Mobile power supply energy-saving control system based on energy consumption analysis

Abstract

The invention relates to the technical field of energy management, in particular to a mobile power supply energy-saving control system based on energy consumption analysis, which comprises a power behavior deconstruction module, a load state judgment module, a power supply behavior trend module, a pressure relief response rhythm module and an energy consumption abnormity labeling module. According to the method, the power output sequence of the mobile power supply in the non-load state is deeply analyzed, the periodic understanding of the power fluctuation mode is realized in combination with the extraction of the voltage change point location and the current trend period, and the power fluctuation mode of the mobile power supply is obtained through the matching of the power peak section and the voltage drop sequence in the load connection state. According to the method, the continuity characteristic of a power supply behavior is judged, the starting and interruption time of a daily power supply interface is marked in a time dimension, and the time intensity of power abrupt change and an abnormal interval is identified in a voltage withdrawing sequence, so that the judgment of the power supply state of the mobile power supply is more rhythmic and trend-oriented; and the redundant energy consumption is timely identified and avoided in a non-loaded or non-periodic power supply state.

Description

Technical Field

[0001] This invention relates to the field of energy management technology, and in particular to a mobile power supply energy-saving control system based on energy consumption analysis. Background Technology

[0002] Energy management technology involves the coordination and control of energy acquisition, conversion, distribution, and use. Core aspects include electrical energy storage, scheduling control, and energy consumption monitoring. The overall energy management system encompasses intelligent adjustment of energy flow relationships between power supply and consumption equipment in the power system, and optimization of energy use based on parameters such as actual load demand. Especially with the rapid proliferation of portable devices, mobile power supply devices have become an important branch of this field. Addressing the energy support needs of mobile terminals, energy management technology is developing towards greater intelligence, efficiency, and low consumption. Among these, a mobile power bank energy-saving control system refers to a system that monitors and controls the power output status to achieve energy-saving operations during the release of mobile power. The key technical issue addressed is how to reduce energy consumption of mobile power banks in non-essential power supply states. This system employs a button-triggered timed shutdown method for the output circuit, controlling the switching circuit state by judging load changes and user operation behavior, thereby reducing ineffective power supply time and achieving energy saving.

[0003] Existing technologies rely on button triggering and load response signal recognition to control the power switch. In cases where user operation is unclear or load behavior is not reflected in time, it is difficult to shut down the output circuit in time. This often results in situations where the power bank continues to output without being cut off in time, especially when portable devices are temporarily disconnected or the load current is weak. This method cannot accurately determine the effectiveness of power supply demand, causing frequent occurrences of ineffective power supply during unnecessary periods, leading to energy waste and reduced equipment operating efficiency. In multiple short-term connections and frequent interruptions, it is easy to miss nodes with sudden increases in energy consumption, and it lacks the ability to continuously track the energy usage status. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a mobile power energy-saving control system based on energy consumption analysis.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a mobile power supply energy-saving control system based on energy consumption analysis includes:

[0006] The power behavior deconstruction module obtains the power output data of the power bank when it is not connected to a terminal load, extracts the voltage change sequence, the number of current slope changes and the power change trajectory points, divides them into multiple periodic segments according to time period, and generates power behavior rhythm segment groups.

[0007] The load state discrimination module calls the power behavior rhythm segment group, extracts the power peak segment and the corresponding voltage drop sequence within the sampling period under the connected load state, compares the overlap ratio of the voltage drop sequence with the time position of the same fluctuation point in the rhythm segment, and obtains the load power supply continuous state identification result.

[0008] Based on the load power supply continuous status identification results, the power supply behavior trend module records the start and interruption times of the daily continuous power supply of the mobile power supply interface, identifies the distribution of start times and stop intervals within the time window, and obtains a power supply behavior density distribution map.

[0009] The voltage relief response rhythm module uses the power supply behavior density distribution map to monitor the equally spaced voltage output points and power response data approaching the voltage relief stage, compares the consistency between the continuous voltage drop stage and the power change direction, and generates a voltage relief response trajectory group.

[0010] As a further aspect of the present invention, the power behavior rhythm segment group includes voltage fluctuation interval identification labels, current change trend period markers, and power trajectory alignment feature point groups; the load power supply continuity status identification results include power supply continuity identifiers, power supply behavior rhythm contrast values, and continuity correlation scores; the power supply behavior density distribution map includes daily power supply period statistics, behavior frequency distribution annotation maps, and concentrated activity period hotspot maps; and the voltage relief response trajectory group includes voltage drop inflection point sequences, power direction change mapping sets, and voltage offset trend trajectory lines.

[0011] As a further aspect of the present invention, the power behavior deconstruction module includes:

[0012] The electrical parameter change extraction submodule acquires the power output data of the power bank when it is not connected to a terminal load, detects the voltage change sequence, the number of current slope changes and the power change points per unit time, calls the voltage value range amplitude, the current slope change frequency and the numerical difference and time interval between the power points, and makes continuous change judgment based on the order of parameters and the amplitude of numerical fluctuation, and generates a set of electrical parameter change trend indicators.

[0013] The trend overlap segmentation module extracts the start and end positions of continuous fluctuation segments based on the time order of power points in the electrical parameter change trend index group and the relationship between the change amplitude of adjacent points. It calls the synchronous change number of voltage change amplitude and current slope change number within the same time period, compares the time extension length and synchronous change frequency, and obtains the rhythm segmentation sequence.

[0014] The rhythm segment grouping submodule calls the time length and internal change frequency value of the segments in the rhythm segmentation sequence, performs pairwise matching of the time length difference and frequency difference between multiple segments, and classifies and marks them according to the preset interval number where the matching difference falls, thus obtaining the power behavior rhythm segment group.

[0015] As a further aspect of the present invention, the load status determination module includes:

[0016] The rhythm segment extraction submodule extracts the power behavior rhythm segment group based on the sampling period power data under the connected load state, identifies the power peak segment within the sampling period, records the time label and amplitude distribution of the power peak segment, and obtains the power peak segment dataset.

[0017] The voltage fluctuation analysis submodule calls the power peak segment dataset, detects the voltage data sequence within the corresponding time period, filters the voltage drop data corresponding to the power peak segment time label, calculates the drop rate and time span of each drop interval, and establishes a voltage drop sequence set.

[0018] The continuous state identification submodule calculates the rhythm overlap fluctuation deviation rate based on the voltage drop sequence set and the degree of overlap between the time period position of each voltage drop sequence and the same fluctuation point within the rhythm segment. It then compares the rhythm overlap fluctuation deviation rate with the rhythm tolerance threshold to determine whether the fluctuation time period position constitutes a continuous feature and generates a load power supply continuous state identification result.

[0019] As a further aspect of the present invention, the power supply behavior trend module includes:

[0020] Based on the continuous power supply status identification result of the load, the continuous status recording submodule obtains the on / off status change data of the mobile power supply interface during the daily time period, divides the duration of the power supply status, extracts the start time point and interruption time point of continuous power supply, and obtains the power supply start and end time distribution value.

[0021] The power supply frequency extraction submodule calls the power supply start and end time distribution value, performs time aggregation judgment on the power supply start behavior within the daily time period, counts the occurrence time of the power supply start event, records the power supply stop time after each start behavior, calculates the interval time between consecutive stop behaviors, and generates the start number and stop interval value.

[0022] The density trend identification submodule divides the daily time period into equal partitions based on the number of starts and the stop interval values, identifies the density of power supply starts and the clustering areas of stop intervals within the partitions, marks the concentrated segments, and obtains a power supply behavior density distribution map.

[0023] As a further aspect of the present invention, the pressure relief response rhythm module includes:

[0024] The voltage output extraction submodule extracts equally spaced voltage output points near the depressurization stage using the power supply behavior density distribution map. It selects a set of points in the voltage data that have the same time interval and aligns them with the power supply behavior time axis to obtain an equally spaced voltage point sequence.

[0025] The power direction determination submodule, based on the equally spaced voltage point sequence, calls the power response data corresponding to each point, identifies the power change direction within the continuous voltage drop interval, determines whether the power change direction is consistent with the voltage drop direction, compares the consistent interval with the adjacent intervals, filters the intervals where the change trend deviates, and obtains the voltage drop offset node set.

[0026] The voltage drop trajectory submodule records the voltage change value and power difference within the equal step interval before and after the voltage drop offset node according to the voltage drop offset node set, calculates the characteristic intensity value of the voltage drop offset node, maps the characteristic intensity value of the voltage drop offset node to the offset node position, constructs a complete response trajectory through point interpolation, and generates a voltage relief response trajectory group.

[0027] As a further aspect of the present invention, the system also includes an energy consumption anomaly labeling module:

[0028] The energy consumption anomaly labeling module extracts the voltage pullback sequence corresponding to the power mutation interval based on the depressurization voltage response trajectory group, determines whether the abnormal power change occurs in the non-periodic power supply stage, and marks the abnormal change density points in the voltage output stage according to the number of occurrences and the degree of overlap of time periods to obtain the abnormal energy consumption associated time period sequence.

[0029] The abnormal energy consumption associated time period sequence includes the locations of sudden voltage pullback intervals, the frequency markers of non-periodic anomalies, and the sections with concentrated abnormal energy consumption density.

[0030] As a further aspect of the present invention, the energy consumption anomaly labeling module includes:

[0031] The power mutation extraction submodule calculates the power change rate based on the time-series power data points in the voltage relief response trajectory group and the numerical difference between adjacent power values, selects the time segment of the rate mutation, calls the corresponding time interval, and generates the voltage pullback interval amplitude value.

[0032] The voltage pullback judgment submodule calls the voltage pullback interval amplitude value, extracts the time range of the non-periodic power supply stage according to the power supply status mark, determines whether the voltage pullback interval overlaps with the time range of the non-periodic power supply stage, records the time segments where the overlap occurs, and generates the voltage abnormal overlap frequency.

[0033] The density point marking submodule calls the voltage anomaly overlap frequency, classifies and summarizes it according to the frequency distribution characteristics and coverage duration ratio in the differentiated time period, filters the time period with concentrated frequency and prominent coverage ratio, marks the points in time order, and generates an abnormal energy consumption associated time period sequence.

[0034] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0035] In this invention, by deeply analyzing the power output sequence of a power bank under non-load conditions, and extracting voltage change points and current trend cycles, the periodicity of power fluctuation patterns is understood. By matching the power peak segment and voltage drop sequence under load conditions, the continuity characteristics of power supply behavior are identified. The start and stop times of the power supply interface are marked daily in the time dimension, and the density changes and concentrated activity periods of power supply behavior are analyzed to capture the trend reversal of power direction consistency during the voltage relief phase. The time density of power mutations and abnormal intervals is identified in the voltage pullback sequence. Through continuous identification, behavior density positioning, and abnormal trajectory marking, an energy consumption behavior structure covering the entire power supply process is constructed, making the judgment of the power supply status of the power bank more rhythmic and trend-oriented, and timely identifying and avoiding redundant energy consumption under no-load or non-periodic power supply conditions. Attached Figure Description

[0036] Figure 1 This is a system flowchart of the present invention;

[0037] Figure 2 This is a flowchart of the power behavior deconstruction module in this invention;

[0038] Figure 3 This is a flowchart of the load status determination module in this invention;

[0039] Figure 4 This is a flowchart of the power supply behavior trend module in this invention;

[0040] Figure 5 This is a flowchart of the pressure relief response rhythm module in this invention;

[0041] Figure 6 This is a flowchart of the energy consumption anomaly labeling module in this invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0043] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0044] Please see Figure 1 The energy-saving control system for mobile power supplies based on energy consumption analysis includes:

[0045] The power behavior deconstruction module acquires the power output data of the power bank when it is not connected to a terminal load, extracts the voltage change sequence, the number of current slope changes and the power change trajectory points, divides it into multiple periodic segments according to time period, performs overlap analysis on the change trend within the time period, and groups them according to the time extension and change rhythm consistency of the overlapping time period to generate power behavior rhythm segment groups.

[0046] The load state discrimination module calls the power behavior rhythm segment group, extracts the power peak segment and the corresponding voltage drop sequence within the sampling period under the connected load state, compares the overlap ratio of the voltage drop sequence and the same fluctuation point in the rhythm segment to determine whether the power supply behavior has continuous characteristics, and obtains the load power supply continuous state identification result.

[0047] Based on the load power supply continuous status identification results, the power supply behavior trend module records the start and interruption times of continuous power supply from the mobile power supply interface every day, identifies the distribution of start times and stop intervals within the time window, extracts the areas of discontinuous change in behavior density and the time periods of concentrated occurrence, and obtains a power supply behavior density distribution map.

[0048] The voltage relief response rhythm module uses the power supply behavior density distribution map to monitor the equally spaced voltage output points and power response data near the voltage relief stage, compares the consistency between the continuous voltage drop stage and the power change direction, marks the voltage drop offset position at the trend inflection point, and generates a voltage relief voltage response trajectory group.

[0049] The energy consumption anomaly labeling module extracts the voltage pullback sequence corresponding to the power mutation interval based on the voltage relief response trajectory group, determines whether the abnormal power change occurs in the non-periodic power supply stage, and marks the abnormal change density points in the voltage output stage according to the number of occurrences and the degree of overlap of time periods, thus obtaining the abnormal energy consumption associated time period sequence.

[0050] The power behavior rhythm segment group includes voltage fluctuation range identification labels, current change trend period markers, and power trajectory alignment feature point groups. The load power supply continuous state identification results include power supply continuity identifiers, power supply behavior rhythm contrast values, and continuity correlation scores. The power supply behavior density distribution map includes daily power supply period statistics, behavior frequency distribution annotation maps, and hot spot maps of concentrated activity periods. The voltage relief response trajectory group includes voltage drop inflection point sequences, power direction change mapping sets, and voltage offset trend trajectory lines. The abnormal energy consumption correlation period sequence includes sudden voltage pullback interval points, non-periodic abnormal occurrence frequency markers, and abnormal energy consumption density concentrated sections.

[0051] Please see Figure 2 The power behavior deconstruction module includes:

[0052] The electrical parameter change extraction submodule acquires the power output data of the power bank when it is not connected to a terminal load, detects the voltage change sequence, the number of current slope changes and the power change points per unit time, calls the voltage value range amplitude, the current slope change frequency and the numerical difference and time interval between the power points, and makes continuous change judgment based on the order of parameters and the amplitude of numerical fluctuation, and generates a set of electrical parameter change trend indicators.

[0053] This process acquires power output data of a power bank when no terminal load is connected. This is achieved by periodically sampling and caching the voltage and current values ​​at the power bank's output terminal using embedded voltage and current sensors. These values ​​are then continuously calculated and filtered during processing. The system assumes that when the power bank is powered on but not connected to any device, there are still slight fluctuations at the output interface. By recording the voltage and current values ​​under this condition over a continuous period, the actual power output curve is derived using multiplication. Simultaneously, combined with predefined voltage change detection rules, the voltage sequence is differentially processed to identify points exceeding a change threshold. Each time the difference exceeds a set value is recorded as a marker. The voltage change event points are recorded. The current data is processed by derivatives to identify obvious trends of current increase or decrease and calculate the frequency of change. The number of times the slope exceeds the threshold per second is counted. Simultaneously, the power change points are extracted based on the number of change points per second. If the number exceeds the set fluctuation threshold, it is identified as a significant change point. Then, the voltage change amplitude, current change frequency, and power change quantity are combined to form a trend triplet. The numerical change trend and continuity between them are analyzed in chronological order to determine which combinations show a stable trend in continuous time. Multiple adjacent continuous points are aggregated into a trend segment through a time sliding window to obtain the electrical parameter change trend index group.

[0054] The trend overlap segmentation module extracts the start and end positions of continuous fluctuation segments based on the time order of power points in the electrical parameter change trend index group and the relationship between the change amplitude of adjacent points. It calls the synchronous change number of voltage change amplitude and current slope change number within the same time period, compares the time extension length and synchronous change frequency, and obtains the rhythm segmentation sequence.

[0055] First, the power mutation time points are organized and arranged in ascending order to form an event sequence on the time axis. By comparing the power change amplitude and time interval between adjacent points one by one, time periods with small changes and short intervals are selected as candidate fluctuation segments. In actual operation, upper limits for change amplitude and time interval can be set, and adjacent points that meet the requirements are connected into continuous segments. Within the fluctuation segment, the voltage sequence is analyzed to find the difference between the maximum and minimum values ​​as the voltage change amplitude. At the same time, the frequency of current change slope is statistically analyzed within the same time period to determine whether the current change is active in the segment, and the time point of slope change is recorded. The timestamps of voltage change event points and current slope change points are compared, and the number of times they overlap within the same time period is counted. If the number of overlaps exceeds a certain threshold, it is determined that there are synchronous changes in electrical parameters within the segment. Then, the time periods of synchronous fluctuation are summarized to form a rhythm segmentation sequence.

[0056] The rhythm segment grouping submodule calls the time length and internal change frequency value of the segments in the rhythm segmentation sequence, performs pairwise matching of the time length difference and frequency difference between multiple segments, and classifies and marks them according to the preset interval number where the matching difference falls, thus obtaining the power behavior rhythm segment group;

[0057] The process involves classifying the duration and internal frequency of change pairs, extracting the start and end times of each segment, calculating the total duration of the segment by the difference, and simultaneously counting the total number or average frequency of voltage and current slope changes within the segment. This constructs basic feature value pairs for each segment, forming a data set such as "time length - frequency of change." Segments are then paired up, and the differences in time length and frequency of change are compared. By setting reasonable intervals, a grouping criterion is established where the time difference is within 1 second and the frequency difference is within 2 times. Segment pairs meeting this criterion are grouped into the same classification number, forming similar rhythmic feature segment labels. The entire process involves sequentially verifying segment combinations, repeatedly pairing them, and grouping them according to preset rules. In the analysis results, different numbers represent different types of rhythmic change patterns. Classifying rhythmic segments in this way allows for rapid identification and clustering of recurring rhythmic behaviors in subsequent processing, resulting in power behavior rhythmic segment groups.

[0058] Please see Figure 3 The load status determination module includes:

[0059] The rhythm segment extraction submodule extracts power data of the sampling period under the connected load state based on the power behavior rhythm segment group, identifies the power peak segment within the sampling period, records the time label and amplitude distribution of the power peak segment, and obtains the power peak segment dataset.

[0060] Power data under load conditions is extracted periodically. The extraction uses raw three-phase voltage and current waveforms recorded by power quality monitoring equipment as input. Instantaneous power sequences are obtained by multiplying the voltage and current within each cycle. The average power of each cycle is then calculated as the criterion for determining peak power ranges. In this example, motor operation sampling data is used, with a sampling frequency of 10kHz and a total sampling duration of 5 seconds, resulting in 50,000 sampling points. Every 50 sampling points are grouped into one sampling cycle, and the average power of each cycle is calculated. After determining the rate value, the power peak interval threshold is set to a value greater than the mean of the sequence plus 1.5 times the standard deviation. That is, if the mean of the average power sequence is 2.1kW and the standard deviation is 0.4kW, then the power peak threshold is 2.1 + 1.5 × 0.4 = 2.7kW. Any period greater than 2.7kW is classified as a power peak segment, and the start timestamp of the segment (such as the 1250th, 2450th, and 3310th sampling points) and the corresponding period length (such as 10 periods, 8 periods, 12 periods, etc.) are recorded to form a power peak segment dataset.

[0061] The voltage fluctuation analysis submodule calls the power peak segment dataset, detects the voltage data sequence within the corresponding time period, filters the voltage drop data corresponding to the power peak segment time label, calculates the drop rate and time span of each drop interval, and establishes a voltage drop sequence set.

[0062] Voltage fluctuation data needs to be extracted within the coverage area of ​​this section. Using the timestamp corresponding to each power peak section as the starting point, extract the three-phase voltage RMS data within its coverage period. Set the voltage drop judgment threshold to 90% of its rated voltage. Taking 220V as an example, the drop threshold value is 198V. Determine if a voltage drop event exists within each period. If the voltage value drops from 210V to 180V in a certain section and the duration is greater than 3 periods, it is determined as a drop event. Record the start time and the time when the drop reaches its lowest point, and calculate the drop rate. ,in, This is the voltage difference (210-180=30V). Given a time span (3 cycles, or 0.06s), the voltage drop rate is 500V / s. This information is then added to the voltage drop sequence set. The same operation is performed on the next power peak segment to establish the voltage drop sequence set.

[0063] The continuous state identification submodule, based on the set of voltage drop sequences and the degree of overlap between the time interval positions of each voltage drop sequence and similar fluctuation points within the rhythm segment, uses the following formula:

[0064] ;

[0065] Calculate the rhythm overlap fluctuation deviation rate, compare the rhythm overlap fluctuation deviation rate with the rhythm tolerance threshold, determine whether the position of the fluctuation period constitutes a continuous feature, and generate the load power supply continuity status identification result.

[0066] in, This represents the deviation rate of rhythm overlap fluctuations. Represents the number of fluctuation points. The fluctuation point representing the voltage drop segment The central time period position, Represents the fluctuation point within a rhythmic segment The central time period position, Represents fluctuation point The duration of the voltage drop phase. Represents fluctuation point The time span;

[0067] The formula calculation logic is as follows: it measures the degree of overlap between each voltage drop sequence and similar fluctuation points within the rhythm segment, and the measurement process incorporates the rhythm overlap fluctuation deviation rate. To perform the measurement, the center time position of the fluctuation point in each rhythm segment needs to be extracted. And extract the center time position of the corresponding voltage drop event. By analyzing the positional differences between each pair of fluctuation points and normalized reference factor and Construct standard coordinates, where This represents the distribution width of all fluctuation points within the voltage drop segment of the rhythm segment, calculated as the standard deviation of the fluctuation point time series. This represents the standard deviation of the time interval between fluctuation points;

[0068] The rhythm overlap fluctuation deviation rate is a standardized distance index used to measure the degree of temporal position matching between fluctuation points and voltage drop events in a rhythm segment. The smaller the value, the higher the degree of overlap between the two on the time axis, indicating a closer correspondence between the rhythm segment and voltage fluctuation characteristics.

[0069] Meaning of parameters and calculation process:

[0070] The number of fluctuation points extracted from the current rhythm segment is set to 6. =6, let its corresponding... =[3.1, 3.4, 4.0, 4.5, 4.9, 5.2] seconds, =[3.0, 3.3, 4.1, 4.6, 4.8, 5.1] seconds, then the position difference is Δ=[0.1, 0.1, -0.1, -0.1, 0.1, 0.1] seconds. Let the corresponding... =0.25 seconds, =0.15 seconds, then the normalization factor is Substitute into the formula:

[0071] ;

[0072] The results show that the rhythm overlap fluctuation deviation rate is 0.3429, which indicates that there is a moderate degree of deviation between the voltage drop fluctuation point within this rhythm segment and the fluctuation point within the rhythm. This is consistent with the empirical threshold. =0.4, if < If so, it is determined to be a continuous feature segment.

[0073] Table 1 lists the values ​​of each participating parameter and their corresponding values ​​in the specific examples. Summary of value calculation results:

[0074] Table 1: Calculation Table of Rhythm Fluctuation Deviation Rate

[0075] Rhythmic segment numbering Fluctuation points Central position (s) Central position (s) （s） （s） value 1 6 [3.1，3.4，…，5.2] [3.0，3.3，…，5.1] 0.25 0.15 0.3429 2 5 [6.0，6.3，…，7.4] [6.1，6.4，…，7.5] 0.30 0.20 0.4714

[0076] As shown in Table 1, the first rhythmic segment... Values ​​below the judgment threshold of 0.4 are classified as continuous segments, while the second segment is classified as... Values ​​exceeding the threshold are excluded from the continuous judgment results.

[0077] Please see Figure 4 The power supply behavior trend module includes:

[0078] The continuous status recording submodule obtains the on / off status change data of the mobile power supply interface during the daily time period based on the continuous status identification results of the load power supply, divides the duration of the power supply status, extracts the start time point and interruption time point of continuous power supply, and obtains the power supply start and end time distribution value.

[0079] The system explicitly collects daily power supply status switching records for each power bank's power interface. These records should originate from data export files of embedded power monitoring devices, using a CSV format time series file generated at a minute-level sampling frequency as the basic input. The "Power Status" field in this data file undergoes preliminary processing, defining a power level greater than 1.5W as a power supply status and less than 0.5W as a power outage status. For values ​​between these two, a linear interpolation method combined with the duration ratio is used for judgment. When a continuous power supply status lasts for more than 30 minutes and the power level remains consistently greater than 1.5W, the start time of this segment is recorded as the starting time point. Conversely, when the power level is consistently less than 0.5W for 15 minutes, it is recorded as the interruption time point. For example, in the monitoring point A device record for July 2, 2025, power supply started at 08:15 and ended at 10:45, which are recorded as the start time 08:15 and the interruption time 10:45, respectively. After extracting the multiple power supply periods generated by this interface throughout the day, the data is sorted by time and stored in a structure sequence, and archived as a power supply start and end time distribution value with the device ID as the primary key.

[0080] The power supply frequency extraction submodule calls the power supply start and end time distribution value, performs time aggregation judgment on power supply start behavior within the daily time period, counts the occurrence time of power supply start events, records the power supply stop time after each start behavior, calculates the interval time between consecutive stop behaviors, and generates the start number and stop interval value.

[0081] Each power supply segment within a day is analyzed individually, extracting the start time of each segment and archiving it by hour. It is also determined whether the segment falls within a defined critical behavior interval, defined as 06:00 to 10:00 and 18:00 to 23:00. The number of power supply starts within a time window is summarized based on the distribution frequency of start times. For example, if device A has 6 power supply start behaviors between 06:00 and 10:00 on July 2nd, the number of starts for that time window is recorded as 6. The time difference between the interruption time and the next start time of each segment is calculated. If the interval between two segments is 30 minutes, the stop interval is recorded as 30 minutes. All stop intervals between segments are extracted sequentially and aggregated by hour to form "start count and stop interval values". During this process, time periods with more than 5 starts are marked as high-frequency segments for subsequent density trend analysis, generating start count and stop interval values.

[0082] The density trend identification submodule divides the daily time period into equal partitions based on the number of starts and the value of the stop interval, identifies the density of power supply start times and the clustering areas of stop intervals within the partitions, marks the concentrated segments, and obtains a power supply behavior density distribution map.

[0083] Before equally partitioning the daily time period, data collection records from each power supply unit are received and parsed, extracting the "start time" and "stop time" fields. The entire time from 0:00 to 24:00 each day is divided into equally spaced time periods of fixed width. With a segment width of 15 minutes, the entire day is divided into 96 segments. Timestamps are extracted and segment attribution is determined for each power supply record from each power supply unit. The "start time" field of a record is read, and it is determined whether its time value falls within the currently processed time period. If so, the power supply unit number corresponding to the start event is recorded in the start count set for that segment. Simultaneously, the "stop time" of the previous stop event is recorded, and the current start and stop times are calculated. The time difference between the last stop is taken as the "stop interval value". This value is compared with the set interval threshold. If the interval is less than 10 minutes, it is judged as a short interval start behavior and recorded in the short interval statistical sequence. After the equal segment processing is completed, the total number of start events in each time period is counted and a start density array is constructed for subsequent density clustering identification submodule to call. The attribution judgment of each start event must be based on the time difference calculation with minute-level precision. At the same time, the current power supply unit data is called to compare its interval one by one to ensure that the value of each density statistical point is a true and time-consistent power supply behavior. A power supply behavior density distribution map is constructed.

[0084] Please see Figure 5 The pressure relief response rhythm module includes:

[0085] The voltage output extraction submodule extracts equally spaced voltage output points near the depressurization stage by using the power supply behavior density distribution map. It selects a set of points in the voltage data that have the same time interval and aligns them with the power supply behavior time axis to obtain an equally spaced voltage point sequence.

[0086] Continuous monitoring of power supply behavior data in the power grid is performed, collecting power supply data and corresponding voltage output data with a time resolution of 0.1 seconds. A data reading interface is set up to sequentially read the power supply behavior identifier at each time point, and a two-dimensional density map is constructed based on this. The vertical axis of the density map represents the average voltage change per unit time, and the horizontal axis represents the average power change. Continuous data within every 10 seconds is summarized and analyzed to form sample units, which are then projected onto the density distribution space. For example, if the voltage change per unit time is 2.5V and the power change is 150W, the projected point is (150, 2.5). After density mapping, the interval with the density decrease rate in the upper 20% is selected as the initial judgment criterion for approaching the depressurization stage. The time step is set. =0.1s, derived from the end of the power supply cycle backwards, at each To perform equidistant sampling, a point sequence group containing n sampling points is generated, where the points in the sequence are as follows: After the sequence is generated, the time axis of the point sequence is aligned according to the voltage relief start position marked in the power supply behavior density map to determine whether each sampling point is in the voltage relief trend range. If the voltage gradient corresponding to the point is... satisfy If so, the segment is determined to be a voltage drop trend segment, and a sequence of equally spaced voltage points is obtained.

[0087] The power direction determination submodule is based on the equally spaced voltage point sequence. It calls the power response data corresponding to each point, identifies the power change direction in the continuous voltage drop interval, determines whether the power change direction is consistent with the voltage drop direction, compares the consistent interval with the adjacent intervals, filters the intervals where the change trend deviates, and obtains the voltage drop offset node set.

[0088] For each sampling point, retrieve the power response value at its corresponding time point. If a certain voltage point Corresponding power =130W, then record the location ( , , This involves judging the voltage change trend and power change direction between two consecutive points, i.e., comparing... and The product sign, if and This indicates that both voltage and power are decreasing, suggesting they are moving in the same direction. For example, if... =230V =227V, =120W =110W, then =-3V, =-10W, consistent direction; after determining the direction of the segment, the intervals with consecutive consistent directions are divided into trend segments, and the criterion for determining the trend reversal is that the power change amplitude meets the threshold condition. >8W, and the change in the previous interval is in the opposite direction. This condition is used to filter out the trend turning point. In actual operation, the threshold for judging the trend turning point of power change is set to ±10W, and a sudden change is detected by using a moving average. The position that meets the condition is marked as the voltage drop offset node set.

[0089] The voltage drop trajectory submodule records the voltage change and power difference within the equal step interval before and after the offset node based on the voltage drop offset node set, using the formula:

[0090] ;

[0091] Calculate the characteristic strength value of the voltage drop offset node, map the characteristic strength value of the voltage drop offset node to the offset node position, construct a complete response trajectory through point interpolation, and generate a set of voltage relief response trajectories;

[0092] in, For the first The characteristic strength value of the pressure drop offset node corresponding to each offset node. Representing the Before and after the first offset node Voltage difference Representing the Before and after the first offset node Bit power response value, Representing the The offset node of the nth offset node Rate of change of voltage drop at location Representing the The offset node of the nth offset node The difference in pressure drop amplitude between equally spaced intervals This represents the total number of sampling points before and after the node.

[0093] Formula calculation logic: Based on the total absolute value of voltage changes before and after a node, the total energy of the power response change (square root of the sum of squares), and the cancellation term of the voltage drop rate change, a comprehensive evaluation index is constructed. The numerator is calculated using... This indicates the severity of local voltage fluctuations at that node. This indicates the energy density of the power response at the node. The impact of dynamic response rate fluctuations is deducted, and the denominator F represents the number of participating nodes. An amplitude instability term is introduced to form a normalized correction, which constitutes a dynamic intensity weighted expression of the voltage power offset characteristics. The difference in power response intensity per unit voltage drop is calculated in the form of a ratio, and the node characteristic index is obtained.

[0094] The voltage drop offset node characteristic strength value is a numerical value used to measure the comprehensive offset of a voltage drop node in the voltage and power response relationship. It reflects the dynamic disturbance intensity of the node in the local voltage drop process and its dispersion with the surrounding response state. The larger the value, the stronger the voltage drop at the node is accompanied by the change in power response, and the more obvious the voltage drop disturbance characteristics are.

[0095] The parameters are defined as follows:

[0096] For nodes front and back The voltage difference between the two points, in volts (V), is determined by... Obtain;

[0097] For nodes Before and after Power response, in W, collected from field monitoring data;

[0098] This represents the rate of change of node voltage drop, calculated as follows: The unit is V / s²;

[0099] This represents the difference in pressure drop between adjacent intervals, expressed in V. (The rest of the text appears to be a series of numerical values ​​and symbols, possibly related to voltage drop differences.) The absolute value is then used to calculate the difference between adjacent points.

[0100] The total number of sampling points is set to [value]. =10; Sampling point data is as follows:

[0101] Table 3: Sampling Table of Pressure Drop Characteristic Parameters

[0102] Sampling sequence number voltage difference Power Response Voltage drop rate Pressure drop difference 1 -2.0 120 -0.5 1.5 2 -1.8 122 -0.4 1.2 3 -2.2 118 -0.6 1.7

[0103] Substitute the data from Table 3 into the calculation, and proceed step by step as follows:

[0104] Sum of the absolute values ​​of the voltage differences:

[0105] ;

[0106] Square root of power:

[0107] ;

[0108] Sum of absolute values ​​of pressure drop rates:

[0109] ;

[0110] Sum of the absolute values ​​of the pressure drop differences:

[0111] ;

[0112] Substitute into the formula to calculate:

[0113] ;

[0114] The results show that the characteristic strength value of the voltage drop offset node is 28.98, which can be used to quantify the voltage drop response strength of the node. The advantage of the formula is that by introducing the voltage drop rate change term and the voltage drop amplitude difference, the multi-dimensional dynamic quantities are included in the numerical measurement, which improves the expressive power of the response trajectory. In the formula, the characteristic strength value of the voltage drop offset node is mapped to the node position, and combined with the linear interpolation technique, a continuous point response trajectory is established to obtain the set of voltage relief response trajectories.

[0115] Please see Figure 6 The energy consumption anomaly labeling module includes:

[0116] The power mutation extraction submodule calculates the power change rate based on the time-series power data points in the voltage relief response trajectory group and the numerical difference between adjacent power values, selects the time segment of the rate mutation, calls the corresponding time interval, and generates the voltage pullback interval amplitude value.

[0117] When processing time-series power data points in the depressurization voltage response trajectory group, it is necessary to extract power sequence data and ensure that the time point corresponding to each power value has strict consistency. The data is then denoised and interpolated to ensure the continuity of the time series. Power differences are extracted from adjacent power values, and abrupt changes are identified by observing the changes in these differences. This process can be achieved by setting abrupt change identification rules, such as comparing the rate of power change with the average rate of power change in the previous period. If the increase exceeds a set percentage, it can be identified as an abrupt change. Then, a sliding window method is used to identify whether multiple consecutive abrupt changes constitute a time segment, and this segment is included in the further processing scope. In practical applications, in an industrial load monitoring scenario, multiple compressors in a factory start up after depressurization, causing a concentrated increase in power values ​​in a short period of time. At this time, the extracted power data points show a clear upward trend. By comparing the differences between the data before and after the abrupt change, the time period of the abrupt change can be quickly identified. Combined with the voltage change trend within this time period, the interval where the voltage pullback occurs can be extracted, and the amplitude value of the voltage pullback interval can be generated.

[0118] The voltage pullback judgment submodule calls the voltage pullback interval amplitude value, extracts the time range of the non-periodic power supply stage based on the power supply status flag, determines whether the voltage pullback interval overlaps with the time range of the non-periodic power supply stage, records the overlapping time segments, and generates the voltage abnormal overlap frequency.

[0119] By combining power supply status marker data to interpret different power supply states, the time range of non-periodic power supply phases is uniformly extracted, and each time period is compared with the voltage drop interval. During the comparison process, it is necessary to check for any time overlap. For overlapping time periods, they are marked as abnormal overlapping segments and recorded. This comparison can be directly judged in conjunction with the time axis. Suppose that in a data center, the UPS power supply records a voltage drop between 13:20 and 13:22 during the switching phase, while the power supply status record shows non-periodic power supply between 13:21 and 13:24. The status, due to the overlap of time periods, is marked as an abnormal overlapping area. The number of such areas is accumulated to form an overlap frequency index. This frequency can be used to measure the voltage stability during the depressurization or switching process. By statistically analyzing the overlap frequency on a daily, hourly or periodic basis, the correlation between voltage pullback and non-periodic power supply behavior can be effectively quantified. In practical scenarios, such as power grid stability monitoring and industrial power alarm, data backtracking and abnormal behavior detection can be performed. It is especially suitable for scenarios with frequent power supply switching, such as hospitals, computer rooms, and rail transit stations, to generate voltage abnormal overlap frequencies.

[0120] The density point marking submodule calls the voltage anomaly overlap frequency, classifies and summarizes it according to the frequency distribution characteristics and coverage duration ratio in the differentiated time period, filters the time period with concentrated frequency and prominent coverage ratio, marks the points in time order, and generates an abnormal energy consumption associated time period sequence.

[0121] The time series is segmented into multiple analysis units by dividing the entire time series into equal time intervals. The number of overlapping anomalies within each unit is calculated, and the anomaly frequency density is determined based on the time length. To ensure the density results are discriminative, the anomaly distribution characteristics within each time interval are categorized. The concentration of anomalous behavior within a given time interval can be determined by calculating the ratio of the proportion of anomalies to the total duration. For example, a scenario is set where an office park experiences multiple instances of concentrated air conditioner activation during the morning rush hour and after lunch break, leading to unstable power supply. Multiple sets of overlapping anomaly intervals are recorded. In the two time periods of 09:00 to 09:30 and 13:10 to 13:40, after summarizing the frequency of anomalies during these periods, it was found that the frequency was much higher than that of the remaining time periods, and the duration was also significantly higher. Therefore, these periods were marked as key anomaly periods. The time periods were output in chronological order to form a marked sequence, providing time anchors for energy consumption diagnosis. In actual deployment, this sequence can be used to assist in energy consumption analysis, pre-schedule equipment operation, and optimize energy allocation strategies. It is especially suitable for scenarios with high requirements for continuous and stable power supply, such as large-scale manufacturing, intelligent buildings, or data centers, to obtain a sequence of time periods associated with abnormal energy consumption.

[0122] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A mobile power supply energy-saving control system based on energy consumption analysis, characterized in that, The system includes: The power behavior deconstruction module obtains the power output data of the power bank when it is not connected to a terminal load, extracts the voltage change sequence, the number of current slope changes and the power change trajectory points, divides them into multiple periodic segments according to time period, and generates power behavior rhythm segment groups. The load state discrimination module calls the power behavior rhythm segment group, extracts the power peak segment and the corresponding voltage drop sequence within the sampling period under the connected load state, compares the overlap ratio of the voltage drop sequence with the time position of the same fluctuation point in the rhythm segment, and obtains the load power supply continuous state identification result. Based on the load power supply continuous status identification results, the power supply behavior trend module records the start and interruption times of the daily continuous power supply of the mobile power supply interface, identifies the distribution of start times and stop intervals within the time window, and obtains a power supply behavior density distribution map. The voltage relief response rhythm module uses the power supply behavior density distribution map to monitor the equally spaced voltage output points and power response data approaching the voltage relief stage, compares the consistency between the continuous voltage drop stage and the power change direction, and generates a voltage relief response trajectory group.

2. The mobile power supply energy-saving control system based on energy consumption analysis according to claim 1, characterized in that, The power behavior rhythm segment group includes voltage fluctuation range identification labels, current change trend period markers, and power trajectory alignment feature point groups. The load power supply continuity status identification results include power supply continuity identifiers, power supply behavior rhythm contrast values, and continuity correlation scores. The power supply behavior density distribution map includes daily power supply period statistics, behavior frequency distribution annotation maps, and hotspot maps of concentrated activity periods. The voltage relief response trajectory group includes voltage drop inflection point sequences, power direction change mapping sets, and voltage offset trend trajectory lines.

3. The mobile power supply energy-saving control system based on energy consumption analysis according to claim 1, characterized in that, The power behavior deconstruction module includes: The electrical parameter change extraction submodule acquires the power output data of the power bank when it is not connected to a terminal load, detects the voltage change sequence, the number of current slope changes and the power change points per unit time, calls the voltage value range amplitude, the current slope change frequency and the numerical difference and time interval between the power points, and makes continuous change judgment based on the order of parameters and the amplitude of numerical fluctuation, and generates a set of electrical parameter change trend indicators. The trend overlap segmentation module extracts the start and end positions of continuous fluctuation segments based on the time order of power points in the electrical parameter change trend index group and the relationship between the change amplitude of adjacent points. It calls the synchronous change number of voltage change amplitude and current slope change number within the same time period, compares the time extension length and synchronous change frequency, and obtains the rhythm segmentation sequence. The rhythm segment grouping submodule calls the time length and internal change frequency value of the segments in the rhythm segmentation sequence, performs pairwise matching of the time length difference and frequency difference between multiple segments, and classifies and marks them according to the preset interval number where the matching difference falls, thus obtaining the power behavior rhythm segment group.

4. The mobile power supply energy-saving control system based on energy consumption analysis according to claim 3, characterized in that, The load status determination module includes: The rhythm segment extraction submodule extracts the power behavior rhythm segment group based on the sampling period power data under the connected load state, identifies the power peak segment within the sampling period, records the time label and amplitude distribution of the power peak segment, and obtains the power peak segment dataset. The voltage fluctuation analysis submodule calls the power peak segment dataset, detects the voltage data sequence within the corresponding time period, filters the voltage drop data corresponding to the power peak segment time label, calculates the drop rate and time span of each drop interval, and establishes a voltage drop sequence set. The continuous state identification submodule calculates the rhythm overlap fluctuation deviation rate based on the voltage drop sequence set and the degree of overlap between the time period position of each voltage drop sequence and the same fluctuation point within the rhythm segment. It then compares the rhythm overlap fluctuation deviation rate with the rhythm tolerance threshold to determine whether the fluctuation time period position constitutes a continuous feature and generates a load power supply continuous state identification result.

5. The mobile power supply energy-saving control system based on energy consumption analysis according to claim 4, characterized in that, The power supply behavior trend module includes: Based on the continuous power supply status identification result of the load, the continuous status recording submodule obtains the on / off status change data of the mobile power supply interface during the daily time period, divides the duration of the power supply status, extracts the start time point and interruption time point of continuous power supply, and obtains the power supply start and end time distribution value. The power supply frequency extraction submodule calls the power supply start and end time distribution value, performs time aggregation judgment on the power supply start behavior within the daily time period, counts the occurrence time of the power supply start event, records the power supply stop time after each start behavior, calculates the interval time between consecutive stop behaviors, and generates the start number and stop interval value. The density trend identification submodule divides the daily time period into equal partitions based on the number of starts and the stop interval values, identifies the density of power supply starts and the clustering areas of stop intervals within the partitions, marks the concentrated segments, and obtains a power supply behavior density distribution map.

6. The mobile power supply energy-saving control system based on energy consumption analysis according to claim 5, characterized in that, The pressure relief response rhythm module includes: The voltage output extraction submodule extracts equally spaced voltage output points near the depressurization stage using the power supply behavior density distribution map. It selects a set of points in the voltage data that have the same time interval and aligns them with the power supply behavior time axis to obtain an equally spaced voltage point sequence. The power direction determination submodule, based on the equally spaced voltage point sequence, calls the power response data corresponding to each point, identifies the power change direction within the continuous voltage drop interval, determines whether the power change direction is consistent with the voltage drop direction, compares the consistent interval with the adjacent intervals, filters the intervals where the change trend deviates, and obtains the voltage drop offset node set. The voltage drop trajectory submodule records the voltage change value and power difference within the equal step interval before and after the voltage drop offset node according to the voltage drop offset node set, calculates the characteristic intensity value of the voltage drop offset node, maps the characteristic intensity value of the voltage drop offset node to the offset node position, constructs a complete response trajectory through point interpolation, and generates a voltage relief response trajectory group.

7. The mobile power supply energy-saving control system based on energy consumption analysis according to claim 1, characterized in that, The system also includes an energy consumption anomaly labeling module: The energy consumption anomaly labeling module extracts the voltage pullback sequence corresponding to the power mutation interval based on the depressurization voltage response trajectory group, determines whether the abnormal power change occurs in the non-periodic power supply stage, and marks the abnormal change density points in the voltage output stage according to the number of occurrences and the degree of overlap of time periods to obtain the abnormal energy consumption associated time period sequence. The abnormal energy consumption associated time period sequence includes the locations of sudden voltage pullback intervals, the frequency markers of non-periodic anomalies, and the sections with concentrated abnormal energy consumption density.

8. The mobile power supply energy-saving control system based on energy consumption analysis according to claim 7, characterized in that, The energy consumption anomaly labeling module includes: The power mutation extraction submodule calculates the power change rate based on the time-series power data points in the voltage relief response trajectory group and the numerical difference between adjacent power values, selects the time segment of the rate mutation, calls the corresponding time interval, and generates the voltage pullback interval amplitude value. The voltage pullback judgment submodule calls the voltage pullback interval amplitude value, extracts the time range of the non-periodic power supply stage according to the power supply status mark, determines whether the voltage pullback interval overlaps with the time range of the non-periodic power supply stage, records the time segments where the overlap occurs, and generates the voltage abnormal overlap frequency. The density point marking submodule calls the voltage anomaly overlap frequency, classifies and summarizes it according to the frequency distribution characteristics and coverage duration ratio in the differentiated time period, filters the time period with concentrated frequency and prominent coverage ratio, marks the points in time order, and generates an abnormal energy consumption associated time period sequence.

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