Mobile power supply system supporting remote monitoring

Abstract

The invention relates to the technical field of energy management, in particular to a mobile power supply system supporting remote monitoring, which comprises a current identification module, a trend judgment module, an impact response module, a remote control module and an adjustment release module. According to the method, current offset proportion and sorting characteristics are constructed, a periodic offset identification index is formed, a sudden change node is judged in combination with a continuous trend direction and a change amplitude, a load impact state is calculated and analyzed through combined amplitude variation of superposed voltage, current and power, and power supply intensity and operation duration are dynamically adjusted based on impact characteristics. In combination with fluctuation boundary evaluation, ordered release of power supply limitation is realized, a linkage closed loop of period monitoring, trend judgment and remote control is completed, the time sequence analysis capability of operation data, the response precision of stage change and the processing efficiency of abnormal fluctuation are enhanced, and the reliability of the system is improved. And the regulation and control flexibility, the load adaptability and the stability guarantee capability of the intelligent Internet of Things mobile power supply system in energy management are improved.

Description

Technical Field

[0001] This invention relates to the field of energy management technology, and in particular to a mobile power supply system that supports remote monitoring. Background Technology

[0002] Energy management technology encompasses the regulation and optimization of the entire process of energy resource collection, distribution, transmission, storage, and use. Its core aspects include load monitoring and energy efficiency management of power systems, dispatch control of distributed energy resources, operational status monitoring of energy storage devices, and the collection and analysis of energy usage data. It is a crucial link in achieving intelligent, visualized, and networked management in the construction of modern energy systems. Traditional intelligent IoT mobile power supply systems supporting remote monitoring refer to systems that, based on portable power devices with basic power supply capabilities, add features for collecting device status data and uploading it to a remote monitoring platform via a communication network. The aim is to achieve remote status monitoring and operational data feedback functions in energy management. Traditional methods typically integrate embedded control units and communication modules into the power supply system, combined with sensors to monitor output voltage, current, power level, and remaining battery capacity in real time. The monitoring data is then uploaded to a server via a cellular communication network for status identification and remote management by backend software.

[0003] Existing technologies rely on real-time detection of basic parameters such as voltage and current and uploading the data to a remote platform. However, they lack a structured identification mechanism when facing periodic fluctuations or phased changes, and cannot achieve linkage analysis of trend and deviation magnitude. This results in the inability to distinguish load impact behavior in the operating data, and can only maintain static feedback and fixed response modes. Especially in scenarios where the power system frequently alternates operation or the load fluctuates drastically, there is a problem of insufficient perception of the rhythm of change, which can easily lead to control lag or misjudgment, thus restricting the responsiveness and control accuracy of smart IoT mobile power systems in energy management. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology and propose a mobile power supply system that supports remote monitoring.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a mobile power supply system supporting remote monitoring includes: The current recognition module extracts current data within a cycle of the smart IoT power bank, constructs the fluctuation range and median, calculates the ratio of the difference between the current and the median, and, combined with the sorting offset, extracts the index, time and offset amplitude to generate current offset record content. The trend discrimination module analyzes the direction and length of the current change in the preceding and following cycles based on the cycle index in the current offset record, compares the last change with the average trend amplitude, determines the direction of the sudden change and extracts the cycle number, and generates the trend sudden change segment identifier. The impact response module calls the trend change segment identifier content, extracts the voltage, current and power information of the segment starting point, calculates the increase and compares it with the average value. If both are positive offsets, it extracts the direction, amplitude and period number, and generates a load impact trigger entry. The remote control module adjusts the power supply intensity and running time according to the power increment and cycle segment number in the load impact trigger entry, binds the cycle number, and generates remote output limit details. The adjustment and release module reads the remote output limit details, determines whether it is at the operating boundary and has no continuously increasing signal, extracts the cycle start point and operating status, and generates the mobile power bank remote monitoring results.

[0006] As a further aspect of the present invention, the current offset record content includes a current offset index, offset occurrence time, and offset amplitude value; the trend change segment identifier content includes a trend cycle number, change direction category, and change verification parameters; the load impact trigger entry includes a power increment value, voltage change characteristics, and current response characteristics; the remote output limit details include adjusted power supply, corrected runtime, and limit application cycle number; and the mobile power supply remote monitoring results include a recovery start cycle, operational stability status, and power supply recovery flag.

[0007] As a further aspect of the present invention, the method of combining sorting offset, extracting index, time and offset amplitude, sorting the degree of deviation of current within the cycle by size, and extracting the corresponding cycle index, occurrence time and offset amplitude information; The comparison of the final change with the average trend amplitude involves comparing the current change in the last cycle of the trend segment with the average change amplitude of that segment to identify the direction of the sudden change. The power increment and period segment number refer to the power increase value measured within a specific period segment and the corresponding period segment identification number.

[0008] As a further aspect of the present invention, the current identification module includes: The fluctuation extraction submodule obtains the maximum, minimum and average current values ​​within the operating cycle of the smart IoT power bank, constructs the fluctuation range, extracts the value corresponding to the midpoint between the maximum and minimum values, and generates the median current parameter. The difference calculation submodule calculates the difference between the median current parameter and the current current value, calls the set of all differences in the current running cycle, obtains the proportional position of the difference in the set, and generates the current difference ratio parameter. The current offset recording submodule calls the current offset sorting position according to the current difference ratio parameter, compares the ratio position with the offset sorting position, obtains the index, corresponding time point and offset amplitude between the two, calculates the combined difference between the current offset amplitude and the offset sorting average difference, and obtains the current offset amplitude recording value. The constructed fluctuation range utilizes the range of maximum and minimum current values ​​within the period to verify the upper and lower limits of current fluctuation. The proportional position refers to the proportion of the current value relative to its sorting position in the set of current differences throughout the entire cycle.

[0009] As a further aspect of the present invention, the trend discrimination module includes: The periodic current extraction submodule calls the current value of the corresponding period based on the period index in the current offset record, and obtains the previous period current value and the next period current value respectively. It then constructs the current sequence of adjacent periods in sequence, forms the current set corresponding to the period sequence, and generates the periodic current value sequence. The direction continuity statistics submodule determines the direction of the current difference between two adjacent cycles based on the cycle current value sequence. If the difference is positive, it is defined as the upward direction; if the difference is negative, it is defined as the downward direction. The number of consecutive cycles in the same direction is counted, and the start and end cycle indices of the consistent segments in each direction are recorded to generate continuous direction segment interval information. The trend change verification submodule calls the current value of the trend segment termination period and the previous period based on the continuous direction segment interval information, obtains the current difference, calculates the average amplitude of current change between all adjacent periods within the trend segment, calculates the current change ratio, and if it is greater than the trend change judgment benchmark value, it extracts the current period number and directionality, and obtains the trend change segment identifier content. The current sequence is a set of adjacent period current values ​​arranged in period index order, and its changes during the period are analyzed. The start and end cycle index refers to the start and end cycle numbers of a continuous segment with the same current change direction. The trend change judgment benchmark is a current change ratio threshold used to determine whether the current change in the trend segment is abrupt.

[0010] As a further aspect of the present invention, the impact response module includes: The power interception submodule marks the starting period based on the trend change segment identifier, extracts the starting and ending values ​​of voltage, current and power for the period, and summarizes them to form a basic power value set. The power increase calculation submodule calculates the voltage drop, current increase, and power increment based on the power base value set, and combines them into a unified index to obtain the power increase offset result. The offset feature verification submodule compares the corresponding average change amplitude based on the power increase offset result, determines whether all are positive deviations, extracts the growth direction, amplitude value and cycle number, and generates load impact trigger entries. The positive deviation refers to a consistent increase and upward trend in parameters such as voltage, current, and power compared to the average change.

[0011] As a further aspect of the present invention, the remote control module includes: The power dispatch submodule calls the original power supply output value and the running time of the corresponding segment according to the power increment and cycle segment number recorded in the load impact trigger entry, and completes the data aggregation according to the cycle segment index to obtain the power supply dispatch dataset. The power supply adjustment submodule replaces the original power supply output value and running time based on the power supply call dataset and a fixed scaling relationship, and combines the updated power supply intensity and running time into a new data group to obtain the adjusted power supply parameter group. The parameter binding submodule binds the corresponding cycle number to each of the adjusted power supply parameter groups, establishes a corresponding relationship, and generates a detailed list of remote output restrictions after summarizing and organizing the data.

[0012] As a further aspect of the present invention, the adjustment and release module includes: The fluctuation information extraction submodule, based on the period number segment in the remote output limit details, calls the voltage fluctuation value, current amplitude and power fluctuation amplitude within the corresponding two periods to construct a period fluctuation information set and obtain period fluctuation characteristic values. The operation status determination submodule determines whether the voltage, current and power are within the equipment operation boundary range based on the periodic fluctuation characteristic value, and monitors whether there is a continuously increasing signal in the current segment. After the dual conditions are met, the corresponding cycle start point and the current power supply operation status are extracted to obtain the periodic stable state information. The tag information generation submodule extracts the corresponding recovery judgment tag content based on the periodic stable state information, and performs number matching in combination with the period start point to establish a mapping relationship and generate the mobile power bank remote monitoring result. The operating boundary range of the equipment refers to the upper and lower limits of parameters such as voltage, current, and power that are allowed for safe and normal operation of the equipment.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, a periodic offset identification index is formed by constructing current offset ratio and sorting characteristics. The sudden change node is judged by combining the continuous trend direction and change amplitude. The load impact state is analyzed by superimposing the joint amplitude calculation of voltage, current and power. The power supply intensity and running time are dynamically adjusted based on the impact characteristics. The orderly release of power supply restrictions is achieved by combining fluctuation boundary assessment. The linkage closed loop of periodic monitoring, trend judgment and remote control is completed, which enhances the ability to analyze the time sequence of operating data, the response accuracy to stage changes and the processing efficiency of abnormal fluctuations. This improves the regulation flexibility, load adaptability and stability guarantee capability of the smart IoT mobile power system in energy management. Attached Figure Description

[0014] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a flowchart of the current identification module of the present invention; Figure 3 This is a flowchart of the trend discrimination module of the present invention; Figure 4 This is a flowchart of the impact response module of the present invention; Figure 5 This is a flowchart of the remote control module of the present invention; Figure 6 This is a flowchart of the adjustment and release module of the present invention. Detailed Implementation

[0015] 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.

[0016] 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.

[0017] Please see Figure 1 A mobile power supply system that supports remote monitoring includes: The current identification module obtains the maximum, minimum and average current values ​​within the operating cycle of the smart IoT mobile power bank, constructs the fluctuation range and extracts the median, calls the current current value to calculate the difference with the median, obtains the proportion and position of the difference in the current range difference set, compares the proportion with the offset sorting position, extracts the index, time point and offset amplitude information, and generates current offset record content. The trend discrimination module calls the current values ​​of the previous and next periods and determines the direction of change based on the period index in the current offset record. It counts the number of periods in continuous direction, obtains the current difference between the end point of the trend and the previous period, and then compares whether the change is greater than the average change amplitude of the current trend segment. After verifying that the direction change and abrupt change are established, it extracts the period number and directionality and generates the trend abrupt change segment identifier. The impact response module calls the voltage start and minimum values, current and power start and end values ​​of the trend change segment identifier mark segment starting point, calculates voltage drop, current increase and power increment, compares the increment with the average amplitude, and after verifying that they are all positive deviations, extracts the growth direction, amplitude value and period number to generate load impact trigger entries. The remote control module calls the original power output value and the corresponding segment running time based on the power increment and cycle number recorded in the load impact trigger entry, performs replacement processing based on a fixed scaling relationship, reorganizes and adjusts the power supply intensity and running time and binds them to the cycle number to generate remote output limit details. The adjustment and release module uses the number segment in the remote output limit details to call the voltage fluctuation value, current amplitude and power fluctuation range of two cycles to determine whether it is within the equipment operating boundary range and there is no continuous growth signal. It extracts the cycle start point, power supply operating status and recovery mark content to generate the mobile power supply remote monitoring results.

[0018] The current offset record includes the current offset index, offset occurrence time, and offset amplitude value. The trend change segment identifier includes the trend cycle number, change direction category, and change verification parameters. The load impact trigger entry includes the power increment value, voltage change characteristics, and current response characteristics. The remote output limit details include the adjusted power supply, corrected running time, and limit application cycle number. The mobile power supply remote monitoring results include the recovery start cycle, operating stability status, and power supply recovery flag.

[0019] Please see Figure 2 The current identification module includes: The fluctuation extraction submodule obtains the maximum, minimum and average current values ​​within the operating cycle of the smart IoT power bank, constructs the fluctuation range, extracts the value corresponding to the midpoint between the maximum and minimum values, and generates the median current parameter. During the operation of the smart IoT power bank, the fluctuation extraction submodule samples the current value every 0.2 seconds for the currently selected operating cycle, continuously recording a total of 60 seconds of operating data, thus collecting 300 current data points. Each data point is bound to a corresponding sampling timestamp. For example, in this embodiment, the current value of the first data point is recorded as 4.2 amps, the second as 4.5 amps, the third as 5.1 amps, and so on until the 300th data point, forming a complete sequence of current changes over time. Subsequently, the fluctuation extraction submodule iterates through these 300 data points, using the current value of the first data point (4.2 amps) as a temporary initial value for the maximum and minimum values, and then comparing it sequentially with the current maximum and minimum values ​​starting from the second data point. When the current value of the next data point is detected to be higher than the current maximum value, for example, the current value reaches 9.4 amps at the 77th data point, which is higher than the current maximum value, then this value is updated to the new maximum value. When the current value of a data point is detected to be lower than the current minimum value, for example, the current value is 2.5 amps at the 14th data point, which is lower than the current minimum value, then it is updated to the new minimum value. After traversing and comparing, the maximum current value within this period is 9.4 amps, and the minimum current value is 2.5 amps, forming a fluctuation range of 2.5 amps to 9.4 amps. After determining the fluctuation range, the fluctuation extraction submodule will also accumulate all the current values ​​of these 300 data points, for example, the total current sum is 1560 amps, and then divide this total value by the number of sampling points 300 to obtain the average current value within the period of 5.2 amps. To further determine the typical location of current fluctuations, the module adds the acquired maximum value of 9.4 A and minimum value of 2.5 A, then halves the result to obtain the median current parameter within this cycle. The calculated median value is approximately 5.95 A, which serves as a typical value representing the current fluctuation range for this cycle and is used for subsequent comparison with actual sampling points. This median value typically falls in the higher-middle range of the battery load under typical charging and discharging conditions of a smart IoT power bank, and occurs more frequently when a household power bank is undergoing constant current discharge to approximately 80% capacity. Therefore, it can be used to identify the baseline of operating fluctuations under non-extreme loads.

[0020] The difference calculation submodule calculates the difference between the median current parameter and the current current value, calls the set of all differences in the current running cycle, obtains the proportional position of the difference in the set, and generates the current difference ratio parameter. After obtaining the median current parameter, the difference calculation submodule compares it with the current value of each real-time sampling point within the cycle, forming a difference data sequence to prepare for subsequent scaling analysis. In this embodiment, the known median current parameter is 5.95 amps, and the current value collected at the 160th sampling point in the cycle is 6.4 amps. The module first performs a simple subtraction between this sampling point current value and the median, obtaining a difference of 0.45 amps. Then, the submodule continues to call 300 sampling data points throughout the cycle, performing a similar subtraction operation each time to calculate the difference between each data point and the median current, and recording the absolute value of the difference. For example, the current value at the first sampling point is 4.2 amps, and its absolute difference from the median is 1.75 amps; the current value at the second sampling point is 4.5 amps, and its absolute difference from the median is 1.45 amps, and so on until the 300th sampling point, forming a complete sequence of absolute difference values. After completing the entire difference sequence, the difference calculation submodule sorts these 300 differences from smallest to largest, forming a sorted list from smallest to largest deviation. In this sorting, the smaller the difference, the closer it is to the typical median of the periodic current fluctuation. The module then finds the position of the difference 0.45 at the current 160th sampling point in the list. For example, if it is the 165th difference, it is the 165th of all 300 differences, and its proportional position in the period is approximately 0.55. This proportionalization parameter reflects the ranking of the current at the current sampling point relative to the typical median current fluctuation within the period. In practical smart IoT power bank scenarios, this proportional position often appears in the 0.4 to 0.6 range, typically corresponding to a moderate offset, such as in everyday office laptop charging scenarios, where it falls within most normal operating fluctuations.

[0021] The current offset recording submodule, based on the current difference ratio parameter, calls the current offset sorting position, compares the ratio position with the offset sorting position, and obtains the index, corresponding time point, and offset magnitude between the two, using the formula: ; The current offset amplitude is recorded by calculating the combined difference between the current offset amplitude and the average difference of the offset sorting. in, Represents the current current value. Represents the median current parameter. Represents the first in the set of current difference values item, Represents the number of samples in the set. The sorting index value represents the current current difference. The sorting index value representing the proportion of the current difference. The recorded value represents the magnitude of the current offset; Based on the current difference ratio parameter of 0.5333, the corresponding sorting index position is 160. Let the index set of the sorted current difference set be... This indicates the current difference's offset sort position within the set. The proportional position sequence corresponds to the index. When the two are equal, their offset difference is 0. Next, substitute this into the formula: ; Substituting the values, the calculation is as follows: Step 1: Calculate the molecular part: ; The second step is to calculate the first part of the denominator: ; The third step is to calculate the root mean square of the sum of the squares of the offset sorting differences: ; Substituting the polynomial: ; The final calculated current offset amplitude recorded value is: ; The results indicate that the recorded current offset amplitude at the 160th sampling point in the current operating cycle is approximately 0.043. This value, combined with the ranking differences among the 300 samples and the normalized current deviation, accurately reflects the degree of offset at this sampling point and can be used to construct a current operating offset curve or for state identification.

[0022] First, the dimensions of each parameter involved in the calculation are analyzed and normalized. The current value and the median current parameter are both in amperes, and the sequence of differences between all current data within the period and the median is also in amperes. During processing, the difference between the current value and the median current of the period is first compared to the sum of the absolute values ​​of the differences between all current values ​​and the median throughout the period. Because both parts are in amperes, their dimensions cancel each other out after division, resulting in a dimensionless ratio. This ensures that subsequent results no longer depend on the specific current value, avoiding direct influence from dimensions.

[0023] Next, in the step of processing the offset sorting difference, for each sampling point, the difference between its actual position in the periodic sorting and its ideal sorting position calculated by the current difference ratio is directly represented as an index value. Since the index is essentially a pure sequence number and has no physical dimension, even operations such as squaring, averaging, or taking the square root only measure the difference in the statistical sequence distribution, and remain purely dimensionless values. This ensures that when combining the normalized current ratio result with the discrete characteristics of the sorting index, all data are in the same dimension, i.e., all are dimensionless results, allowing for direct numerical comparison and multiplication, avoiding comparison distortion caused by different physical units.

[0024] The so-called "combined difference between the current offset amplitude and the average offset sorting difference" can be understood as follows: This value represents the normalized offset formed by comparing the current value at the current sampling point with the median of the periodic current. It further considers the degree of deviation between the current sampling point's position and its proportionally expected position within the sequence of all current values ​​during the period. In other words, it is a numerical indicator that comprehensively describes both the magnitude of the current sampling point's offset and its deviation from the periodic normal behavior at the sequence sorting distribution level.

[0025] From a computational perspective, the process begins by calculating the single-point offset between the current value and the median of the period. Then, this offset is compared to the total offsets generated by the current fluctuations throughout the entire period to obtain the proportion of the current offset within the overall fluctuation context. Next, the differences between the actual positions of all sampling points in the current offset ranking within the period and their positions according to proportional theory are calculated. This difference sequence is used to measure the dispersion of the overall ranking, reflecting the stability or anomaly of the current sample within the periodic sequence. Finally, these two quantification results from different perspectives are multiplied and fused together to form a single combined difference, which visually characterizes the degree of anomaly exhibited by the current sampling point within the current period.

[0026] The design concept of this combined difference is to take into account both sudden fluctuations at a single point and deviations in the global sequence ranking by multiplying the normalized offset magnitude percentage by the average difference in sequence ranking. The core principle is that if a single point has a small offset magnitude but a severe ranking anomaly, or a large offset magnitude even if the ranking doesn't deviate too much, both will be reflected in the final combined result. Therefore, it can more precisely distinguish seemingly ordinary sampling points that may hide anomalies in sequence behavior during periodic analysis.

[0027] Please see Figure 3 The trend identification module includes: The periodic current extraction submodule calls the current value of the corresponding period based on the period index in the current offset record, and obtains the current value of the previous period and the current value of the next period respectively. It then constructs the current sequence of adjacent periods in sequence, forms the current set corresponding to the periodic sequence, and generates the periodic current value sequence. First, the system extracts the current working cycle index from the current offset record table. Assuming the current processing object is cycle number 20, based on the structural information in the record, the system also needs to retrieve the numbers of the preceding and following cycles, corresponding to cycles 19 and 21 respectively. Then, it enters the cycle current data caching module or database reading module to obtain the current monitoring values ​​for the above three cycles one by one. Taking actual monitoring data as an example, the current values ​​corresponding to cycles 19, 20, and 21 are 2.35 amps, 2.80 amps, and 2.42 amps respectively. These three values ​​form the set of preceding, middle, and following current values ​​for the current cycle point. Next, according to the window width set by the current module, the system further extends forward and backward to extract current data for more cycles to form a complete cycle sequence. Assuming the window width is set to 7 cycles, the system needs to retrieve data from the two cycles before and two cycles after the current cycle, extending forward to cycles 17 and 18, and backward to cycles 22 and 23. The system continues to read the current values ​​for these cycles. For example, the readings show a current value of 2.22 amps for cycle 17, 2.28 amps for cycle 18, 2.33 amps for cycle 22, and 2.26 amps for cycle 23. This forms a set of cycle current values ​​containing 7 elements [2.22, 2.28, 2.35, 2.80, 2.42, 2.33, 2.26]. This set represents the sequence of cycle current values ​​centered on the current cycle. In the actual data acquisition system, current data is recorded once per second by current sensors installed at each cycle sampling point and stored in the database as average values. Therefore, there are certain time stability requirements during the cycle. To ensure data integrity, the system performs missing data detection on all retrieved cycle data. If any cycle data is missing, the missing value is automatically filled according to the compensation mechanism. For example, if data for cycle 18 is missing, the system will look up the current values ​​for cycles 17 and 19, which are 2.22 amps and 2.35 amps respectively. An averaging operation will be performed to obtain the filler value: (2.22 + 2.35) / 2 = 2.285 amps. After filling this value into the data sequence, the sequence will be updated to [2.22, 2.285, 2.35, 2.80, 2.42, 2.33, 2.26]. The entire data extraction and filler process is driven by index commands, ensuring the current value set is complete and continuous, providing a valid data foundation for the subsequent directional analysis module.

[0028] The direction continuity statistics submodule determines the direction of the current difference between two adjacent cycles based on the periodic current value sequence. If the difference is positive, it is defined as the upward direction; if the difference is negative, it is defined as the downward direction. It counts the number of consecutive cycles in the same direction and records the start and end cycle indices of the consistent segments in each direction, generating continuous direction segment interval information. The directional continuity statistics submodule takes a sequence of periodic current values ​​as input. During execution, the system starts from the first item of the sequence and calculates the difference in current values ​​between adjacent periods one by one, determining the sign and direction of the difference. Taking the current sequence [2.22, 2.285, 2.35, 2.80, 2.42, 2.33, 2.26] as an example, it first calculates the difference in current between the 18th and 17th periods. Subtracting 2.22 from 2.285 yields 0.065 amperes, which is positive and defined as an "upward direction." The starting direction is recorded as upward. Then, the difference in current between the 19th and 18th periods is calculated. Subtracting 2.285 from 2.35 still yields 0.065 amperes, and the direction remains unchanged, continuing to be judged as upward. The difference in current between the 20th and 19th periods is calculated. Subtracting 2.35 from 2.80 yields 0.45 amperes, and the direction is still upward. Thus, periods 17 to 20 constitute a continuous upward direction segment, and the current direction segment terminates at period 20. The system currently marks the start and end periods of the current direction segment as [17, 20], and the direction attribute as "ascending". In the subsequent judgment, the current difference between the 21st and 20th cycles is 2.42 minus 2.80. At 0.38 amperes, the direction changes; the marker direction shifts from upward to downward, the current direction segment ends, and the new direction segment begins from cycle 21. Continue to determine the current difference between cycle 22 and cycle 21; 2.33 minus 2.42 equals... 0.09 amperes, the direction remains downward. Next, determine the difference between the 22nd and 23rd cycles: 2.26 minus 2.33 equals... With a current of 0.07 amperes and a consistent direction, cycles 21 to 23 constitute a continuous downward direction segment. The system records the start and end cycles of the second direction segment as [21, 23], with the direction being "decreasing". Throughout the direction determination process, the system uses ±0.01 amperes as the threshold for identifying the direction change. If the absolute value of the current difference between adjacent cycles is less than 0.01 amperes, it is determined to be a stable segment, and the system skips the direction recording. For example, if the current difference between a pair of cycles is 0.008 amperes, the system will not classify it into any direction segment and will directly skip that pair of cycles. In this embodiment, all differences are greater than this threshold, thus clearly identifying the direction change segment. Finally, the system generates two direction segment information, representing two stages: a continuous rise in current value and a subsequent continuous decline in current value, providing interval information of the continuous direction segment for the trend change verification module. Throughout the process, each step of the judgment and recording is based on the current cycle index recursively, without relying on external models, and is only based on the actual difference in current values ​​between adjacent cycles for logical judgment, thereby achieving the initial division of the current trend direction.

[0029] The trend change verification submodule, based on the continuous direction segment interval information, retrieves the current value between the trend segment termination period and the previous period to obtain the current difference, and then calculates the average amplitude of the current change between all adjacent periods within the trend segment using the formula: ; The current mutation ratio is calculated. If it is greater than the trend mutation judgment benchmark value, the current cycle number and directionality are extracted, and the trend mutation segment identifier is obtained. in, This represents the current value at the end of the trend period. This indicates the current value of the previous cycle. and This represents the current value in the i-th period and the previous period within the trend segment. It represents the number of adjacent periods in the trend segment, and ΔI is the ratio of current changes during a sudden trend change; The trend abrupt change verification submodule verifies the abrupt change ratio based on the direction segments [17, 20, rising] and [21, 23, falling] generated in the second segment. First, it takes the first rising segment, whose termination period is the 20th cycle, corresponding to a current value of 2.80A. The previous cycle is the 19th cycle, corresponding to a current value of 2.35A. The difference between the two is calculated as follows: ; Next, the absolute values ​​of the current differences between adjacent cycles within the trend segment are calculated and averaged. This segment comprises cycles 17 to 20, involving three sets of adjacent cycles: (17, 18): 2.22 → 2.285, (18, 19): 2.285 → 2.35, and (19, 20): 2.35 → 2.80. The differences are as follows: ; ; ; The sum of the three differences is 0.58A, and the number of adjacent period groups is 3. The average current variation is: ; Substitute into the formula: ; in: Current value at the end of the trend cycle; : Current value of the previous cycle; The sum of the absolute values ​​of current changes in adjacent cycles within a trend segment; Number of adjacent periodic groups; : The calculated mutation ratio.

[0030] If the benchmark value for determining a sudden trend change is set to 1.8, the basis for this setting is that the standard mean of the periodic current variation in a large number of experimental statistics is 0.21A, and the standard deviation is 0.16A. The benchmark value is taken as a weighted average of 1.8 times the ratio of the mean to the standard deviation, calculated as follows: ; However, in the current paragraph it is: ; Therefore, the mutation condition is met, and the mutation period number is extracted as 20, with an upward direction. This mutation segment is recorded as a trend mutation identifier and passed to the subsequent processing module. This result indicates that the current difference between the end of the trend segment and the previous period is much greater than the fluctuation amplitude within the trend segment, indicating a significant mutation phenomenon, thus constituting a trend mutation judgment result.

[0031] Dimensional normalization of parameters in the formula for judging sudden current changes: In the process of judging trend abrupt changes, the key parameters involved all have a clear unit dimension, mainly the current unit "Ampere". The difference in current between the trend termination period and the previous period is expressed as the difference between two data points with the unit "Ampere", and the result is still "Ampere"; the difference in current value between all adjacent periods within the trend segment is also in "Ampere" unit. These differences are summed and then divided by the number of period groups to obtain a value representing the average current change within the trend segment, which is still in "Ampere" unit.

[0032] Therefore, in calculating the trend abrupt change ratio, two current values ​​with the same unit are compared. That is, the aforementioned difference in current abrupt change is divided by the average current change amplitude within the trend segment. The result no longer carries units, becoming a dimensionless pure numerical value. This normalization process ensures comparability under different trend segments and different current amplitude backgrounds, allowing the abrupt change judgment to focus only on the relative intensity of change, without being disturbed by the absolute current level, thus possessing universality.

[0033] Definition of current jump ratio: The current abrupt change ratio refers to the ratio between the magnitude of the current jump at the end of a trend segment and the average magnitude of the periodic fluctuations within that segment. More specifically, it represents whether the degree of a sudden and significant change in the current value over a period is greater than the normal level of change within that trend segment. If the current change in a certain period is much higher than the usual change within that segment, i.e., the abrupt change ratio is large, then this period will be identified as a critical point where the current trend abruptly changes.

[0034] This ratio does not focus on whether the current itself reaches a certain upper limit, but rather on whether the "intensity of change" of the current is abnormal. For example, in a trend segment where the current changes by only 0.1 amperes per cycle on average, if a jump of 0.4 amperes occurs, then the cycle can be considered to have a sudden change characteristic.

[0035] The principle of calculating the current mutation ratio: The calculation process for the current mutation ratio consists of three main steps. First, extract the current values ​​from the last cycle of the trend segment and its immediately preceding cycle, calculating the current change amplitude between them as the source of the mutation difference. Second, trace back the entire trend segment, sequentially extracting the current change amplitude between all adjacent cycles within the segment, calculating their absolute change values, and summing all these differences. Then, divide by the number of adjacent cycle pairs to obtain the typical change amplitude within the trend segment, i.e., the average fluctuation value. Third, ratio the mutation difference obtained in the first step with the average fluctuation value from the second step to obtain a unitless numerical value.

[0036] This ratio reflects the relative position of the cycle's abrupt change intensity within the context of the current fluctuation. If its value is significantly greater than one, such as greater than 1.5 or 2, it indicates that the degree of current abrupt change in this cycle is significantly higher than the average fluctuation behavior of this segment, and it can be identified as a trend abrupt change cycle. Conversely, if the ratio is close to or less than one, it indicates that the current change amplitude in this cycle is within the fluctuation range of the trend segment and does not have abrupt change characteristics. The entire judgment process does not rely on the absolute magnitude of the current data, but only on the relative intensity of the periodic fluctuation, thus it is applicable to stability identification under different electrical systems or load conditions.

[0037] Please see Figure 4The shock response module includes: The power capture submodule marks the start period based on the trend change segment identifier, extracts the start and minimum voltage values, and the start and end values ​​of current and power for the period, and summarizes them to form a basic power value set. First, the module reads the cycle number and direction information recorded in the abrupt change segment identifier. Assuming the current trend abrupt change identifier is cycle number 30 and the direction is upward, this module uses cycle 30 as the starting point for extraction. Combined with the context cycle window range parameter setting (e.g., setting the extraction cycle length to 5 cycles), cycles 30 to 34 are selected as the processing range. Within this cycle range, voltage data for each cycle is extracted sequentially. First, the voltage value at the start of cycle 30 is obtained, set to 225.6 volts. Then, the lowest voltage value within this cycle is retrieved, set to 219.8 volts. After recording these two sets of data, current value extraction is performed. Samples are taken from the previous and next time nodes within cycle 30 to obtain the starting and ending current values, set to 2.80 amperes and 3.65 amperes respectively. Next, power value extraction is performed, referencing the current and voltage data. Within the linkage interval, power data at the beginning and end of the cycle are obtained through equal-interval sampling, set at 620 watts and 790 watts respectively. This data comes from high-frequency sampling records of the power monitoring system, recorded once per second. A set of boundary values ​​for power variation is constructed by filtering the start, lowest, or end points. If the voltage fluctuation amplitude within a certain cycle is less than the preset accuracy threshold of 0.5 volts, the lowest value is discarded, and only the starting value is retained for power calculation. The accuracy threshold is set based on the system's historical average fluctuation and sensor resolution. Assuming the sensor voltage accuracy is 0.1 volts and the historical average fluctuation is 0.32 volts, the threshold is set to the sum of these two values, 0.42 volts, rounded up to 0.5 volts. This operation ensures that the voltage interception logic is not affected by minor disturbances. All extracted basic power data are uniformly organized into a set of basic power values ​​in the structure according to cycle numbering for subsequent indicator calculations.

[0038] The power increase calculation submodule calculates the voltage drop, current increase, and power increment based on the power base value set, and combines them into a unified index to obtain the power increase offset result. First, the voltage drop, current increase, and power increase are calculated separately for each cycle. The difference between the starting voltage and the lowest voltage of the cycle is calculated first. For example, in cycle 30, the starting voltage is 225.6 volts and the lowest voltage is 219.8 volts, so the voltage drop is 5.8 volts. The voltage drop threshold is set by the system's standard voltage reduction, with a reference rated voltage of 230 volts and an allowable voltage fluctuation limit of 5%, or 11.5 volts. Therefore, 5.8 volts is considered a medium fluctuation. Next, the current increase is calculated. The starting current is 2.80 amps, and the ending current is 3.65 amps, resulting in a current increase of 0.85 amps. The increase threshold is preset to 0.6 amps based on the fluctuation under variable load conditions. Therefore, the current change exceeds the judgment benchmark. Finally, the power increase is calculated... The initial power is 620 watts, and the final power is 790 watts, with a difference of 170 watts. The reference benchmark is set by the device's power response rate. It is assumed that the maximum allowable power increase per unit cycle does not exceed 150 watts, so this value also exceeds the limit. When the three indicators are combined, they are performed using a standardized weighted method. Each indicator is converted into a relative proportion value between 0 and 1 according to its unit range. In this cycle, the voltage drop accounts for 0.504 of the upper limit of 11.5 volts, the current increase accounts for 0.567 of the upper limit of 1.5 amperes, and the power increase accounts for 0.567 of the upper limit of 300 watts. The three are simply averaged to obtain a comprehensive power increase offset value of 0.546. This operation is repeated for all cycles, and the offset index sequence under continuous cycles is output to finally obtain the power increase offset result.

[0039] The offset feature verification submodule compares the corresponding average change magnitude based on the power increase offset result, determines whether all are positive deviations, extracts the growth direction, magnitude value and cycle number, and generates load impact trigger entries. The offset feature verification submodule, based on the power increase offset results, compares the differences of various offset indicators with the reference average of the trend segment on a cycle-by-cycle basis. First, it summarizes all voltage declines, current increases, and power increases within the cycle segment period by cycle, and calculates the average change value of the overall trend segment. Assuming the average voltage decline is 5.2 volts, the average current increase is 0.73 amperes, and the average power increase is 160 watts, the three actual change values ​​for each cycle are directly compared with the above averages. In the 30th cycle, these are 5.8 volts, 0.85 amperes, and 170 watts, all greater than their corresponding averages, thus indicating a positive deviation. The deviation judgment logic is: if any value is lower than its corresponding average by a percentage... The ninety-fifth condition, i.e., voltage less than 4.94 volts, current less than 0.6935 amperes, and power less than 152 watts, is considered a negative offset; otherwise, it is considered a positive offset. When all three values ​​are positive offsets in a cycle, the load is determined to enter a surge response state in that cycle. The current cycle number is the trigger cycle for the load impact, set to cycle 30. At the same time, the three actual change values ​​of this cycle are recorded as the trigger amplitude content. The judgment process continues for the next cycle. If the positive offset condition is met for multiple consecutive cycles, the corresponding continuous impact segment is recorded; otherwise, only the single-point impact behavior is recorded. Finally, the cycle number, the set of electrical amplitudes, and the positive offset judgment content are output to generate a load impact trigger entry.

[0040] Please see Figure 5 The remote control module includes: The power dispatch submodule retrieves the original power supply output value and the running time of the corresponding segment based on the power increment and cycle segment number recorded in the load impact trigger entry, and completes data aggregation by cycle segment index to obtain the power supply dispatch dataset. First, extract the cycle index and corresponding power increment value involved in the trigger entry. For example, suppose the trigger cycle is cycle 34 and the power increment is 170 watts. This module calls the power output value of the corresponding cycle segment in the original power supply database through the cycle number. Assuming that the original power supply in cycle 34 is 850 watts and the running time is 9 seconds, after locating the relevant power supply record through the cycle segment index information, it calls the data of adjacent cycle segments to help determine the stability of the power supply status. If the original power supply values ​​of cycle 33 and cycle 35 are 830 watts and 880 watts respectively, and the running time is 9 seconds for both, then the original power value group corresponding to the cycle segment group [33, 34, 35] is [ [830, 850, 880] watts, with a running time group of [9, 9, 9] seconds, are summarized and organized in one go. This data aggregation operation adopts a matching method based on the cycle number index to ensure that records are not lost or duplicated. The running time data is read from the sampling controller's record file with second-level precision. If the running time data of a certain cycle is missing, it is supplemented by the average value of the preceding and following cycles. For example, if the duration of cycle 34 is missing, the average value of cycles 33 and 35 (9+9) / 2=9 seconds is taken to supplement it. This process uniformly constructs the power value sequence and duration sequence to form a complete power supply call dataset, which serves as the basis for subsequent power supply adjustments.

[0041] The power supply adjustment submodule replaces the original power supply output value and runtime based on the power supply call dataset and a fixed scaling relationship. The updated power supply intensity and runtime are combined into a new data group to obtain the adjusted power supply parameter group. First, the original power output value is proportionally adjusted according to the scaling relationship set by the system. For example, the adjustment coefficient is set to 0.85, which means the original power output value is compressed to 85%. Then, the running time is extended accordingly to maintain energy constancy. The first cycle is processed, with an original power of 850 watts, which is adjusted to 850 × 0.85 = 722.5 watts. To ensure the power supply remains unchanged, the running time needs to be adjusted synchronously. The original duration was 9 seconds, so the adjusted running time should be the product of the original power and the original duration divided by the adjusted power, i.e., (850 × 9) / 722.5 ≈ 10.59 seconds. Other cycles are processed synchronously. The adjusted power of cycle 33 is 830 × 0.85 = 705.5 watts, and the running time is adjusted to (830 × 9) / 705.5 ≈ 10.59 seconds. After adjustment, the power is 880 × 0.85 = 748 watts, and the running time is (880 × 9) / 748 ≈ 10.59 seconds. Since the original running time of all cycles is consistent, the running time after adjustment is uniformly 10.59 seconds. This scaling relationship ensures that the total power supply energy of each cycle remains stable. Power supply control is achieved only by adjusting the power intensity and duration. The scaling factor of 0.85 is the system's preset power limit ratio, which is set according to the on-site load protection level. If the load category is Class II equipment, the maximum scaling factor shall not be lower than 0.75 and not higher than 0.95. In this implementation, 0.85 is selected, which is in the middle range and is reasonable. Finally, the adjusted power values ​​and running time values ​​of all cycle segments are recombined into a new power supply parameter group to complete the power supply adjustment process.

[0042] The parameter binding submodule binds the corresponding cycle number to each of the adjusted power supply parameter groups, establishes a corresponding relationship, and generates a detailed list of remote output limits after summarizing and organizing the data. First, according to the order of the power sequence and runtime sequence in the power supply parameter group, match the corresponding original cycle number sequence. For example, if the adjusted power value group is [705.5, 722.5, 748] watts, the corresponding runtime group is [10.59, 10.59, 10.59] seconds, and the original cycle number is [33, 34, 35]. The module binds the first group of parameters to cycle 33, the second group to cycle 34, and the third group to cycle 35 according to the subscript index method. Each group forms a triplet record, represented as "cycle number, adjusted power value, adjusted runtime". Data integrity must be ensured during the binding operation. Verification confirms that each parameter group has a corresponding number. If the number of parameter groups is inconsistent with the number of cycle groups, a data re-sampling mechanism will be triggered to ensure that the parameter matching logic does not misalign or overlap. All binding results are sorted and summarized to form a structured record set. The record format standard is: the cycle number field is an integer, the power value field is a floating-point type with one decimal place, and the runtime field is a floating-point type with two decimal places. The final output of this set forms the remote output limit detail data item for use by the remote control module. The number of detail records is consistent with the number of power supply call cycles, and they correspond one-to-one to ensure that the remote power supply execution system has the operability of cycle-based control.

[0043] Please see Figure 6 The adjustment and release module includes: The fluctuation information extraction submodule uses the cycle number segment in the remote output limit details to call the voltage fluctuation value, current amplitude and power fluctuation amplitude within the corresponding two cycles, constructs the cycle fluctuation information set, and obtains the cycle fluctuation characteristic value. First, the cycle group represented by each number is retrieved. Voltage data from two consecutive cycles within each group is extracted, and the difference between their maximum and minimum values ​​is calculated to form the voltage fluctuation value. Current data from the same cycle group is retrieved, and the maximum current value at the beginning and end of each cycle is used to calculate the amplitude. Simultaneously, the corresponding power change amplitude within that segment is extracted. This operation uses the cycle number as the retrieval key. Assuming the number segment is cycles 41 to 42, the original data records for cycles 41 and 42 are retrieved. In cycle 41, the highest voltage is 229.6 volts, and the lowest is 224.0 volts, resulting in a voltage fluctuation value of 5.6 volts and a current fluctuation value of... The starting value is 2.2 amps, the ending value is 3.05 amps, the amplitude is 0.85 amps, the power change is from 680 watts to 855 watts, the fluctuation range is 175 watts. In the 42nd cycle, the voltage fluctuation value is 4.8 volts, the current amplitude is 0.63 amps, and the power fluctuation is 142 watts. The combination of these three data points forms the electrical fluctuation information group for this cycle. The fluctuation data of the cycle segment are used to construct the cycle fluctuation information set after field matching. This set combines the cycle number, voltage fluctuation value, current amplitude, and power fluctuation range in a unified format to form the cycle fluctuation characteristic value, which is used for subsequent judgment module analysis.

[0044] The operation status determination submodule determines whether the voltage, current and power are within the equipment operation boundary range based on the periodic fluctuation characteristic value, and monitors whether there is a continuously increasing signal in the current segment. After the dual conditions are met, the corresponding cycle start point and the current power supply operation status are extracted to obtain the periodic stable state information. The operating status determination submodule performs interval judgment operations on voltage, current, and power data within a cycle segment based on periodic fluctuation characteristic values, and identifies continuously increasing signals. First, it sets operating boundary intervals: voltage fluctuation is allowed to be no higher than 6.0 volts, current fluctuation no higher than 1.2 amps, and power fluctuation no higher than 200 watts. If all three parameters do not exceed their corresponding thresholds within the current cycle, it is considered to be within the stable boundary range. Taking cycles 41 and 42 as examples, their voltage fluctuations are 5.6 volts and 4.8 volts respectively, both below 6.0 volts; current fluctuations are 0.85 amps and 0.63 amps, below 1.2 amps; and power fluctuations are 175 watts and 142 watts, also below 200 watts. All conditions are met for stability. Then, the continuous growth signal is judged. The judgment method is whether the voltage, current and power values ​​are increasing within the current cycle group. For example, in cycle 41, the current increases from 2.2 to 3.05 and the power increases from 680 to 855, which meets the continuous upward trend. If the three data continuously increase for more than two cycles, it is considered as a continuous upward signal. If only one cycle meets the condition, it is not judged as continuous. In the current scenario, although cycle 42 is still rising, the amplitude is slowing down, so it can be judged that there is a continuous growth signal within the segment. After meeting the dual conditions of boundary interval and trend, cycle 41 is extracted as the starting cycle, and the electrical operation state of this cycle is marked as "stable and continuously rising", and it is organized into a cycle stability state information record.

[0045] The tag information generation submodule extracts the corresponding recovery judgment tag content based on the periodic stable state information, and performs number matching in combination with the period start point to establish a mapping relationship and generate the mobile power bank remote monitoring results. First, the running status and number of the marked cycle 41 are extracted and used as the core basis for generating the recovery judgment mark content. Then, the position mapping relationship of the cycle number in the full cycle running log is retrieved in the system index table. For example, if cycle 41 is located in segment "B3" in the task scheduling mapping table, it is recorded as "B3-41". This number combination is bound with the status information to form a mapping key-value pair. At the same time, the current status is defined as "Recovery Judgment: Yes". After the status is bound with the number, it is written into the remote monitoring data structure to form a set of "number-status" pairs. The data is further processed and output as a structured detail. Each record in this detail consists of three fields: cycle number, recovery judgment status, and source status description, ensuring that there is a corresponding monitoring record item for each stable cycle. Finally, this set is written into the monitoring system reporting queue as the remote monitoring result.

[0046] 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 system supporting remote monitoring, characterized in that, The system includes: The current recognition module extracts current data within a cycle of the smart IoT power bank, constructs the fluctuation range and median, calculates the ratio of the difference between the current and the median, and, combined with the sorting offset, extracts the index, time and offset amplitude to generate current offset record content. The trend discrimination module analyzes the direction and length of the current change in the preceding and following cycles based on the cycle index in the current offset record, compares the last change with the average trend amplitude, determines the direction of the sudden change and extracts the cycle number, and generates the trend sudden change segment identifier. The impact response module calls the trend change segment identifier content, extracts the voltage, current and power information of the segment starting point, calculates the increase and compares it with the average value. If both are positive offsets, it extracts the direction, amplitude and period number, and generates a load impact trigger entry. The remote control module adjusts the power supply intensity and running time according to the power increment and cycle segment number in the load impact trigger entry, binds the cycle number, and generates remote output limit details. The adjustment and release module reads the remote output limit details, determines whether it is at the operating boundary and has no continuously increasing signal, extracts the cycle start point and operating status, and generates the mobile power bank remote monitoring results.

2. The mobile power supply system supporting remote monitoring according to claim 1, characterized in that, The current offset record includes the current offset index, offset occurrence time, and offset amplitude value. The trend change segment identifier includes the trend cycle number, change direction category, and change verification parameters. The load impact trigger entry includes the power increment value, voltage change characteristics, and current response characteristics. The remote output limit details include the adjusted power supply, corrected runtime, and limit application cycle number. The mobile power bank remote monitoring results include the recovery start cycle, operating stability status, and power supply recovery flag.

3. The mobile power supply system supporting remote monitoring according to claim 1, characterized in that, The process involves combining sorting offsets to extract the index, time, and offset magnitude. The deviation of the current within a cycle is sorted by size, and the corresponding cycle index, occurrence time, and offset magnitude information are extracted. The comparison of the final change with the average trend amplitude involves comparing the current change in the last cycle of the trend segment with the average change amplitude to identify the direction of the sudden change. The power increment and the period segment number, the power increase value measured within the period segment and the corresponding period segment identification number.

4. The mobile power supply system supporting remote monitoring according to claim 3, characterized in that, The current identification module includes: The fluctuation extraction submodule obtains the maximum, minimum and average current values ​​within the operating cycle of the smart IoT power bank, constructs the fluctuation range, extracts the value corresponding to the midpoint between the maximum and minimum values, and generates the median current parameter. The difference calculation submodule calculates the difference between the median current parameter and the current current value, calls the set of all differences in the current running cycle, obtains the proportional position of the difference in the set, and generates the current difference ratio parameter. The current offset recording submodule calls the current offset sorting position according to the current difference ratio parameter, compares the ratio position with the offset sorting position, obtains the index, corresponding time point and offset amplitude between the two, calculates the combined difference between the current offset amplitude and the offset sorting average difference, and obtains the current offset amplitude recording value. The constructed fluctuation range utilizes the range of maximum and minimum current values ​​within the period to verify the upper and lower limits of current fluctuation. The proportional position refers to the proportion of the current value to the sorting position in the set of current differences throughout the entire cycle.

5. The mobile power supply system supporting remote monitoring according to claim 4, characterized in that, The trend discrimination module includes: The periodic current extraction submodule calls the current value of the corresponding period based on the period index in the current offset record, and obtains the previous period current value and the next period current value respectively. It then constructs the current sequence of adjacent periods in sequence, forms the current set corresponding to the period sequence, and generates the periodic current value sequence. The direction continuity statistics submodule determines the direction of the current difference between two adjacent cycles based on the cycle current value sequence. If the difference is positive, it is defined as the upward direction; if the difference is negative, it is defined as the downward direction. The number of consecutive cycles in the same direction is counted, and the start and end cycle indices of the consistent segments in each direction are recorded to generate continuous direction segment interval information. The trend change verification submodule calls the current value of the trend segment termination period and the previous period based on the continuous direction segment interval information, obtains the current difference, calculates the average amplitude of current change between all adjacent periods within the trend segment, calculates the current change ratio, and if it is greater than the trend change judgment benchmark value, it extracts the current period number and directionality, and obtains the trend change segment identifier content. The current sequence is a set of adjacent period current values ​​arranged in period index order, and its changes during the period are analyzed. The start and end cycle index refers to the start and end cycle numbers of a continuous segment with the same current change direction. The trend change judgment benchmark is a current change ratio threshold used to determine whether the current change in the trend segment is abrupt.

6. The mobile power supply system supporting remote monitoring according to claim 5, characterized in that, The shock response module includes: The power interception submodule marks the starting period based on the trend change segment identifier, extracts the starting and ending values ​​of voltage, current and power for the period, and summarizes them to form a basic power value set. The power increase calculation submodule calculates the voltage drop, current increase, and power increment based on the power base value set, and combines them into a unified index to obtain the power increase offset result. The offset feature verification submodule compares the corresponding average change amplitude based on the power increase offset result, determines whether all are positive deviations, extracts the growth direction, amplitude value and cycle number, and generates load impact trigger entries. The positive deviation refers to the increase and upward trend in voltage, current, and power parameters compared to the average change, all in the same direction.

7. The mobile power supply system supporting remote monitoring according to claim 6, characterized in that, The remote control module includes: The power dispatch submodule calls the original power supply output value and the running time of the corresponding segment according to the power increment and cycle segment number recorded in the load impact trigger entry, and completes the data aggregation according to the cycle segment index to obtain the power supply dispatch dataset. The power supply adjustment submodule replaces the original power supply output value and running time based on the power supply call dataset and a fixed scaling relationship, and combines the updated power supply intensity and running time into a new data group to obtain the adjusted power supply parameter group. The parameter binding submodule binds the corresponding cycle number to each of the adjusted power supply parameter groups, establishes a corresponding relationship, and generates a detailed list of remote output restrictions after summarizing and organizing the data.

8. The mobile power supply system supporting remote monitoring according to claim 7, characterized in that, The adjustment and release module includes: The fluctuation information extraction submodule, based on the period number segment in the remote output limit details, calls the voltage fluctuation value, current amplitude and power fluctuation amplitude within the corresponding two periods to construct a period fluctuation information set and obtain period fluctuation characteristic values. The operation status determination submodule determines whether the voltage, current and power are within the equipment operation boundary range based on the periodic fluctuation characteristic value, and monitors whether there is a continuously increasing signal in the current segment. After the dual conditions are met, the corresponding cycle start point and the current power supply operation status are extracted to obtain the periodic stable state information. The tag information generation submodule extracts the corresponding recovery judgment tag content based on the periodic stable state information, and performs number matching in combination with the period start point to establish a mapping relationship and generate the mobile power bank remote monitoring result. The operating boundary range of the equipment refers to the upper and lower limits of voltage, current, and power parameters within which the equipment is safe and operating normally.

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