Method and device for activating a nuclear power plant dc power source battery

Abstract

The application discloses a kind of nuclear power station direct-current power source battery activation method and device, for battery state parameter and activation configuration specification implementation matching degree check generates activation adaptation coefficient;Polarization degree statistics is generated, and polarization characteristic spectrum is extracted usable charge-discharge cycle generates charge-discharge period group, and polarization characteristic spectrum is mapped to charge-discharge period group and constructs standard activation sequence;With the help of activation adaptation coefficient, composite activation instruction is generated by applying coefficient modulation, and according to saturation early warning, ladder execution node is established;Composite activation instruction is generated to generate feedback data set, and residual correction parameter is generated by extracting polarization residual signal, energy monitoring is generated to generate energy balance curve and trend analysis is generated to generate activation response coefficient;Carry out recovery cycle analysis, use recovery cycle to adjust activation frequency to generate performance confirmation file, compare performance confirmation file with battery state parameter to generate activation effect evaluation report, realize activation process self-adapting adjustment and accurate control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of nuclear power plant DC power supply system maintenance, in particular to a nuclear power plant DC power supply battery activation method and device. BACKGROUND

[0002] The battery pack in the DC power supply system of the nuclear power plant undertakes the task of providing uninterrupted power supply for key loads such as protection systems, monitoring instruments and emergency lighting under accident conditions. Under the long-term floating operation mode, the electrode surface of the battery pack will gradually form a passivation layer, resulting in attenuation of available capacity, increase of internal resistance, and weakening of charge-discharge response capability, which threatens the safety of power supply guarantee capability of the nuclear power plant.

[0003] The existing battery activation technology mainly adopts periodic charge-discharge cycle to eliminate polarization effect. The typical method is to use constant current charging and then deep discharge, and to promote the dissolution of electrode passivation layer through multiple cycles. However, this method has obvious shortcomings: the charge-discharge parameters are usually fixed, lacking adaptability to the performance differences of different battery packs; the activation process lacks real-time monitoring and dynamic adjustment mechanism, and is prone to over-activation or under-activation; the activation effect evaluation method is relatively rough, relying only on single indicators such as capacity test, and cannot comprehensively evaluate the performance recovery degree. Therefore, a new activation method is needed to solve at least one of the above problems. SUMMARY

[0004] The present application discloses a nuclear power plant DC power supply battery activation method and device, which matches and checks the battery state and activation configuration, constructs a targeted activation sequence through polarization characteristic mapping, realizes stage-by-stage execution control through coefficient modulation and saturation evaluation, and forms an evaluation report containing performance recovery details and health degree comparison through polarization residual extraction, energy monitoring, trend analysis and recovery cycle evaluation.

[0005] The present application discloses a nuclear power plant DC power supply battery activation method and device, which matches and checks the battery state and activation configuration, constructs a targeted activation sequence through polarization characteristic mapping, realizes stage-by-stage execution control through coefficient modulation and saturation evaluation, and forms an evaluation report containing performance recovery details and health degree comparison through polarization residual extraction, energy monitoring, trend analysis and recovery cycle evaluation.

[0006] Collecting battery state parameters and activation configuration specifications of the activation equipment, and generating an activation adaptation coefficient by implementing matching degree checking of the battery state parameters and the activation configuration specifications;

[0007] Generating a polarization characteristic spectrum by developing polarization degree statistics for the battery state parameters, extracting available charge-discharge cycles along the activation configuration specifications to generate a charge-discharge time period group, and mapping the polarization characteristic spectrum to the charge-discharge time period group to construct a standard activation sequence;

[0008] Generating a composite activation instruction by applying coefficient modulation to the standard activation sequence with the aid of the activation adaptation coefficient, generating a saturation warning by developing execution saturation evaluation for the composite activation instruction, and establishing a ladder execution node according to the saturation warning;

[0009] The energy monitoring module is configured to generate a feedback data set by transmitting the composite activation instruction to the battery according to the step execution node, generate a residual correction parameter by extracting a polarization residual signal from the feedback data set, generate an energy balance curve by performing energy monitoring on the feedback data set based on the residual correction parameter, and generate an activation response coefficient by performing trend analysis by using the energy balance curve.

[0010] The evaluation output module is configured to generate a recovery period by performing recovery period analysis on the feedback data set according to the activation response coefficient, generate a performance confirmation file by adjusting an activation frequency by using the recovery period, and generate an activation effect evaluation report by comparing the performance confirmation file with the battery state parameter in terms of health degree.

[0011] The second aspect of the present application provides a nuclear power station direct current power supply battery activation device, comprising:

[0012] The data acquisition module is configured to acquire a battery state parameter of the battery and an activation configuration specification of the activation equipment, and generate an activation adaptation coefficient by performing matching degree checking on the battery state parameter and the activation configuration specification.

[0013] The sequence construction module is configured to generate a polarization feature spectrum by performing polarization degree statistics on the battery state parameter, generate a charge-discharge time period group by extracting available charge-discharge periods along the activation configuration specification, and construct a standard activation sequence by mapping the polarization feature spectrum to the charge-discharge time period group.

[0014] The instruction modulation module is configured to generate a composite activation instruction by applying coefficient modulation on the standard activation sequence by means of the activation adaptation coefficient, generate a saturation warning by performing execution saturation evaluation on the composite activation instruction, and establish a step execution node according to the saturation warning.

[0015] The energy monitoring module is configured to generate a feedback data set by transmitting the composite activation instruction to the battery according to the step execution node, generate a residual correction parameter by extracting a polarization residual signal from the feedback data set, generate an energy balance curve by performing energy monitoring on the feedback data set based on the residual correction parameter, and generate an activation response coefficient by performing trend analysis by using the energy balance curve.

[0016] The evaluation output module is configured to generate a recovery period by performing recovery period analysis on the feedback data set according to the activation response coefficient, generate a performance confirmation file by adjusting an activation frequency by using the recovery period, and generate an activation effect evaluation report by comparing the performance confirmation file with the battery state parameter in terms of health degree.

[0017] The beneficial effects of the present application are embodied in the following points: first, the matching degree of the battery state parameter and the activation configuration specification is adopted to check the mechanism, the activation adaptation coefficient reflecting the adaptation degree of the device capacity and the battery state is generated, and the polarization characteristic spectrum extracted by the polarization degree statistics is combined to construct the standard activation sequence by mapping to the charge and discharge period group. This technical solution solves the problem that the traditional method cannot adapt to the performance difference of different storage battery groups by using fixed parameters, so that the activation sequence can be configured differently according to the actual polarization state of each monomer, thereby improving the matching precision of the activation scheme and the storage battery deterioration state. Secondly, compared with the traditional method, the process control is easy to lead to the defects of excessive activation or insufficient activation, the standard activation sequence is modulated by means of the activation adaptation coefficient, the execution saturation degree evaluation is carried out for the generated composite activation instruction, and the step execution node is established according to the saturation early warning, so that the monomers with different polarization degrees are distributed to the appropriate execution stage. The step execution strategy effectively avoids the mutual interference between the monomers, and ensures the safety and balance of the activation process. Finally, a complete evaluation chain from polarization residual extraction, energy monitoring, trend analysis to recovery cycle evaluation is established. Specifically, the polarization residual signal is extracted from the feedback data set and the residual correction parameter is generated, the energy monitoring is carried out based on the parameter to generate the energy balance curve, the activation response coefficient is obtained through trend analysis, and then the recovery cycle analysis is carried out and the activation frequency is adjusted, and finally the performance confirmation file is compared with the battery state parameter to form the activation effect evaluation report. This multi-dimensional evaluation system overcomes the limitations of the traditional method which only relies on a single capacity index, and can fully reflect the performance recovery degree and consistency improvement effect of the storage battery. BRIEF DESCRIPTION OF DRAWINGS

[0018] The drawings herein show specific examples of the technical solutions described in the present application, and constitute part of the specification together with the specific embodiments, for explaining the technical solutions, principles and effects of the present application.

[0019] Unless specifically stated or defined otherwise, the same reference signs in different drawings represent the same or similar technical features, and different reference signs may also be used to represent the same or similar technical features.

[0020] Figure 1 is a flowchart of a nuclear power station DC power source storage battery activation method of the present application.

[0021] Figure 2 is a structural block diagram of a nuclear power station DC power source storage battery activation device of the present application. DETAILED DESCRIPTION

[0022] With reference to the drawings and the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work are within the protection scope of the present application.

[0023] It should be noted that all directional indications, such as upper, lower, left, right, front, back, and the like, used in the embodiments of the present application are only used to explain the relative position relationship, movement condition and the like between components in a certain posture (as shown in the drawings), and if the certain posture changes, the directional indications also change accordingly.

[0024] It should also be noted that when an element is referred to as being "fixed" or "disposed" on another element, it can be directly on the other element or can have a middle element. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or can have a middle element.

[0025] In addition, the description involving "first", "second" and the like in the present application is only for the purpose of description, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first", "second" can explicitly or implicitly include at least one of the features. In addition, the technical solutions of various embodiments can be combined with each other, but it must be based on the fact that a person of ordinary skill in the art can realize it, and when the combination of technical solutions contradicts each other or cannot be realized, it should be considered that the combination of technical solutions does not exist, and is not within the protection scope claimed by the present application.

[0026] The technical solutions of the embodiments of the present application will be introduced below.

[0027] As shown in Figure 1 The embodiment of the present application provides a nuclear power station direct current power supply battery activation method, which comprises the following steps S110-S150:

[0028] In step S110, the battery state parameters of the battery and the activation configuration specifications of the activation equipment are collected, and the activation matching degree check is performed on the battery state parameters and the activation configuration specifications to generate an activation adaptation coefficient.

[0029] Specifically, the battery state parameters of the battery and the activation configuration specifications of the activation device are collected. In the DC power supply system of the nuclear power plant, the battery state parameters are collected by the battery management unit. The battery state parameters include the terminal voltage, internal resistance and capacity attenuation rate of each single battery. The voltage of each single battery is measured by the collection device, and the terminal voltage value of each single battery is recorded. The internal resistance of each single battery is measured, and the internal resistance value is obtained by the alternating current impedance method. The capacity attenuation of each single battery is counted, and the capacity attenuation rate is obtained by comparing the current capacity with the rated capacity. At the same time, the activation configuration specifications of the activation device are obtained. The activation configuration specifications include the voltage regulation accuracy, current output stability and capacity equalization ability of the device. The technical specification of the activation device is consulted, the voltage regulation accuracy of the device is recorded, and the fine degree of voltage regulation of the device is confirmed. The current output stability of the device is measured, and the ability of the device to maintain stable current output under different load conditions is evaluated. The capacity equalization ability of the device is detected, and the ability level of the device to implement differentiated treatment on single batteries with different capacities is determined.

[0030] In some embodiments, the matching degree check between the battery state parameters and the activation configuration specifications generates an activation adaptation coefficient, including: extracting a single battery dispersion index set from the battery state parameters to form a dispersion index set; performing capability matching on the dispersion index set and the activation configuration specifications to form an index-device correspondence relationship; identifying a single difference compensation gap along the index-device correspondence relationship to form a capability gap domain; and forming an activation adaptation coefficient through the coverage strength of the capability gap domain.

[0031] The single battery discreteness index set is formed according to the battery state parameter extraction. The key performance data of each single battery is extracted from the battery state parameter, and the performance difference degree between each single battery is analyzed. For each single battery terminal voltage in the battery state parameter, the voltage discreteness coefficient is obtained by comparing each single terminal voltage with the average terminal voltage, and the formula is CV_V=σ_V / μ_V, wherein CV_V is the voltage discreteness coefficient, σ_V is the standard deviation of each single terminal voltage, and μ_V is the average terminal voltage. The battery group of the nuclear power station has been operated for 8 years. Due to the different aging speeds of each single battery, the terminal voltage of part of the single battery decreases obviously, and the terminal voltage of part of the single battery remains good, which leads to the increase of the voltage discreteness coefficient and the deterioration of the consistency of the battery group. For each single battery internal resistance in the battery state parameter, the deviation of each single internal resistance and the average internal resistance is compared, and the internal resistance discreteness coefficient is obtained, which is expressed by the coefficient of variation, reflecting the concentration or dispersion degree of the internal resistance distribution of each single battery. For each single battery capacity attenuation rate in the battery state parameter, the distribution range of each single capacity attenuation rate is counted, and the capacity attenuation discreteness coefficient is obtained, which characterizes the consistency of the capacity degradation speed of each single battery. The standby battery group of the nuclear power station is in the floating state for a long time, and the capacity attenuation of part of the single battery is accelerated due to the high environmental temperature, while the attenuation of other single batteries is slow, the capacity attenuation discreteness coefficient increases, and the capacity difference between the single batteries expands. The voltage discreteness coefficient, the internal resistance discreteness coefficient and the capacity attenuation discreteness coefficient are collected to form the discreteness index set. The discreteness index set includes the voltage discreteness coefficient, the internal resistance discreteness coefficient, the capacity attenuation discreteness coefficient and the corresponding numerical values.

[0032] The index-equipment corresponding relationship is formed by matching the dispersion index set and the activation configuration specification. Each dispersion coefficient in the dispersion index set is matched and analyzed with the equipment capacity parameter in the activation configuration specification. For the voltage dispersion coefficient in the dispersion index set, the voltage regulation precision parameter of the equipment is extracted from the activation configuration specification to determine whether the voltage regulation precision of the equipment can eliminate the voltage dispersion among the single units. The voltage regulation precision of the activation equipment in the nuclear power station is limited, while the voltage dispersion coefficient of the battery in the station has reached a high level after 10 years of operation. The voltage regulation precision of the equipment is insufficient to completely eliminate the voltage difference among the single units, and the corresponding relationship between the voltage dispersion coefficient and the voltage regulation precision needs to be established to evaluate the matching degree of the equipment regulation capacity. For the internal resistance dispersion coefficient in the dispersion index set, the current output stability parameter of the equipment is extracted from the activation configuration specification to determine whether the equipment can maintain stable output under the condition of internal resistance difference. The internal resistance of the battery group in the nuclear power station is unevenly distributed, and the internal resistance of some single units has significantly increased. The equipment needs to switch among the single units with different internal resistances during the activation process. If the current output fluctuation of the equipment is large, it is difficult to achieve balanced activation, and the corresponding relationship between the internal resistance dispersion coefficient and the current output stability needs to be established. For the capacity attenuation dispersion coefficient in the dispersion index set, the capacity balancing capability parameter of the equipment is extracted from the activation configuration specification to determine whether the equipment can implement differential treatment on the single units with large capacity difference. Each coefficient in the dispersion index set is paired with the corresponding capacity parameter in the activation configuration specification to form a complete index-equipment corresponding relationship. The index-equipment corresponding relationship includes three groups of mappings: voltage dispersion-regulation precision pairing, internal resistance dispersion-current stability pairing, and capacity dispersion-balancing capability pairing.

[0033] A monomer difference compensation gap forming capability gap domain is identified along the index-equipment correspondence relationship. Based on the index-equipment correspondence relationship, the values of the discrete degree indexes and the values of the equipment capability parameters are compared item by item to identify the regions where the equipment capability is insufficient. For the pairing of the voltage discrete coefficient and the voltage regulation precision in the index-equipment correspondence relationship, when the discrete degree index exceeds the range that can be covered by the equipment regulation precision, there is a voltage regulation capability gap. The end voltages of each monomer of a group of batteries in a nuclear power plant are seriously uneven, and the voltage difference between the highest monomer and the lowest monomer is obvious. The voltage regulation precision of the equipped activation equipment is limited, and it is unable to adjust all monomer voltages to a consistent level, forming a voltage regulation capability gap. The voltage regulation capability gap is recorded as one component of the capability gap domain. For the pairing of the internal resistance discrete coefficient and the current output stability in the index-equipment correspondence relationship, when the monomer internal resistance difference is large, the equipment may have current output fluctuations when switching activation between different monomers. If the fluctuations exceed the allowed range, a current stability gap is formed. In the battery group of a nuclear power plant, the internal resistance of some monomers increases to several times the normal value, and the current output of the activation equipment is unstable when processing high internal resistance monomers, making it difficult to achieve balanced charging and discharging, forming a current stability gap. The current stability gap is recorded as another component of the capability gap domain. For the pairing of the capacity attenuation discrete coefficient and the capacity balancing capability in the index-equipment correspondence relationship, when the capacity difference between monomers exceeds the equipment balancing processing capability, a capacity balancing gap is formed. The various capability gaps are summarized, including the voltage regulation gap, the current stability gap, and the capacity balancing gap, to form a complete capability gap domain. The capability gap domain includes three values: the voltage regulation gap value, the current stability gap value, and the capacity balancing gap value.

[0034] An activation adaptation coefficient is formed by the coverage strength of the capability gap field. Each gap value in the capability gap field is extracted to calculate the coverage strength. The coverage strength is the ratio of the equipment capability parameter and the corresponding dispersion index. The voltage regulation coverage strength C_V is the ratio of the voltage regulation accuracy and the voltage dispersion coefficient. The current stability coverage strength C_I is the ratio of the current output stability and the internal resistance dispersion coefficient. The capacity balance coverage strength C_C is the ratio of the capacity balance capability and the capacity attenuation dispersion coefficient. A coverage strength greater than 1 indicates that the equipment capability completely covers the demand, and a coverage strength less than 1 indicates that there is a capability gap. The coverage strengths corresponding to each gap in the capability gap field are weighted and integrated. The formula is S = W1*C_V + W2*C_I + W3*C_C, where S is the activation adaptation coefficient, C_V is the voltage regulation coverage strength, C_I is the current stability coverage strength, C_C is the capacity balance coverage strength, and W1, W2, and W3 are the weight coefficients of each capability. A group of batteries serving for 12 years in a nuclear power plant are evaluated for activation. Due to long-term operation, the performance differences between individual batteries have widened. The activation equipment provided has basic capabilities in voltage regulation and capacity balance to meet the demand, but it is insufficient in current output stability. The activation adaptation coefficient obtained by the formula indicates that the matching degree of the equipment configuration and the battery state is at a medium level. The weight coefficients are allocated according to the influence of each capability on the activation effect. The voltage regulation capability plays a leading role in restoring the consistency of individual voltages, and has a higher weight. The weights of current stability and capacity balance capability are relatively low. After the nuclear power plant replaces a new high-performance activation equipment, the voltage regulation accuracy, current output stability, and capacity balance capability of the equipment are significantly improved. For the same group of batteries, the activation adaptation coefficient is significantly improved, indicating that the matching degree of the new equipment configuration and the battery state is improved.

[0035] In step S120, a polarization feature spectrum is generated by developing a polarization degree statistic for the battery state parameter. A charge-discharge period group is generated by extracting available charge-discharge cycles along the activation configuration specification. A standard activation sequence is constructed by mapping the polarization feature spectrum to the charge-discharge period group.

[0036] Specifically, the polarization degree statistics are generated for the battery state parameters to generate the polarization feature spectrum. A short pulse current is applied to each single battery, and the voltage recovery process after the pulse is removed is monitored. The speed of voltage recovery reflects the severity of polarization. In a battery pack that has been in a floating state for a long time, the voltage of some single batteries slowly recovers after the pulse is removed, indicating that the polarization degree is heavy, and the voltage of another part of the single battery quickly recovers to the steady state, indicating that the polarization degree is light. The time constant of the voltage recovery process of each single battery is recorded, and the larger the time constant, the more serious the polarization. The polarization correlation is analyzed in combination with the internal resistance data in the battery state parameters. The increase in internal resistance is often accompanied by an increase in polarization. In a battery that has been in service for a long time, the internal resistance of some single batteries increases significantly, and the polarization degree of these single batteries is usually heavy. There is a positive correlation between the capacity decay rate in the battery state parameters and the polarization. Single batteries with obvious capacity decay usually have more serious polarization. The polarization degrees of each single battery are sorted according to the severity to form a polarization intensity distribution sequence. The polarization intensity of the battery pack of a nuclear power station is found to be in a step distribution after polarization testing. The polarization of the front single battery is slight, the polarization of the middle single battery is moderate, and the polarization of the rear single battery is heavy. According to the polarization intensity distribution of the battery state parameters, each single battery is divided into different polarization grades to generate a polarization feature spectrum. The polarization feature spectrum includes two indicators of the polarization grade and the polarization time constant of each single battery.

[0037] The available charge and discharge periods are extracted along the activation configuration specification to generate a charge and discharge period group. The voltage regulation accuracy in the activation configuration specification determines the range of charge periods that the device can perform. The charge period needs to meet the voltage regulation capability constraint. The charge duration cannot be shorter than the establishment time required for the device voltage regulation to stabilize the output. When the device voltage regulation accuracy is high, a shorter charge period can be supported. When the device voltage regulation accuracy is low, the charge period needs to be extended to ensure charge stability. The discharge period range is constrained by the current output stability in the activation configuration specification. The discharge period needs to consider the discharge depth limit and the discharge current stability requirement. A device with good current output stability can perform deeper discharge depth without causing battery damage. A device with poor current output stability needs to limit the discharge depth to avoid over-discharge. The interval requirement for charge and discharge switching depends on the capacity balancing capability in the activation configuration specification. The device needs a certain response time when switching from charge mode to discharge mode. A device with strong capacity balancing capability has a short response time and can have a smaller switching interval. A device with weak capacity balancing capability has a long response time and needs to set a larger switching interval. The available charge period, discharge period, and switching interval are combined to form a charge and discharge period group. The charge and discharge period group includes two types of charge and discharge cycle configurations: fast mode and deep mode. The fast mode charge and discharge cycle is short and suitable for single batteries with mild polarization. The deep mode charge and discharge cycle is long and suitable for single batteries with heavy polarization.

[0038] In some embodiments, the mapping of the polarization feature spectrum to the charge-discharge time period group constructs a standard activation sequence, including: dividing the polarization feature spectrum into a heavy polarization section and a light polarization section; establishing a time period docking channel from the heavy polarization section to the light polarization section in 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; locating an optimal matching position along the matching point sequence to generate a standard activation sequence.

[0039] The polarization feature spectrum is divided into a heavy polarization section and a light polarization section. A polarization degree threshold is set according to the polarization intensity of each monomer in the polarization feature spectrum. The capacity recovery difficulty of a monomer significantly increases when the polarization time constant exceeds 1.5 times the average value or exceeds a set threshold T_th. The value of T_th is determined according to the battery type and service life, and serves as the threshold for distinguishing between heavy polarization and light polarization. Monomers with polarization intensity higher than the threshold are classified into the heavy polarization section. These monomers need longer charge-discharge cycles and deeper discharge depths to be effectively depolarized. In a group of nuclear power plant batteries that have been in service for many years, the polarization time constants of monomers in the heavy polarization section are significantly higher, and the capacity attenuation is severe. These monomers cannot recover on their own during regular floating charging and must be activated to improve performance. Monomers with polarization intensity lower than the threshold are classified into the light polarization section. The depolarization of these monomers is relatively easy, and shorter charge-discharge cycles can be used. The light polarization section of a newly commissioned battery group usually contains most of the monomers, which have slight polarization and can be restored to normal state through simple charge-discharge cycles. The division of the heavy polarization section and the light polarization section records the monomer numbers and corresponding polarization time constant ranges of each section.

[0040] A time period docking channel is established from the heavy polarization section to the light polarization section in the charge-discharge time period group. The deepest mode in the charge-discharge time period group corresponds to the heavy polarization section, which has the longest charge-discharge cycle and the deepest discharge depth. The long cycle charge-discharge of the deepest mode can effectively break the concentration polarization and electrochemical polarization inside the heavy polarization monomers, allowing the active material to redistribute and restore the battery capacity. The fastest mode in the charge-discharge time period group corresponds to the light polarization section, which has the shortest charge-discharge cycle. The short cycle charge-discharge of the fastest mode is sufficient to eliminate the surface polarization of light polarization monomers, while avoiding unnecessary capacity loss caused by excessive activation. The time period docking channel is established from the heavy polarization section to the light polarization section, and contains multiple transition gears. Each gear corresponds to a charge-discharge time period configuration, realizing a gradual transition from the deepest mode to the fastest mode. The charge-discharge cycle of each gear decreases in turn. When activating a group of old batteries, the nuclear power plant allocates the heaviest polarization monomers to the deepest mode gear for long cycle activation, the lightest polarization monomers to the fastest mode gear for short cycle activation, and the monomers with intermediate polarization degree to the transition gears for moderate cycle activation.

[0041] The marking of the positions of the fitting points of the features on the time-period-connection channel to form a fitting point sequence includes: determining a detection interval according to the time-period-connection channel to identify a polarization jump characteristic, the polarization jump characteristic including an ascending gradient, a peak duration, and a decay rate; tracking a polarization change process along the detection interval to form a polarization change spectrum; extracting time-period positioning values of each fitting point from the polarization change spectrum; and arranging the fitting point sequence according to the fitting degrees of the time-period positioning values.

[0042] The detection interval is determined according to the time-period-connection channel to identify a polarization jump characteristic. The change rule of the polarization intensity is observed along the time-period-connection channel to identify a region where the polarization intensity appears to jump rapidly. The polarization jump characteristic includes three elements, namely, an ascending gradient, a peak duration, and a decay rate. The ascending gradient reflects the change speed of the polarization degree from light to heavy. When the polarization time constant of some monomers in the polarization feature spectrum corresponding to the time-period-connection channel suddenly increases greatly, it indicates that the polarization degrees of these monomers and the previous monomers are significantly different, and there is an obvious polarization jump boundary. The peak duration reflects the number of monomers in the heavy polarization section. The longer the duration, the more monomers in the heavy polarization section. The peak duration of the battery pack of the nuclear power station is relatively long, which indicates that the overall aging degree of the battery pack needs to be focused on. The decay rate reflects the transition speed of the polarization degree from heavy to light. The faster the decay rate, the fewer the transition monomers, and the polarization distribution presents a two-pole polarization feature. The slower the decay rate, the more the transition monomers, and the polarization distribution is relatively continuous. According to the polarization jump characteristic in the time-period-connection channel, a detection interval that needs to be focused on is determined. The detection interval includes a region where the ascending gradient is the largest, the start and end positions of the peak duration, and a region where the decay rate changes obviously.

[0043] The polarization change spectrum is formed by tracking the polarization change process along the detection interval. The change details of the polarization intensity are recorded in detail in the detection interval. The change trajectory of the polarization time constant of each monomer is tracked to observe how the polarization time constant transitions from one level to another. When analyzing the polarization state of the battery of the nuclear power station, it is found that the polarization time constant at the start position of the detection interval suddenly jumps from the light polarization level to the heavy polarization level. The jump amplitude is obvious and is accompanied by a synchronous increase in the capacity decay rate. This synchronous change indicates that there is a strong correlation between the polarization degree and the capacity loss. The change trajectory of the internal resistance of each monomer in the detection interval presents a synchronization with the polarization aggravation. The internal resistance growth region is highly coincident with the polarization jump region. The polarization time constant of the monomer whose internal resistance suddenly increases also increases accordingly. The polarization change spectrum is formed by comprehensively tracking the change of the polarization time constant and the change of the capacity decay rate. The polarization change spectrum includes two groups of data, namely, the polarization time constant sequence and the capacity decay rate sequence of each monomer in the detection interval.

[0044] The time period positioning value of each kiss point is extracted from the polarization change spectrum. In the heavy polarization region of the polarization change spectrum, the position where the polarization intensity and the depth mode charging and discharging period configuration are highly consistent is identified. The polarization time constant and capacity decay rate of these single bodies are consistent with the polarization type targeted by the depth mode, and the long period configuration of the depth mode can effectively improve the polarization state of these single bodies. The time period positioning value of the consistent position is recorded, and the time period positioning value includes the corresponding charging time, discharging time and charging and discharging period parameters. In the light polarization region of the polarization change spectrum, there are positions highly consistent with the fast mode. The polarization degree of these single bodies is slight, and the short period configuration of the fast mode can achieve ideal activation effect. The time period positioning value of these positions is extracted. The positions consistent with the transition gear in the transition region also need to be identified and the corresponding time period positioning value is extracted. Through the above method, the nuclear power station identifies the single body group that needs deep activation in the heavy region of the polarization change spectrum, the single body group that needs medium intensity activation in the transition region, and the single body group that only needs rapid activation in the light region, and records the time period positioning value of each group.

[0045] The kiss point sequence is generated by arranging the time period positioning values according to the consistency degree. The matching degree of the time period positioning value of each kiss point with the actual polarization characteristics is evaluated, and the matching degree is quantified by the consistency degree M, M = 1 - |T_actual - T_target| / T_target, where T_actual is the actual polarization time constant of the single body, T_target is the middle value of the target polarization time constant range of the corresponding mode, and the closer the consistency degree to 1, the better the matching. The time period positioning value of the kiss point located in the most polarized single body corresponds to the longest charging and discharging period of the depth mode, and the consistency degree is the highest. The configuration parameters of the depth mode completely match the activation requirements of the heavy polarization single body. The time period positioning value of the kiss point located in the lightest polarization single body corresponds to the shortest charging and discharging period of the fast mode, and the consistency degree is also high. The fast mode can efficiently process the light polarization single body. The time period configuration of the intermediate kiss point corresponding to the transition single body and the transition gear has a medium consistency degree. The kiss points are arranged in order from high to low according to the consistency degree to form a kiss point sequence. The kiss point sequence of the nuclear power station presents a consistency degree distribution characteristic of high at both ends and low in the middle. The matching degree of the kiss points at both ends of the polarization distribution with the corresponding mode is the best, and the activation effect is the most predictable. The matching degree of the kiss points in the transition region is slightly lower but still within an acceptable range and can be effectively processed through the transition gear.

[0046] The optimal anastomosis position is located along the anastomosis point sequence to generate a standard activation sequence. A number of positions with the highest anastomosis degree in the anastomosis point sequence are selected as standard anchor points, which represent typical states of heavy polarization and light polarization, and can be used as a reference benchmark for dividing activation sections. The standard anchor points determined based on the anastomosis point sequence divide the entire polarization feature spectrum into multiple activation sections, each of which corresponds to a charge and discharge period configuration. The battery pack of the nuclear power plant is divided into three parts, namely, a heavy polarization activation section, a transition activation section, and a light polarization activation section, according to the anchor points of the anastomosis point sequence, and the boundaries between the sections are determined by the anchor point positions. The cells in the heavy polarization activation section adopt a deep mode and are set with a longer charge and discharge period and a larger number of repetitions to ensure that the heavy polarization is fully eliminated. The cells in the light polarization activation section adopt a fast mode and are set with a shorter charge and discharge period to ensure the activation effect while improving the processing efficiency. The transition activation section adopts an intermediate gear configuration, and the charge and discharge period and the number of repetitions are between the deep mode and the fast mode. The charge and discharge period parameters of each activation section are arranged in execution order to form a standard activation sequence, which includes the cell number list of each activation section, the charge and discharge period parameters, and the execution order.

[0047] In step S130, a standard activation sequence is subjected to coefficient modulation by means of an activation adaptation coefficient to generate a composite activation instruction, saturation warning is generated by executing saturation evaluation on the composite activation instruction, and a ladder execution node is established according to the saturation warning.

[0048] Specifically, the standard activation sequence is subjected to coefficient modulation by means of an activation adaptation coefficient to generate a composite activation instruction. The period parameters in the standard activation sequence are combined with the activation adaptation coefficient to modulate the period parameters. When the activation adaptation coefficient is high, it indicates that the device capacity is sufficient to execute according to the standard activation sequence, and when the activation adaptation coefficient is low, the standard activation sequence needs to be modified to compensate for the insufficient device capacity. The charging time in the standard activation sequence is modulated, and the modulation formula is T_adj=T_charge×(1+α×(1-S)), where T_adj is the modulated charging time, T_charge is the standard charging time, S is the activation adaptation coefficient, and a is the modulation coefficient. The activation equipment of the nuclear power plant has a low activation adaptation coefficient due to a long service life, and the charging time of the deep mode is appropriately extended according to the modulation formula to compensate for the influence of the decline in the voltage regulation accuracy of the device. The discharge time in the standard activation sequence is similarly modulated, and the discharge depth is ensured to meet the depolarization requirements according to the activation adaptation coefficient. The charge and discharge period and the number of repetitions are also modulated accordingly to form the activation parameters subjected to coefficient modulation. The modulated parameters are integrated to generate a composite activation instruction, which includes the charging time, the discharge time, the charge and discharge period, and the execution order of each activation section subjected to adaptation coefficient modification.

[0049] In some embodiments, the performing the execution saturation evaluation on the composite activation instruction generates a saturation warning, including: performing monomer balanced load capacity identification on the composite activation instruction to generate a load deviation critical area; performing monomer pressure difference evaluation according to the load deviation critical area to form a pressure coefficient; generating a continuous pressure distribution by interval interpolation through the pressure coefficient; and performing critical extraction using the continuous pressure distribution to generate a saturation warning.

[0050] The monomer balanced load capacity identification is performed on the composite activation instruction to generate a load deviation critical area. The load intensity exerted by the composite activation instruction on each monomer battery is analyzed, and the load intensity is represented by the depth of discharge in the composite activation instruction. The greater the depth of discharge, the higher the load intensity. The balanced load capacity that each monomer battery can withstand under the current performance state is identified, which refers to the maximum depth of discharge that the monomer can withstand without excessive stress. The balanced load capacity is negatively correlated with the capacity decay rate and the internal resistance growth rate of the monomer. The higher the capacity decay rate or the greater the internal resistance growth rate, the lower the balanced load capacity. The load deviation is identified by comparing the load intensity of the composite activation instruction with the balanced load capacity of each monomer. When the balanced load capacity of some poor performance monomers is significantly lower than the load intensity exerted by the composite activation instruction, there is a load deviation. In a group of batteries that have been in service for many years in a nuclear power plant, the monomers near the heat source location of the machine room have accelerated aging due to long-term exposure to high temperature environments. The capacity and internal resistance of these monomers have been significantly degraded, and the depth of discharge they can withstand is much lower than that of normal monomers. However, the depth of discharge required by the deep mode in the composite activation instruction is large, exceeding the capacity of these degraded monomers. The area where the load deviation exceeds the safety threshold is marked as the load deviation critical area. The monomers in the load deviation critical area may experience excessive stress and have a saturation risk when executing the composite activation instruction.

[0051] The pressure coefficient is formed by evaluating the pressure bearing difference of the monomer according to the load deviation critical zone. The difference in the ability of the monomer in the load deviation critical zone to bear the activated load is evaluated. Although the monomer with better performance in the load deviation critical zone is located in the critical zone, the pressure bearing capacity is relatively strong. The monomer with poor performance in the load deviation critical zone has weak pressure bearing capacity and faces greater risk. The nuclear power station subdivides and evaluates the monomers in the load deviation critical zone, and finds that some monomers are included in the critical zone because of slight performance decline near the maintenance period. These monomers have a lighter degree of degradation and are expected to recover after activation. Another part of the monomers is because of the process defects in the early installation, which leads to long-term performance lagging behind the monomers of the same batch. These monomers have a heavy degree of degradation and need to be cautious when activated. The pressure difference of each monomer is quantified to form a pressure coefficient. The formula is P=(L_applied-L_capacity) / L_capacity, where P is the pressure coefficient, L_applied is the load intensity, and L_capacity is the balanced load capacity of the monomer. The greater the pressure coefficient, the greater the stress the monomer bears and the higher the saturation risk.

[0052] A continuous pressure distribution is generated by interval interpolation through the pressure coefficient. The pressure coefficients of the monomers are arranged in sequence according to the monomer serial number to form a discrete pressure coefficient sequence. The monomers located in the load deviation critical zone have positive pressure coefficient values, and the pressure coefficients of the remaining monomers are zero or negative. The discrete pressure coefficient sequence is interpolated to fill the intermediate values between the monomers with known pressure coefficients to generate a continuous pressure distribution curve. The purpose of interpolation is to identify the spatial distribution law of the pressure coefficient to facilitate the discovery of the concentrated risk area. The interpolation method uses piecewise linear interpolation or spline interpolation to ensure the smoothness of the pressure distribution curve, forming a continuous pressure distribution covering all monomers. The continuous pressure distribution curve shows that the pressure coefficient is concentrated in some areas, forming a high pressure peak. The nuclear power station finds that the high pressure monomers are concentrated in the middle of the battery pack rather than randomly distributed through the continuous pressure distribution curve. After the on-site investigation by the operation and maintenance personnel, it is confirmed that the corresponding rack in this area is long-term in the ventilation dead angle of the machine room, and the air conditioner cold air is difficult to cover effectively, resulting in high environmental temperature. The middle monomers therefore have a faster performance degradation rate than the two end monomers, showing the typical distribution characteristics of high middle and low ends on the continuous pressure distribution. This finding provides a basis for subsequent optimization of the ventilation layout of the machine room.

[0053] Saturated warning is generated by critical extraction of continuous pressure distribution. Critical threshold is set on the continuous pressure distribution curve, which corresponds to the safety boundary of saturation risk. The critical threshold is divided into three levels of yellow warning threshold, orange warning threshold and red warning threshold according to the statistical distribution characteristics of the single pressure bearing capacity. The area exceeding the critical threshold in the continuous pressure distribution is identified, and the single in these areas has saturation risk. The nuclear power station identifies multiple areas exceeding the yellow warning threshold on the continuous pressure distribution curve, which are distributed in the middle and tail of the battery pack. The corresponding single has obvious performance decline due to more deep discharge cycles in the history operation. According to the degree of exceeding the threshold, the saturation risk is divided into different levels. The single whose pressure coefficient exceeds the yellow warning threshold but does not reach the orange threshold is marked as yellow warning, indicating that there is a mild saturation risk. The single whose pressure coefficient exceeds the orange warning threshold but does not reach the red threshold is marked as orange warning, indicating that there is a moderate saturation risk. The single whose pressure coefficient exceeds the red warning threshold is marked as red warning, indicating that there is a serious saturation risk. The saturation warning is generated by summarizing the warning level and the single position involved. The saturation warning contains two contents of warning level and single position involved.

[0054] According to the saturation warning, the ladder execution node is established. The composite activation instruction is divided into multiple execution stages according to the level and position of the saturation warning. For single with lower saturation warning level, the composite activation instruction can be executed continuously without interruption. The single with stronger pressure bearing capacity in the nuclear power battery pack has green saturation warning level, and these single can complete the whole activation cycle continuously. For single with higher saturation warning level, the rest node is inserted into the composite activation instruction to change the continuous execution to ladder execution. Single with yellow and orange saturation warning level cannot continuously bear long-time high-intensity activation and need to be executed in stages. The position of the ladder execution node is determined. The node is set before the critical point of the saturation warning. The first ladder execution node is set after several charge and discharge cycles in the deep mode to allow the single to rest for a period of time before continuing the subsequent cycle. The subsequent ladder execution node is set after several charge and discharge cycles in the standard mode as needed. The rest time of each ladder execution node is set. The rest time is determined according to the single pressure bearing capacity and the saturation warning level. The yellow warning single is set with a shorter rest time, the orange warning single is set with a longer rest time, and the red warning single needs to significantly reduce the activation intensity or suspend the activation. The nuclear power station sets multiple ladder execution nodes in the composite activation instruction according to the saturation warning analysis result, which divides the originally continuous activation process into multiple execution stages with appropriate rest intervals between stages. The position and rest time of the ladder execution node are added to the composite activation instruction to form a complete activation scheme containing execution node control.

[0055] In step S140, the feedback data set is generated by transmitting the composite activation instruction to the battery according to the step execution node, the residual polarization signal is extracted from the feedback data set to generate the residual correction parameter, the energy monitoring is performed on the feedback data set based on the residual correction parameter to generate the energy balance curve, and the trend analysis is performed on the energy balance curve to generate the activation response coefficient.

[0056] Specifically, the feedback data set is generated by transmitting the composite activation instruction to the battery according to the step execution node. The activation equipment is controlled by the step execution node in the composite activation instruction to implement the charging and discharging operation on the battery pack in a staged manner. In the first execution stage, the deep mode charging and discharging instruction is transmitted to the single body in the heavy polarization stage, the equipment charges these single bodies according to the modulated charging time, and the terminal voltage, charging current and temperature change of each single body are recorded during the charging process. After the charging is completed, the single body is deeply discharged in the discharging mode, and the terminal voltage, discharging current and temperature data are recorded during the discharging process. When the first stage completes the preset charging and discharging cycle, the equipment enters the rest period of the first step execution node, and the voltage recovery and temperature drop of each single body are monitored during the rest period. The voltage recovery speed of part of the heavy polarization single body of the nuclear power plant battery pack during the rest period of the first step execution node is obviously slower than that of other single bodies, which is recorded in the feedback data set as an important basis for subsequent analysis. After the rest period is over, the second execution stage is entered, the standard mode charging and discharging instruction is transmitted to the single body in the moderate polarization stage, and the parameter data of the charging and discharging process are continuously collected. The voltage data, current data, temperature data and time stamp information collected in real time during the whole staged execution process are summarized to form the feedback data set, and the feedback data set contains the charging and discharging response characteristics of each single body in different execution stages and the whole process data from the start of activation to the completion of each stage.

[0057] In some embodiments, the residual polarization signal is extracted from the feedback data set to generate the residual correction parameter, including: constructing a voltage rebound file according to the feedback data set; extracting a rebound peak value bit along the voltage rebound file to form a peak anchor point; setting an attenuation threshold for the peak anchor point to divide the voltage rebound file into an effective residual band and an invalid residual band; and quantifying the distribution characteristics of the effective residual band and the invalid residual band to form the residual correction parameter.

[0058] 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.

[0059] 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.

[0060] 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 units where the internal resistance has increased significantly due to long-term operation, indicating that these units have a lot of polarization residues that are difficult to eliminate. In contrast, in units with good performance, the range of ineffective residual bands is narrower or even absent, and effective residual bands dominate.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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 nuclear power plants, 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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:

[0081] 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.

[0082] 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.

[0083] 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;

[0084] 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.

[0085] 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.

[0086] 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 embodiment can be referred to the contents of the above method embodiments, and will not be repeated in this embodiment.

[0087] 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.

[0088] 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: The process involves collecting battery state parameters and activation configuration specifications of the activation equipment, and performing a matching degree check between the battery state parameters and the activation configuration specifications to generate an activation adaptation coefficient. This includes: extracting individual battery dispersion indicators based on the battery state parameters to form a dispersion indicator set; matching the dispersion indicator set with the activation configuration specifications to form an indicator-equipment correspondence; identifying individual cell difference compensation gaps along the indicator-equipment correspondence to form a capability gap domain; and generating an activation adaptation coefficient based on the coverage strength of the capability gap domain. The process involves: statistically analyzing the polarization degree of the battery state parameters to generate a polarization feature spectrum; extracting available charge / discharge cycles along the activation configuration specifications to generate charge / discharge time period groups; and mapping the polarization feature spectrum to the charge / discharge time period groups to construct a standard activation sequence. This 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. 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 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.

3. 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.

4. 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.

5. 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.

6. The method according to claim 1, 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.

7. The method according to claim 3, 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.

8. 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 battery state parameters of the storage battery and activation configuration specifications of the activation equipment. It performs a matching degree check between the battery state parameters and the activation configuration specifications to generate an activation adaptation coefficient. This includes: extracting individual battery dispersion indicators based on the battery state parameters to form a dispersion indicator set; matching the dispersion indicator set with the activation configuration specifications to form an indicator-equipment correspondence; identifying individual cell difference compensation gaps along the indicator-equipment correspondence to form a capability gap domain; and generating an activation adaptation coefficient based on the coverage strength of the capability gap domain. 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 specifications 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. This 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. 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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