Distribution box multi-dimensional state on-line monitoring system based on Internet of Things

Abstract

The invention discloses a distribution box multi-dimensional state online monitoring system based on Internet of Things, which relates to the technical field of distribution box monitoring and comprises a dynamic voltage turn-back capture module, an energy migration path construction module, a retention section delineation module, a resonance evolution line analysis module and a dynamic suppression path traction module. According to the invention, by constructing a dynamic voltage turn-back capture band and an energy migration path, gathering, staying and enhancing processes of energy in the distribution box can be identified in advance, and early-stage pre-judgment of a potential closed energy cavity is realized. And retention energy is dragged and dispersed in order through a dynamic inhibition path, so that the internal energy density of the distribution box is maintained in a safe range, thereby effectively avoiding sudden structural damage and improving the overall safety.

Description

Technical Field

[0001] This invention relates to the field of distribution box monitoring technology, specifically to an online multi-dimensional status monitoring system for distribution boxes based on the Internet of Things. Background Technology

[0002] Internet of Things (IoT)-based multi-dimensional online monitoring of distribution boxes refers to the use of distributed sensors, communication nodes, and edge terminals to digitally collect, aggregate, and continuously track key operating parameters inside the distribution box in real time, and achieve remote online visualization through the IoT network. Its core lies in the simultaneous collection of data across multiple dimensions, including temperature, humidity, load current, voltage fluctuations, switch actions, cable heating trends, and internal environmental anomalies. This data is then combined with trend analysis at the edge and status recognition in the cloud to form continuous online monitoring of operating status, environmental changes, and potential risks. This allows for early identification and alerts before anomalies such as overload overheating, sudden humidity increases, insulation degradation, poor contact, and aging electrical components occur, thus achieving real-time assurance of safe operation and a proactive early warning mechanism for potential faults.

[0003] The existing technology has the following shortcomings:

[0004] During the operation of the distribution box, if voltage backflow occurs in a local circuit, a closed energy retention zone will gradually form inside the busbar section. Driven by continuous fluctuations, this retention zone will evolve into a resonant cavity with coupling characteristics, causing electromagnetic energy to continuously circulate and superimpose within a narrow space. As the intensity of energy superposition continues to increase, the metal surface of the busbar will undergo microscale structural erosion in a very short time, further triggering surface peeling and localized melting and diffusion. This type of damage is characterized by its suddenness and insidiousness. Before the resonant cavity reaches the critical energy level, conventional monitoring data such as temperature, current, and voltage remain normal, making it difficult to effectively identify through traditional online monitoring. Once the energy within the closed section exceeds the material's tolerance threshold, explosive damage will be triggered without warning, ultimately resulting in busbar breakage, arcing, and failure of the entire cabinet.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide an online monitoring system for the multi-dimensional status of distribution boxes based on the Internet of Things (IoT) to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a multi-dimensional online monitoring system for distribution boxes based on the Internet of Things, including a dynamic voltage back-reversal capture module, an energy migration path construction module, a stagnation section delineation module, a resonance evolution line analysis module, and a dynamic suppression path traction module;

[0008] Dynamic voltage back-tracing capture module: A dynamic voltage back-tracing capture zone is established around the busbar structure of the distribution box. The continuous voltage fluctuations collected by the Internet of Things are unfolded in time sequence, and the abnormal trend of voltage fluctuations gathering in a single segment is extracted in the dynamic voltage back-tracing capture zone to form continuous data for subsequent spatial analysis.

[0009] Energy migration path construction module: Utilize the obtained abnormal trends to construct energy migration paths, connect the abnormal trends point by point according to the spatial position of the parent row, so that the energy migration path presents a spatial form that converges towards the center, and limit the scope of the suspicious area based on this spatial form.

[0010] Delineation module for stagnant zones: Delineates stagnant zones within a defined suspicious area, and traces the trajectory of changes in energy residence time and energy density through continuous voltage fluctuations, giving the stagnant zones a quantifiable dynamic morphology, providing a basis for subsequent evolution identification;

[0011] Resonance Evolution Line Analysis Module: Constructs a resonance evolution line based on the dynamic morphology of the stagnant section, tracks the changes in cyclic superposition intensity along the resonance evolution line, continuously unfolds the periodic enhancement behavior, and forms a triggering benchmark for anomaly control;

[0012] Dynamic suppression path traction module: Based on the triggering reference, a dynamic suppression path is introduced. A phase traction ring array is arranged inside the dynamic suppression path. The cyclic superposition intensity along the resonance evolution line is dispersed segment by segment according to the phase sequence to achieve energy traction, thereby blocking the formation of the closed energy cavity and realizing dynamic suppression of sudden structural damage to the distribution box.

[0013] Preferably, the step of establishing a dynamic voltage foldback capture zone around the busbar structure of the distribution box includes the following sub-steps:

[0014] The voltage fluctuations collected from multiple voltage acquisition points at different locations on the busbar of the distribution box are acquired one by one and arranged into a continuous voltage fluctuation time sequence chain according to the order of acquisition time. During the arrangement process, the original voltage change of each sampling cycle is maintained so that the voltage fluctuation time sequence chain can fully reflect the real rhythm of the busbar operation process.

[0015] The voltage fluctuation time sequence chain is mapped one by one to the spatial structure of the busbar, so that each voltage fluctuation is clearly linked to the actual geometric position on the busbar, and a dynamic voltage backtracking capture zone is formed along the busbar. The voltage fluctuation points that fall back in space are marked in the dynamic voltage backtracking capture zone to record possible backtracking signs.

[0016] By comparing the spatial distribution of voltage in continuous time slices within the dynamic voltage foldback capture band, it is possible to extract whether voltage fluctuations tend to cluster towards a fixed segment. When voltage fluctuations in multiple time slices repeatedly cluster towards the same segment in space, the corresponding segment is identified as a potential area for voltage fluctuation focusing, which can be used for subsequent energy migration and retention analysis.

[0017] Preferably, the steps of constructing energy migration paths and defining the scope of the suspected area include:

[0018] Each abnormal trend point obtained from the dynamic voltage return capture band is confirmed one by one according to the actual position of the busbar when it appears, and its position is calibrated in three-dimensional space according to the geometric orientation of the busbar, so that each abnormal trend point forms a set of trend points with spatial orientation.

[0019] The trend points are connected in series on the busbar structure according to the order in which the abnormal trend points appear, and adjacent abnormal trend points are connected in a continuous manner according to the actual direction of the busbar, so that the abnormal trend points form an energy migration path inside the busbar that can reflect the true offset trajectory.

[0020] The energy migration path after series connection is continuously compared. By comparing the changes in the spacing between abnormal trend points and the overall direction change, it is confirmed whether the energy migration path gradually converges towards the central section of the main row in space, forming a spatial form with convergence characteristics.

[0021] Based on the convergence characteristics of the focal segment formed within the busbar, and combined with the starting point, ending point, and spatial relationship of the focal segment, the actual range of the focal segment on the busbar is marked to obtain the range of the suspected area.

[0022] Preferably, when confirming the convergence pattern of the energy migration path, the positional changes of multiple consecutive abnormal trend points in different time slices are compared to confirm whether the abnormal trend points have the characteristic of repeatedly falling back to the same focal segment in time. This ensures that the range of the suspicious area is finally limited only under the condition of satisfying both spatial convergence and temporal repetition, thereby improving the accuracy of the range of the suspicious area.

[0023] Preferably, the step of plotting the trajectory of energy residence time variation versus energy density variation through continuous voltage fluctuations includes:

[0024] The defined suspicious area is expanded segment by segment in the actual location of the busbar structure. The continuous voltage fluctuations of all voltage acquisition points within the suspicious area are compared hourly in different time slices. Voltage fluctuation points that repeatedly stay or fall back at the same position are extracted from the time slices, so that a continuous fluctuation trend with dwelling characteristics is formed in the middle of the suspicious area, forming an energy dwell time change trajectory.

[0025] Based on the continuous fluctuation trend, the voltage fluctuation amplitude, persistence and repeatability at each location in adjacent time slices are compared. By analyzing whether the fluctuations repeatedly return, maintain local peaks or superimpose and enhance in a short period of time, the location with energy density accumulation characteristics in the middle of the suspicious area is determined, and the corresponding energy density change trajectory is formed.

[0026] Cross-confirm the energy residence time change trajectory with the energy density change trajectory, continuously calibrate adjacent positions that simultaneously possess residence and accumulation characteristics, and delineate residence segments with temporal continuity and spatial coherence within the suspicious area based on the continuous performance of the calibrated positions in multiple time slices, so that the residence segments have a quantifiable dynamic form.

[0027] Preferably, the dynamic morphology of the stagnation segment includes the energy residence time, peak energy density, energy density distribution width, and energy density change rate. By continuously recording the lateral expansion trajectory and longitudinal energy enhancement trajectory of the stagnation segment in multiple time slices, the energy evolution trend of the stagnation segment has visual characteristics and can be used as a basis for subsequent resonance identification.

[0028] Preferably, the step of tracking the change in cyclic superposition intensity along the resonance evolution line to continuously unfold the periodic enhancement behavior and simultaneously form a triggering reference for anomaly control includes:

[0029] Within the defined retention zone, the energy residence time change trajectory and energy density change trajectory in each time slice are unfolded segment by segment, and the key points that show energy decline and re-enhancement in continuous time slices are extracted as the basic node group.

[0030] The basic node groups are connected in series according to their spatial location and temporal order within the stagnation zone, so that the connection relationship between adjacent nodes can reflect the actual propagation direction and enhancement trend of energy within the stagnation zone, thereby forming a continuous evolutionary skeleton.

[0031] Based on the continuous evolutionary framework, key trends reflecting periodic enhancement behavior are identified, and these key trends are depicted as resonant evolution lines in the form of continuous curves. The periodic enhancement behavior is then unfolded along the resonant evolution lines.

[0032] The enhancement amplitude, repetition frequency and peak duration on the resonant evolution line are comprehensively confirmed, and a triggering benchmark for active anomaly control is established in combination with the dynamic morphology of the stagnation section to determine whether the stagnation section has reached the critical state for intervention.

[0033] Preferably, the establishment of the triggering benchmark includes threshold comparison of the amplitude change rate, peak density region and enhancement acceleration of the periodic enhancement behavior on the resonance evolution line. When the enhancement amplitude continues to rise in multiple consecutive time slices without obvious attenuation phase, the corresponding continuous enhancement state is determined as the triggering condition for active anomaly control, so as to ensure early intervention before the formation of a closed energy cavity in the stagnation section.

[0034] Preferably, the step of dispersing the cyclic superposition intensity along the resonant evolution line segment by segment in phase order to achieve energy traction includes:

[0035] On the resonant evolution line, the changes in cyclic superposition intensity are compared in real time according to the established triggering benchmark. When the enhancement threshold, duration or peak density characteristics are met, a dynamic suppression path is established between the outer edge of the stagnation section and the adjacent busbar section. The starting point and direction of the dynamic suppression path are determined according to the dynamic morphology of the stagnation section.

[0036] Within the dynamic suppression path, phase traction rings are arranged according to the time change sequence and phase change relationship of energy on the resonance evolution line, so that the phase traction rings correspond to the high points, low points and turning points of the cyclic superposition in sequence, and form an energy traction gradient from strong to weak.

[0037] The cyclic superposition intensity along the resonance evolution line inside the stagnant section is mapped segment by segment to the phase traction ring array in the dynamic suppression path, so that the energy is gradually transferred from the inside to the outside along the phase sequence, realizing the staged release and dispersion of energy in the stagnant section.

[0038] During the energy traction process, the energy diffusion state on the dynamic suppression path is continuously observed. When the energy density decreases, the residence time shortens, and the cyclic superposition peak weakens, it is confirmed that the formation of the closed energy cavity is effectively blocked, thereby achieving dynamic suppression of sudden structural damage to the distribution box.

[0039] Preferably, during the deployment of the phase traction ring array, the spacing and sequence of the phase traction rings are adjusted according to the energy density distribution difference between the stagnation section and the starting point of the dynamic suppression path. The phase traction rings close to the stagnation section are arranged in a dense manner to enhance the energy traction capability, while the phase traction rings far from the stagnation section are arranged in a gradually sparse manner to form a continuously decreasing energy traction gradient. This allows for smooth diffusion and prevents energy backflow as energy moves outward along the path.

[0040] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0041] This invention establishes a continuously unfolding dynamic voltage reflection and capture zone inside the distribution box, synchronizing the time series of voltage fluctuations with the spatial location of the busbar. This allows for the early identification of accumulation trends hidden in weak fluctuations before external signs such as temperature rise, noise, or current distortion appear on the busbar. Through the step-by-step construction of energy migration paths, stagnation sections, and resonant evolution lines, key processes such as energy convergence, residence, and amplification within the busbar are continuously visualized. This transforms potential damage from previously imperceptible internal behavior into a continuously trackable evolutionary trajectory, thereby enabling early perception of the formation process of enclosed energy cavities and early prediction of sudden risks.

[0042] This invention introduces a dynamic suppression path under the action of a triggering reference, dispersing the gradually increasing cyclic superposition behavior in the stagnant section to different sections of the phase traction ring. This allows concentrated energy to be directed out along the traction sequence, preventing energy from repeatedly accumulating in a confined structure and triggering explosive damage. In this way, the stress, field strength, and energy density inside the busbar are always maintained within the material's tolerance range. The highly concealed and rapidly accumulating energy coupling effect is actively weakened, transforming the high-risk sudden damage process into a sustainable and controllable stable state, thereby significantly improving the overall safety and controllability of the distribution box operation. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0044] Figure 1 This is a schematic diagram of a multi-dimensional online status monitoring system for distribution boxes based on the Internet of Things according to the present invention;

[0045] Figure 2 This is a flowchart of the method for online monitoring of the multi-dimensional status of a power distribution box according to the present invention;

[0046] Figure 3 This is a schematic diagram illustrating the principle of establishing a dynamic voltage feedback capture zone around the busbar structure of the distribution box in this invention.

[0047] Figure 4 This is a schematic diagram illustrating the principle of the present invention in depicting the trajectory of energy residence time and energy density changes through continuous voltage fluctuations. Detailed Implementation

[0048] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0049] like Figures 1 to 4 As shown, the present invention provides a multi-dimensional online monitoring system for distribution boxes based on the Internet of Things, including a dynamic voltage back-feedback capture module, an energy migration path construction module, a stagnation section delineation module, a resonance evolution line analysis module, and a dynamic suppression path traction module;

[0050] Dynamic voltage back-tracing capture module: A dynamic voltage back-tracing capture zone is established around the busbar structure of the distribution box. The continuous voltage fluctuations collected by the Internet of Things are unfolded in time sequence, and the abnormal trend of voltage fluctuations gathering in a single segment is extracted in the dynamic voltage back-tracing capture zone to form continuous data for subsequent spatial analysis.

[0051] The specific steps for extracting the abnormal trend of voltage fluctuations clustering into a single segment in the dynamic voltage foldback capture band are as follows:

[0052] First, voltage fluctuations are collected one by one from multiple voltage acquisition points installed at different locations on the busbar of the distribution box. Each record is then arranged into a continuous voltage fluctuation time-series chain according to the chronological order of its acquisition. To ensure that each fluctuation point reflects the true state at the time of acquisition, no compression or merging of the original voltage changes is performed during the construction of the voltage fluctuation time-series chain. Instead, the specific voltage values ​​of each sampling period are directly arranged according to the acquisition sequence, so that the time-series chain maintains the true rhythm of the busbar operation from beginning to end. During this process, to make the voltage fluctuation time-series chain more readable for subsequent analysis, the voltage fluctuations at the same acquisition point at different times are kept at a constant interval. This ensures that the amplitude, upward trend, downward trend, sudden jumps, and gradual shifts of voltage changes are all continuously represented in the voltage fluctuation time-series chain. For example, when the acquisition point records small increases, decreases, or repeated fluctuations at intervals of 10 milliseconds, 20 milliseconds, and 30 milliseconds, these change points are presented in an equidistant arrangement, allowing this implementation to directly observe whether there are rapid, repetitive, micro-amplitude fluctuations at specific locations on the busbar within a short period. Furthermore, different sampling points at different locations on the busbar often exhibit different response characteristics. Therefore, when constructing the voltage fluctuation time series, it is necessary to ensure that the voltage fluctuations at each sampling point correspond to each other within the same time period, so that the overall voltage profile of the entire busbar at the same moment can be displayed through lateral comparison. Through this construction method, the resulting voltage fluctuation time series not only fully preserves the actual voltage changes of the busbar at each location, but also provides a stable foundation for subsequent observation of foldback behavior on the time axis.

[0053] After obtaining the complete voltage fluctuation time sequence, this time sequence is mapped one by one to the specific spatial structure of the busbar, ensuring that each voltage fluctuation in the time sequence is clearly associated with its actual geometric location on the busbar, ultimately forming a dynamic voltage reflection capture band arranged around the busbar. During this process, the arrangement of each acquisition point on the busbar is confirmed segment by segment. For example, the busbar passes through multiple turning points from the inlet to the outlet. The length, bending angle, spacing between adjacent conductors, and distance from the cabinet wall of each segment are used as references when constructing the spatial relationship, ensuring that the voltage fluctuations not only exhibit continuity on the time axis but also have directionality in the spatial dimension. To ensure that the capture band accurately reflects the actual situation inside the busbar, all voltage fluctuations are arranged sequentially from the source end of the busbar and unfolded along the actual direction of the busbar, allowing the voltage changes of all acquisition points in each time slice to extend sequentially from the source end to the outlet in spatial order. Furthermore, when constructing the capture band, the spatial reversal points of voltage fluctuations are marked. When a voltage fluctuation at a certain moment suddenly returns from the end to near the source in the spatial position sequence, it is recorded as a possible reversal indicator. In this way, voltage reversals are presented in the form of abnormal spatial directional changes in the dynamic voltage reversal capture band, allowing reversal behavior previously hidden in single-point data to be identified. For example, when a certain intermediate section of the busbar shows fluctuations continuously converging towards the same position from adjacent acquisition points in multiple time slices, the capture band can accurately reflect and retain this continuous directionality, forming the basic data for spatial analysis.

[0054] After the dynamic voltage backtracking capture band is constructed, the spatial distribution of voltage in consecutive time slices within the capture band is compared segment by segment. By repeatedly confirming the voltage position in each time slice, it is possible to extract whether voltage fluctuations are showing a trend of converging towards a fixed segment. To ensure the repeatability of this trend identification, the voltage fluctuation positions in multiple consecutive time slices are compared point by point in the acquisition sequence, recording whether they are spatially converging towards the same narrow segment. For example, if a certain middle segment of the busbar repeatedly shows voltage fluctuation points gradually converging towards a position closer to that segment in more than a dozen adjacent time slices, then that segment is determined to be a potential area for voltage fluctuation focusing. In this process, it is necessary not only to confirm whether the spatial convergence is prolonged, but also to confirm whether the convergence points have a recurring characteristic of returning to the same segment in time. For example, if a convergence towards a certain segment occurs in the first time slice, the convergence trend continues in the second time slice, and the convergence recurs in the third to fifth time slices, these recurring directional behaviors constitute an abnormal clustering trend. When multiple sampling points in the capture band exhibit similar spatial convergence behavior over a relatively long period, it can be considered that voltage fluctuations are converging towards a single segment. This implies that there may be initial signs of repeated back-and-forth electromagnetic energy within this segment, and this early sign is precisely a key precursor to the subsequent formation of stagnation zones and resonant cavities. Through this trend extraction, it is possible not only to determine whether voltage fluctuations exhibit repetitive changes over time, but also to clarify whether they point to the same busbar location in space. This allows the convergence behavior, which traditional monitoring methods cannot capture, to be visualized in the capture band. This provides continuous data support for subsequent analysis of where electrical energy migrates, whether it will stagnate in a certain segment, and whether this stagnation will further evolve into resonance. This enables the entire dynamic voltage back-and-forth capture band to guide subsequent spatial analysis.

[0055] Energy migration path construction module: Utilize the obtained abnormal trends to construct energy migration paths, connect the abnormal trends point by point according to the spatial position of the parent row, so that the energy migration path presents a spatial form that converges towards the center, and limit the scope of the suspicious area based on this spatial form.

[0056] The energy migration path is made to exhibit a spatial pattern that converges towards the center, and the scope of the suspected area is defined based on this spatial pattern. The specific steps are as follows:

[0057] Each abnormal trend point obtained from the dynamic voltage foldback capture band is individually confirmed according to its corresponding actual busbar position at the time of its occurrence, ensuring that each trend point has a clear spatial location on the busbar structure. During this process, the overall geometry of the busbar is comprehensively analyzed, including its actual trajectory from the incoming line, its bending relationships at different locations, its relative height within the cabinet, the distance between adjacent metal components and the busbar, the spacing between busbar phases, and the interval between the busbar and the cabinet wall panel. This ensures that the location of each abnormal trend point can be accurately described in three-dimensional space. By precisely marking the location of each abnormal trend point, a set of abnormal trend points is formed that includes both temporal relationships and corresponding positions on the busbar structure, making the spatial correspondence of these points the basis for subsequent interconnections. To ensure that this set of locations accurately reflects the direction of abnormal trend propagation, each abnormal trend point is arranged in chronological order of its appearance, and its position is marked sequentially on the busbar structure in this order, giving the abnormal trend a traceable linear trajectory in the spatial dimension.

[0058] After spatially locating and sequentially arranging the abnormal trend points, these points are connected point-by-point according to their actual spatial positions on the busbar, ensuring that the connection order between each abnormal trend point completely follows the geometric distribution of the busbar. During the connection process, it is crucial to ensure that the connections accurately reflect the natural path of electrical energy propagation within the busbar. Therefore, when connecting two adjacent abnormal trend points, it is necessary to confirm whether they are located on the same busbar segment or connected through a natural bend in the busbar, and then connect them as a continuous path according to the actual direction of the busbar. In this process, if an abnormal trend point shows a significant spatial offset from its preceding and following counterparts, but exhibits a tendency to converge towards the same region in the time series, this offset is retained as a critical feature in the connection, ensuring that the energy migration path fully reflects the actual offset trajectory of electrical energy within the busbar structure, rather than simply reflecting a simple geometric straight-line connection. Through this point-by-point connection method, a coherent path reflecting the true flow direction of abnormal trends can be formed within the busbar structure, integrating the originally scattered abnormal trend points into a continuous migration trajectory.

[0059] After connecting the abnormal trend points, the entire connected trend path is continuously compared. By comparing the changes in distance, connection direction, and overall orientation between multiple abnormal trend points, it is determined whether the migration path exhibits a clear overall spatial pattern of gradually converging towards the central section. During this process, the overall pattern of the migration path is continuously observed. For example, if multiple abnormal trend points spatially show a gradual approach from the outside towards a certain middle section, or if the migration path repeatedly converges towards the same position at multiple stages, then the migration path is determined to exhibit a structural characteristic of convergence towards the central region. In further observation, when the distance between abnormal trend points continuously decreases over time, and multiple abnormal trend points converge towards the same position on the main line, it is considered that the energy migration path is converging along a certain section of the main line, thus forming a continuous and directional converging trend. Through this judgment of the overall pattern, the accuracy of identifying potential energy concentration points within the main line can be significantly improved, transforming the originally dispersed trends that only exist in time and location points into spatial paths with overall regularity.

[0060] After confirming that the migration path exhibits a spatial convergence towards the center, the actual position of the convergence segment formed by the focal area of ​​the migration path within the busbar is clearly marked, and this convergence segment serves as the basis for defining the suspected area. During this process, the start point, end point, width, and proximity of the convergence segment on the busbar are clearly marked, ensuring that the suspected area is clearly defined not only in the linear direction of the busbar but also in its lateral position, longitudinal height, and distance from adjacent components. By precisely defining the focal area of ​​the migration path, potential factors causing voltage backflow can be identified in that area, such as micro-gap changes, local impedance abrupt changes, unseen surface damage, or material stress concentration, laying the foundation for subsequent assessments of whether a stagnation zone will form. Since the extent of the suspected area is directly determined by the convergence pattern of the migration path, this definition method not only has high accuracy but also reflects the development direction and concentration characteristics of abnormal trends within the busbar, enabling this implementation to identify potential risk points at an early stage.

[0061] Delineation module for stagnant zones: Delineates stagnant zones within a defined suspicious area, and traces the trajectory of changes in energy residence time and energy density through continuous voltage fluctuations, giving the stagnant zones a quantifiable dynamic morphology, providing a basis for subsequent evolution identification;

[0062] By depicting the trajectory of energy residence time and energy density changes through continuous voltage fluctuations, the residence region can be given a quantifiable dynamic form. The specific steps are as follows:

[0063] The suspected area defined in the previous stage is expanded segment by segment within the actual location of the busbar structure. Continuous voltage fluctuations at all voltage sampling points within the suspected area are extracted in different time slices. Following the linear orientation of the busbar structure, the voltage changes at each sampling point are compared hourly to clearly show whether voltage fluctuations persist at each location within the area across consecutive time slices. During the extraction process, fluctuations with relatively stable voltage amplitudes, slow changes, or recurring returns to the same position are all included in the analysis scope of the suspected area. These fluctuations are arranged point by point according to their chronological order of occurrence, forming a continuous trajectory from the start to the end of the time frame—the energy residence time change trajectory. To determine whether energy stagnates in a certain segment, the same location is observed to repeatedly exhibit fluctuation values ​​that fall back, pause, and then increase again across multiple time points. For example, if the fluctuation value in a certain time slice approaches that location, does not move too far away in the next time slice, and continues to fluctuate repeatedly near that location in subsequent time slices, it can be preliminarily considered that this location has the basic characteristics of energy stagnation. In this way, all voltage fluctuations within the suspected area can form a continuous trend with dwell characteristics in the time dimension, providing a continuous basis for the subsequent identification of the lingering section.

[0064] Based on the continuous fluctuation extraction described above, the voltage fluctuation changes at each location in adjacent time slices are compared. By comparing the speed, amplitude, persistence, and repeatability of the fluctuation changes, it is further determined which locations within the suspicious area exhibit obvious signs of energy density accumulation. To visualize the trajectory of energy density changes, the spatial distribution density of each voltage fluctuation is continuously observed. For example, when a location repeatedly exhibits voltage fluctuation backflow, amplitude increase, and local peak superposition within a short period, it is considered that the energy density at that location is significantly higher than that of the surrounding area during that time period. In further processing, the amplitude changes of fluctuations in adjacent time slices are compared extensively. For example, if a significant increase in amplitude occurs in one time slice, and this amplitude does not immediately dissipate or shift in the next time slice but continues to maintain a high level at that location, it is considered that there is an energy accumulation trend at that location. In addition, it is necessary to continuously observe the locations where fluctuations repeatedly converge. For example, if fluctuations from multiple different sampling points gradually approach the same location in multiple time slices and maintain the fluctuation within a certain time range after convergence, this location is an important node in the energy density change trajectory. By comprehensively observing these trajectories, the changes in energy density within the suspected area over different time periods can be clearly depicted, presenting the existence, rate, and location of energy accumulation as continuous trajectories. In this way, previously dispersed voltage fluctuations can be integrated into continuously observable density change curves, making the energy accumulation process within the suspected area quantifiable.

[0065] Subsequently, using the energy residence time and energy density change trajectories obtained in the first two steps, all locations exhibiting clear residence and accumulation characteristics are cross-checked. Within the suspected area, a final residence segment is delineated, ensuring that this segment not only has a definite spatial location but also possesses comprehensive characteristics such as residence time spanning multiple time slices, continuously increasing energy density, and repeated convergence of fluctuations. When delineating residence segments, the continuity between multiple locations is comprehensively considered. For example, on the busbar structure, if multiple adjacent locations exhibit clear energy residence characteristics across multiple time slices, these adjacent locations should be collectively classified as a residence segment, rather than selecting only a single point. To further quantify the dynamic morphology of the residence segment, the energy residence time, peak energy density, energy density distribution width, and energy density change rate of the segment in different time slices are all recorded in a continuous trajectory, allowing the residence segment to display its dynamic behavior in the form of a time curve. For example, when a stagnation zone exhibits a narrow energy retention range at a certain moment, followed by an expansion of the retention range over multiple time slices, this can be recorded as a lateral expansion trajectory of the stagnation zone. Similarly, when the energy density at a certain location within a stagnation zone continuously increases over several time slices, this can be recorded as a longitudinal energy enhancement trajectory. Finally, after all trajectories are plotted, they are integrated into a dynamic form of the stagnation zone, allowing the zone to accurately represent the energy retention patterns and accumulation trends within that region. This dynamic form not only possesses spatial directionality and temporal continuity but also visually demonstrates the overall evolutionary trend of energy within the region, providing an accurate basis for subsequent judgments on whether the stagnation zone will further evolve into a resonant zone.

[0066] Resonance Evolution Line Analysis Module: Constructs a resonance evolution line based on the dynamic morphology of the stagnant section, tracks the changes in cyclic superposition intensity along the resonance evolution line, continuously unfolds the periodic enhancement behavior, and forms a triggering benchmark for anomaly control;

[0067] By tracking the changes in cyclic superposition intensity along the resonance evolution line, the periodic enhancement behavior is continuously unfolded, and a triggering reference for anomaly control is formed. The specific steps are as follows:

[0068] Within the defined retention segment, the energy residence time and energy density variation trajectories in each time slice are unfolded segment by segment. This allows the energy behavior of the retention segment in different time slices to be fully displayed in a sequential manner, and the basic nodes required to construct the resonant evolution line are determined from these trajectories. In this process, information such as the expansion and contraction, density fluctuations, residence position shifts, peak accumulation, and peak duration of the retention segment in consecutive time slices are unfolded one by one, allowing the temporal behavior of the retention segment to be clearly presented in a time-progressive manner. Furthermore, in this sequential unfolding process, special attention is paid to points within the retention segment that show significant fluctuations, declines, and then intensifications in consecutive time slices, as these points often represent the initial stages of energy cyclical superposition in local regions. Through such point-by-point observation, a basic node group consisting of multiple key change points can be formed, enabling these nodes to serve as structural support points for constructing the resonant evolution line. These nodes not only clearly indicate the accumulation and decline relationship of the stagnation segment in each time slice, but also further demonstrate whether the stagnation segment shows an evolutionary direction from weak to strong, from loose to compact, and from diffusion to focus, laying the foundation for establishing an evolutionary path with continuous logic.

[0069] Based on the aforementioned basic node group, these nodes are continuously connected in series according to their spatial location and temporal order within the retention zone. This ensures that the connections between nodes reflect the actual propagation direction and reinforcement patterns of energy within the retention zone. When connecting these nodes, the relative spatial position, temporal order of appearance, and the magnitude of changes in energy residence time and energy density trajectory of each node are comprehensively considered, ensuring that each connection reflects the dynamic continuity between nodes. For example, if a node has a short residence time in a previous time slice but a significantly longer residence time in a subsequent time slice, the connection between this node and its successor is established as a trend of increasing energy residence time within the retention zone. Similarly, if a node exhibits a slight increase in local energy density in a previous time slice but a sharp increase in density in a subsequent time slice, the connection between these two nodes is expressed as a trend of rapid energy superposition within the region. By connecting these points one by one, a path that truly reflects the continuity of energy behavior can be formed. This path gradually forms a continuous evolutionary framework with clear directionality, coherence, and time progression, and provides a directly usable continuous structure for the final construction of the resonant evolution line.

[0070] After completing the aforementioned continuous evolution framework, the energy behavior within the stagnation zone is further observed based on this framework. By extracting the patterns of node changes on the framework, key trends reflecting periodic enhancement behavior are identified, and a resonant evolution line is constructed using these key trends as the main lines. In this process, the focus is on node relationships on the framework that repeatedly exhibit energy backflow, energy refocusing, repeated superposition of local peaks, and periodic increases in density across multiple time slices. These recurring enhancement behaviors are depicted along the framework as continuous curves, presenting these periodic enhancement behaviors with clear trend trajectories. Furthermore, the periodic contraction and expansion, periodic focusing and outward expansion morphological changes exhibited by the stagnation zone across multiple time slices need to be integrated into the evolution line, so that the evolution line not only reflects changes in the intensity of energy accumulation but also represents the periodic oscillation characteristics of the stagnation zone in spatial morphology. By describing these continuous aspects, the periodic enhancement behavior within the stagnant section can be continuously unfolded along the resonance evolution line. This allows the implementation to identify whether the stagnant section is evolving into resonance behavior with closed-loop characteristics, providing a strong structural basis for subsequent judgment on whether abnormal control intervention is needed.

[0071] After the resonant evolution line is constructed, key characteristics such as amplitude changes, repetition frequency, peak density, and enhancement acceleration of the periodic enhancement behavior on the resonant evolution line are comprehensively confirmed. By comparing these characteristics with the overall dynamic morphology of the stagnant section, a triggering benchmark is formed to trigger abnormal control. This benchmark can accurately indicate whether the resonant evolution has reached a state requiring active intervention. When determining the triggering benchmark, special attention is paid to phenomena in the resonant evolution line that show rapid increases in local density, significantly accelerated periodic enhancement frequency, and significantly prolonged peak duration, as these phenomena often indicate that the stagnant section is approaching a critical state that may form a closed energy cavity. In addition, it is necessary to comprehensively judge the overall trend of change on the evolution line. For example, when the resonant evolution line shows a continuous superposition of enhancement trends in multiple consecutive time slices without a significant attenuation stage, this continuous enhancement state can be used as the core indicator of the triggering benchmark. Through such comprehensive settings, the resonant evolution line not only becomes an important reference for observing the superposition of energy cycles, but also provides precise triggering opportunities for the subsequent introduction of dynamic suppression measures, transforming the entire protection process from passive response to active prevention.

[0072] Dynamic suppression path traction module: Based on the triggering reference, a dynamic suppression path is introduced. A phase traction ring is arranged inside the dynamic suppression path to disperse the cyclic superposition intensity along the resonance evolution line segment by segment according to the phase sequence, so that the energy is pulled out from the stagnant section, thereby blocking the formation of the closed energy cavity and realizing the dynamic suppression of sudden structural damage to the distribution box.

[0073] The cyclic superposition intensity along the resonance evolution line is dispersed segment by segment in phase order, so that energy is drawn out from the stagnant segment. The specific steps are as follows:

[0074] Real-time comparison is performed on the established triggering benchmark along the resonance evolution line. When the cyclic superposition intensity change on the resonance evolution line satisfies any one or more of the characteristics of the enhancement threshold, enhancement duration, enhancement acceleration trend, or peak density corresponding to the triggering benchmark in a continuous time slice, a dynamic suppression path is immediately established between the outer edge of the stagnation section and the adjacent busbar section, so that the suppression path has a clear spatial orientation on the actual structure of the busbar. When establishing the dynamic suppression path, referring to the dynamic morphology of the stagnation section constructed in the previous stage, the section with the longer energy residence time, higher energy density, and most prominent cyclic superposition performance within the stagnation section is taken as the starting point of the dynamic suppression path, so that the dynamic suppression path can unfold closely along the energy focusing center formed by the stagnation section. Subsequently, according to the actual spatial structure of the busbar extending outward from the stagnation section, the extension direction of the dynamic suppression path is determined along the actual orientation, bending direction, and distance from other components of the busbar, so that the dynamic suppression path can not only conform to the structural characteristics of the busbar, but also provide a continuous spatial channel for the subsequent segment-by-segment dispersion of cyclic superposition intensity. In this process, the direction of the dynamic suppression path needs to avoid the area within the stagnation zone where energy has already converged, so that the path becomes the primary pathway to guide energy out of the stagnation zone, thereby providing a spatial basis for subsequent phase traction.

[0075] After the dynamic suppression path is formed, a series of phase traction rings are arranged segment by segment along the path, according to the temporal and phase changes of energy along the resonant evolution line. Each phase traction ring is designed to guide the cyclic superposition intensity from the stagnant section to the outside of the path point by point. When arranging the phase traction rings, the phase relationship of the cyclic superposition intensity along the resonant evolution line is first determined, such as the high points, low points, turning points, sustained enhancement points, and enhancement drop points of cyclic superposition in continuous time slices. This ensures that each change node corresponds to a specific phase traction ring on the dynamic suppression path. This arrangement of phase traction rings spatially mirrors the phase sequence of the resonant evolution line, allowing energy to be drawn sequentially within the dynamic suppression path according to the same phase rhythm as the resonant evolution line. Meanwhile, during the deployment process, the spacing and sequence of the phase traction rings are adjusted according to the difference in energy density between the stagnation section and the starting point of the dynamic suppression path. This allows the phase traction rings closer to the stagnation section to be set up more densely, so as to form a stronger traction capability for the energy-dense area, while the phase traction rings farther away from the stagnation section can be appropriately spaced out, so that the dynamic suppression path can gradually reduce the density of energy transfer in the subsequent stages, thereby forming an energy traction gradient from strong to weak.

[0076] After the phase traction rings are deployed, the cyclic superposition intensity along the resonance evolution line within the stagnation section is mapped segment by segment to the phase traction rings within the dynamic suppression path. This allows each phase traction ring to actively carry the energy of its corresponding stage according to the phase sequence of the resonance evolution line. When the corresponding cyclic superposition intensity on the resonance evolution line in a certain time slice reaches or approaches the trigger reference, it can be transferred sequentially from the center of the stagnation section to the outside of the dynamic suppression path in phase sequence. During this process, each phase traction ring must receive the energy of the corresponding phase from the stagnation section and immediately export the energy to the next ring in the traction direction, thus forming a continuous segment-by-segment dispersion chain. As the phase of the cyclic superposition intensity continuously changes, the energy will continuously move outward in a segment-by-segment manner within the suppression path, so that the energy within the stagnation section no longer remains in its original position, nor does it further superimpose along the original path. Instead, it is gradually carried out of the stagnation section by a phase chain with a clear sequence and a clear direction of advancement. This mapping method allows the energy to be dispersed in a timely manner when the resonant evolution line is in the enhancement stage, avoiding the formation of a closed loop structure within the stagnant section, thereby effectively interrupting the energy concentration trend.

[0077] After the energy traction process achieves a stable, segmented advancement effect, the energy dispersed along the dynamic suppression path is further diffused, causing it to gradually attenuate over a larger area of ​​the busbar structure. This prevents the energy within the stagnant section from returning and forming a closed energy cavity, thus completely blocking its formation. During this process, the end of the dynamic suppression path is ensured to have sufficient extensibility, allowing the energy drawn along the path to naturally dissipate into a larger space after reaching the outer area of ​​the busbar, gradually dispersing to a lower density over multiple time slices, preventing it from forming a reverse flow or refocusing tendency. Furthermore, the entire suppression process is continuously observed. When the energy density within the stagnant section gradually decreases, the energy residence time significantly shortens, and the peak value of the cyclic superposition shows a significant attenuation, it is confirmed that the dynamic suppression path has successfully blocked the formation of the closed energy cavity, effectively suppressing the risk of sudden structural damage to the distribution box busbar in this area.

[0078] Through the above steps, the dynamic suppression path can intervene in a timely manner when the triggering reference conditions are met, and gradually remove the cyclic superposition intensity inside the stagnant section by phase traction, thereby achieving proactive intervention in the energy concentration trend and effectively suppressing sudden structural damage to the distribution box busbar.

[0079] This invention establishes a continuously unfolding dynamic voltage reflection and capture zone inside the distribution box, synchronizing the time series of voltage fluctuations with the spatial location of the busbar. This allows for the early identification of accumulation trends hidden in weak fluctuations before external signs such as temperature rise, noise, or current distortion appear on the busbar. Through the step-by-step construction of energy migration paths, stagnation sections, and resonant evolution lines, key processes such as energy convergence, residence, and amplification within the busbar are continuously visualized. This transforms potential damage from previously imperceptible internal behavior into a continuously trackable evolutionary trajectory, thereby enabling early perception of the formation process of enclosed energy cavities and early prediction of sudden risks.

[0080] This invention introduces a dynamic suppression path under the action of a triggering reference, dispersing the gradually increasing cyclic superposition behavior in the stagnant section to different sections of the phase traction ring. This allows concentrated energy to be directed out along the traction sequence, preventing energy from repeatedly accumulating in a confined structure and triggering explosive damage. In this way, the stress, field strength, and energy density inside the busbar are always maintained within the material's tolerance range. The highly concealed and rapidly accumulating energy coupling effect is actively weakened, transforming the high-risk sudden damage process into a sustainable and controllable stable state, thereby significantly improving the overall safety and controllability of the distribution box operation.

[0081] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A multi-dimensional online status monitoring system for distribution boxes based on the Internet of Things, characterized in that, It includes a dynamic voltage back-and-forth capture module, an energy migration path construction module, a stagnation zone delineation module, a resonance evolution line analysis module, and a dynamic suppression path traction module; Dynamic voltage back-tracing capture module: A dynamic voltage back-tracing capture zone is established around the busbar structure of the distribution box. The continuous voltage fluctuations collected by the Internet of Things are unfolded in time sequence, and the abnormal trend of voltage fluctuations gathering in a single segment is extracted in the dynamic voltage back-tracing capture zone. Energy migration path construction module: Constructs energy migration paths using the obtained abnormal trends, connects the abnormal trends point by point according to the spatial position of the parent row to obtain the spatial shape of the energy migration path, and limits the scope of the suspicious area based on the spatial shape. Delineation module for stagnant zones: Delineates stagnant zones within a defined suspicious area, and traces the trajectory of changes in energy residence time and energy density through continuous voltage fluctuations to obtain the dynamic morphology of the stagnant zones; Resonance Evolution Line Analysis Module: Constructs a resonance evolution line based on the dynamic morphology of the stagnant section, tracks the changes in the cyclic superposition intensity along the resonance evolution line, and simultaneously forms a triggering reference for anomaly control; Dynamic suppression path traction module: Based on the triggering reference, a dynamic suppression path is introduced, and a phase traction ring array is arranged inside the dynamic suppression path to disperse the cyclic superposition intensity along the resonance evolution line segment by segment in phase order to achieve energy traction.

2. The IoT-based multi-dimensional online status monitoring system for distribution boxes according to claim 1, characterized in that, The steps for establishing a dynamic voltage foldback capture zone around the busbar structure of the distribution box include the following sub-steps: The voltage fluctuations collected from multiple voltage acquisition points installed at different locations on the busbar of the distribution box are acquired one by one and arranged into a continuous voltage fluctuation time sequence chain according to the order of acquisition time. The voltage fluctuation timing chain is mapped one by one to the spatial structure of the busbar, and a dynamic voltage back-return capture zone is formed along the busbar direction. The voltage fluctuation points that appear to fall back in space are marked in the dynamic voltage back-return capture zone. By comparing the spatial distribution of voltage in continuous time slices within the dynamic voltage foldback capture band, it is possible to extract whether voltage fluctuations tend to cluster towards a fixed segment. When voltage fluctuations in multiple time slices repeatedly cluster towards the same segment in space, the corresponding segment is identified as a potential area for voltage fluctuation focusing.

3. The IoT-based multi-dimensional online status monitoring system for distribution boxes according to claim 2, characterized in that, The steps for constructing energy migration paths and defining the scope of suspected areas include: Each abnormal trend point obtained from the dynamic voltage return capture band is confirmed one by one according to the actual position of the busbar when it appears, and its position is calibrated in three-dimensional space according to the geometric orientation of the busbar, so that each abnormal trend point forms a set of trend points with spatial orientation. The trend points are connected one by one on the mother row structure according to the order in which the abnormal trend points appear, and adjacent abnormal trend points are connected in a continuous manner according to the actual direction of the mother row to form an energy migration path. By continuously comparing the energy migration paths after the connection, and comparing the changes in the spacing between abnormal trend points with the overall direction change, it is confirmed whether the energy migration paths gradually converge towards the central section of the main row in space, forming a spatial form with convergence characteristics. Based on the convergence characteristics of the focused section formed inside the busbar, and combined with the starting point, ending point and spatial relationship of the focused section, the actual range of the focused section on the busbar is marked to obtain the range of the suspected area.

4. The IoT-based multi-dimensional online status monitoring system for distribution boxes according to claim 3, characterized in that, When confirming the convergence pattern of the energy migration path, the positional changes of multiple consecutive abnormal trend points in different time slices are compared to confirm whether the abnormal trend points have the characteristic of repeatedly falling back to the same focal segment in time, so that the scope of the suspicious area can be finally limited only under the condition of satisfying both spatial convergence and temporal repetition.

5. The IoT-based multi-dimensional online status monitoring system for distribution boxes according to claim 3, characterized in that, The steps for plotting the trajectory of energy residence time changes versus energy density changes through continuous voltage fluctuations include: The defined suspicious area is expanded segment by segment in the actual location of the busbar structure. The continuous voltage fluctuations of all voltage acquisition points within the suspicious area are compared hourly in different time slices. Voltage fluctuation points that repeatedly stay or fall back at the same position are extracted from the time slices, so that a continuous fluctuation trend with dwelling characteristics is formed in the middle of the suspicious area, forming an energy dwell time change trajectory. Based on the continuous fluctuation trend, the voltage fluctuation amplitude, persistence and repeatability at each location in adjacent time slices are compared. By analyzing whether the fluctuations repeatedly flow back, maintain local peaks or superimpose and enhance, the location with energy density accumulation characteristics in the middle of the suspicious area is determined, and the corresponding energy density change trajectory is formed. Cross-confirm the energy residence time change trajectory with the energy density change trajectory, continuously calibrate adjacent locations that simultaneously possess residence and accumulation characteristics, and delineate residence segments with temporal continuity and spatial coherence within the suspicious area based on the continuous performance of the calibrated locations in multiple time slices.

6. The IoT-based multi-dimensional online status monitoring system for distribution boxes according to claim 5, characterized in that, The dynamic morphology of the stagnation zone includes energy residence time, peak energy density, energy density distribution width and energy density change rate, and is continuously recorded by the lateral expansion trajectory and longitudinal energy enhancement trajectory of the stagnation zone in multiple time slices.

7. The IoT-based multi-dimensional online status monitoring system for distribution boxes according to claim 5, characterized in that, The steps of tracking the changes in cyclic superposition intensity along the resonance evolution line and simultaneously forming a triggering reference for anomaly control include: Within the defined retention zone, the energy residence time change trajectory and energy density change trajectory in each time slice are unfolded segment by segment, and key points that show energy decline and re-enhancement in continuous time slices are extracted as basic node groups. The basic node groups are connected in sequence according to their spatial location and temporal order within the retention section to form a continuous evolutionary skeleton. Based on the evolutionary framework, key trends reflecting periodic enhancement behavior are identified, and these key trends are depicted as resonant evolution lines in the form of continuous curves. The periodic enhancement behavior is then unfolded along the resonant evolution lines. The enhancement amplitude, repetition frequency and peak duration on the resonant evolution line are comprehensively confirmed, and a triggering benchmark for active anomaly control is established in combination with the dynamic morphology of the stagnation section to determine whether the stagnation section has reached the critical state for intervention.

8. The IoT-based multi-dimensional online status monitoring system for distribution boxes according to claim 7, characterized in that, The establishment of the triggering benchmark includes threshold comparison of the amplitude change rate, peak density region and enhancement acceleration of the periodic enhancement behavior on the resonance evolution line. When the enhancement amplitude continues to rise in multiple consecutive time slices without a decay phase, the corresponding continuous enhancement state is determined as the triggering condition for active abnormal control.

9. A multi-dimensional online status monitoring system for distribution boxes based on the Internet of Things according to claim 7, characterized in that, The steps to achieve energy traction by dispersing the cyclic superposition intensity along the resonance evolution line segment by segment in phase order include: On the resonant evolution line, the changes in cyclic superposition intensity are compared in real time according to the established triggering benchmark. When the enhancement threshold, duration or peak density characteristics are met, a dynamic suppression path is established between the outer edge of the stagnation section and the adjacent busbar section. The starting point and direction of the dynamic suppression path are determined according to the dynamic morphology of the stagnation section. Within the dynamic suppression path, phase traction rings are arranged according to the time sequence and phase change relationship of energy on the resonance evolution line, and an energy traction gradient is formed. The cyclic superposition intensity along the resonance evolution line within the stagnant section is mapped segment by segment to the phase traction loop array within the dynamic suppression path; During the energy traction process, the energy diffusion state on the dynamic suppression path is continuously observed. When the energy density decreases, the residence time shortens, and the cyclic superposition peak weakens, it is confirmed that the formation of the closed energy cavity is effectively blocked.

10. A multi-dimensional online status monitoring system for distribution boxes based on the Internet of Things according to claim 9, characterized in that, During the deployment of the phase traction ring array, the spacing and sequence of the phase traction rings are adjusted according to the energy density distribution difference between the stagnation section and the starting point of the dynamic suppression path. The phase traction rings close to the stagnation section are arranged in a dense manner, while the phase traction rings far from the stagnation section are arranged in a gradually sparse manner, forming a continuously decreasing energy traction gradient.

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