target alignment time point The generated synchronization data value, Indicates the original non-uniform time point The collected data values, It is a Lagrange polynomial, where N is the number of adjacent original data points used for interpolation. In practice, this alignment and interpolation operation is performed on all data streams, ultimately generating a synchronized data source that contains aligned current, voltage, power factor, temperature, concentration, and alarm status values ​​at each identical time reference point.

[0030] Example 2: See Figure 3The energy consumption data from the synchronous data source is divided into multiple time-domain segments according to a preset period. Fast Fourier Transform is performed on the current and voltage waveforms within each time-domain segment to extract the amplitude and phase information of the fundamental and harmonic components. Based on the correlation of energy consumption and the similarity of harmonic spectra between time-domain segments, a time-domain-energy efficiency correlation network is constructed, with nodes representing time-domain segments and edges representing correlation strength. The local clustering coefficient and edge weight standard deviation of each node in the time-domain-energy efficiency correlation network are calculated. Nodes with clustering coefficients below a threshold and edge weight standard deviations above a threshold are identified as abnormal energy consumption nodes. A multi-dimensional environmental variable sequence of temperature, humidity, and particulate matter concentration is separated from the source. For each environmental variable sequence, a dynamic evolution model is constructed with time as the independent variable and environmental parameter values ​​as the dependent variable. The dynamic evolution model fits the parameters using a sliding window regression method. The dynamic evolution model is used to predict the short-term environmental parameter values ​​in the future and calculate the residual between the predicted values ​​and the actual monitored values ​​in the synchronous data source. When the residual continuously exceeds a preset threshold, the starting time point of the continuous exceedance of the preset threshold and the corresponding environmental variable type are recorded to form a thermal disturbance feature. All thermal disturbance features constitute a thermal disturbance feature set.

[0031] In the specific implementation, the synchronous data source is sliced ​​in the time domain and transformed in the frequency domain to construct a time-domain-energy efficiency correlation network and identify abnormal energy consumption nodes in the network. The synchronous data source includes aligned current waveform sequences, voltage waveform sequences, and environmental parameter sequences. In the specific implementation, the energy consumption data in the synchronous data source is divided into continuous time domain segments according to a preset 5-minute period. For example, the first time domain segment includes all current and voltage sampling points within the time stamp interval from 14:30:00 to 14:35:00. In the specific implementation, a Fast Fourier Transform is performed on the current waveform within a time domain segment. This current waveform consists of 30,000 discrete sampling points. After the transform, the amplitude of the fundamental 50Hz component is extracted as 150.5 Amps, and the amplitude of the third harmonic 150Hz component is extracted as 4.8 Amps. The phase angle is recorded. The same operation is performed on the voltage waveform within the same segment to extract the corresponding amplitude and phase information.

[0032] In some embodiments, a time-domain-energy efficiency correlation network is constructed based on the correlation of power consumption and the similarity of harmonic spectra between time-domain segments. Specifically, the correlation of power consumption between time-domain segment A and time-domain segment B is calculated. The total power consumption of time-domain segment A is 15.3 kWh, and the total power consumption of time-domain segment B is 16.1 kWh. Their correlation is quantified using a correlation coefficient. In another embodiment, the similarity of the harmonic spectra between time-domain segment A and time-domain segment B is calculated by comparing the amplitude distributions of the two segments at the 3rd, 5th, and 7th harmonics. Their similarity is defined by the reciprocal of the Euclidean distance. A graph structure is constructed where each node represents a 5-minute time-domain segment. If the correlation coefficient of power consumption between two time-domain segments is greater than 0.8 and the harmonic spectrum similarity value is greater than a preset threshold, an edge is established between the nodes representing these two time-domain segments. The weight of the edge is the weighted sum of the correlation coefficient of power consumption and the harmonic spectrum similarity value, thus forming the time-domain-energy efficiency correlation network.

[0033] In some embodiments, the local clustering coefficient and the standard deviation of edge weights for each node in the time-domain-energy efficiency correlation network are calculated. Specifically, for a node Node_X in the time-domain-energy efficiency correlation network, which is connected to three other nodes Node_M, Node_N, and Node_P, the actual number of connecting edges between Node_M, Node_N, and Node_P is checked. Assuming Node_M is connected to Node_N and Node_P, but there is no connection between Node_N and Node_P, the actual number of connecting edges is 2. The maximum number of connecting edges between these three nodes is 3. Therefore, the local clustering coefficient of Node_X is 2 divided by 3. In a specific implementation, the weight values ​​of all connected edges of node Node_X are calculated. For example, the weight of the edge connected to Node_M is 1.2, the weight of the edge connected to Node_N is 1.5, and the weight of the edge connected to Node_P is 0.9. Then, the standard deviation of these three weight values ​​is calculated. In practice, the threshold for the local clustering coefficient is set to 0.4, and the threshold for the standard deviation of the edge weight is set to 0.25. Nodes with a local clustering coefficient lower than 0.4 and an edge weight standard deviation higher than 0.25 are identified as nodes with abnormal energy consumption.

[0034] In the specific implementation, multi-dimensional environmental variables are extracted from the synchronous data source, which includes aligned temperature, humidity, and particulate matter concentration sequences. Specifically, the temperature sequence, recorded at one data point per minute, is separated from the synchronous data source; for example, the temperature sequence value between 10:00 and 11:00 is [25.1, 25.3, 25.6, ..., 26.0] degrees Celsius. A dynamic evolution model is constructed for the temperature sequence. This model is a univariate linear regression model, with time sequence number as the independent variable and temperature value as the dependent variable. A sliding window regression method with a window width of 10 data points is used to fit the slope and intercept parameters of the model. Finally, the dynamic evolution model fitted in the current window is used to predict the temperature value at the next time point. For example, based on the data from the previous 10 minutes, the predicted temperature for the 11th minute is 25.8 degrees Celsius, while the actual monitored value for the 11th minute in the synchronous data source is 26.5 degrees Celsius; the calculated prediction residual is 0.7 degrees Celsius. The calculation of the predicted values ​​of the dynamic evolution model can be expressed as follows:

[0035] in: In time sequence Predicted values ​​of environmental parameters at the location, This represents the intercept parameter of the dynamic evolution model fitted by the current sliding window. This represents the slope parameter of the dynamic evolution model fitted by the current sliding window.

[0036] In practice, thermal disturbance features are recorded when the prediction residuals continuously exceed a preset threshold. The preset temperature residual threshold is 0.5 degrees Celsius. If the prediction residuals at three consecutive time points are 0.7, 0.6, and 0.8 degrees Celsius, respectively, all exceeding 0.5 degrees Celsius, a thermal disturbance feature is recorded. This feature includes the starting time point "10:11" and the corresponding environmental variable type "temperature". In practice, the same dynamic evolution model construction, prediction residual calculation, and judgment process is performed on the humidity sequence and particulate matter concentration sequence. All recorded features constitute a thermal disturbance feature set. In practice, when constructing the dynamic evolution model for the humidity sequence, the same implementation process as for the temperature sequence is adopted, i.e., separating the humidity sequence recorded at one data point per minute from the synchronous data source, for example, a humidity value sequence of [45.2, 45.5, 45.8, ..., 46.3] percentage relative humidity. For humidity sequences, a univariate linear regression model with time sequence as the independent variable and humidity parameter as the dependent variable is constructed as a dynamic evolution model. The slope and intercept parameters of the model are fitted using a sliding window regression method, with the sliding window width set to 10 data points. The dynamic evolution model fitted in the current window is used to predict the humidity value at the next time point, and the residual between the predicted value and the actual monitored value in the synchronous data source is calculated. When the humidity residual continuously exceeds a preset threshold, the starting time point of the continuous exceedance of the preset threshold and the corresponding environmental variable type "humidity" are recorded, forming a thermal perturbation feature. The same process is performed for particulate matter concentration sequences, constructing a dynamic evolution model, calculating the prediction residual, and determining the threshold. All recorded features, including thermal perturbation features of temperature, humidity, and particulate matter concentration types, together constitute a thermal perturbation feature set.

[0037] Example 3: Extract the physical distribution circuit identifier and time information corresponding to each energy consumption anomaly node in the time-domain-energy efficiency correlation network, extract the monitoring point location identifier and time information corresponding to each feature in the thermal disturbance feature set, align the energy consumption anomaly node and thermal disturbance feature in the time window, and compare the physical adjacency relationship between the distribution circuit identifier and the monitoring point location identifier in the spatial location. Mark the distribution circuit corresponding to the energy consumption anomaly node that meets the time alignment and has a physical adjacency relationship as the energy consumption safety unit that needs to be regulated.

[0038] In practical implementation, the energy consumption anomaly nodes in the time-domain-energy efficiency correlation network are spatially matched with the thermal disturbance feature set to identify the energy safety units that need to be regulated. The time-domain-energy efficiency correlation network contains multiple identified energy consumption anomaly nodes, and the thermal disturbance feature set contains multiple recorded thermal disturbance features. In practical implementation, the physical distribution circuit identifier and time information corresponding to an energy consumption anomaly node in the time-domain-energy efficiency correlation network are extracted. The physical distribution circuit identifier of this energy consumption anomaly node is "A3 distribution cabinet-07 outgoing circuit", and its corresponding time information is a 5-minute time domain segment from "14:30:00" to "14:35:00".

[0039] In some embodiments, the location identifier and time information of the monitoring point corresponding to a feature in the thermal disturbance feature set are extracted. In a specific implementation, the location identifier of the monitoring point corresponding to a thermal disturbance feature in the thermal disturbance feature set is "A3 distribution cabinet - inside door panel - T_Sensor_12", and the starting time point recorded by this thermal disturbance feature is "14:32:17".

[0040] In some embodiments, energy consumption anomaly nodes and thermal disturbance characteristics are aligned on a time window. In a specific implementation, a time alignment window is set, with a span of ±3 minutes. It is determined whether the time range of the energy consumption anomaly node "14:30:00-14:35:00" and the starting time point of the thermal disturbance characteristic "14:32:17" meet the window alignment condition. Since "14:32:17" falls entirely within the "14:30:00-14:35:00" interval, the time alignment requirement is met.

[0041] In practical implementation, the physical adjacency relationship between the power distribution circuit identifier and the monitoring point location identifier is compared spatially. The determination of physical adjacency is based on a pre-defined power distribution site topology mapping table. This table defines the physical cabinet area associated with each power distribution circuit identifier and the environmental monitoring point identifier deployed within that area. In practice, querying the mapping table, the power distribution circuit identifier "A3 power distribution cabinet - outgoing circuit No. 07" is mapped to the physical location "A3 cabinet, right middle section of the wiring area," while the monitoring point location identifier "A3 power distribution cabinet - inside door panel - T_Sensor_12" is mapped to the physical location "A3 cabinet, inside panel of the cabinet door directly opposite the right middle section of the wiring area." According to the mapping table definition, these two physical locations are directly adjacent.

[0042] It is understandable that the spatial matching process involves traversing and comparing all energy-abnormal nodes with all thermal disturbance features. In specific implementation, the physical power distribution circuit of another energy-abnormal node is identified as "B1 distribution cabinet - No. 12 outgoing circuit", with time information of "09:15:00-09:20:00". Simultaneously, there is a monitoring point for a thermal disturbance feature identified as "B1 distribution cabinet - top air duct - PM_Sensor_02", with a starting time of "09:10:05". Although the time point "09:10:05" and the time window "09:15:00-09:20:00" of the energy-abnormal node are not aligned within the set ±3-minute window, the matching condition is not met even if the physical locations are related. The spatial adjacency determination can be expressed using a formula: in: This indicates the matching result. This indicates the time information of nodes with abnormal power usage. The function represents the temporal information of the thermal disturbance characteristics. The output value is 1 when time-aligned, otherwise it is 0. The physical power distribution circuit identifier indicates the node with abnormal energy consumption. The location identifier of the monitoring point representing the characteristics of thermal disturbance, function The output value is 1 if the physical locations are adjacent, otherwise it is 0.

[0043] It is understandable that marking energy safety units is a direct result of spatial matching. In practice, when an energy-abnormal node and a thermal disturbance feature are identified that simultaneously meet the time alignment condition and have a physical adjacency, the physical power distribution circuit corresponding to this energy-abnormal node, namely "A3 distribution cabinet-07 outgoing circuit," is formally marked as an energy safety unit requiring regulation. In practice, the marking information is recorded as a structured entry, including the marked circuit identifier, the energy-abnormal node number that triggered the matching, the associated thermal disturbance feature number, and the timestamp of the matching occurrence.

[0044] See Figure 4This is a biaxial line graph representing the data acquisition and synchronization phase in the energy-saving control of the distribution cabinet. The trends of current, temperature, and humidity show a certain correlation (e.g., peak current corresponds to high values ​​in the environmental signal), and the synchronization error remains consistently low (<5). The time-series matching between energy consumption data and environmental signals is high, the synchronization error is stable, and the data source quality is reliable. The peak current near time point 12.5 corresponds to a high value in the environmental signal, indicating a close correlation between equipment load and environmental thermal conditions during this period, requiring close monitoring. This type of chart is used to verify the synchronization of energy consumption data and environmental signals. By displaying the time-series correlation of multi-dimensional data, the reliability of the synchronization data source is confirmed, providing a foundation for subsequent anomaly identification and control.

[0045] Example 4: Synchronously acquiring the effective current value sequence of the energy safety unit at multiple consecutive time points to... The temperature sequence of the cabinet connection point is collected by the infrared sensor at the corresponding time point. The cross-correlation function between the current effective value sequence and the cabinet connection point temperature sequence is calculated to find the time delay that makes the cross-correlation function reach its peak. According to the time delay, the cabinet connection point temperature sequence is translated along the time axis so that the starting point of the translated temperature sequence corresponds to the starting point of the current effective value sequence in time. The ratio of the current-temperature change rate after alignment is calculated. The distribution variance of the ratio in the observation period is statistically analyzed and the distribution variance is quantified as the distortion level of current-thermal coupling. The real-time power factor of the energy safety unit is continuously monitored and the periodic sampling value of the insulation resistance to ground is collected. A scatter plot is drawn with the power factor as the horizontal axis and the insulation resistance value as the vertical axis. Ellipse fitting is performed on the scatter plot to obtain the slope of the major axis and the length of the minor axis of the fitted ellipse. The product of the absolute value of the slope of the major axis and the length of the minor axis is defined as the quantitative index of the insulation-power degradation trend, where the slope of the major axis reflects the interaction sensitivity and the length of the minor axis reflects the fluctuation range.

[0046] In the specific implementation, the covariance mode of the current waveform and thermal imaging characteristics of the energy safety unit is analyzed to quantitatively assess the distortion level of current-thermal coupling. The energy safety unit is a specific power distribution circuit that has been marked. In the specific implementation, the effective current value sequence of the energy safety unit at multiple consecutive time points is acquired synchronously, with a time point interval of 10 seconds. The effective current value sequence at time points t1, t2, t3, and t4 is recorded as [152.1, 153.0, 155.3, 154.8] amperes, and the corresponding cabinet connection point temperature sequence collected by the infrared sensor at the same time point is recorded as [43.1, 43.5, 44.9, 45.2] degrees Celsius. In the specific implementation, the cross-correlation function between the effective current value sequence and the cabinet connection point temperature sequence is calculated. The calculation of the cross-correlation function incorporates a series of time delay assumptions. The time delay that causes the cross-correlation function to reach its peak is identified as a sampling interval, i.e., 10 seconds.

[0047] In some embodiments, the cabinet connection point temperature sequence is shifted along the time axis according to the time delay. In a specific implementation, the cabinet connection point temperature sequence [43.1, 43.5, 44.9, 45.2] is shifted forward by 10 seconds along the time axis, so that the starting point of the shifted temperature sequence corresponds in time to the starting point of the current effective value sequence. The shifted and aligned temperature sequence becomes [43.5, 44.9, 45.2], and the corresponding current effective value sequence is truncated as [152.1, 153.0, 155.3]. In a specific implementation, the ratio of the aligned current-temperature change rate is calculated. For the first interval, the current change rate is (153.0-152.1) / 10=0.09 amperes / second, and the temperature change rate is (44.9-43.5) / 10=0.14 degrees Celsius / second. Their ratio is 0.09 / 0.14≈0.643.

[0048] It is understandable that the distortion level of current-temperature coupling is obtained through statistical analysis of the ratio of the rates of change. In practice, within a complete observation period, for example, 300 seconds, an aligned current-temperature rate of change ratio is calculated every 10 seconds, resulting in 30 ratio data points. The calculation of the current-temperature rate of change ratio can be expressed as:

[0049] in: This represents the ratio of current to temperature change rate over a certain alignment time interval. This indicates the change in the effective value of the current in the energy safety unit during this time interval. This represents the temperature change at the cabinet connection points within the same time interval. In practice, the variance of these 30 ratio data points is calculated. The variance value is quantified as the distortion level of current-thermal coupling; a higher variance value indicates poorer stability in the relationship between current and temperature changes. See Table 1.

[0050] Table 1: Sampling Data of Power Factor and Insulation Resistance ; In some embodiments, the interaction between power factor changes and insulation parameter fluctuations in an energy-safe unit is analyzed to quantitatively assess the insulation-power degradation trend. In a specific implementation, the real-time power factor of the energy-safe unit is continuously monitored, with the monitoring data updated at one recording point per minute. Simultaneously, periodic sampling values ​​of the insulation resistance to ground are collected using an insulation tester, with a sampling interval of 2 hours. In a specific implementation, a scatter plot is drawn based on the sampling data within a working day, with the power factor on the horizontal axis and the insulation resistance value on the vertical axis. The data points are derived from the table "Example of Sampling Data for Power Factor and Insulation Resistance".

[0051] In practice, an ellipse fitting is performed on the scatter plot. The least squares method is used to fit an ellipse that best represents the distribution trend of the data points. The result of the ellipse fitting is the slope of the major axis and the length of the minor axis. The slope of the major axis reflects the direction and degree of the interaction sensitivity between power factor changes and insulation resistance fluctuations, while the length of the minor axis reflects the range of insulation resistance fluctuations at the corresponding power factor. It can be understood that the quantitative index of the insulation-power degradation trend is derived from the geometric parameters of the fitted ellipse. In practice, the product of the absolute value of the major axis slope and the length of the minor axis is defined as the quantitative index of the insulation-power degradation trend. The absolute value of the major axis slope reflects the interaction sensitivity, and the length of the minor axis reflects the fluctuation range. This product value serves as a comprehensive index to assess the degradation trend of insulation performance with changes in the power factor.

[0052] Example 5: The quantitative indices of current-thermal coupling distortion level and insulation-power degradation trend are normalized to obtain normalized distortion index and normalized degradation index. The normalized distortion index and normalized degradation index are input into a preset instruction decision matrix. The instruction decision matrix defines the control instruction type and control parameters corresponding to different index combinations. The instruction decision matrix is ​​queried and outputs a comprehensive control instruction containing instruction type and control parameters. The comprehensive control instruction is sent to the intelligent circuit breaker in the distribution cabinet through the building automation system integration platform. The intelligent circuit breaker executes the power outage, current limiting or alarm operation defined by the instruction type and adjusts the intensity or threshold of the operation according to the control parameters.

[0053] In practical implementation, a comprehensive control command is generated by integrating the distortion level of current-thermal coupling and the degradation trend of insulation-power. Both the distortion level of current-thermal coupling and the degradation trend of insulation-power have been quantified into specific indicators. In practical implementation, for a marked energy safety unit, the quantified index value of its current-thermal coupling distortion level is 0.15, and the quantified index value of its insulation-power degradation trend is 12.6. These two quantified indices are normalized using a preset maximum-minimum scaling method. The reference maximum value of current-thermal coupling distortion level is set to 0.2, and the reference minimum value is set to 0. The reference maximum value of insulation-power degradation trend is set to 20.0, and the reference minimum value is set to 0. The normalized distortion index and the normalized degradation index are obtained by calculating (current value - reference minimum value) / (reference maximum value - reference minimum value). After calculation, the normalized distortion index is 0.75, and the normalized degradation index is 0.63.

[0054] In some embodiments, the normalized distortion index and the normalized degradation index are input into a preset instruction decision matrix. The instruction decision matrix is ​​a two-dimensional lookup table, where the horizontal and vertical axes represent discretized partitions of the normalized distortion index and the normalized degradation index, respectively. Each partition cell defines the corresponding control instruction type and control parameters. In a specific implementation, the instruction decision matrix defines that when the normalized distortion index is in the interval [0.7, 0.9) and the normalized degradation index is in the interval [0.6, 0.8), the output control instruction type is "current limiting operation," and the control parameter is "current upper limit setting: 120A." The mapping process of inputting the index into the instruction decision matrix can be formulaically expressed as: in: This indicates the type of control instruction obtained after querying the instruction decision matrix. This represents the control parameters obtained after querying the decision matrix of the instruction. Represents the normalized distortion index. Represents the normalized degradation index. and These are the total number of discretized partitions of the instruction decision matrix in the corresponding dimension, with symbols... This indicates a round-down operation. The mapping function represents the instruction decision matrix.

[0055] In practical implementation, the query instruction decision matrix outputs a comprehensive control instruction. Based on a normalized distortion index of 0.75 and a normalized degradation index of 0.63, the corresponding cell in the instruction decision matrix is ​​located, and a comprehensive control instruction containing the instruction type "current limiting operation" and the control parameter "current upper limit setting value: 120A" is retrieved. This instruction is encapsulated as a structured data message. In some embodiments, the comprehensive control instruction is sent to the intelligent circuit breaker in the distribution cabinet through the building automation system integration platform. The building automation system integration platform sends the data message to the network address of the intelligent circuit breaker corresponding to the target energy safety unit through a standard industrial communication protocol.

[0056] It is understandable that the intelligent circuit breaker executes the operation defined by the command type. In specific implementation, after receiving the comprehensive control command, the intelligent circuit breaker parses the command type as "current limiting operation" and then adjusts its internal current protection threshold according to the control parameter "current upper limit setting: 120A". When the circuit current is detected to continuously exceed 120 amps, the intelligent circuit breaker performs a current limiting operation to control the current below the set value. In specific implementation, if the command type is "alarm operation", the intelligent circuit breaker triggers the local audible and visual alarm according to the alarm level defined in the control parameters and uploads the status information. If the command type is "power failure operation", the intelligent circuit breaker performs a tripping and power failure action according to the delay time defined in the control parameters.

[0057] See Figure 5 This is a multi-dimensional composite chart analyzing the energy consumption anomaly identification stage of the distribution cabinet. Energy consumption anomaly nodes (marked with "×") all meet the conditions of "clustering coefficient below the threshold and edge weight standard deviation above the threshold." The energy consumption fluctuations corresponding to these anomaly nodes are large (e.g., around time segments 5 and 15), and the harmonic spectrum similarity differs from normal periods, indicating abnormal energy consumption patterns during these periods. This type of chart is used for anomaly node identification in the time-domain-energy efficiency correlation network. By integrating energy consumption, spectrum similarity, and network characteristics (clustering coefficient, edge weight standard deviation), it locates the time segments with abnormal energy consumption, providing targets for subsequent regulation.

[0058] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0059] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An energy-saving control method for a building intelligent expandable power distribution cabinet, characterized in that, This can be achieved through the following process: Collect energy consumption data streams and environmental status signals from the power distribution cabinet, and perform time-series alignment and signal fusion to form a synchronous data source; Time-domain slicing and frequency-domain transformation are performed on the synchronous data source to construct a time-domain-energy efficiency correlation network and identify abnormal energy consumption nodes in the network; Multi-dimensional environmental variables are extracted from synchronous data sources, and a set of thermal disturbance features is generated by constructing a dynamic evolution model of environmental parameters. Spatial matching of energy consumption anomaly nodes in the time-domain-energy efficiency correlation network with thermal disturbance feature sets identifies energy consumption safety units that require regulation. For energy safety units, we analyze the covariation mode of their current waveform and thermal imaging characteristics to quantitatively assess the distortion level of current-thermal coupling; For energy-safe units, the interaction between power factor changes and insulation parameter fluctuations is analyzed to quantitatively assess the degradation trend of insulation-power ratio. By integrating the distortion level of current-thermal coupling with the degradation trend of insulation-power, comprehensive control commands are generated to drive the actuators in the distribution cabinet.

2. The energy-saving control method for a building intelligent expandable power distribution cabinet according to claim 1, characterized in that, The system collects energy consumption data streams and environmental status signals from the power distribution cabinet, performs time alignment and signal fusion to form a synchronous data source, specifically: The voltage waveform, current waveform and power factor of each circuit in the distribution cabinet are continuously collected by the multi-functional energy meter to form the original monitoring data stream; Through the digital and analog input interfaces of the multi-functional energy meter, temperature distribution signals, air quality concentration signals and fire alarm status signals inside and around the power distribution cabinet are collected to form a composite environmental status stream. Add a unique timestamp identifier to each data point in the original monitoring data stream and the composite environmental status stream; Based on the timestamp identifier, the original monitoring data stream and the composite environmental status stream are aligned and interpolated to generate a synchronous data source with a unified time series reference.

3. The energy-saving control method for a building intelligent expandable power distribution cabinet according to claim 1, characterized in that, The synchronous data source is sliced ​​in the time domain and transformed in the frequency domain to construct a time-domain-energy efficiency correlation network, and nodes with abnormal energy consumption in the network are identified. Specifically: The energy consumption data in the synchronous data source is divided into multiple time-domain segments according to a preset period; Fast Fourier transform is performed on the current and voltage waveforms in each time domain segment to extract the amplitude and phase information of the fundamental component and each harmonic component. Based on the correlation of power consumption and the similarity of harmonic spectrum among different time domain segments, a time-domain-energy efficiency correlation network is constructed in which nodes represent time domain segments and edges represent correlation strength. Calculate the local clustering coefficient and edge weight standard deviation of each node in the time-domain-energy efficiency correlation network, and identify nodes with clustering coefficients below the threshold and edge weight standard deviations above the threshold as nodes with abnormal energy consumption.

4. The energy-saving control method for a building intelligent expandable power distribution cabinet according to claim 1, characterized in that, Multi-dimensional environmental variables are extracted from the synchronous data source, and a dynamic evolution model of environmental parameters is constructed to generate a set of thermal disturbance features, specifically: Separate multi-dimensional environmental variable sequences of temperature, humidity, and particulate matter concentration from synchronous data sources; For each environmental variable sequence, a dynamic evolution model is constructed with time as the independent variable and environmental parameter values ​​as the dependent variable. The parameters of the dynamic evolution model are fitted using a sliding window regression method. The dynamic evolution model is used to predict the short-term environmental parameter values ​​in the future, and the residual between the predicted values ​​and the actual monitored values ​​in the synchronous data source is calculated. When the residual continuously exceeds a preset threshold, the starting time point of the continuous exceeding of the preset threshold and the corresponding environmental variable type are recorded to form a thermal disturbance feature. All thermal disturbance features constitute a thermal disturbance feature set.

5. The energy-saving control method for a building intelligent expandable power distribution cabinet according to claim 1, characterized in that, Spatial matching of energy consumption anomaly nodes in the time-domain-energy efficiency correlation network with thermal disturbance feature sets identifies energy consumption safety units requiring regulation. Specifically: Extract the physical power distribution circuit identifier and time information corresponding to each energy consumption anomaly node in the time-domain-energy efficiency correlation network; Extract the monitoring point location identifier and time information corresponding to each feature in the thermal disturbance feature set; Align energy consumption anomaly nodes with thermal disturbance characteristics within the time window, and compare the physical adjacency relationship between power distribution circuit identifiers and monitoring point location identifiers in terms of spatial location; The power distribution circuits corresponding to energy-abnormal nodes that meet time alignment and have physical adjacency are marked as energy-safe units that need to be regulated.

6. The energy-saving control method for a building intelligent expandable power distribution cabinet according to claim 1, characterized in that, For energy safety units, the covariance modes of their current waveforms and thermal imaging characteristics are analyzed to quantitatively assess the distortion level of current-thermal coupling, specifically: The current effective value sequence of the energy safety unit at multiple consecutive time points is acquired simultaneously, as well as the temperature sequence of the cabinet connection point collected by the infrared sensor at the corresponding time points; Calculate the cross-correlation function between the effective current value sequence and the temperature sequence at the cabinet connection point, and find the time delay that makes the cross-correlation function reach its peak value; Based on the time delay, the temperature sequence of the cabinet connection point is translated along the time axis so that the starting point of the translated temperature sequence corresponds in time to the starting point of the current effective value sequence, thus completing the sequence alignment. And calculate the ratio of the current-temperature change rate after alignment; The distribution variance of the ratio within the observation period is statistically analyzed, and the distribution variance is quantified as the distortion level of current-thermal coupling.

7. The energy-saving control method for a building intelligent expandable power distribution cabinet according to claim 1, characterized in that, For energy-safe units, the interaction between power factor changes and insulation parameter fluctuations is analyzed to quantitatively assess the insulation-power degradation trend, specifically: Continuously monitor the real-time power factor of the energy safety unit and collect periodic sampling values ​​of the insulation resistance to ground; Draw a scatter plot with power factor on the horizontal axis and insulation resistance on the vertical axis; An ellipse is fitted to the scatter plot to obtain the slope of the major axis and the length of the minor axis of the fitted ellipse. The product of the absolute value of the major axis slope and the minor axis length is defined as a quantitative indicator of the degradation trend of insulation-power, where the major axis slope reflects the interaction sensitivity and the minor axis length reflects the fluctuation range.

8. The energy-saving control method for a building intelligent expandable power distribution cabinet according to claim 1, characterized in that, By integrating the distortion level of current-thermal coupling with the degradation trend of insulation-power, a comprehensive control command is generated, specifically: The quantitative indices of distortion level of current-thermal coupling and degradation trend of insulation-power are normalized to obtain normalized distortion index and normalized degradation index. The normalized distortion index and the normalized degradation index are input into a preset instruction decision matrix, which defines the control instruction type and control parameters corresponding to different index combinations. The query instruction decision matrix outputs a comprehensive control instruction that includes the instruction type and control parameters.

9. The energy-saving control method for a building intelligent expandable power distribution cabinet according to claim 8, characterized in that, The integrated control command is sent to the smart circuit breaker in the power distribution cabinet through the building automation system integration platform. The smart circuit breaker executes the power outage, current limiting or alarm operation defined by the command type, and adjusts the intensity or threshold of the operation according to the control parameters.

10. A building intelligent expandable power distribution cabinet, characterized in that, include: The data acquisition module is used to collect energy consumption data streams and environmental status signals from the power distribution cabinet. The data processing and communication module includes a processor and a memory, wherein the memory stores a computer program; The control execution module is connected to the data processing and communication module and is used to drive the actuator in the power distribution cabinet. The processor is configured to execute a computer program in the memory to implement the energy-saving control method for the building intelligent expandable power distribution cabinet as described in any one of claims 1 to 9.

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