Control method and system for emergency energy storage power supply

Abstract

This invention discloses a control method and system for emergency energy storage power supplies, relating to the fields of electrical automation and energy storage control technology. The method includes the following steps: S001, establishing a coupled observation baseline between the electromagnetic field and the control bus signal; by constructing a reflection probe sequence with the same frequency as the control bus signal, collecting and reconstructing the bit edge signals generated during communication, and generating an interference map; S002, based on the interference map, analyzing the start and end positions of the data frame, bit sampling nodes, and edge transition trajectories, extracting unique phase fingerprints, and constructing an interference feature library to identify interference sources and their frequency bands that cause signal aliasing and sequence inversion. This invention achieves interference source identification and signal reconstruction by constructing an interference map using reflection probes, extracting phase fingerprints, and establishing an interference feature library; combined with time delay calibration, reliability assessment, and conjugate protection, it constructs a multi-path redundancy control mechanism to ensure the control stability and execution safety of the emergency energy storage system under electromagnetic interference environments.

Description

Technical Field

[0001] This invention relates to the fields of electrical automation and energy storage control technology, specifically to control methods and systems for emergency energy storage power supplies. Background Technology

[0002] Emergency energy storage power supply control refers to the comprehensive, refined, and dynamic management and control of energy flow, power distribution, and safety protection of energy storage systems under emergency scenarios such as sudden power outages, extreme disasters, communication interruptions, and power system instability. This is achieved by addressing the core requirements of energy storage power supply in terms of power supply stability, battery life, and equipment safety, relying on the multi-dimensional perception and intelligent algorithm optimization of the smart grid architecture. First, a high-precision multi-parameter monitoring module is built at the software layer to collect key operational data in real time, such as remaining battery charge (SOC), terminal voltage, operating temperature, and load power, forming a self-calibrating, visualized data closed loop. This fundamentally solves the problems of limited perception dimensions and delayed decision-making response in traditional energy storage control. Second, based on the hierarchical scheduling logic of the smart grid and the priority of emergency tasks (such as medical equipment, communication support, and basic lighting), a dynamic power distribution algorithm is designed to intelligently adjust the output power according to the load level. In situations of energy storage capacity shortage or power outage, priority is given to ensuring the continuous operation of critical loads, thereby achieving a balance between high efficiency and strategic planning in energy distribution. Meanwhile, the control process incorporates multi-dimensional protection sub-modules for overcharge, over-discharge, over-temperature, and short circuit, combined with multi-layered safety thresholds and adaptive diagnostic mechanisms. This enables millisecond-level response, rapid isolation, and self-healing recovery upon detecting abnormal operating conditions, effectively preventing high-risk events such as energy backflow, cell thermal runaway, and link overload. Furthermore, the system supports deep interconnection with the smart grid cloud platform, allowing real-time monitoring of power status and dynamic adjustment of operating parameters via mobile or remote terminals. Customized control strategies can be implemented based on different emergency scenarios (such as field rescue, home emergency, and industrial backup). Practical verification has shown that this method significantly improves the power supply efficiency, dispatch response speed, and battery cycle life of energy storage power supplies in a smart grid collaborative environment, achieving a unified balance of safety, stability, and intelligence in emergency power supply. This provides solid technical support for energy resilience and continuous power supply in complex environments.

[0003] The existing technology has the following shortcomings: In existing technologies, emergency energy storage power supplies generally rely on a control bus to achieve real-time communication and coordinated energy scheduling among multiple nodes in scenarios where multiple devices operate in parallel. However, when high-power inverters, high-frequency charging modules, or fast switching devices are operating simultaneously within the system, the resulting strong electromagnetic fields can easily cause high-frequency interference to the control bus signals, distorting the originally stable communication waveforms. Under the coupling effect of electromagnetic interference and signal transmission paths, the control bus signals may superimpose anti-aliasing waveforms with phase errors, disrupting the bit sequence of data packets. Such waveforms are very similar in form to normal signals and are easily misinterpreted by the main control chip as legitimate communication data, thus triggering incorrect command responses in the emergency energy storage power supply control logic and causing a reversal of the charging and discharging logic. For example, the system may be mistakenly triggered to reverse charge when it should be discharging to supply energy, or misidentified as discharging mode while charging, resulting in an energy flow contrary to the control objective and causing serious consequences such as reverse voltage injection from the bus, over-discharge of cells, and inverter breakdown.

[0004] 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

[0005] The purpose of this invention is to provide a control method and system for emergency energy storage power supplies to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a control method and system for an emergency energy storage power supply, comprising the following steps: S001, establish the coupled observation baseline of electromagnetic field and control bus signal, and collect and reconstruct bit edge signal generated during communication by constructing a reflection probe sequence with the same frequency as the control bus signal, and generate an interference mapping map containing energy distribution and phase delay characteristics. S002, based on the interference map, analyzes the start and end positions of the data frame, bit sampling nodes and edge transition trajectories, extracts unique phase fingerprints, and constructs an interference feature library to identify the interference sources and their frequency bands that cause signal aliasing and sequence inversion; S003, based on the interference feature library, establish a time delay reversible calibration chain, reverse register the sampling clock and the decision threshold, inject phase differential perturbation at both ends of the control bus, realize waveform stripping and data bit order restoration, and restore the communication channel synchronization state; S004, in the calibrated communication channel, perform dual-track comparison, generate instruction confidence weight based on the comparison result of the key frame of the previous control cycle and the current frame, implement soft isolation for low-weight frames, and prevent interference instructions from entering the control chain; S005, based on the evolution of the credibility weight, performs dynamic regulation, triggers phase conjugate protection and Hamilton variational routing mechanism, drives the majority of voters to perform weighted correction of the control chain, and realizes closed-loop unification of interference identification, data calibration, causal verification and control decision.

[0007] Preferably, step S001 includes: After constructing a sequence of reflection probes with the same frequency as the control bus signal, the observation area is selected to cover the communication line between the main controller and the end load. An initial model of electromagnetic distribution is established by spatial scanning to locate the high-frequency radiation area. The bit edge signals on the control bus are acquired and normalized. A waveform slice set is constructed according to a unified time base. The signal stream is reconstructed by delay superposition. The timing features and local energy amplitude of the transition points are extracted to form a multi-dimensional bit edge descriptor set. The description subset is interpolated and mapped to the initial model to generate an interference density map, and the time dimension is introduced to form an interference mapping map. Based on the disturbance frequency, disturbance amplitude, and edge deformation ratio of each point in the interference mapping diagram, the phase response delay is calculated, and the phase response features are clustered to construct the electromagnetic interference path, thereby realizing the localization of the interference source and the identification of nodes affected by the communication path.

[0008] Preferably, after constructing the interference mapping map, the phase drift and time deviation information of the spatial coordinate points are extracted by the phase fluctuation continuity between adjacent reflection probe detection points, a phase response table is generated, and clustering and sorting are performed based on the phase response characteristics to identify the divergence starting point, wavefront propagation range and the nodes that play a role in the communication path of the electromagnetic interference.

[0009] Preferably, step S002 includes: After generating the interference map, waveform data from multiple complete communication cycles are selected. Based on the energy disturbance density map and phase response table, the interference-affected area of ​​the signal edge is delineated and a jump event index chain is established. Representative transition behaviors are extracted from the index chain and superimposed with corresponding electromagnetic disturbance data to construct a feature set containing time displacement, phase drift, energy disturbance and bit number. The feature combinations are inductively encoded to construct a phase transition trajectory map and form a unique set of phase fingerprints; Using the phase fingerprint index table as the core, an interference feature dataset is constructed, and statistical analysis is used to achieve accurate location of electromagnetic interference sources and frequency band attribution.

[0010] Preferably, step S003 includes: Based on the phase fingerprint index in the interference feature library, the interfered bits in the current communication cycle are marked, a sampling window is established and resampling is performed, and the timing of the sampling points is adjusted back to the standard position based on the time offset information. After completing the reverse registration of the sampling clock, the amplitude curve is reconstructed by combining the edge amplitude fluctuation characteristics, and the optimal decision threshold is calculated to correct the misjudged edge state. Phase differential perturbation signals are injected at both ends of the control bus to achieve interference branch stripping and main signal trajectory restoration; The calibrated transition points are reordered and aligned with the frame structure fields to complete the reordering of communication data bits and restore the synchronous and stable state of the communication channel.

[0011] Preferably, the phase differential perturbation signal consists of two conduction pulses with opposite polarities and amplitudes lower than the main signal. The injection frequency is consistent with the main communication signal, which is used to create edge timing misalignment at the signal receiving end in order to identify and remove the interference branch.

[0012] Preferably, step S004 includes: Extract the key data frames that have been calibrated and verified in the previous control cycle as reference templates, establish a timing control group with the communication frames that match the structure in the current cycle, compare the transition characteristic parameters bit by bit, and generate a set of deviation values. The deviation value set is quantified and calculated to construct the instruction credibility weight evaluation matrix. The credibility weight of the current communication frame is calculated and compared with the set threshold. For communication frames below a threshold, perform soft isolation and establish a high-risk marking and re-comparison mechanism based on weight performance within a continuous period; A new reference frame sequence is constructed based on communication frames with high credibility. The most representative frame is selected by a sliding window, and the reference information is updated cyclically to form a dynamic credibility evolution chain.

[0013] Preferably, the soft isolation operation includes delaying the triggering time of the communication frame in the control chain and introducing an acknowledgment condition before execution. The update of the reference frame is based on the communication frames with the highest similarity in multiple cycles, and the frame with the strongest edge feature consistency is selected as the reference benchmark for the next cycle through a sliding window mechanism.

[0014] Preferably, step S005 includes: Based on the evolution of instruction confidence weights within a continuous cycle, control frames with declining weights are identified and marked as control targets that need to be corrected. Phase conjugate protection is then performed to stabilize their control effects. Based on Hamiltonian variational logic analysis, the energy cost and response efficiency of each logic node in the control path are analyzed, a control path weight matrix is ​​constructed, and the optimal path is activated to replace the original control link. For control frames with the same logic bit in multiple cycles, perform majority voting calculation, allocate voting coefficients according to the credibility evolution trend, and output weighted decision instructions; For instruction groups affected by interference, adaptive weighting is performed, and the control strategy is optimally adjusted by combining battery status and load information to achieve closed-loop optimized execution of charging and discharging logic.

[0015] The control system for emergency energy storage power supplies includes an interference modeling module, a feature extraction module, a time delay calibration module, a signal filtering module, and a dynamic control module. The interference modeling module establishes a coupled observation baseline between the electromagnetic field and the control bus signal. By constructing a sequence of reflection probes with the same frequency as the control bus signal, it collects and reconstructs the bit edge signals generated during communication, and generates an interference map containing energy distribution and phase delay characteristics. The feature extraction module, based on the interference map, analyzes the start and end positions of the data frame, bit sampling nodes and edge transition trajectories, extracts unique phase fingerprints, and constructs an interference feature library to identify interference sources and their frequency bands that cause signal aliasing and sequence inversion. The delay calibration module establishes a reversible delay calibration chain based on the interference feature library, performs reverse registration between the sampling clock and the decision threshold, injects phase differential perturbations at both ends of the control bus, realizes waveform stripping and data bit order restoration, and restores the synchronization state of the communication channel. The signal filtering module performs a dual-track comparison in the calibrated communication channel. Based on the comparison results between the key frame of the previous control cycle and the current frame, it generates instruction confidence weights and implements soft isolation for low-weight frames to prevent interference instructions from entering the control chain. The dynamic control module performs dynamic control based on the evolution of the credibility weights, triggers phase conjugate protection and Hamilton variational routing mechanism, drives the majority of voters to perform weighted correction of the control chain, and realizes closed-loop unification of interference identification, data calibration, causal verification and control decision-making.

[0016] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention constructs a reflection probe sequence with the same frequency as the control bus to obtain a high-precision interference map, enabling spatial localization and frequency band attribution of interference sources. Furthermore, relying on phase fingerprint extraction and the establishment of an interference feature library, a traceable interference behavior labeling system is formed, effectively avoiding misjudgments caused by signal aliasing and sequence reversal. Simultaneously, through a reversible time-delay calibration chain and differential perturbation injection, the true data bit sequence is successfully restored and the synchronization of the communication channel is reconstructed. Based on this, a cross-cycle reliability evolution evaluation mechanism is introduced to dynamically identify and softly isolate abnormal commands, ensuring accurate execution of control logic. Finally, combining phase conjugate protection and variational path selection strategies, a multi-path redundancy decision-making mechanism is constructed to effectively resist the impact of transient interference on energy management commands, achieving stable, continuous, and intelligent closed-loop control of the control chain in emergency energy storage power supply scenarios, comprehensively improving the safety, reliability, and response efficiency of the energy storage system in complex environments. Attached Figure Description

[0017] 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.

[0018] Figure 1 This is a flowchart of the control method for the emergency energy storage power supply of the present invention.

[0019] Figure 2 This is a schematic diagram of the control system of the emergency energy storage power supply of the present invention. Detailed Implementation

[0020] 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.

[0021] This invention provides, for example Figure 1 The control method for the emergency energy storage power supply shown includes the following steps: S001, establish a spatiotemporal coupling observation baseline between electromagnetic field and control bus signal. By constructing a reflection probe sequence with the same frequency as the control bus signal, continuously sample and reconstruct the bit edge signal generated during communication to obtain an interference mapping map containing energy distribution information and phase delay characteristics. To achieve accurate observation and reconstruction of control bus communication waveforms under the influence of electromagnetic interference, an interference identification method based on spatiotemporal coupling observation is proposed. By constructing a dedicated probe array and a continuous waveform reconstruction mechanism, the method effectively extracts the characteristics of the effect of electromagnetic interference on the control signal and forms an interference map. The specific steps are as follows: A representative communication segment within the target emergency energy storage power supply was selected as the observation area, covering the control bus path from the main controller to the end load. A spatial electromagnetic distribution initial model was established by spatially scanning the layout of various power electronic devices and the routing of their wiring within the energy storage device. This identified potential structural locations that could trigger high-frequency radiation, including the inverter bridge arm area, the high-frequency resonant charging path, the switching relays, and their adjacent busbar areas. Subsequently, based on the actual transmission frequency of the control bus along this path, a sequence of reflection probes with identical frequencies was designed. The probes were made of shielded coaxial material and featured micro-pitch sensing electrodes with capacitive bias at their front ends to ensure high sensitivity to minute phase shifts in the signal. The probe array was arranged parallel to the control bus on both sides of the communication line, with a spacing of 3 to 5 millimeters to obtain high-density spatial electromagnetic samples. By precisely locating and activating the probe array, continuous sensing of the upper edge signal of the control bus within a unit of time was achieved, and the probe sensing results were input to a waveform buffer array in a time-synchronized manner.

[0022] After receiving the continuous signal streams from each reflection probe, all signals are normalized, and a waveform slice set based on bit edge characteristics is constructed with a time base of 2 microseconds. Each waveform slice represents the signal morphology within that time window, including positive transitions, negative transitions, plateau regions, and possible high-frequency wave superposition regions. Based on the waveform slice set, a multi-segment signal reconstruction operation is performed, using a delayed linear superposition method to stitch adjacent slices into a continuous signal stream. During the stitching process, the original temporal structure of the waveform is maintained to ensure that the actual edge transition trajectory is restored to the greatest extent possible without compromising data integrity. In the reconstructed complete signal stream, the actual occurrence time, rise time, duration of the stable region, and corresponding local energy change amplitude of each transition point are further extracted to form a multi-dimensional bit edge descriptor subset. Each record in the descriptor subset has a unique position and dynamic energy parameters to express the signal response morphology under the current spatiotemporal state.

[0023] By combining the bit edge descriptor set with the initial electromagnetic distribution model, the descriptors are mapped back to the original control bus structure through spatial interpolation. The energy change amplitude of each bit is then superimposed onto the electromagnetic spatial coordinates, forming a two-dimensional interference density map with both positional distribution density and energy response characteristics. This density map, with the spatial coordinate axis as the horizontal axis and the energy disturbance amplitude as the vertical axis, displays the actual electromagnetic disturbance intensity along the entire control bus. Furthermore, a time dimension is introduced, stacking the interference density maps for each control cycle into a time series graph, forming a complete interference mapping map. This graph not only reflects the energy disturbance change trend of each specific location in continuous cycles but also identifies whether certain high-frequency interference events are periodic or regular, thus providing a visual basis for tracing interference behavior.

[0024] After obtaining the interference map, the phase response delay at each spatial coordinate point is calculated over the entire communication cycle based on the disturbance frequency, disturbance amplitude, and edge deformation ratio. This delay is then compared with the original signal frequency to generate a comprehensive phase response table containing spatial location, phase drift, and time deviation. The table is then clustered and sorted according to frequency interference trends to extract representative interference structural features. Based on this, the electromagnetic interference path is constructed according to the phase fluctuation continuity between adjacent probe detection points, identifying the initial divergence point of the interference source, the effective wavefront range, and the locations of influencing nodes in the corresponding communication path. This process, through joint analysis with the initially established control bus structure model, ultimately forms a complete interference map that accurately expresses the propagation path of interference behavior, phase change patterns, and signal distortion trends. This provides a fundamental reference for subsequent identification, correction, and interference elimination of communication signals.

[0025] S002, based on the interference map, performs fine analysis on the starting position, bit sampling node and edge transition trajectory of the communication data frame, extracts a unique phase fingerprint, and constructs an interference feature library composed of phase fingerprints to identify the electromagnetic interference source points and their operating frequency bands that cause control bus signal aliasing and sequence inversion. To utilize the spatial electromagnetic interference distribution information revealed by interference mapping maps to quantitatively identify and attribute the potential distortions in control bus communication data, an interference identification method based on phase feature reconstruction is proposed. Through multi-dimensional time series analysis and interference feature archiving, a stable identification reference for interference behavior is established. The specific steps are as follows: Based on the generated interference mapping, multiple complete data cycles in the control bus communication process are selected as the analysis objects. The communication waveform within each cycle is unfolded into a standard time axis according to its sampling sequence. Based on the energy disturbance density map and phase response table established in the previous step, the location region most susceptible to interference at the signal edge is determined, and the sampling window in the waveform that needs to be analyzed in detail is delineated accordingly. Within each window, a multi-parameter transition record set is established by extracting the rise time, completion time, steady-state delay time, and edge tilt of the actual edge transition point. Furthermore, within each record set, the data frame position, bit period interval, and offset from the period center of the transition point are marked, forming a jump event index chain with logical indexes to fully express the temporal evolution characteristics of the transition behavior in the current communication process.

[0026] Representative transition behaviors in the transition event index chain are categorized according to their respective communication cycles and superimposed with their corresponding electromagnetic disturbance data in the interference map, forming a quaternary feature set containing time displacement, phase drift, energy disturbance, and bit number. This feature set uses transition events as the smallest granular unit, recording the dynamic behavior characteristics of these events under interference conditions. Time displacement reflects the deviation of the sampling clock, phase drift reveals the degree of interference on edge morphology, energy disturbance describes the intensity of electromagnetic disturbance in the region where the point is located, and bit number is used for data structure alignment and logic tracing. By synchronously comparing and analyzing the evolution of the feature set across multiple communication cycles, feature combinations with stable spatiotemporal offset characteristics that repeat in multiple cycles are selected as typical response modes of electromagnetic interference acting on the control bus in the current environment.

[0027] The selected feature combinations are inductively encoded to transform their temporal evolution paths into sequential pointing relationships, and a phase jump trajectory map is constructed by combining their phase drift directions. This trajectory map uses the time axis as the horizontal axis and the phase response offset as the vertical axis, representing each jump behavior as a path node. Features are quantified through dimensions such as the number of path bends, fluctuation amplitude, and continuity, ultimately forming a uniquely identifiable set of phase fingerprints. Each phase fingerprint corresponds to a recognizable signal deformation behavior occurring in control bus communication under specific electromagnetic interference backgrounds, exhibiting strong stability and interference specificity. By comparing and clustering phase fingerprint sets generated in multiple communication frames, fingerprint sets with similar jump trajectory patterns and the same interference response characteristics are uniformly classified, forming a phase fingerprint index table with classification labels and time identifiers.

[0028] Based on the completed phase fingerprint dataset, a complete interference feature dataset is established with the phase fingerprint index table as the core. This dataset uses the control cycle as the basic unit, associating all valid phase fingerprints appearing within the current cycle. It combines these fingerprints with their corresponding interference map coordinates, perturbation frequency range, signal distortion type, and corresponding bit frame logical positions to achieve multi-dimensional attribution from signal behavior to interference source characteristics. Through high-frequency statistics and fingerprint spectrum analysis of this dataset, the main interference sources causing signal aliasing and sequence inversion, along with their corresponding frequency bands, can be accurately located. Furthermore, by identifying the coupling patterns between the interference sources and the communication path, the asymmetric perturbation characteristics they generate on the communication waveform in different time periods are further revealed, providing a basis for interference localization and differential stripping for subsequent data repair and waveform calibration.

[0029] S003, based on the results of the interference feature library, establishes a time delay reversible calibration chain, performs reverse registration step by step on the sampling clock and signal decision threshold, injects phase differential perturbation signals at both ends of the control bus, strips the overlapping waveforms and restores the original data bit order, and restores the synchronous stable state of the communication signal channel; Based on the constructed interference feature library and phase fingerprint index table, a calibration method for communication waveform distortion is further proposed. Through step-by-step time registration and differential interference stripping, the bit sequence structure of the original data is accurately restored, ensuring the synchronous stability of the control bus communication link under interference conditions. The specific steps are as follows: Data frames identified as affected by interference within the current communication cycle are selected. A frame-by-frame comparison is performed based on the fingerprint index recorded in the interference feature database. Bits exhibiting significant phase drift, transition anomalies, and edge distortion are marked one by one, forming a list of interfered nodes. Subsequently, with each node as the center, the process extends two bit cycles forward and backward, extracting the original sampling points within the extended region to establish a local sampling window set. Resampling is then performed on all sampling points in this set. During this resampling process, relying on the time offset and phase change rate information recorded in the interference feature database, the timestamp of each sampling point is reversed, reverting it from the actual recording time to the theoretically corrected timing position where a normal edge should appear. Through this reverse mapping process, the standard timing logic can be restored without changing the original sampled data values, ensuring high reference consistency for subsequent edge discrimination and threshold determination.

[0030] After completing the reverse registration of the sampling clock, amplitude reconstruction processing is performed on the edge signals in the bit period. Specifically, based on the edge amplitude fluctuation characteristics stored in the phase fingerprint set, the amplitude fluctuation process experienced by abnormal nodes at their corresponding positions within the current communication period is analyzed, and the nonlinear transition trend between their upper and lower edges is identified. Combining the difference between the actual amplitude curve in the sampling window and the ideal edge template, an amplitude deviation model is constructed, and the optimal correction amount for the signal decision threshold is calculated. This correction amount is applied to the decision process of the current period, enabling the signal threshold line to adapt to amplitude variations under interference superposition environments, achieving effective repair of misjudged edge states. The corrected decision threshold no longer uses a fixed median method but is flexibly adjusted according to the actual amplitude fluctuations, effectively improving the accuracy of edge recognition.

[0031] To further eliminate waveform ghosting caused by interference superposition between adjacent transition events, a phase differential perturbation signal is introduced at both ends of the communication path of the control bus. This perturbation signal consists of two conduction pulses of opposite polarity and extremely low amplitude, coupled into the bus at a frequency close to the main communication signal. By disturbing the energy superposition path during the edge transition of the main signal, the perturbation signal causes a slight shift in the spatial propagation path of the overlapping waveform, thereby guiding the main component of the original signal and the interference component to form a timing misalignment at the receiving end. The receiving side identifies the signal branch belonging to the interference component by comparing the edge positions of the original signal in the perturbation-free state, and separates this branch from the overall waveform, preserving the true transition shape of the main signal trajectory. This differential stripping mechanism effectively eliminates redundant edges caused by electromagnetic reflection, path coupling, or crosstalk, greatly improving the singularity and logical integrity of the signal waveform.

[0032] After clock registration, judgment correction, and waveform stripping, bit sequence reassembly is performed on the full-cycle signal. This step, using bit periods as units, reorders each calibrated transition point according to its theoretical timeline position, restoring the timing structure of the original data frame. During this process, the bit synchronization signal, start bit, stop bit, and valid data bits are aligned bit by bit, and the logical boundaries are determined based on the time intervals between transition points, reconstructing the periodic relationships between bits. To ensure the reassembled data frame is recognizable by the communication protocol, the synchronized and calibrated frame structure is matched and verified against the protocol frame header template to ensure complete boundary logic, consistent bit width, and frame length meeting the expected specifications. After all reassemblies are completed, the communication signal channel returns to a stable synchronized state, possessing normal start detection capabilities, frame parsing capabilities, and subsequent command triggering capabilities, providing accurate basic data support for the next stage of command screening and response.

[0033] S004, in the calibrated signal channel, the key data frame of the previous control cycle is called and compared with the current communication frame in a dual-track manner. The instruction confidence weight corresponding to each frame is generated based on the comparison deviation, and the frame signal below the set threshold is softly isolated to prevent erroneous control instructions from being executed. After completing clock registration, amplitude correction, and bit sequence reassembly of the communication waveform, to ensure the security of instruction execution, a method for evaluating instruction reliability based on historical data frame comparison is proposed. Through timing consistency analysis and dynamic weight allocation, abnormal control instructions are identified and isolated. The specific steps are as follows: A key data frame with a complete structure, clear edges, and no interference characteristics is extracted from the previous control cycle. This data frame was determined in the previous cycle to have a complete frame header, valid data fields, and correct stop bits, and its clock and signal threshold accuracy was verified through the aforementioned calibration process. Therefore, it can serve as a reference template for the data frame in the current cycle. Subsequently, in the current control cycle, data frames with matching lengths are selected according to the same communication protocol structure to establish a data control group for the old and new timing channels. During the comparison process, using the bit period as the basic unit, each valid transition point in the current frame is paired bit by bit with the corresponding transition information in the historical frame, including parameters such as start edge type, transition completion time, duration of the stable region, and slope of signal amplitude change. The above comparison results are uniformly encoded to form a set of deviation values ​​to reflect the degree of difference between the current communication frame and the reference frame in terms of structure and behavior.

[0034] The deviation value set is quantified to establish a command credibility weight evaluation matrix for each communication frame. In this matrix, each parameter dimension corresponds to a credibility sub-item weight. For example, a transition time difference of less than 0.5 microseconds is assigned full weight, while a difference greater than 2 microseconds is reduced to zero. Transition patterns consistent with historical templates retain their original weights, while reverse waveforms, double edges, or gradual changes result in correspondingly lower weight coefficients. Signal amplitude slopes with high consistency with reference values ​​are assigned high weights, while those with drastic changes are assigned low weights. All sub-item weights are accumulated after standard normalization to form the total credibility weight of the current frame. This value is expressed as a percentage, theoretically ranging from 0% to 100%, representing the acceptability and execution risk level of the current communication frame after interference correction. By comparing this weight with a set weight threshold, it can be determined whether the frame is a potential abnormal command carrier.

[0035] Based on the evaluation results, soft isolation is performed on communication frames that fall below the set confidence weight threshold. Isolation methods include, but are not limited to: preventing the frame from entering the subsequent instruction parsing process, transferring the frame to a backup channel for secondary verification, delaying the frame's trigger time in the control chain, blanking the frame's data, and adding confirmation conditions before the corresponding control terminal executes the action. To avoid critical control delays due to a single misjudgment, a cumulative confidence observation mechanism for multiple consecutive cycles is also required. If a communication frame with the same address identification shows insufficient confidence in more than three cycles, it is marked as a high-risk frame and enters the subsequent channel isolation zone. However, if the current frame only shows a critical decrease in confidence in a single cycle, it retains the right to re-compare in the next cycle. This two-cycle confirmation mechanism ensures the stability of the overall communication link and the continuity of instruction execution.

[0036] While implementing the soft isolation strategy, remaining communication frames still within the valid signal judgment range are retained, and a new reference frame sequence is established for comparison in the next control cycle. This operation requires selecting frames with clear edge characteristics, normal signal slope, and stable amplitude fluctuations as new benchmarks based on several communication frames that have passed the reliability assessment in the current cycle. To enhance anti-interference capabilities, a ratio sliding window mechanism is introduced during the reference frame update process. By cross-comparing and sorting the similarity of multiple candidate frames, the most representative frame is selected as the basic reference for the next cycle. This iterative process maintains the updating and synchronization of reference information in the communication chain under high interference environments, effectively improving comparison accuracy and judgment reliability. Ultimately, it forms a dynamically adaptable reliability evolution chain, fundamentally avoiding command misjudgment and control anomalies caused by instantaneous waveform distortion.

[0037] S005, based on the continuous evolution of the instruction credibility weight, executes a dynamic adjustment process, triggers the phase conjugate protection mechanism and the routing coordination mechanism based on Hamilton variational logic, drives the cross-cycle majority voter to dynamically weight and correct the control decision chain, realizes the adaptive weighted execution and optimal control strategy selection of the charging and discharging logic in a strong electromagnetic interference environment, and completes the unified closed loop of interference identification, data calibration, causal verification and control decision. After completing the reliability weight assessment of communication commands and soft isolation of abnormal frames, a dynamic control process oriented towards the control link is further introduced. Through a multi-dimensional strategy linkage mechanism, adaptive optimization adjustment of the charging and discharging logic under interference environments is achieved, thereby constructing a stable closed-loop control response chain. The specific steps are as follows: Based on the reliability weight evaluation results of each communication frame in the previous control cycle, the weight change trends of all command frames over multiple consecutive cycles are statistically analyzed, forming a set of weight evolution curves. This set uses the unique address of each communication frame as an index to record its weight fluctuations, number of jumps, critical interval dwell time, and smooth transitions with preceding and following cycles in each control cycle. Weight trajectories showing a significant downward trend are marked with high priority, identifying them as target command points in areas with strong potential interference. Simultaneously, the scope of influence of these command points in the control logic is analyzed to determine whether they participate in current switching control, energy storage path conversion, or voltage maintenance strategies. If they belong to core control chain nodes, they are marked as control targets requiring priority correction. Based on this identification result, the phase conjugate protection mechanism in the electrical execution link is activated, applying symmetrical electrical parameter adjustments to control points currently at the forefront of command response. For example, in energy storage release logic with excessively high trigger frequencies, a phase-reversed conjugate control reference is introduced to suppress potential energy release anomalies caused by erroneous commands, keeping them within a controllable and safe range.

[0038] During the execution of the phase conjugate protection mechanism, a control command coordination mechanism based on path analysis is simultaneously activated. Following the principle of minimum action path in Hamiltonian variational logic, the signal response chain from the communication receiving point to the physical execution end within the current control cycle is reconstructed. This response chain encompasses the command parsing unit, judgment logic nodes, execution triggering interface, and state feedback entry point. Weighted analysis is performed on all logic jump nodes in this path, recording the energy triggering cost, control lag time, and state traceability parameters for each jump segment. By constructing a weight matrix of multiple selectable paths, the path chain with the minimum action energy and optimal regulation efficiency under the current electrical execution environment is selected as the actual control path and prioritized for activation throughout the decision-making process. Thus, when the original control path is softly isolated due to insufficient command reliability, the backup path immediately takes over the control responsibilities and completes the action triggering under the protection of the conjugate mechanism, ensuring that energy storage control maintains dynamic continuity even under conditions of high interference.

[0039] For all instruction groups participating in the control logic within the current cycle, a cross-cycle majority voter is established, endowed with dynamic weighting capabilities based on the evolution trend of credibility. This voter, using time as the main axis, categorizes instructions with the same logical bit from three or more consecutive cycles. Each category of instructions calculates a voting coefficient based on its historical credibility weight. The higher the weight, the stronger the voting influence. If an instruction consistently performs stably throughout historical cycles and its performance tends to align with the current cycle, it is considered a priority execution candidate. The voter makes a comprehensive judgment on all valid instructions within the current cycle based on the weighted results, outputting a final execution control command to replace the original frame's direct triggering logic. This mechanism effectively introduces temporal and data redundancy, improving the control logic's anti-interference and fault tolerance capabilities. Especially when instruction weights fluctuate critically, historical weight accumulation can smooth the current control judgment, avoiding erroneous control responses due to individual anomalies.

[0040] When executing the charging and discharging control logic based on the voting results, an adaptive weighting mechanism is introduced. For all instruction groups affected by high-risk interference, the weight in the execution priority is automatically adjusted, and the output strategy is optimally modified according to the battery charge status, bus voltage fluctuation range, and external load level. If the current strategy is to discharge to supply energy to the main bus, but the voter identifies a control anomaly in the previous cycle, the output proportion of this command is reduced during actual execution, and a backup strategy is invoked to divert some energy to the internal buffer energy storage unit, reducing system risk. Conversely, when an instruction group exhibits high consistency and reliability, and matches well with the feedback from the previous cycle's execution results, its priority is increased and its output proportion is strengthened, achieving logic reinforcement. This adaptive weighting adjustment mechanism iterates continuously across multiple control cycles, forming a complete closed loop from interference identification, data correction, causal judgment to control response through continuous comparison, dynamic correction, and path optimization. This ensures the operational stability and control accuracy of the emergency energy storage power supply in complex electromagnetic interference environments.

[0041] This invention constructs a reflection probe sequence with the same frequency as the control bus to obtain a high-precision interference map, enabling spatial localization and frequency band attribution of interference sources. Furthermore, relying on phase fingerprint extraction and the establishment of an interference feature library, a traceable interference behavior labeling system is formed, effectively avoiding misjudgments caused by signal aliasing and sequence reversal. Simultaneously, through a reversible time-delay calibration chain and differential perturbation injection, the true data bit sequence is successfully restored and the synchronization of the communication channel is reconstructed. Based on this, a cross-cycle reliability evolution evaluation mechanism is introduced to dynamically identify and softly isolate abnormal commands, ensuring accurate execution of control logic. Finally, combining phase conjugate protection and variational path selection strategies, a multi-path redundancy decision-making mechanism is constructed to effectively resist the impact of transient interference on energy management commands, achieving stable, continuous, and intelligent closed-loop control of the control chain in emergency energy storage power supply scenarios, comprehensively improving the safety, reliability, and response efficiency of the energy storage system in complex environments.

[0042] This invention provides, for example Figure 2 The control system of the emergency energy storage power supply shown includes an interference modeling module, a feature extraction module, a time delay calibration module, a signal filtering module, and a dynamic control module. The interference modeling module establishes a coupled observation baseline between the electromagnetic field and the control bus signal. By constructing a sequence of reflection probes with the same frequency as the control bus signal, it collects and reconstructs the bit edge signals generated during communication, and generates an interference map containing energy distribution and phase delay characteristics. The feature extraction module, based on the interference map, analyzes the start and end positions of the data frame, bit sampling nodes and edge transition trajectories, extracts unique phase fingerprints, and constructs an interference feature library to identify interference sources and their frequency bands that cause signal aliasing and sequence inversion. The delay calibration module establishes a reversible delay calibration chain based on the interference feature library, performs reverse registration between the sampling clock and the decision threshold, injects phase differential perturbations at both ends of the control bus, realizes waveform stripping and data bit order restoration, and restores the synchronization state of the communication channel. The signal filtering module performs a dual-track comparison in the calibrated communication channel. Based on the comparison results between the key frame of the previous control cycle and the current frame, it generates instruction confidence weights and implements soft isolation for low-weight frames to prevent interference instructions from entering the control chain. The dynamic control module performs dynamic control based on the evolution of the credibility weights, triggers phase conjugate protection and Hamilton variational routing mechanism, drives the majority of voters to perform weighted correction of the control chain, and realizes closed-loop unification of interference identification, data calibration, causal verification and control decision-making.

[0043] The control method for emergency energy storage power provided in this embodiment of the invention is implemented through the control system of the aforementioned emergency energy storage power. For details of the specific methods and processes of the control system of the emergency energy storage power, please refer to the embodiment of the control method for the aforementioned emergency energy storage power, which will not be repeated here.

[0044] 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 control method and system for emergency energy storage power supplies, characterized in that, Includes the following steps: S001, Establish the coupled observation baseline of electromagnetic field and control bus signal, and collect and reconstruct bit edge signal generated during communication by constructing a reflection probe sequence with the same frequency as the control bus signal, and generate interference mapping map; S002, based on the interference map, analyzes the start and end positions of the data frame, bit sampling nodes and edge transition trajectories, extracts unique phase fingerprints, and constructs an interference feature library to identify the interference sources and their frequency bands that cause signal aliasing and sequence inversion; S003, based on the interference feature library, establish a time delay reversible calibration chain, reverse register the sampling clock and the decision threshold, inject phase differential perturbation at both ends of the control bus, realize waveform stripping and data bit order restoration, and restore the communication channel synchronization state; S004, in the calibrated communication channel, perform dual-track comparison, generate instruction confidence weight based on the comparison result of the key frame of the previous control cycle and the current frame, implement soft isolation for low-weight frames, and prevent interference instructions from entering the control chain; S005, based on the evolution of the credibility weight, performs dynamic adjustment, triggers phase conjugate protection and Hamilton variational routing mechanism, and drives the majority of voters to make weighted corrections to the control chain.

2. The control method and system for emergency energy storage power supply according to claim 1, characterized in that, Step S001 includes: After constructing a sequence of reflection probes with the same frequency as the control bus signal, the observation area is selected to cover the communication line between the main controller and the end load. An initial model of electromagnetic distribution is established by spatial scanning to locate the high-frequency radiation area. The bit edge signals on the control bus are acquired and normalized. A waveform slice set is constructed according to a unified time base. The signal stream is reconstructed by delay superposition. The timing features and local energy amplitude of the transition points are extracted to form a multi-dimensional bit edge descriptor set. The description subset is interpolated and mapped to the initial model to generate an interference density map, and the time dimension is introduced to form an interference mapping map. Based on the disturbance frequency, disturbance amplitude, and edge deformation ratio at each point in the interference mapping diagram, the phase response delay is calculated, and the phase response characteristics are clustered to construct the electromagnetic interference action path.

3. The control method and system for emergency energy storage power supply according to claim 2, characterized in that, After constructing the interference map, the phase drift and time deviation information of spatial coordinate points are extracted by detecting the phase fluctuation continuity between adjacent reflection probe detection points, generating a phase response table, and clustering and sorting based on the phase response characteristics to identify the divergence starting point, wavefront propagation range and the nodes that play a role in the communication path of electromagnetic interference.

4. The control method and system for emergency energy storage power supply according to claim 1, characterized in that, Step S002 includes: After generating the interference map, waveform data from multiple complete communication cycles are selected. Based on the energy disturbance density map and phase response table, the interference-affected area of ​​the signal edge is delineated and a jump event index chain is established. Representative transition behaviors are extracted from the index chain and superimposed with corresponding electromagnetic disturbance data to construct a feature set containing time displacement, phase drift, energy disturbance and bit number. The feature combinations are inductively encoded to construct a phase transition trajectory map and form a unique set of phase fingerprints; Using the phase fingerprint index table as the core, an interference feature dataset is constructed, and statistical analysis is used to achieve accurate location of electromagnetic interference sources and frequency band attribution.

5. The control method and system for emergency energy storage power supply according to claim 1, characterized in that, Step S003 includes: Based on the phase fingerprint index in the interference feature library, the interfered bits in the current communication cycle are marked, a sampling window is established and resampling is performed, and the timing of the sampling points is adjusted back to the standard position based on the time offset information. After completing the reverse registration of the sampling clock, the amplitude curve is reconstructed by combining the edge amplitude fluctuation characteristics, and the optimal decision threshold is calculated to correct the misjudged edge state. Phase differential perturbation signals are injected at both ends of the control bus to achieve interference branch stripping and main signal trajectory restoration; The calibrated transition points are reordered and aligned with the frame structure fields to complete the reordering of communication data bits and restore the synchronous and stable state of the communication channel.

6. The control method and system for emergency energy storage power supply according to claim 5, characterized in that, The phase differential perturbation signal consists of two conduction pulses with opposite polarities and amplitudes lower than the main signal. The injection frequency is consistent with the main communication signal, and it is used to create edge timing misalignment at the signal receiving end.

7. The control method and system for emergency energy storage power supply according to claim 1, characterized in that, Step S004 includes: Extract the key data frames that have been calibrated and verified in the previous control cycle as reference templates, establish a timing control group with the communication frames that match the structure in the current cycle, compare the transition characteristic parameters bit by bit, and generate a set of deviation values. The deviation value set is quantified and calculated to construct the instruction credibility weight evaluation matrix. The credibility weight of the current communication frame is calculated and compared with the set threshold. For communication frames below a threshold, perform soft isolation and establish a high-risk marking and re-comparison mechanism based on weight performance within a continuous period; A new reference frame sequence is constructed based on communication frames with high credibility. The most representative frame is selected by a sliding window, and the reference information is updated cyclically to form a dynamic credibility evolution chain.

8. The control method and system for emergency energy storage power supply according to claim 7, characterized in that, The soft isolation operation includes delaying the triggering time of communication frames in the control chain and introducing acknowledgment conditions before execution. The update of the reference frame is based on the communication frames with the highest similarity in multiple cycles. The frame with the strongest edge feature consistency is selected as the reference benchmark for the next cycle through a sliding window mechanism.

9. The control method and system for emergency energy storage power supply according to claim 1, characterized in that, Step S005 includes: Based on the evolution of instruction confidence weights within a continuous cycle, control frames with declining weights are identified and marked as control targets that need to be corrected. Phase conjugate protection is then performed to stabilize their control effects. Based on Hamiltonian variational logic analysis, the energy cost and response efficiency of each logic node in the control path are analyzed, a control path weight matrix is ​​constructed, and the optimal path is activated to replace the original control link. For control frames with the same logic bit in multiple cycles, perform majority voting calculation, allocate voting coefficients according to the credibility evolution trend, and output weighted decision instructions; For instruction groups affected by interference, adaptive weight reduction is performed, and the control strategy is optimally adjusted based on battery status and load information.

10. A control system for an emergency energy storage power supply, used to implement the control method and system for the emergency energy storage power supply according to any one of claims 1-9, characterized in that, It includes an interference modeling module, a feature extraction module, a time delay calibration module, a signal filtering module, and a dynamic control module. The interference modeling module establishes a coupled observation baseline between the electromagnetic field and the control bus signal. By constructing a sequence of reflection probes with the same frequency as the control bus signal, it collects and reconstructs the bit edge signals generated during communication to generate an interference map. The feature extraction module, based on the interference map, analyzes the start and end positions of the data frame, bit sampling nodes and edge transition trajectories, extracts unique phase fingerprints, and constructs an interference feature library to identify interference sources and their frequency bands that cause signal aliasing and sequence inversion. The delay calibration module establishes a reversible delay calibration chain based on the interference feature library, performs reverse registration between the sampling clock and the decision threshold, injects phase differential perturbations at both ends of the control bus, realizes waveform stripping and data bit order restoration, and restores the synchronization state of the communication channel. The signal filtering module performs a dual-track comparison in the calibrated communication channel. Based on the comparison results between the key frame of the previous control cycle and the current frame, it generates instruction confidence weights and implements soft isolation for low-weight frames to prevent interference instructions from entering the control chain. The dynamic control module performs dynamic control based on the evolution of the credibility weight, triggers phase conjugate protection and Hamilton variational routing mechanism, and drives the majority of voters to make weighted corrections to the control chain.

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