Method and device for activating storage battery of direct-current power supply of nuclear power station

Abstract

The invention discloses a nuclear power station direct-current power supply storage battery activation method and device. The method comprises the steps that matching degree checking is carried out on battery state parameters and activation configuration specifications to generate an activation adaptation coefficient; carrying out polarization degree statistics to generate a polarization characteristic spectrum, extracting an available charging and discharging period to generate a charging and discharging period group, and mapping the polarization characteristic spectrum to the charging and discharging period group to construct a standard activation sequence; applying coefficient modulation by means of the activation adaptation coefficient to generate a composite activation instruction, and determining a step execution node according to saturation early warning; transmitting a composite activation instruction to generate a feedback data set, extracting a polarization residual signal to generate a residual correction parameter, executing energy monitoring to generate an energy balance curve, and performing trend analysis to generate an activation response coefficient; and carrying out recovery cycle analysis, adjusting the activation frequency by using the recovery cycle to generate a performance confirmation file, and carrying out health degree comparison on the performance confirmation file and the battery state parameters to generate an activation effect evaluation report, thereby realizing self-adaptive adjustment and accurate control in the activation process.

Description

Technical Field

[0001] This invention relates to the field of nuclear power plant DC power system maintenance technology, and in particular to a method and apparatus for activating nuclear power plant DC power battery. Background Technology

[0002] The battery banks in the DC power system of a nuclear power plant are responsible for providing uninterrupted power to critical loads such as protection systems, monitoring instruments, and emergency lighting during accident conditions. Under long-term float charging operation, a passivation layer gradually forms on the electrode surface of the battery banks, leading to a decrease in usable capacity, an increase in internal resistance, and a weakening of charge and discharge response capabilities, thus threatening the safe power supply capability of the nuclear power plant.

[0003] Existing battery activation technologies primarily employ cyclic charge-discharge cycles to eliminate polarization effects. A typical method involves constant current charging followed by deep discharge, with multiple cycles promoting the dissolution of the electrode passivation layer. However, these methods have significant drawbacks: charge and discharge parameters are typically fixed, lacking adaptability to performance differences among different battery packs; the activation process lacks real-time monitoring and dynamic adjustment mechanisms, easily leading to over-activation or under-activation; and the evaluation methods for activation effectiveness are relatively crude, relying solely on single indicators such as capacity testing, failing to comprehensively assess the degree of performance recovery. Therefore, a novel activation method is needed to address at least one of the aforementioned problems. Summary of the Invention

[0004] This invention discloses a method and apparatus for activating DC power storage batteries in nuclear power plants. It constructs a targeted activation sequence by matching and verifying the battery state with the activation configuration and mapping polarization characteristics. It achieves phased execution control by using coefficient modulation and saturation assessment. It also generates an assessment report containing performance recovery details and health comparison through polarization residue extraction, energy monitoring, trend analysis and recovery cycle assessment.

[0005] The first aspect of this invention provides a method for activating a DC power supply battery in a nuclear power plant, comprising the following steps: Collect battery status parameters of the storage battery and activation configuration specifications of the activation equipment, and perform a matching degree check on the battery status parameters and activation configuration specifications to generate an activation adaptation coefficient; Polarization feature spectrum is generated by performing polarization degree statistics on the battery state parameters, and available charge-discharge cycles are extracted along the activation configuration specifications to generate charge-discharge time period groups. The polarization feature spectrum is then mapped to the charge-discharge time period groups to construct a standard activation sequence. The standard activation sequence is modulated by applying the activation adaptation coefficient to generate a composite activation instruction. An execution saturation assessment is performed on the composite activation instruction to generate a saturation warning. A tiered execution node is established based on the saturation warning. According to the stepped execution node, the composite activation command is transmitted to the battery to generate a feedback dataset. The residual polarization signal is extracted from the feedback dataset to generate residual correction parameters. Based on the residual correction parameters, energy monitoring is performed on the feedback dataset to generate an energy balance curve. The energy balance curve is used to perform trend analysis to generate an activation response coefficient. Based on the activation response coefficient, a recovery cycle analysis is performed on the feedback dataset to generate a recovery cycle. The activation frequency is adjusted using the recovery cycle to generate a performance confirmation profile. The performance confirmation profile is then compared with the battery state parameters to generate an activation effect evaluation report.

[0006] A second aspect of the present invention provides an activation device for a DC power supply battery in a nuclear power plant, comprising: The data acquisition module is used to collect the battery status parameters of the storage battery and the activation configuration specifications of the activation equipment, and to perform a matching degree check on the battery status parameters and the activation configuration specifications to generate an activation adaptation coefficient. The sequence construction module is used to perform polarization degree statistics on the battery state parameters to generate a polarization feature spectrum, extract available charge-discharge cycles along the activation configuration specification to generate a charge-discharge time period group, and map the polarization feature spectrum to the charge-discharge time period group to construct a standard activation sequence. The instruction modulation module is used to apply coefficient modulation to the standard activation sequence using the activation adaptation coefficient to generate a composite activation instruction, perform execution saturation assessment on the composite activation instruction to generate a saturation warning, and establish a tiered execution node based on the saturation warning; The energy monitoring module is used to transmit the composite activation command to the battery according to the stepped execution node to generate a feedback dataset, extract the polarization residual signal from the feedback dataset to generate residual correction parameters, perform energy monitoring on the feedback dataset based on the residual correction parameters to generate an energy balance curve, and use the energy balance curve to perform trend analysis to generate an activation response coefficient. The evaluation output module is used to perform recovery cycle analysis on the feedback dataset based on the activation response coefficient to generate a recovery cycle, adjust the activation frequency using the recovery cycle to generate a performance confirmation file, and compare the performance confirmation file with the battery state parameters to generate an activation effect evaluation report.

[0007] The beneficial effects of this invention are reflected in the following points: First, by employing a matching degree verification mechanism between battery state parameters and activation configuration specifications, an activation adaptation coefficient reflecting the degree of compatibility between equipment capabilities and battery state is generated. Combined with polarization characteristic spectra extracted statistically by polarization degree, a standard activation sequence is constructed by mapping these parameters to charge / discharge time period groups. This technical solution solves the problem that traditional methods using fixed parameters cannot adapt to the performance differences of different battery packs, enabling the activation sequence to be configured differently for the actual polarization state of each individual cell, thereby improving the matching accuracy between the activation scheme and the battery degradation state. Second, compared to the shortcomings of traditional methods that lack process control and are prone to over-activation or under-activation, this invention applies coefficient modulation to the standard activation sequence using the activation adaptation coefficient and performs execution saturation assessment on the generated composite activation commands. Based on saturation warnings, tiered execution nodes are established, allocating cells with different polarization degrees to appropriate execution stages. This tiered execution strategy effectively avoids mutual interference between cells, ensuring the safety and balance of the activation process. Finally, a complete evaluation chain is established, from residual polarization extraction, energy monitoring, trend analysis to recovery cycle assessment. Specifically, residual polarization signals are extracted from the feedback dataset to generate residual correction parameters. Based on these parameters, energy monitoring is performed to generate an energy balance curve. The activation response coefficient is obtained through trend analysis, followed by recovery cycle analysis and adjustment of the activation frequency. Finally, the performance confirmation file is compared with the battery state parameters to generate an activation effect evaluation report. This multi-dimensional evaluation system overcomes the limitations of traditional methods that rely solely on a single capacity indicator, and can comprehensively reflect the degree of battery performance recovery and consistency improvement. Attached Figure Description

[0008] The accompanying drawings illustrate specific examples of the technical solutions described in this invention and, together with the detailed embodiments, form part of the specification, serving to explain the technical solutions, principles, and effects of this invention.

[0009] Unless otherwise specified or defined, the same reference numerals in different figures represent the same or similar technical features, and different reference numerals may be used to represent the same or similar technical features.

[0010] Figure 1 This is a schematic flowchart of a method for activating a DC power supply battery for a nuclear power plant according to the present invention.

[0011] Figure 2 This is a structural block diagram of a DC power supply battery activation device for nuclear power plants according to the present invention. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0013] It should be noted that all directional indicators (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicator will also change accordingly.

[0014] It should also be noted that when a component is described as "fixed to" or "set on" another component, it can be directly on the other component or there may be an intervening component present. When a component is described as "connected to" another component, it can be directly connected to the other component or there may be an intervening component present.

[0015] Furthermore, the use of terms such as "first" and "second" in this application is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed in this application.

[0016] The technical solutions of the embodiments of this application are described below.

[0017] like Figure 1 As shown, this embodiment of the invention provides a method for activating a DC power supply battery in a nuclear power plant, including the following steps S110-S150: Step S110: Collect the battery status parameters of the storage battery and the activation configuration specifications of the activation equipment, and perform a matching degree check on the battery status parameters and activation configuration specifications to generate an activation adaptation coefficient.

[0018] Specifically, the battery status parameters of the storage batteries and the activation configuration specifications of the activation equipment are collected. In the DC power system of the nuclear power plant, battery status parameters are collected through the battery management unit. These parameters include the terminal voltage, internal resistance, and capacity decay rate of each individual cell. The acquisition device measures the voltage of each individual cell and records its value. The internal resistance of each individual cell is measured using the AC impedance method. The capacity decay of each individual cell is statistically analyzed, and the current capacity is compared with the rated capacity to obtain the capacity decay rate. Simultaneously, the activation configuration specifications of the activation equipment are obtained. These specifications include the voltage regulation accuracy, current output stability, and capacity balancing capability of the equipment. The technical specifications of the activation equipment are reviewed, and the voltage regulation accuracy is recorded to confirm the fineness of the adjustable voltage. The current output stability of the equipment is measured to assess its ability to maintain stable current output under different load conditions. The capacity balancing capability of the equipment is tested to determine its ability to differentiate between cells of different capacities.

[0019] In some embodiments, the step of performing a matching degree verification between the battery state parameters and the activation configuration specifications to generate an activation adaptation coefficient includes: extracting individual cell dispersion indicators based on the battery state parameters to form a dispersion indicator set; performing capability matching between the dispersion indicator set and the activation configuration specifications to form an indicator-device correspondence; identifying individual cell difference compensation gaps along the indicator-device correspondence to form a capability gap domain; and forming an activation adaptation coefficient based on the coverage strength of the capability gap domain.

[0020] Based on the battery state parameters, a set of dispersion indices is formed by extracting individual cell dispersion indices. Key performance data for each individual cell is extracted from the battery state parameters to analyze the performance differences between them. For the terminal voltage of each individual cell in the battery state parameters, the terminal voltage of each cell is compared with the average terminal voltage to obtain the voltage dispersion coefficient, calculated as CV_V = σ_V / μ_V, where CV_V is the voltage dispersion coefficient, σ_V is the standard deviation of the terminal voltage of each cell, and μ_V is the average terminal voltage. In a nuclear power plant battery pack that has been in operation for 8 years, due to the different aging rates of individual cells, some cells experience significant voltage drops while others maintain relatively good voltage, leading to an increased voltage dispersion coefficient and poorer battery pack consistency. For the internal resistance of each individual cell in the battery state parameters, the deviation of each cell's internal resistance from the average internal resistance is compared to obtain the internal resistance dispersion coefficient, expressed as the coefficient of variation, reflecting the concentration or dispersion of the internal resistance distribution of each cell. For the capacity decay rate of each individual cell in the battery state parameters, the distribution range of the capacity decay rate of each cell is statistically analyzed to obtain the capacity decay dispersion coefficient, characterizing the consistency of the capacity degradation rate of each individual cell. The backup battery banks in nuclear power plants are in a float charging state for extended periods. Some individual cells experience accelerated capacity decay due to higher ambient temperatures, while others decay more slowly, leading to an increased capacity decay dispersion coefficient and a widening capacity difference between cells. A dispersion index set is formed by summing the voltage dispersion coefficient, internal resistance dispersion coefficient, and capacity decay dispersion coefficient. This dispersion index set includes the three indicators and their corresponding values.

[0021] A capability matching relationship between the index set and the activation configuration specifications is established by using an index-equipment correspondence. The dispersion coefficients in the index set are matched with the equipment capability parameters in the activation configuration specifications. For the voltage dispersion coefficient in the index set, the voltage regulation accuracy parameter of the equipment is extracted from the activation configuration specifications to determine whether the equipment's voltage regulation accuracy can eliminate voltage dispersion between individual cells. The domestically produced activation equipment in the nuclear power plant has limited voltage regulation accuracy, while the voltage dispersion coefficient of a group of batteries that has been operating for 10 years in this plant has reached a high level. The equipment's voltage regulation accuracy is insufficient to completely eliminate voltage differences between individual cells, necessitating the establishment of a correspondence between the voltage dispersion coefficient and voltage regulation accuracy to assess the matching degree of equipment regulation capability. For the internal resistance dispersion coefficient in the index set, the current output stability parameter of the equipment is extracted from the activation configuration specifications to determine whether the equipment can maintain stable output under conditions of internal resistance differences. The internal resistance distribution of the battery packs in the nuclear power plant is uneven, with some individual cells exhibiting significantly increased internal resistance. During the activation process, the equipment needs to switch between cells with different internal resistances. If the equipment's current output fluctuates significantly, it is difficult to achieve balanced activation. Therefore, a correspondence between the internal resistance dispersion coefficient and current output stability is established. For the capacity decay dispersion coefficient in the dispersion index set, the capacity balancing capability parameters of the equipment are extracted from the activation configuration specifications to determine whether the equipment can implement differentiated processing for individual units with large capacity differences. Each coefficient in the dispersion index set is paired one by one with the corresponding capability parameters in the activation configuration specifications to form a complete index-equipment correspondence. The index-equipment correspondence includes three sets of mappings: voltage dispersion-regulation accuracy pairing, internal resistance dispersion-current stability pairing, and capacity dispersion-balancing capability pairing.

[0022] Identify the capacity gap domain by following the indicator-equipment correspondence relationship to address the individual cell difference compensation gap. Based on the indicator-equipment correspondence relationship, compare the values ​​of the dispersion index with the values ​​of the equipment capacity parameters to identify areas where the equipment capacity is insufficient. For the pairing of voltage dispersion coefficient and voltage regulation accuracy in the indicator-equipment correspondence relationship, a voltage regulation capacity gap exists when the dispersion index exceeds the range that the equipment regulation accuracy can cover. In a nuclear power plant, the terminal voltages of individual cells in a battery bank are severely uneven, with a significant difference between the highest and lowest voltage cells. However, the voltage regulation accuracy of the activated equipment is limited, making it impossible to adjust the voltages of all cells to a uniform level, thus forming a voltage regulation capacity gap. The voltage regulation capacity gap is recorded as a component of the capacity gap domain. For the pairing of internal resistance dispersion coefficient and current output stability in the indicator-equipment correspondence relationship, when the internal resistance difference between cells is large, current output fluctuations may occur when the equipment switches between different cells for activation. If the fluctuations exceed the allowable range, a current stability gap is formed. In the battery bank of a nuclear power plant, the internal resistance of some cells increases to several times the normal value. When the activated equipment processes high-resistance cells, the current output is unstable, making it difficult to achieve balanced charging and discharging, thus forming a current stability gap. The current stability gap is recorded as another component of the capacity gap domain. For the pairing of capacity decay dispersion coefficient and capacity balancing capability in the indicator-equipment correspondence, a capacity balancing gap is formed when the capacity difference between individual units exceeds the equipment's balancing capacity. All capacity gaps are summarized, including the voltage regulation gap, current stability gap, and capacity balancing gap, to form a complete capacity gap domain. The capacity gap domain contains three values: voltage regulation gap value, current stability gap value, and capacity balancing gap value.

[0023] The activation adaptation coefficient is formed by the coverage intensity of the capacity gap domain. Various gap values ​​are extracted from the capacity gap domain, and the coverage intensity for each value is calculated. Coverage intensity is the ratio of the equipment capacity parameter to its corresponding dispersion index. Voltage regulation coverage intensity C_V = voltage regulation accuracy / voltage dispersion coefficient; current stability coverage intensity C_I = current output stability / internal resistance dispersion coefficient; capacity balancing coverage intensity C_C = capacity balancing capability / capacity decay dispersion coefficient. A coverage intensity greater than 1 indicates that the equipment capacity fully covers the requirement, while a value less than 1 indicates a capacity gap. The coverage intensities corresponding to each gap in the capacity gap domain are weighted and summarized using the formula S = W1 × C_V + W2 × C_I + W3 × C_C, where S is the activation adaptation coefficient, C_V is the voltage regulation coverage intensity, C_I is the current stability coverage intensity, C_C is the capacity balancing coverage intensity, and W1, W2, and W3 are the weighting coefficients for each capability. A nuclear power plant conducted an activation assessment on a group of batteries that had been in service for 12 years. Due to long-term operation, the performance differences between individual cells had widened. The existing activation equipment generally met the requirements in terms of voltage regulation and capacity equalization, but it was insufficient in terms of current output stability. The activation fit coefficient obtained by the formula indicated that the matching degree between the equipment configuration and the battery condition was at a moderate level. The weighting coefficients were allocated according to the degree of influence of each capability on the activation effect. Voltage regulation capability played a dominant role in restoring the consistency of individual cell voltages and had a higher weight, while current stability and capacity equalization capability had relatively lower weights. After the nuclear power plant replaced the equipment with new high-performance activation equipment, the voltage regulation accuracy, current output stability, and capacity equalization capability of the equipment were significantly improved. A reassessment of the same group of batteries showed a significant increase in the activation fit coefficient, indicating that the matching degree between the new equipment configuration and the battery condition was improved.

[0024] Step S120: Perform polarization degree statistics on battery state parameters to generate polarization feature spectrum, extract available charge-discharge cycles along activation configuration specifications to generate charge-discharge time period groups, and map the polarization feature spectrum to the charge-discharge time period groups to construct standard activation sequence.

[0025] Specifically, polarization characteristic spectra are generated by statistically analyzing the polarization degree of battery state parameters. Short-term pulsed currents are applied to each individual cell, and the voltage recovery process after the pulse is removed is monitored; the speed of voltage recovery reflects the severity of polarization. In battery packs that have been in a float-charge state for a long time, some individual cells show slow voltage recovery after the pulse is removed, indicating severe polarization, while others recover quickly to a steady state, indicating less severe polarization. The time constant of the voltage recovery process for each individual cell is recorded; a larger time constant indicates more severe polarization. The correlation between polarization and internal resistance data in the battery state parameters is analyzed. Increased internal resistance is often accompanied by increased polarization. In batteries with longer service lives, the internal resistance of some cells increases significantly, and these cells usually also have more severe polarization. There is a positive correlation between capacity decay rate and polarization in the battery state parameters; cells with significant capacity decay tend to have more severe polarization. The polarization of individual battery cells is sorted according to its intensity, forming a polarization intensity distribution sequence. Polarization testing of battery packs in nuclear power plants revealed a stepped distribution of polarization intensity after sorting: cells at the front have slight polarization, cells in the middle have moderate polarization, and cells at the rear have heavy polarization. Based on the polarization intensity distribution of battery state parameters, each cell is divided into different polarization levels, generating a polarization characteristic spectrum. The polarization characteristic spectrum includes two indicators: the polarization level of each cell and the polarization time constant.

[0026] The available charge / discharge cycles are extracted from the activated configuration specifications to generate charge / discharge period groups. The voltage regulation accuracy in the activated configuration specifications determines the range of charging periods that the device can execute. The charging period needs to meet the voltage regulation capability constraints; the charging time cannot be shorter than the setup time required for the device's voltage to stabilize. When the device's voltage regulation accuracy is high, it can support shorter charging periods; when the device's voltage regulation accuracy is low, the charging period needs to be extended to ensure charging stability. The range of discharging periods is constrained by the current output stability in the activated configuration specifications. The discharging period needs to consider the depth of discharge limitation and the discharge current stability requirements. Devices with good current output stability can perform deeper discharges without causing battery damage, while devices with poor current output stability need to limit the depth of discharge to avoid over-discharge. The interval requirement for charge / discharge switching depends on the capacity balancing capability in the activated configuration specifications. The device needs a certain response time when switching from charging mode to discharging mode. Devices with strong capacity balancing capability have shorter response times and can have smaller switching intervals, while devices with weak capacity balancing capability have longer response times and need to set larger switching intervals. The available charging periods, discharging periods, and switching intervals are combined to form charge / discharge period groups. The charge / discharge period group includes two types of charge / discharge cycle configurations: fast mode and deep mode. The fast mode has a short charge / discharge cycle and is suitable for mildly polarized cells, while the deep mode has a long charge / discharge cycle and is suitable for heavily polarized cells.

[0027] In some embodiments, mapping the polarization feature spectrum to the charge / discharge time period group to construct a standard activation sequence includes: dividing the polarization feature spectrum into a heavily polarized segment and a lightly polarized segment; establishing a time period docking channel from the heavily polarized segment to the lightly polarized segment within the charge / discharge time period group; marking the positions of feature matching points on the time period docking channel to form a matching point sequence; and locating the optimal matching position along the matching point sequence to generate a standard activation sequence.

[0028] The polarization characteristic spectrum is divided into a heavily polarized segment and a lightly polarized segment. A polarization degree threshold is set based on the polarization intensity of each cell in the polarization characteristic spectrum. Cells with a polarization time constant exceeding 1.5 times the average value or exceeding the set threshold T_th have significantly increased capacity recovery difficulty. T_th is determined based on the battery type and service life, and this value serves as the boundary threshold between heavily polarized and lightly polarized cells. Cells with polarization intensities higher than the threshold are classified into the heavily polarized segment. These cells require longer charge-discharge cycles and deeper discharge depths to effectively depolarize. In a nuclear power plant's battery bank, cells in the heavily polarized segment exhibit significantly higher polarization time constants and severe capacity decay, which cannot be self-recovered during conventional float charging and require specialized activation treatment to improve performance. In the polarization characteristic spectrum, cells with polarization intensity below the threshold are classified into the slightly polarized segment. These cells are relatively easy to depolarize using shorter charge-discharge cycles. The slightly polarized segment of newly commissioned battery packs typically contains most of the cells, which have only slight polarization and can be eliminated and restored to normal state through simple charge-discharge cycles. The classification results of the heavily polarized segment and the slightly polarized segment record the cell numbers and corresponding polarization time constant ranges contained in each segment.

[0029] Within the charge / discharge time interval group, a time interval transition channel is established from the heavily polarized segment to the lightly polarized segment. The deep mode, with the longest charge / discharge cycle and the greatest discharge depth in the charge / discharge time interval group, corresponds to the heavily polarized segment. The long charge / discharge cycle of the deep mode effectively breaks down the concentration polarization and electrochemical polarization within the heavily polarized cells, allowing for the redistribution of active materials and restoring battery capacity. The fast mode, with the shortest charge / discharge cycle in the charge / discharge time interval group, corresponds to the lightly polarized segment. The short charge / discharge cycle of the fast mode is sufficient to eliminate the surface polarization of the lightly polarized cells, while avoiding unnecessary capacity loss due to over-activation. A time interval transition channel is established from the heavily polarized segment to the lightly polarized segment, containing multiple transition levels, each corresponding to a specific charge / discharge time interval configuration, achieving a gradual transition from the deep mode to the fast mode. The charge / discharge cycle of each level decreases sequentially. When activating a group of old batteries, a nuclear power plant uses a time-sharing docking channel to assign the most polarized cells to the deep mode for long-cycle activation, assign the least polarized cells to the fast mode for short-cycle activation, and assign cells with intermediate polarization to the transition mode for moderate-cycle activation.

[0030] For example, marking the positions of characteristic matching points on the time-segment docking channel to form a matching point sequence includes: determining a detection interval based on the polarization jump characteristics identified by the time-segment docking channel, wherein the polarization jump characteristics include the rising gradient, peak duration, and decay rate; tracking the polarization change process along the detection interval to form a polarization change spectrum; extracting the time-segment location value of each matching point from the polarization change spectrum; and arranging the matching points according to the degree of matching of the time-segment location values ​​to generate a matching point sequence.

[0031] The detection interval is determined by identifying polarization jump characteristics based on the time-segment docking channel. The variation pattern of polarization intensity is observed along the time-segment docking channel to identify regions where rapid jumps in polarization intensity occur. Polarization jump characteristics include three elements: the gradient of polarization, the duration of the peak value, and the decay rate. The gradient of polarization reflects the rate of change from light to heavy polarization. When the polarization time constant of some cells in the polarization characteristic spectrum corresponding to the time-segment docking channel suddenly increases significantly, it indicates that the polarization degree of these cells differs significantly from that of the preceding cells, and there is a clear polarization jump boundary. The duration of the peak value reflects the number of cells in the heavily polarized segment. The longer the duration, the more heavily polarized cells there are. In nuclear power plant battery banks, a longer peak duration indicates that the overall aging of the battery bank is more severe and requires close monitoring. The decay rate reflects the speed of the transition from heavy to light polarization. A fast decay rate indicates fewer transition cells and a polarization distribution exhibiting a bipolar characteristic, while a slow decay rate indicates the presence of more transition cells and a more continuous polarization distribution. Based on the polarization jump characteristics in the time-sharing docking channel, the detection intervals that need to be focused on are determined. The detection intervals include the region with the largest upward gradient, the start and end positions of the peak duration, and the region with significant changes in decay rate.

[0032] A polarization change spectrum is formed by tracing the polarization change process along the detection interval. Detailed records of polarization intensity changes are kept within the detection interval, and the trajectory of the polarization time constant of each cell is tracked to observe how the time constant transitions from one level to another. In analyzing the polarization state of batteries, nuclear power plants have found that the polarization time constant at the beginning of the detection interval suddenly jumps from a mild polarization level to a severe polarization level, with a significant jump accompanied by a synchronous increase in capacity decay rate. This synchronous change indicates a strong correlation between polarization degree and capacity loss. The trajectory of the internal resistance change of each cell within the detection interval shows synchronicity with the increase in polarization; the region of internal resistance growth highly overlaps with the region of polarization jump, and the polarization time constant of cells with a sudden increase in internal resistance also increases accordingly. The tracked changes in polarization time constant and capacity decay rate are combined to form a polarization change spectrum, which includes two sets of data: the polarization time constant sequence and the capacity decay rate sequence of each cell within the detection interval.

[0033] The time-period positioning values ​​of each matching point are extracted from the polarization variation spectrum. In the heavily polarized region of the polarization variation spectrum, locations where the polarization intensity highly matches the deep mode charge / discharge period configuration are identified. The polarization time constant and capacity decay rate of these cells are consistent with the polarization type targeted by the deep mode. The long-period configuration of the deep mode can effectively improve the polarization state of these cells. The time-period positioning values ​​of these matching locations are recorded, including the corresponding charging duration, discharging duration, and charge / discharge cycle parameters. In the lightly polarized region of the polarization variation spectrum, there are locations that highly match the fast mode. These cells have only slight polarization and can achieve ideal activation effects using the short-period configuration of the fast mode. The time-period positioning values ​​of these locations are extracted. Locations in the transition region that match the transition level also need to be identified, and the corresponding time-period positioning values ​​are extracted. The nuclear power plant uses the above method to identify cell groups requiring deep activation in the heavily polarized region of the polarization variation spectrum, cell groups requiring medium-intensity activation in the transition region, and cell groups requiring only rapid activation in the lightly polarized region. The time-period positioning values ​​of each group are recorded.

[0034] The matching point sequence is generated by arranging the matching values ​​of the time period positioning values ​​according to their degree of agreement. The degree of matching between the time period positioning values ​​of each matching point and the actual polarization characteristics is evaluated. The degree of matching is quantified by the degree of agreement M, M = 1 - |T_actual - T_target| / T_target, where T_actual is the actual polarization time constant of the cell, and T_target is the median value of the target polarization time constant range for the corresponding mode. The closer the degree of agreement is to 1, the better the match. The matching point located at the cell with the heaviest polarization has the highest degree of agreement for the longest charge-discharge cycle in the deep mode, and the configuration parameters of the deep mode perfectly match the activation requirements of the heavily polarized cell. The matching point located at the cell with the lightest polarization also has a high degree of agreement for the shortest charge-discharge cycle in the fast mode, and the fast mode can efficiently handle the lightly polarized cell. The time period configuration matching degree of the intermediate matching points corresponding to the transition cells and transition gears is at a moderate level. Arrange the matching points in descending order of matching degree to form a matching point sequence. The matching point sequence of nuclear power plants shows a matching degree distribution characteristic of high at both ends and low in the middle. The matching points at both ends of the polarization distribution have the best matching degree with the corresponding mode and the activation effect is most predictable. The matching degree of the matching points in the transition region is slightly lower but still within an acceptable range and can be effectively treated through the transition level.

[0035] A standard activation sequence is generated by locating the optimal matching position along the matching point sequence. Several positions with the highest matching degree are selected from the matching point sequence as standard anchor points. These anchor points represent typical states of severe and mild polarization and can serve as reference benchmarks for dividing activation segments. Based on the standard anchor points determined by the matching point sequence, the entire polarization characteristic spectrum is divided into multiple activation segments. Each activation segment corresponds to a charge / discharge time configuration. The battery packs of the nuclear power plant are divided into three parts according to the anchor points of the matching point sequence: a severe polarization activation segment, a transitional activation segment, and a mild polarization activation segment. The boundaries between each segment are determined by the anchor point positions. Individual cells in the severe polarization activation segment use a deep mode with a longer charge / discharge cycle and a higher number of repetitions to ensure sufficient elimination of severe polarization. Individual cells in the mild polarization activation segment use a fast mode with a shorter charge / discharge cycle to improve processing efficiency while ensuring activation effect. The transitional activation segment uses an intermediate configuration, with the charge / discharge cycle and number of repetitions between the deep and fast modes. The charging and discharging time parameters of each activation segment are arranged in the order of execution to form a standard activation sequence. The standard activation sequence includes three parts: a list of individual unit numbers of each activation segment, charging and discharging time parameters, and execution order.

[0036] Step S130: Apply coefficient modulation to the standard activation sequence using activation adaptation coefficient to generate a composite activation instruction; perform execution saturation assessment on the composite activation instruction to generate a saturation warning; and establish a tiered execution node based on the saturation warning.

[0037] Specifically, a composite activation command is generated by applying coefficient modulation to the standard activation sequence using the activation adaptation coefficient. The various time-period parameters in the standard activation sequence are combined with the activation adaptation coefficient to modulate the time-period parameters. A high activation adaptation coefficient indicates sufficient equipment capacity to execute the standard activation sequence; a low activation adaptation coefficient requires correction of the standard activation sequence to compensate for insufficient equipment capacity. The charging duration in the standard activation sequence is modulated using the formula T_adj = T_charge × (1 + α × (1 - S)), where T_adj is the modulated charging duration, T_charge is the standard charging duration, S is the activation adaptation coefficient, and α is the modulation coefficient. Due to their long service life, the activation equipment in nuclear power plants often has a low activation adaptation coefficient. Therefore, the charging duration in the depth mode is appropriately extended according to the modulation formula to compensate for the decreased voltage regulation accuracy of the equipment. A similar modulation is applied to the discharge duration in the standard activation sequence, also based on the activation adaptation coefficient, to ensure that the discharge depth meets the depolarization requirements. The charge / discharge cycle and repetition count are also modulated accordingly to form the coefficient-modulated activation parameters. The modulated parameters are integrated to generate a composite activation instruction, which includes the charging duration, discharging duration, charging and discharging cycle, and execution order of each activation segment after being corrected by the adaptation coefficient.

[0038] In some embodiments, the step of performing an execution saturation assessment and generating a saturation warning for the composite activation command includes: identifying the individual balanced load capacity of the composite activation command to generate a load deviation critical zone; assessing the individual pressure bearing difference based on the load deviation critical zone to form a pressure coefficient; generating a continuous pressure distribution through interval interpolation of the pressure coefficient; and using the continuous pressure distribution to perform critical extraction to generate a saturation warning.

[0039] The composite activation command is used to identify the balanced load capacity of individual cells, thus generating a critical region for load deviation. The load intensity applied to each individual cell by the composite activation command is analyzed; the load intensity is characterized by the depth of discharge in the composite activation command, with a higher depth of discharge resulting in a higher load intensity. The balanced load capacity that each individual cell can withstand under its current performance state is identified. The balanced load capacity refers to the maximum depth of discharge that a cell can withstand without excessive stress. The balanced load capacity is negatively correlated with the cell's capacity decay rate and internal resistance growth rate; a higher capacity decay rate or a higher internal resistance growth rate results in a lower balanced load capacity. Load deviation is identified by comparing the load intensity of the composite activation command with the balanced load capacity of each individual cell. Load deviation exists when the balanced load capacity of some poor-performing cells is significantly lower than the load intensity applied by the composite activation command. In a battery bank that has been in service for many years in a nuclear power plant, cells near the heat source in the generator room have experienced accelerated aging due to prolonged exposure to high temperatures. The capacity and internal resistance of these cells have significantly deteriorated, and their depth of discharge is far lower than that of normal cells. However, the depth of discharge required by the deep mode in the composite activation command is large, exceeding the tolerance of these deteriorated cells. The area where the load deviation exceeds the safety threshold is marked as the load deviation critical zone. The monomers in the load deviation critical zone may be subjected to excessive stress and are at risk of saturation when executing the composite activation command.

[0040] Pressure coefficients are derived by assessing the pressure-bearing differences of individual units within the load deviation critical zone. The ability of units within the load deviation critical zone to withstand activation loads is evaluated. Units with better performance, although located in the critical zone, have relatively stronger pressure-bearing capacity, while units with poorer performance face greater risks due to weaker pressure-bearing capacity. Nuclear power plants conduct detailed assessments of units within the load deviation critical zone, finding that some units were included in the critical zone due to slight performance degradation nearing their maintenance cycle. These units have relatively minor deterioration and are expected to recover after activation. Other units have more severe deterioration due to early installation defects, resulting in long-term performance lagging behind other units in the same batch. Activation of these units requires extra caution. The pressure-bearing differences of each unit are quantified to form a pressure coefficient, calculated as P = (L_applied - L_capacity) / L_capacity, where P is the pressure coefficient, L_applied is the load intensity, and L_capacity is the unit's equilibrium load capacity. A higher pressure coefficient indicates greater stress and a higher risk of saturation for the unit.

[0041] A continuous pressure distribution is generated through interval interpolation of the pressure coefficients. The pressure coefficients of each cell are arranged according to their cell number to form a discrete pressure coefficient sequence. Cells located in the critical load deviation zone have positive pressure coefficient values, while the pressure coefficients of other cells are zero or negative. Interpolation is performed on the discrete pressure coefficient sequence, filling in intermediate values ​​between cells with known pressure coefficients to generate a continuous pressure distribution curve. The purpose of interpolation is to identify the spatial distribution pattern of the pressure coefficients to facilitate the discovery of concentrated risk areas. Piecewise linear interpolation or spline interpolation is used to ensure the smoothness of the pressure distribution curve, forming a continuous pressure distribution covering all cells. The continuous pressure distribution curve shows that the pressure coefficients rise concentratedly in certain areas, forming obvious high-pressure peaks. The nuclear power plant discovered through the continuous pressure distribution curve that high-pressure cells are concentrated in the middle section of the battery pack rather than being randomly dispersed. After on-site investigation, maintenance personnel confirmed that the racks corresponding to this area have long been in a ventilation dead zone of the equipment room, where air conditioning cannot effectively cover the area, resulting in higher ambient temperatures. Therefore, the performance of cells in the middle section degrades faster than that of cells at the ends, exhibiting a typical distribution characteristic of high pressure in the middle and low pressure at the ends in the continuous pressure distribution. This discovery provides a basis for subsequent optimization of the equipment room ventilation layout.

[0042] Criticality extraction and saturation early warning are generated using continuous pressure distribution. A critical threshold is set on the continuous pressure distribution curve, corresponding to the safety boundary of saturation risk. Based on the statistical distribution characteristics of the pressure-bearing capacity of individual cells, the critical threshold is divided into three levels: yellow, orange, and red. Regions exceeding the critical threshold in the continuous pressure distribution are identified; cells within these regions have a saturation risk. The nuclear power plant identified several regions exceeding the yellow warning threshold on the continuous pressure distribution curve. These regions are distributed in the middle and end sections of the battery pack, and the corresponding cells have undergone numerous deep discharge cycles during historical operation, resulting in significant performance degradation. Saturation risk is classified into different levels according to the degree of exceeding the threshold: cells with pressure coefficients exceeding the yellow warning threshold but not reaching the orange threshold are marked as yellow (indicating mild saturation risk); cells with pressure coefficients exceeding the orange warning threshold but not reaching the red threshold are marked as orange (indicating moderate saturation risk); and cells with pressure coefficients exceeding the red warning threshold are marked as red (indicating severe saturation risk). The saturation early warning is generated by summarizing each warning level and the locations of the affected cells. The saturation early warning includes both the warning level and the location of the affected cells.

[0043] Establish tiered execution nodes based on saturation warnings. Divide the composite activation command into multiple execution stages according to the level and location of the saturation warning. For cells with lower saturation warning levels, the composite activation command can be executed continuously without interruption. Cells with strong pressure resistance in the nuclear power plant battery bank have a green saturation warning level, and these cells can continuously complete the entire activation cycle. For cells with higher saturation warning levels, rest nodes need to be inserted into the composite activation command to change continuous execution to tiered execution. Cells with yellow and orange saturation warning levels cannot withstand continuous high-intensity activation and need to be executed in stages. Determine the location of the tiered execution nodes, setting them before the saturation warning critical point. Based on saturation warning analysis, the first tiered execution node is set after several charge / discharge cycles in deep mode, allowing the cell to rest for a period before continuing subsequent cycles. Subsequent tiered execution nodes are set as needed after several charge / discharge cycles in standard mode. Set the rest duration for each tiered execution node. The rest duration is determined based on the cell's pressure resistance and saturation warning level. Yellow warning cells have a shorter rest duration, orange warning cells have a longer rest duration, and red warning cells require a significant reduction in activation intensity or a halt to activation. Based on saturation early warning analysis results, the nuclear power plant has set multiple tiered execution nodes in the composite activation command, dividing the originally continuous activation process into multiple execution stages, with appropriate rest intervals between each stage. The location and rest duration parameters of the tiered execution nodes are added to the composite activation command to form a complete activation scheme that includes execution node control.

[0044] Step S140: Based on the tiered execution node, the composite activation command is transmitted to the battery to generate a feedback dataset. The residual polarization signal is extracted from the feedback dataset to generate residual correction parameters. Based on the residual correction parameters, energy monitoring is performed on the feedback dataset to generate an energy balance curve. The energy balance curve is used for trend analysis to generate the activation response coefficient.

[0045] Specifically, based on the tiered execution nodes, composite activation commands are transmitted to the batteries to generate feedback datasets. The activation equipment, controlled according to the tiered execution nodes established in the composite activation commands, performs charge and discharge operations on the battery pack in a phased manner. In the first execution phase, deep-mode charge and discharge commands are transmitted to the cells in the heavily polarized stage. The equipment charges these cells according to the modulated charging duration, continuously recording the terminal voltage, charging current, and temperature changes of each cell during charging. After charging is completed, the system switches to discharge mode to perform deep discharge on the cells, recording the terminal voltage, discharge current, and temperature data during discharge. After the first phase completes the preset charge and discharge cycle, the equipment enters the rest period of the first tiered execution node, monitoring the voltage recovery and temperature drop of each cell during the rest period. During the rest period of the first tiered execution node in the nuclear power plant, it was found that the voltage recovery rate of some heavily polarized cells was significantly slower than that of other cells; this characteristic was recorded in the feedback dataset as an important basis for subsequent analysis. After the rest period ends, the second execution phase begins, transmitting standard-mode charge and discharge commands to the cells in the moderately polarized stage, continuing to collect various parameter data of the charge and discharge process. The voltage, current, temperature, and timestamp data collected in real time throughout the phased execution process are aggregated to form a feedback dataset. The feedback dataset contains the charge and discharge response characteristics of each individual cell at different execution stages and the full process data from activation to the completion of each stage.

[0046] In some embodiments, the step of extracting polarization residual signals from the feedback dataset to generate residual correction parameters includes: constructing a voltage rebound profile based on the feedback dataset; extracting rebound peak positions along the voltage rebound profile to form peak anchor points; setting an attenuation threshold for the peak anchor points to divide the voltage rebound profile into effective residual bands and ineffective residual bands; and quantifying the distribution characteristics of the effective residual bands and the ineffective residual bands to form residual correction parameters.

[0047] A voltage rebound profile was constructed based on the feedback dataset. The feedback dataset contains voltage change data for each cell after charging is completed. When the charging current is cut off, the cell's terminal voltage drops from the charging completion voltage to the open-circuit voltage; this drop reflects the release of polarization. The terminal voltage of each cell at the moment charging ends in the feedback dataset is recorded as the starting voltage, and the voltage drop trajectory over time is continuously monitored after charging. Cells in nuclear power plants that have undergone long-term deep charging typically have higher terminal voltages at the end of charging, followed by a rapid drop within minutes, then a slow drop phase, eventually stabilizing at the open-circuit voltage. The voltage drop trajectory of each cell is unfolded along the time axis to form a voltage-time curve. The voltage drop curves of each cell in the feedback dataset are normalized, and cells with different capacities and initial voltages are compared under a unified scale. The normalized voltage drop curves are arranged according to cell number to form a voltage rebound profile. The voltage rebound profile presents the voltage drop characteristics of each cell in a two-dimensional data structure, with the horizontal axis representing the time after charging ends, the vertical axis representing the cell number, and the data values ​​representing the normalized voltage drop amplitude.

[0048] Peak rebound points are extracted along the voltage rebound range to form peak anchor points. In the voltage rebound range, the voltage drop curves of each individual cell typically exhibit a rapid decline at the beginning of charging; the starting point of this rapid decline corresponds to the charging end voltage, which is the peak position of the voltage rebound. The peak positions of the voltage drop curves of each individual cell in the voltage rebound range are recorded, including both time and voltage coordinates. In heavily polarized cells, the charging end voltage is significantly higher than that of lightly polarized cells, resulting in higher rebound peak positions. Conversely, in lightly polarized cells, the charging end voltage is close to the stable open-circuit voltage, leading to lower rebound peak positions. The rebound peak positions of each individual cell are marked in the voltage rebound range, and these marked points constitute a set of peak anchor points. After sorting the peak anchor points, the nuclear power plant found that the group of cells with the highest peak values ​​is concentrated in the middle section of the battery pack. These cells have been exposed to a high-temperature environment for a long time, resulting in severe polarization accumulation. Conversely, the group of cells with the lowest peak values ​​is distributed at both ends of the battery pack, near the air inlet and outlet, where temperature conditions are better and polarization is less severe. The backup battery bank at another nuclear power plant, having been in a float-charge standby state for an extended period, exhibited a generally high and relatively uniform peak anchor point distribution, indicating a moderate degree of polarization accumulation across all cells. The span of the peak anchor point distribution reflects the magnitude of polarization differences between cells.

[0049] A decay threshold is set for the peak anchor point to divide the voltage rebound range into effective and ineffective residual bands. The voltage decay process of each cell, starting from the rebound peak corresponding to the peak anchor point, is divided into a rapid decay segment and a slow decay segment. The rapid decay segment reflects easily eliminated polarization components, which can be effectively removed through charge-discharge cycles during activation. The slow decay segment reflects difficult-to-eliminate polarization components, which remain even after multiple charge-discharge cycles. A decay threshold is set to distinguish between rapid and slow decay. The threshold is selected at the point where the voltage decay rate corresponding to the peak anchor point changes significantly; the inflection point where the decay rate changes from rapid to slow is used as the decay threshold. Regions in the voltage rebound range with voltage decay rates higher than the threshold are classified as effective residual bands. This region corresponds to the rapid decay segment, indicating that the residual polarization in this region can be effectively eliminated through continued activation. Regions with voltage decay rates lower than the threshold are classified as ineffective residual bands. This region corresponds to the slow decay segment, indicating that the residual polarization in this region is close to the inherent characteristics of the cell and cannot be further eliminated through conventional activation. In nuclear power plants, the range of ineffective residual bands is wider in the cells that have experienced a significant increase in internal resistance due to long-term operation, indicating that these cells contain a lot of polarization residues that are difficult to eliminate. In contrast, in cells with good performance, the range of ineffective residual bands is narrower or even absent, and effective residual bands dominate.

[0050] For example, quantifying the distribution characteristics of the effective residual band and the invalid residual band to form residual correction parameters includes: transforming the residual sequence of the effective residual band into an energy accumulation chain; shifting and fusing the residual sequence of the invalid residual band into the energy accumulation chain to form an energy difference spectrum; extracting energy mutation increments from the energy difference spectrum; and forming residual correction parameters based on the degree of aggregation of the energy mutation increments.

[0051] The residual sequence of the effective residual band is transformed into an energy accumulation chain. The polarization residual amount of each cell in the effective residual band is characterized by the voltage decay amplitude. The voltage decay amplitude of each cell within the effective residual band is integrated to obtain the corresponding polarization residual energy of each cell. The polarization residual energies corresponding to the effective residual band are arranged according to the cell number to form a residual energy sequence. The residual energy sequence is accumulated, and the accumulated energy of the i-th cell is defined as the sum of the residual energies of the first i cells, with the formula E_cum(i)=Σ(k=1toi)E_res(k), where E_cum is the accumulated energy and E_res is the residual energy of the cell. The energy sequence after accumulation transformation forms an energy accumulation chain. The value of the energy accumulation chain increases monotonically, and its growth rate reflects the distribution density of the residual energy of each cell. When analyzing the energy accumulation chain, nuclear power plants found that the curve shows a gentle upward trend in the front section of the battery pack, and the slope suddenly increases in the middle section, indicating that the removable polarization residuals of the cells in the middle section are concentrated and numerous. These cells are the groups of cells that frequently undertook emergency discharge tasks in the historical operation record, and frequent deep discharges lead to accelerated polarization accumulation. In the energy accumulation chain, the segments with a larger slope correspond to monomer groups with dense residual energy, and these monomers have more polarization residues. The segments with a smaller slope correspond to monomer groups with sparse residual energy, and these monomers have fewer polarization residues.

[0052] The residual sequences of invalid residual bands are offset and fused into an energy accumulation chain to form an energy difference spectrum. The amount of persistent polarization residue in each cell within the invalid residual band represents the degree of performance degradation of that cell. The voltage decay amplitude of each cell within the invalid residual band is converted into equivalent energy, and the invalid residual energy sequence is obtained using the same integration method as for the valid residual band. The invalid residual energy sequence corresponding to the invalid residual band is fused with the energy accumulation chain. During fusion, an offset coefficient is applied to the invalid residual energy, determined based on the energy ratio of the invalid residual band to the valid residual band; the higher the proportion of invalid residual band energy, the larger the offset coefficient. During the fusion process, it was found that the invalid residual band energy of some cells accounted for more than 60% of the total residual energy. These cells had been under-maintained for a long time, resulting in severe plate sulfation, and most of the polarization had been converted into irreversible degradation. Larger offset coefficients were applied to these cells to highlight the severity of their performance degradation. Cells with an invalid residual band proportion of only about 20% mainly exhibit recoverable polarization accumulation, and smaller offset coefficients were applied. Through offset fusion, the distribution characteristics of the valid and invalid residual bands are integrated into a unified energy spectrum, and the fused energy distribution forms the energy difference spectrum. The nuclear power plant's energy difference spectrum revealed that energy jumps were concentrated in the middle section of the battery pack. These locations corresponded to significant differences in the performance of individual cells. Further investigation revealed that these cells were from different production batches than those at the ends, indicating they were replacement parts. Subtle differences in electrochemical characteristics resulted in a lower performance match with the original cells. In contrast, the energy values ​​at the ends of the battery pack showed smooth changes, with adjacent cells exhibiting similar performance. These cells were all original and had undergone consistent service conditions.

[0053] Energy mutation increments were extracted from the energy difference spectrum. The energy changes between adjacent cells in the energy difference spectrum directly reflect the magnitude of performance differences. The location of energy mutations corresponds to the performance boundary points of individual cells, and the energy mutation increment is represented by the energy difference between adjacent cells. All adjacent cell pairs in the energy difference spectrum were traversed, and locations where the energy mutation increment exceeded a threshold were identified. In battery banks with good activation effects in nuclear power plants, it was found that the performance of each cell tended to be consistent after activation, the energy mutation increments in the energy difference spectrum were small and evenly distributed, and the energy jump amplitude between adjacent cells was controlled at a low level. However, in another group of batteries with poor activation effects, some cells showed significantly lagging recovery due to severe sulfation of internal plates. Significant energy mutation increments appeared between these cells and adjacent normal cells in the energy difference spectrum, forming obvious energy gaps indicating the existence of performance differences. All extracted energy mutation increments were sorted by value, and the most significant mutation locations were identified. These locations are weak points that need to be focused on during the activation process.

[0054] Residual correction parameters are formed based on the degree of aggregation of energy mutation increments. The distribution pattern of energy mutation increments in the individual cell sequence reflects the concentration of performance differences. When energy mutation increments are dispersed across various locations, it indicates that the residual polarization differences of each cell are relatively uniform, and the activation effect is relatively consistent overall. When energy mutation increments are concentrated in certain specific regions, it indicates that there are concentrated performance differences and uneven activation effects among the cells in these regions. The distribution characteristics of energy mutation increments of two sets of batteries in a nuclear power plant were compared. The mutation locations of the first set of batteries were randomly dispersed throughout the entire cell sequence with a low degree of aggregation, indicating that although there are individual differences among the cells, the overall balance is good. The mutation locations of the second set of batteries were concentrated in a continuous region at the end of the battery pack with a high degree of aggregation. Investigation revealed that the cells in this region had experienced over-discharge stress during a deep discharge accident, leading to a collective performance degradation. The aggregation degree index quantifies the distribution characteristics of energy mutation increments, with the formula C=(σ_ΔE / μ_ΔE)×(κ_ΔE / κ_ref), where C is the aggregation degree, σ_ΔE is the standard deviation of the energy mutation increment, μ_ΔE is the mean of the energy mutation increment, κ_ΔE is the kurtosis of the energy mutation increment, and κ_ref is the reference kurtosis. A higher aggregation degree value indicates a more concentrated energy mutation increment and more significant performance differences between monomers. Residual correction parameters are generated based on the aggregation degree and the overall characteristics of the energy difference spectrum. These parameters include both the aggregation degree index and the ratio of effective to ineffective residual bands.

[0055] Energy monitoring is performed on the feedback dataset based on residual correction parameters to generate energy balance curves. The energy data in the feedback dataset is corrected using residual correction parameters. The clustering index in the residual correction parameters is used to determine the correction intensity; a higher clustering intensity indicates a more concentrated performance difference between individual cells, requiring a greater correction intensity. When a nuclear power plant was dealing with a group of batteries with highly concentrated energy fluctuations, it was found that cells in the clustered areas had been damaged in the same deep discharge accident, exhibiting systematic bias, while cells in the dispersed areas only required slight correction. The ratio of effective to ineffective residual bands in the residual correction parameters is used to distinguish between correctable and uncorrectable portions. Cells with a high proportion of effective residual bands show polarization that is mainly reversible and can be improved through activation, while cells with a high proportion of ineffective residual bands exhibit irreversible damage such as plate corrosion; their energy data is kept unchanged to accurately reflect the deterioration state. The corrected feedback dataset more accurately reflects the actual energy conversion situation for each cell in terms of charging and discharging energy. Comparing the charging and discharging energy yields the energy efficiency of each cell, which reflects the energy conversion capability of the cell during charge-discharge cycles. By tracking the energy efficiency trends of individual cells during charge-discharge cycles, it was found that some heavily polarized cells had low energy efficiency in the initial activation phase of the nuclear power plant. As the charge-discharge cycle progressed, the energy efficiency of these cells gradually increased, indicating a reduction in polarization and recovery of energy conversion capabilities. Energy efficiency data for each cell under different charge-discharge cycles were connected chronologically to form energy efficiency curves for each cell. Analysis of the differences between these curves identified cells with uneven energy recovery. The energy difference information from these cells was then integrated to form an energy balance curve. The slope of the energy balance curve indicates the rate of energy recovery, and the fluctuation amplitude reflects the degree of imbalance between cells.

[0056] In some embodiments, the step of using the energy balance curve to perform trend analysis to generate an activation response coefficient includes: identifying slope changes in the energy balance curve to form a set of trend inflection points; extracting energy recovery rates based on the set of trend inflection points to form a recovery rate sequence; performing stability assessment on the recovery rate sequence to generate a stability index; and comprehensively quantifying the stability index and the recovery rate sequence to form an activation response coefficient.

[0057] The slope changes of the energy balance curve are identified to form a set of trend inflection points. The slope of the energy balance curve reflects the rate of energy recovery. In the early stages of activation, the energy efficiency of heavily polarized cells is low, and the slope of the energy balance curve is small, indicating slow energy recovery. As the charge-discharge cycle progresses, polarization gradually diminishes, energy efficiency improves, and the slope of the energy balance curve increases, indicating accelerated energy recovery. When activation reaches a certain stage, cell performance approaches its optimal state, and the energy recovery rate reaches its peak. Further activation reduces the improvement, and the slope of the energy balance curve begins to decline and flatten. Piecewise linear fitting of the energy balance curve is used to identify locations where the slope changes significantly. Locations where the slope increases correspond to inflection points where energy recovery begins to accelerate, locations where the slope decreases correspond to inflection points where energy recovery begins to decelerate, and locations where the slope approaches zero correspond to inflection points where energy recovery approaches saturation. Each identified slope change location is marked as a trend inflection point. In a typical nuclear power plant activation process, the energy balance curve includes three trend segments: a slow recovery segment, a rapid recovery segment, and a saturated stable segment. The corresponding set of trend inflection points includes two key locations: acceleration inflection points and deceleration inflection points. The time and energy coordinates of each trend turning point are recorded to form a set of trend turning points.

[0058] Energy recovery rates are extracted from a set of trend inflection points to form a recovery rate sequence. The trend inflection point set divides the energy equilibrium curve into several trend segments, each exhibiting different characteristics in energy recovery rate. The energy recovery rate between adjacent inflection points in the trend inflection point set is defined as the increase in energy efficiency per unit time. The recovery rate within each trend segment is obtained by the ratio of the energy efficiency difference between the starting and ending points to the time difference. The energy recovery rates of each trend segment divided by the trend inflection point set are calculated sequentially to form a recovery rate sequence. In nuclear power plants with well-activated battery banks, the recovery rate sequence shows a trend of first increasing and then decreasing. The initial recovery rate is low, the mid-term recovery rate reaches a peak, and the late-term recovery rate decreases and approaches zero. This pattern conforms to the typical S-shaped recovery curve characteristics. The peak recovery rate and its corresponding time position are extracted from the numerical distribution of the recovery rate sequence. The peak recovery rate reflects the maximum energy recovery efficiency during activation, and the time to reach the peak reflects the speed of the activation response.

[0059] A stability index is generated by evaluating the stability of the recovery rate sequence. The fluctuation characteristics of the recovery rate sequence reflect the stability of the energy recovery process. In a stable recovery process, the recovery rate sequence changes smoothly with natural transitions between trend segments, while in an unstable recovery process, the recovery rate sequence fluctuates sharply, with some trend segments showing abnormally high or low recovery rates. The fluctuation amplitude of the recovery rate sequence is quantified by the standard deviation and the coefficient of variation. The standard deviation reflects the absolute degree of fluctuation in the recovery rate, and the coefficient of variation reflects the relative degree of fluctuation. In battery banks where the activation process is stable in nuclear power plants, a smaller standard deviation and coefficient of variation in the recovery rate sequence indicate stable and controllable energy recovery. Conversely, in battery banks where the activation process is disturbed, a larger standard deviation and coefficient of variation in the recovery rate sequence indicate uncertainty in energy recovery. A stability index is generated by combining the standard deviation and coefficient of variation, with the formula S_stab = 1 / (1 + CV_R), where S_stab is the stability index and CV_R is the coefficient of variation of the recovery rate sequence. The closer the stability index value is to 1, the more stable the recovery process; the closer the value is to 0, the more unstable the recovery process.

[0060] The activation response coefficient is formed by comprehensively quantifying the stability index and the recovery rate sequence. The characteristic parameters of the stability index and the recovery rate sequence together characterize the overall level of activation effect. Representative parameters in the recovery rate sequence include peak recovery rate, average recovery rate, and the time span of the recovery rate. The peak recovery rate reflects the maximum response capability of the activation process, the average recovery rate reflects the overall efficiency of the activation process, and the time span of the recovery rate reflects the time required for the activation process to reach stability. These parameters, after normalization, are combined with the stability index to construct a quantitative model for the activation response coefficient, with the formula: A_resp=W1×(R_peak / R_ref)+W2×(R_avg / R_ref)+W3×S_stab-W4×(T_span / T_ref), where A_resp is the activation response coefficient, R_peak is the peak recovery rate, R_avg is the average recovery rate, R_ref is the reference recovery rate, S_stab is the stability index, T_span is the time span, T_ref is the reference time span, and W1, W2, W3, and W4 are weighting coefficients. In a nuclear power plant, the activation response coefficient of a battery bank that has undergone a complete activation cycle typically increases first and then tends to stabilize with the charge-discharge cycle. When the activation response coefficient reaches a preset threshold and remains stable, it indicates that the activation process can be terminated and the battery has been restored to a good condition.

[0061] Step S150: Based on the activation response coefficient, perform recovery cycle analysis on the feedback dataset to generate a recovery cycle, use the recovery cycle to adjust the activation frequency to generate a performance confirmation file, and compare the performance confirmation file with the battery state parameters to generate an activation effect evaluation report.

[0062] Specifically, recovery cycles are generated based on the activation response coefficient and the feedback dataset. The evolution of the activation response coefficient with charge-discharge cycles reflects the periodic characteristics of battery performance recovery. In the initial stage of activation, the activation response coefficient is low and rises slowly. After several charge-discharge cycles, the activation response coefficient begins to rise rapidly. Once it reaches a stable high value, the effect of continuing charge-discharge cycles on improving the activation response coefficient weakens. The total number of charge-discharge cycles required for the activation response coefficient to rise from its initial value to its stable value is recorded. This total number of cycles reflects the time required for the battery pack to complete one effective activation. This total number of cycles is defined as the recovery cycle, the length of which depends on the initial polarization degree and performance degradation state of the battery. Nuclear power plants found that the recovery cycle is long when activating battery packs that have been in a float charge state for a long time and are severely polarized, requiring many charge-discharge cycles to stabilize the activation response coefficient. Conversely, the recovery cycle is relatively short when activating battery packs that are frequently used and have less polarization. The correlation between the performance indicators of each cell in the feedback dataset under different charge-discharge cycles, including capacity, internal resistance, and charge-discharge efficiency, and the activation response coefficient is analyzed. When the activation response coefficient reaches a stable state, the capacity of each cell in the feedback dataset should recover to a high proportion of its rated capacity, the internal resistance should decrease to an acceptable range, and the charge / discharge efficiency should reach an ideal level. The number of charge / discharge cycles corresponding to the stable activation response coefficient is used as a marker of the recovery cycle, and the degree of performance recovery of each cell in the feedback dataset at that moment is recorded.

[0063] In some embodiments, the step of adjusting the activation frequency using the recovery cycle to generate a performance verification profile includes: transforming the recovery cycle into a capacity recovery rate distribution array; locating a recovery inflection point reference position along the capacity recovery rate distribution array; performing frequency extension starting from the recovery inflection point reference position to form an initial effective domain; and performing range locking on the initial effective domain to form a performance verification profile.

[0064] The recovery cycle is transformed into a capacity recovery rate distribution array. The recovery cycle records the number of charge-discharge cycles experienced by each cell from activation to performance stabilization. Capacity test data for each cell at the end of each charge-discharge cycle within the recovery cycle is extracted, and the recovery ratio of each cell relative to its rated capacity is calculated. The capacity recovery rate of cell j at the end of the i-th charge-discharge cycle within the recovery cycle is defined as the ratio of the measured capacity to the rated capacity at that moment, using the formula R_ij = C_ij / C_rated, where R_ij is the capacity recovery rate, C_ij is the measured capacity, and C_rated is the rated capacity. The capacity recovery rates of each cell in each charge-discharge cycle within the recovery cycle are arranged according to the cell number and cycle number, forming a two-dimensional data structure. When analyzing the activation data of a set of batteries, the nuclear power plant organizes the capacity recovery rate data of all cells within the recovery cycle into a matrix form. The rows of the matrix correspond to the cell number, the columns correspond to the charge-discharge cycle number, and the data values ​​are the capacity recovery rates at the corresponding positions. The capacity recovery rate data in matrix form constitutes a capacity recovery rate distribution array, which intuitively presents the temporal evolution characteristics of the capacity recovery of each individual cell and the recovery differences between individual cells.

[0065] The baseline position of the recovery inflection point is located along the capacity recovery rate distribution array. In the capacity recovery rate distribution array, the capacity recovery rate of each cell shows a trend of first rapid and then slowing down with the increase of charge-discharge cycles. The position where the recovery rate changes from fast to slow corresponds to the recovery inflection point. For each cell in the capacity recovery rate distribution array, the slope change of its capacity recovery rate curve is analyzed to identify the turning point where the slope changes from large to small. The nuclear power plant found that the recovery inflection point of most cells in the capacity recovery rate distribution array appears at similar charge-discharge cycle positions, indicating that the recovery characteristics of these cells are relatively consistent. However, the recovery inflection point of a few cells is significantly delayed, and these cells have a higher degree of polarization and require more cycles to enter the slow recovery stage. The positions of the recovery inflection points of each cell in the capacity recovery rate distribution array are statistically analyzed, and the mean and distribution range of the inflection point positions are calculated. The nuclear power plant compared the recovery inflection point distribution of different batches of batteries and found that the inflection point positions of battery packs with shorter service life are concentrated and earlier, while the inflection point positions of battery packs with longer service life are dispersed and generally later. By combining the statistical characteristics of the recovery inflection point positions of each individual cell, a recovery inflection point benchmark representing the recovery characteristics of the entire battery pack is determined. The recovery inflection point benchmark is the weighted average of the inflection point positions of each individual cell, with the weights allocated according to the capacity ratio of each individual cell.

[0066] The initial effective domain is formed by extending the activation frequency based on the recovery inflection point benchmark. The recovery inflection point benchmark marks the critical point where the battery pack transitions from a rapid recovery phase to a slow recovery phase, and this benchmark is used to determine the effective range of activation frequencies. Extending forward from the recovery inflection point benchmark, the earliest effective activation cycle is determined, corresponding to the point where the capacity recovery rate begins to rise significantly. Nuclear power plants, in analyzing activation data, found that the capacity recovery rate increased most significantly in the several cycles before the recovery inflection point benchmark; this interval is the core period where the activation effect is most significant. Extending backward from the recovery inflection point benchmark, the period of diminishing marginal returns to activation is determined, corresponding to the point where continued activation yields only a small increase in recovery rate. Nuclear power plants found that continued activation after the recovery inflection point benchmark, while still resulting in capacity improvement, gradually decreases in magnitude; when the improvement falls below a set threshold, continued activation becomes less economical. The range between the forward-extended effective cycle and the backward-extended diminishing marginal returns cycle is defined as the initial effective domain, which encompasses the range of charge-discharge cycles where activation is significant and economically reasonable. The initial effective domain determined by the nuclear power plant based on the recovery inflection point benchmark shows that the effective activation range of this group of batteries is concentrated in the middle of the recovery cycle, while the initial start-up phase and the subsequent maintenance phase are not within the initial effective domain.

[0067] A performance verification file is generated by locking the initial effective domain. The initial effective domain defines the effective activation period range, and the range is locked based on actual operation and maintenance requirements. A recommended activation start time is determined based on the start period of the initial effective domain; activation should be initiated when the battery performance indicators drop to the state corresponding to that time. A nuclear power plant uses the capacity recovery rate corresponding to the start period of the initial effective domain as the activation start threshold; the activation process is triggered when periodic monitoring detects that the battery capacity is below this threshold. A recommended activation termination time is determined based on the end period of the initial effective domain; activation should be terminated when this period is reached to avoid over-processing. A nuclear power plant uses the capacity recovery rate corresponding to the end period of the initial effective domain as the activation termination threshold; activation is stopped when the capacity recovery rate reaches this threshold during the activation process. The recommended activation interval and duration for a single activation are obtained by converting the period span of the initial effective domain. A nuclear power plant determines the recommended activation interval for its battery bank based on the span of the initial effective domain, setting it to several months and the duration of each activation cycle to several days. These parameters are highly correlated with the battery's service life and historical operating conditions; batteries with longer service lives have shorter activation intervals and longer activation cycles. Capacity test data for each cell at the end of the initial effective domain cycle are extracted, and the measured capacity of each cell after activation is recorded. The internal resistance of each cell at the end of the initial effective domain cycle is measured, using the same AC impedance method as before activation to ensure data comparability. The charge-discharge efficiency of each cell at the end of the initial effective domain cycle is calculated; charge-discharge efficiency is the ratio of released energy to charged energy. The locked activation parameters, along with the capacity, internal resistance, and charge-discharge efficiency data for each cell, are compiled to form a performance confirmation file. The performance confirmation file includes three items: capacity data, internal resistance data, and charge-discharge efficiency data for each cell after activation.

[0068] An activation effect evaluation report is generated by comparing the performance confirmation file with the battery state parameters to assess their health. Post-activation performance data recorded in the performance confirmation file is extracted and compared with the pre-activation battery state parameters. The capacity data in the performance confirmation file is compared with the capacity decay rate of each cell in the battery state parameters. The capacity recovery rate is used to evaluate the improvement effect of activation on capacity, defined as (post-activation capacity - pre-activation capacity) / pre-activation capacity. In a nuclear power plant, under good activation conditions, the capacity recovery rate of most cells reached above the expected threshold, indicating a significant polarization elimination effect. However, cells with low capacity recovery rates in the performance confirmation file highly overlapped with cells in the battery state parameters that originally had severe capacity decay. These cells may have irreversible performance degradation and require special attention. The internal resistance data in the performance confirmation file is compared with the internal resistance data of each cell in the battery state parameters. The reduction in internal resistance reflects the degree of polarization elimination by activation. In effectively activated cells, the internal resistance usually decreases significantly, while cells with insignificant or even increasing internal resistance may have internal structural deterioration. The charge / discharge efficiency data in the performance confirmation file is compared with the efficiency baseline before activation. The improvement in efficiency indicates a reduction in energy conversion loss. The activation effect of each cell is rated based on the comprehensive capacity recovery rate, internal resistance change rate, and efficiency improvement, classifying cells into four levels: excellent, good, average, and poor. The battery pack health index is calculated using the formula H = (C_avg / C_rated) × (R_rated / R_avg) × (1 - CV_C), where H is the health level, C_avg is the average capacity, C_rated is the rated capacity, R_avg is the average internal resistance, R_rated is the rated internal resistance, and CV_C is the capacity variation coefficient. The health level after activation is compared with that before activation; the improvement in health level directly reflects the overall activation effect. The results of the above comparative analysis are compiled into an activation effect evaluation report, which includes three parts: activation effect rating of each cell, health level comparative analysis, and identification of abnormal cells.

[0069] To implement the above-described method embodiments, a method for activating a DC power supply battery in a nuclear power plant is provided to achieve the corresponding functions and technical effects. See also... Figure 2 , Figure 2 This diagram illustrates a structural block diagram of a nuclear power plant DC power battery activation device 200 according to an embodiment of this application. For ease of explanation, only the parts relevant to this embodiment are shown. The nuclear power plant DC power battery activation device 200 according to an embodiment of this application includes: The data acquisition module 201 is used to acquire the battery status parameters of the storage battery and the activation configuration specifications of the activation equipment, and to perform a matching degree check on the battery status parameters and the activation configuration specifications to generate an activation adaptation coefficient. The sequence construction module 202 is used to perform polarization degree statistics on the battery state parameters to generate a polarization feature spectrum, extract available charge-discharge cycles along the activation configuration specification to generate a charge-discharge time period group, and map the polarization feature spectrum to the charge-discharge time period group to construct a standard activation sequence. The instruction modulation module 203 is used to apply coefficient modulation to the standard activation sequence using the activation adaptation coefficient to generate a composite activation instruction, perform an execution saturation assessment on the composite activation instruction to generate a saturation warning, and establish a tiered execution node based on the saturation warning; Energy monitoring module 204 is used to transmit the composite activation command to the battery according to the stepped execution node to generate a feedback dataset, extract the polarization residual signal from the feedback dataset to generate residual correction parameters, perform energy monitoring on the feedback dataset based on the residual correction parameters to generate an energy balance curve, and use the energy balance curve to perform trend analysis to generate an activation response coefficient. The evaluation output module 205 is used to perform recovery cycle analysis on the feedback dataset based on the activation response coefficient to generate a recovery cycle, adjust the activation frequency using the recovery cycle to generate a performance confirmation file, and compare the performance confirmation file with the battery state parameters to generate an activation effect evaluation report.

[0070] The aforementioned nuclear power plant DC power supply battery activation device 200 can implement a nuclear power plant DC power supply battery activation method according to the above method embodiments. Optional embodiments of the above method are not detailed here. The remaining contents of this application's embodiments can be referred to the contents of the above method embodiments, and will not be repeated in this embodiment.

[0071] The purpose of the above embodiments is to reproduce and derive the technical solution of the present invention by way of example, and to fully describe the technical solution, purpose and effect of the present invention. The purpose is to enable the public to have a more thorough and comprehensive understanding of the disclosure of the present invention, and not to limit the scope of protection of the present invention.

[0072] The above embodiments are not an exhaustive list based on the present invention, and there may be many other embodiments not listed. Any substitutions and improvements made without departing from the concept of the present invention are within the protection scope of the present invention.

Claims

1. A method for activating a DC power supply battery in a nuclear power plant, characterized in that, include: Collect battery status parameters of the storage battery and activation configuration specifications of the activation equipment, and perform a matching degree check on the battery status parameters and activation configuration specifications to generate an activation adaptation coefficient; Polarization feature spectrum is generated by performing polarization degree statistics on the battery state parameters, and available charge-discharge cycles are extracted along the activation configuration specifications to generate charge-discharge time period groups. The polarization feature spectrum is then mapped to the charge-discharge time period groups to construct a standard activation sequence. The standard activation sequence is modulated by applying the activation adaptation coefficient to generate a composite activation instruction. An execution saturation assessment is performed on the composite activation instruction to generate a saturation warning. A tiered execution node is established based on the saturation warning. According to the stepped execution node, the composite activation command is transmitted to the battery to generate a feedback dataset. The residual polarization signal is extracted from the feedback dataset to generate residual correction parameters. Based on the residual correction parameters, energy monitoring is performed on the feedback dataset to generate an energy balance curve. The energy balance curve is used to perform trend analysis to generate an activation response coefficient. Based on the activation response coefficient, a recovery cycle analysis is performed on the feedback dataset to generate a recovery cycle. The activation frequency is adjusted using the recovery cycle to generate a performance confirmation profile. The performance confirmation profile is then compared with the battery state parameters to generate an activation effect evaluation report.

2. The method according to claim 1, characterized in that, The step of performing a matching degree verification between the battery state parameters and the activation configuration specifications to generate an activation adaptation coefficient includes: Based on the battery state parameters, a set of dispersion indicators is formed by extracting the dispersion index of individual cells. The capability matching between the set of dispersion indicators and the activation configuration specifications is used to form an indicator-equipment correspondence; Identify the gap domain of individual difference compensation gap formation capability along the aforementioned index-equipment correspondence; The activation adaptation coefficient is formed by the coverage strength of the capability gap domain.

3. The method according to claim 1, characterized in that, The step of mapping the polarization feature spectrum to the charge / discharge time group to construct a standard activation sequence includes: The polarization characteristic spectrum is divided into a heavily polarized segment and a lightly polarized segment; Within the charge / discharge time period group, a time period docking channel is established from the heavily polarized segment to the lightly polarized segment; The positions of the characteristic matching points on the time-segment docking channel are marked to form a matching point sequence; The optimal anastomosis position is located along the anastomosis point sequence to generate a standard activation sequence.

4. The method according to claim 1, characterized in that, The step of performing an execution saturation assessment and generating a saturation warning for the composite activation command includes: The composite activation command is used to identify the individual load capacity for load balancing to generate a critical load deviation zone. Based on the aforementioned load deviation critical zone, the pressure bearing difference of individual units is evaluated to form a pressure coefficient; A continuous pressure distribution is generated by interval interpolation of the pressure coefficient; The continuous pressure distribution is used to perform critical extraction and generate saturation early warning.

5. The method according to claim 1, characterized in that, The step of extracting the polarization residual signal from the feedback dataset to generate residual correction parameters includes: A voltage rebound setting is constructed based on the feedback dataset; Extract the peak value of the rebound along the voltage rebound range to form a peak anchor point; Setting an attenuation threshold for the peak anchor point divides the voltage rebound band into an effective residual band and an ineffective residual band. The distribution characteristics of the effective and ineffective residual bands are quantified to form residual correction parameters.

6. The method according to claim 1, characterized in that, The step of using the energy balance curve to perform trend analysis and generate activation response coefficients includes: The slope changes of the energy balance curve are identified to form a set of trend inflection points; Based on the set of trend inflection points, the energy recovery rate is extracted to form a recovery rate sequence; The stability of the recovery rate sequence is evaluated to generate a stability index; The activation response coefficient is formed by combining the stability index and the recovery rate sequence.

7. The method according to claim 1, characterized in that, The process of generating a performance verification profile by adjusting the activation frequency using the recovery cycle includes: The recovery cycle is transformed into a capacity recovery rate distribution array; The recovery inflection point reference position is located along the capacity recovery rate distribution array; Starting from the recovery inflection point reference position, frequency extension is performed to form an initial effective domain; A performance verification profile is generated by performing range locking on the initial valid domain.

8. The method according to claim 3, characterized in that, The step of marking the positions of characteristic matching points on the time-segment docking channel to form a matching point sequence includes: The detection interval is determined based on the polarization jump characteristics of the docking channel during the specified time period. These polarization jump characteristics include the rise gradient, peak duration, and decay rate. The polarization change process is traced along the detection interval to form a polarization change spectrum; Extract the time period location values ​​of each coincidence point from the polarization change spectrum; The matching point sequence is generated by arranging the matching values ​​according to the degree of similarity of the time period positioning values.

9. The method according to claim 5, characterized in that, The step of quantifying the distribution characteristics of the effective and ineffective residual bands to form residual correction parameters includes: The residual sequence of the effective residual band is transformed into an energy accumulation chain; The residual sequence of the invalid residual band is shifted and fused into the energy accumulation chain to form an energy difference spectrum; Extract the energy mutation increment from the energy difference spectrum; Residual correction parameters are formed based on the degree of aggregation of the energy mutation increments.

10. A device for activating a DC power supply battery in a nuclear power plant, characterized in that, include: The data acquisition module is used to collect the battery status parameters of the storage battery and the activation configuration specifications of the activation equipment, and to perform a matching degree check on the battery status parameters and the activation configuration specifications to generate an activation adaptation coefficient. The sequence construction module is used to perform polarization degree statistics on the battery state parameters to generate a polarization feature spectrum, extract available charge-discharge cycles along the activation configuration specification to generate a charge-discharge time period group, and map the polarization feature spectrum to the charge-discharge time period group to construct a standard activation sequence. The instruction modulation module is used to apply coefficient modulation to the standard activation sequence using the activation adaptation coefficient to generate a composite activation instruction, perform execution saturation assessment on the composite activation instruction to generate a saturation warning, and establish a tiered execution node based on the saturation warning; The energy monitoring module is used to transmit the composite activation command to the battery according to the stepped execution node to generate a feedback dataset, extract the polarization residual signal from the feedback dataset to generate residual correction parameters, perform energy monitoring on the feedback dataset based on the residual correction parameters to generate an energy balance curve, and use the energy balance curve to perform trend analysis to generate an activation response coefficient. The evaluation output module is used to perform recovery cycle analysis on the feedback dataset based on the activation response coefficient to generate a recovery cycle, adjust the activation frequency using the recovery cycle to generate a performance confirmation file, and compare the performance confirmation file with the battery state parameters to generate an activation effect evaluation report.

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