Energy management strategy optimization method for communication base station

Abstract

The invention discloses an energy management strategy optimization method for a communication base station, which relates to the technical field of energy management, and comprises the following steps: carrying out high-frequency synchronous sampling on an electrical connection point, obtaining a full-period voltage and current sampling sequence, calculating a ratio of a voltage change rate to a current change rate in unit time, and calculating a current change rate; constructing an impedance jump feature vector used for representing the contact state; and constructing a time sliding window increment matrix based on the impedance jump feature vector, extracting an impedance mean jump amplitude, and generating a thermoelectric coupling joint abnormal factor in combination with a temperature change trend. By constructing a diagnosis mechanism based on high-frequency impedance characteristics and thermoelectric vibration coupling analysis, real-time identification and positioning of abnormal contact are achieved, online self-repairing is achieved through reverse trace energy supply, the power supply safety and the energy efficiency level of the communication base station are improved, and the service life of an energy storage battery is prolonged.

Description

Technical Field

[0001] This invention relates to the field of energy management technology, and more specifically to a method for optimizing energy management strategies for communication base stations. Background Technology

[0002] "Energy management strategy optimization for communication base stations" refers to the dynamic analysis and intelligent control of energy supply and demand during the operation of communication base stations, combined with safety diagnosis and prediction methods of energy storage battery management systems, to achieve efficient energy utilization and stable supply. Specifically, on the one hand, the battery management system (BMS) is used to monitor multi-dimensional parameters such as voltage, current, and temperature of energy storage batteries in real time. Combined with a safety diagnosis model, potential risks such as overcharging, over-discharging, and thermal runaway are identified, and predictive algorithms are used to pre-determine the battery's health status and remaining lifespan. On the other hand, the diagnosis and prediction results are fed back to the base station's energy scheduling layer to dynamically adjust the energy allocation strategy between the battery pack, mains power, and renewable energy sources. Priority is given to ensuring the stability and safety of equipment operation, while energy storage is charged during off-peak hours and discharged during peak hours to maximize energy utilization efficiency. This approach not only significantly improves the power supply security and energy efficiency of communication base stations in complex environments but also effectively extends the lifespan of energy storage batteries, achieving the goals of green, low-carbon, and intelligent energy management.

[0003] Existing technologies have the following shortcomings: In existing technologies, communication base stations typically operate in complex and variable environments, especially in areas with geological micro-vibrations. The electrical connection points of energy storage batteries are highly susceptible to periodic or random vibrations. These vibrations often cause slight loosening of the crimped connections, resulting in a sudden increase in contact resistance within a very short time. However, this type of impedance surge is highly random and unpredictable. Traditional energy management strategies and battery management systems mostly rely on macroscopic measurement indicators such as temperature and voltage, lacking diagnostic mechanisms for the dynamic evolution of electrical contact states. Therefore, when contact resistance abnormally increases within a short period, existing technologies often cannot detect and determine this in time, leading to current accumulation at the obstructed location and causing localized overheating. If this overheating effect is not suppressed, it can further trigger side reactions within the battery cell, causing thermal runaway and potentially leading to a chain reaction of combustion throughout the battery pack, seriously threatening the power supply safety and operational stability of the communication base station.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method for optimizing energy management strategies for communication base stations, in order to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing energy management strategies for communication base stations, comprising the following steps: High-frequency synchronous sampling is performed on electrical connection points to obtain full-cycle voltage and current sampling sequences. The ratio of voltage change rate to current change rate per unit time is calculated to construct an impedance jump characteristic vector to characterize the contact state. Based on the impedance jump feature vector, a time sliding window increment matrix is ​​constructed to extract the impedance mean jump amplitude, and a thermo-electric coupling joint anomaly factor is generated by combining the temperature change trend. Geological vibration intensity parameters are introduced, and weighting factors are set and superimposed on the joint anomaly factor to output a contact anomaly probability score characterizing the impact of vibration disturbance; By setting a dynamic judgment threshold through the contact anomaly probability score of the connection point, and performing backtracking and cross-validation on multiple connection points based on the contact anomaly probability score, a set of target electrical connection points with the risk of impedance surge is obtained. High-frequency impedance resampling is performed on the target electrical connection point set. Combined with temperature trends and historical response residuals, the surge duration label is calculated to generate a trigger signal for dynamic control. Based on the trigger signal, a short-term reverse micro-power supply is performed on the healthy battery clusters near the connection point. Electron migration is guided by the local voltage difference to repair the contact interface and reduce the contact impedance.

[0007] The preferred steps for constructing the impedance jump eigenvector to characterize the contact state are as follows: The full-cycle voltage and current sampling sequences of the electrical connection points of the energy storage battery in the communication base station are obtained, and a unified clock source is used to synchronously sample at a high frequency. The voltage and current rate of change per unit time are calculated based on the voltage and current sampling sequences, respectively, and the continuous impedance change sequence is obtained by dividing the voltage rate of change by the current rate of change. The impedance change value sequence is divided into equal-width time windows. The impedance mean value in each time window is calculated, an increment matrix is ​​constructed, and the impedance jump amplitude between adjacent time windows is extracted. By combining the impedance jump amplitude and the temperature change trend, a thermoelectric coupling factor vector is constructed, which includes the impedance change trend, impedance jump amplitude, temperature difference, and temperature growth rate, and is used as the output of the impedance jump characteristic vector.

[0008] The preferred steps for generating the thermoelectric coupling joint anomaly factor are as follows: The impedance jump eigenvector is divided into multiple time sliding windows with overlapping time ranges, and the average impedance change value within each time window is calculated. Extract the difference in the mean impedance between adjacent time windows as the jump amplitude, and mark the continuous jump trend segments; Extract the temperature data sequence within the corresponding time window, calculate the average temperature and temperature growth trend index, and construct a thermoelectric binary feature group composed of impedance jump amplitude and temperature rise trend. All thermoelectric binary characteristic groups are connected in chronological order, normalization is performed, and combined indicators of sudden impedance rise and intensified temperature rise in a short period of time are extracted to generate thermoelectric coupling joint anomaly factor.

[0009] The preferred steps for outputting the contact anomaly probability score are as follows: Triaxial vibration acceleration sensors are deployed near the battery pack mounting bracket, base, or electrical connection point to collect geological disturbance signals; The vibration signal is segmented and processed to extract the root mean square value of acceleration, peak value, energy density and frequency characteristics, and to identify the time window of strong disturbance. The vibration intensity level is fused with the thermo-electric coupling anomaly factor, and vibration weights are introduced to weight and correct the anomaly score. A fusion score curve was constructed based on the weighted correction results, and the continuous high value intervals, slope changes, and locations of perturbation abrupt change points were analyzed. The contact anomaly probability score is output based on the fusion scoring curve, serving as the triggering basis for risk identification and control strategies in energy management.

[0010] The steps for selecting the preferred set of target electrical connection points with the risk of impedance surge are as follows: A dynamic judgment threshold is set based on the contact anomaly probability scoring sequence of the connection point, and the mean score, standard deviation, vibration energy and load state are introduced to correct the threshold. When the score exceeds the dynamic judgment threshold, time backtracking verification is performed to extract the score rise slope, duration and historical stability features to determine the validity of the score. Cross-validation was performed by selecting clustered connection points, comparing the peak time difference of scores, the rising slope and the temperature rise trend, and identifying the structural consistency of score spikes. Retain connection points that simultaneously satisfy the requirements of score surge effectiveness and structural consistency, form a target electrical connection point set, and output it to the energy dispatching stage.

[0011] If a connection point in the preferred set of target electrical connection points has a score that is higher than the dynamic judgment threshold for 30 consecutive minutes, a temperature rise rate that is more than twice the historical average, and a vibration energy density that is more than 1.5 times the average, it is marked as an object requiring active intervention, and an abnormal notification is output to the energy dispatch layer.

[0012] The preferred steps for generating the trigger signal for dynamic control are as follows: High-frequency impedance sampling is performed on the target set of electrical connection points, with a sampling frequency of no less than 50,000 times per second. Voltage, current and temperature data are acquired simultaneously during the sampling process to construct the impedance change time-series curve of the electrical connection points during operation. The impedance change curve is time-aligned with the temperature evolution trend. The temperature rise rate, temperature rise lag time and impedance increase magnitude of the impedance surge segment are extracted and compared with historical standard features to determine the degree of anomaly. A multidimensional feature vector is constructed based on impedance jump amplitude, surge duration, and thermal response hysteresis time. A surge duration label is generated and a threshold is set. When the label value exceeds the preset upper limit, a control trigger signal is output.

[0013] Preferred steps for performing short-term reverse micro-power supply to repair the contact interface of electrical connection points include: The repair triggering conditions are determined based on the score value of the contact impedance surge duration label, and healthy battery clusters with the shortest electrical path to the target electrical connection point, stable load, and long-term low historical scores are selected as reverse energy sources. Set the voltage, current and duration parameters for reverse power supply, and connect the reverse power supply path through channel switching to ensure that the voltage difference range, power supply stability and safety meet the conditions for non-destructive intervention. High-frequency sampling is performed during the reverse power supply process to record data on voltage, impedance, temperature and current changes. After the power supply is completed, monitoring continues to determine whether the contact interface has been repaired. The effectiveness of the repair is calculated based on the impedance drop magnitude, temperature change rate, and state stabilization time. The status label of the connection point is updated to repair confirmed, repair observed, or repair failed, and a decision is made on whether to enter the next round of intervention cycle accordingly.

[0014] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention overcomes the limitations of existing technologies that rely on macroscopic indicators such as temperature and voltage by using a high-frequency synchronous sampling and impedance differential feature construction mechanism. It is the first to achieve real-time modeling and response identification of sudden contact resistance jumps. By introducing a weighted mechanism of temperature evolution and geological micro-vibration intensity, a three-dimensional coupled thermoelectric vibration analysis framework is established, which effectively improves the accuracy and adaptability of anomaly judgment. By setting dynamic thresholds and a backtracking cross-validation mechanism, connection points with fault precursors are accurately screened, enabling proactive location of risky locations. Furthermore, through reverse micro-power supply and differential pressure-guided electron migration strategies, contact interface self-repair can be completed without interrupting power supply or manual intervention. This significantly reduces the risk of local overheating, thermal runaway, and chain combustion caused by contact degradation, extends the service life of energy storage equipment, and provides a new technical path and practical foundation for building green, safe, and intelligent communication energy systems. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0016] Figure 1 This is a flowchart of the method for optimizing energy management strategies for communication base stations according to the present invention. Detailed Implementation

[0017] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0018] This invention provides, for example Figure 1 The energy management strategy optimization method for communication base stations shown includes the following steps: The full-cycle voltage and current sampling sequences of the electrical connection points of the energy storage battery in the communication base station are obtained during operation. High-frequency synchronous sampling is used, and the ratio of the voltage change rate to the current change rate per unit time is calculated based on the voltage and current sampling sequences to construct an impedance jump feature vector characterizing the electrical contact state of the connection point. To identify contact anomalies at electrical connection points of energy storage batteries in communication base stations under micro-vibration conditions, full-cycle voltage and current signals are first acquired. Then, a high-resolution impedance transition feature vector is constructed to characterize the dynamic evolution trend of the contact state. The specific steps include: In the operational state of the energy storage device at the communication base station, multiple electrical connection points located between the battery pack output and the busbar were selected as monitoring targets. Voltage and current samplers with differential input channels and clock synchronization mechanisms were used to collect the raw time-series data of the terminal voltage and current at each electrical connection point. To ensure the continuity and accuracy of the samples, the voltage signal sampling range was set to 0V to 5V, with a sampling accuracy of no less than 12 bits. The current signal sampling range covered the entire process from 0A to the maximum discharge current, with a sampling error of less than 1%. All channels used a unified clock source for sampling synchronization, with a sampling frequency set to 10,000 times per second to ensure complete capture of electrical signal disturbance characteristics caused by microscopic contact anomalies within a millisecond timescale. During sampling, real-time bias calibration, electromagnetic shielding, and multi-point temperature compensation were used to avoid the influence of cable transmission loss, environmental temperature drift, and interference voltage on the authenticity of the raw signals. After acquisition, the raw voltage and current sequences were aligned with timestamps to obtain a one-to-one corresponding dataset under a synchronized time axis, providing a strictly matched input source for subsequent rate of change calculation and impedance analysis.

[0019] Differential calculations are performed on the voltage and current sampling sequences. The voltage values ​​between adjacent time points are subtracted to calculate the voltage change per unit time. Similarly, the same process is performed on the current sampling sequence to obtain the current change per unit time. A calculation cycle of 10 milliseconds is used, and the corresponding voltage change rate is the voltage change value within that cycle divided by 10 milliseconds. The current change rate is calculated similarly. Dividing the voltage change rate by the current change rate yields the impedance change value within that time segment. This process is repeated to generate a continuous impedance change value sequence for each time slice, forming a time-series data chain reflecting the dynamic contact impedance evolution behavior of electrical connection points under operating conditions. This impedance sequence not only preserves the continuous fluctuation characteristics of the contact state over time but also identifies structural information such as abrupt change points, gradual change sections, and fluctuation frequency bands. Unlike traditional methods that only measure steady-state voltage or current, this method effectively characterizes the short-time resistance instability characteristics of connection points caused by specific changes such as physical stress relaxation, mechanical deformation, contact surface aging, or contaminant layer peeling.

[0020] The impedance change sequence is divided into equal-width time windows, each covering 100 consecutive data points, and an incremental matrix is ​​constructed according to the window order. Within each time window, the mean impedance change value is calculated to form the representative value of that window. By comparing the mean difference between adjacent windows, the evolution of the impedance jump amplitude is quantified. If the mean impedance difference between two consecutive windows exceeds a preset stability threshold, the connection point is considered to be in the contact state fluctuation stage. Furthermore, the temperature change value of the electrical connection point casing during the corresponding time period is incorporated into the analysis. The temperature difference between the connection point surface and the internal temperature sampling point, and the growth rate of this difference within the same time period, are selected as thermal disturbance characteristic indicators. The impedance jump amplitude and temperature disturbance trend are combined to obtain a thermoelectric coupling factor vector that can simultaneously reflect changes in electrical parameters and thermal diffusion effects. To enhance the discriminative power of this vector, a temperature change rate weighting factor is introduced during the calculation process to amplify the influence of temperature response lag, thereby improving the sensitivity to identify early, subtle anomalies.

[0021] The generated thermoelectric coupling factor vector is structurally represented, defining the complete feature set of each connection point within each time window, and using this as the final output for constructing the impedance jump feature vector. This output structure contains multiple components such as impedance change trend, impedance jump amplitude, temperature difference, temperature growth rate, and window ratio, forming a multi-dimensional, multi-time-period fusion of high-order vector sets to comprehensively characterize the contact state evolution of electrical connection points. In subsequent processing, this impedance jump feature vector is used as a direct input parameter for contact anomaly probability modeling, dynamic threshold determination, backtracking verification, and micro-power supply control, achieving a closed-loop process from initial sampling to behavior recognition and then to regulatory intervention.

[0022] Based on the impedance jump feature vector, a time sliding window increment matrix is ​​constructed to extract the jump amplitude of the average impedance within adjacent time windows. Combined with the temperature change trend of the electrical connection point, a thermoelectric coupling joint anomaly factor is generated to reflect the thermoelectric state imbalance of the connection point. To accurately identify anomalous thermoelectric coupling behavior at connection points during the operation of energy storage batteries in communication base stations, after constructing the impedance jump feature vector, a time-sliding window and temperature data fusion process are further introduced to construct a quantifiable joint anomaly factor. This process includes the following steps: The impedance transition feature vector constructed based on high-frequency synchronous sampling is segmented in chronological order to form multiple time windows of fixed length. Each time window contains 200 consecutive impedance data points, with a time coverage of approximately 20 milliseconds. The window sliding step is set to 50 data points, covering 5 milliseconds, to ensure continuity and overlap in time coverage. The sliding window design allows for precise capture of local changes in impedance data while avoiding the average masking of abrupt changes due to excessively wide time slices. In practice, the start and end times of each window are strictly aligned with the original sampling sequence to ensure window boundary alignment and prevent sampling omissions or resampling. For each constructed time window, the internal impedance transition data is extracted point by point, and the arithmetic mean of all data points within the window is calculated to obtain the average impedance value of that window within that time period.

[0023] Two adjacent impedance averages within two time windows are selected, and the difference between them is calculated as the jump amplitude for that time period. The calculation method is to subtract the mean of the previous window from the mean of the subsequent window, retaining the sign of the difference to determine whether the impedance is trending upwards or downwards, ensuring the ability to distinguish different directions of contact state evolution. If multiple consecutive jump amplitudes are positive and their absolute values ​​gradually increase, it indicates that the contact performance at the connection point is continuously deteriorating; conversely, if they are consecutively negative, it indicates that the connection performance has recovered somewhat. To enhance the responsiveness to real anomalies, each jump amplitude is compared with the fluctuation range under historical stable conditions. If it exceeds the upper limit of this range, it is marked as a suspicious impedance mutation event. In addition, continuous window trend identification is performed on the jump amplitude sequence to identify trend segments that continuously rise or suddenly drop for more than a set duration, used to determine whether there is chronic contact deterioration or a short-term transition state.

[0024] After extracting the impedance jump amplitude, the temperature data sequence corresponding to each time window is retrieved and matched one-to-one with the impedance change data. Temperature data is collected by thermocouple sensors deployed on the metal surface of the electrical connection point and its adjacent insulators, with a sampling frequency set to 10,000 times per second to ensure strict synchronization with impedance sampling. The average temperature value is calculated for the temperature data within each time window, and the average temperature difference between the first and second halves of the window is further extracted to obtain a temperature growth trend index. If this temperature growth trend index is positive and the growth rate is greater than an empirically set temperature rise rate threshold, it indicates that the connection point is experiencing localized heat accumulation. This temperature rise rate value and the impedance jump amplitude of the corresponding time window are combined to form a binary feature set for subsequent thermoelectric coupling analysis. In this binary set, the first component reflects the amplitude of electrical parameter fluctuations, and the second component quantifies the temperature rise trend; both can be used to assess the thermoelectric imbalance state of the connection point over a short period. This feature set possesses triple attributes of time, direction, and amplitude, giving it higher discriminative power in characterizing complex physical evolution processes.

[0025] All thermoelectric binary feature groups within the time windows are connected sequentially to form a thermoelectric coupling sequence. To make this sequence suitable for subsequent anomaly identification and diagnosis, it is normalized to ensure a unified basis for comparison between physical quantities of different dimensions. After normalization, a sliding interval scan is performed on the thermoelectric coupling sequence to identify intervals where impedance surges and temperature increases occur simultaneously within a short period. Corresponding combined index values ​​are extracted from these intervals as anomaly factors. Each anomaly factor carries a complete time index, impedance change trend attributes, temperature rise intensity attributes, and temperature response hysteresis characteristics, constituting a thermoelectric coupling joint anomaly factor reflecting the integrity of the connection point's operational status during that time period. This factor can be used to identify potential localized failures caused by loose crimping, contact surface corrosion, or electrochemical side reactions.

[0026] Based on the thermo-electric coupling joint anomaly factor, the geological vibration intensity parameter is introduced, and the weighting factor is set according to the geological vibration intensity. The influence of geological vibration disturbance is superimposed on the thermo-electric coupling joint anomaly factor, and the connection point contact anomaly probability score representing the influence of vibration disturbance is output. When communication base stations are deployed in areas with geological disturbances, the electrical connection points of energy storage batteries are highly susceptible to vibration. To improve the ability to identify contact anomalies under micro-vibration disturbances, it is necessary to incorporate geological vibration intensity parameters into the thermoelectric coupling anomaly factor for fusion scoring, generating more accurate contact anomaly probability values. This method includes the following steps: Based on the structural conditions of the communication base station operating site, seismic-grade triaxial vibration accelerometers are deployed near the mounting brackets, battery compartment bases, or electrical connection points of the battery pack to acquire geological disturbance signals in real time. The vibration sensors used should have a dynamic measurement range of ±2g to ±16g, a minimum resolution of no more than 0.001g, and a sampling frequency of no less than 2000Hz to ensure simultaneous coverage of low-frequency (1Hz to 10Hz) and high-frequency (above 10Hz) vibration components. Through directional adjustments during actual installation, the crimping direction between the sensor's main sensing axis and the electrical connection point is maintained within 15° to ensure that the vibration signal reflects the actual disturbance acting on the connection point to the greatest extent possible. After the vibration signal is connected to the acquisition circuit via a shielded cable, it undergoes bandpass filtering and digital processing to obtain stable, interference-resistant raw vibration acceleration data for subsequent feature extraction operations.

[0027] The collected raw triaxial vibration acceleration data is divided into multiple fixed-length time segments, with a recommended sampling window of 10 seconds. Within each window, parameters such as the effective value (root mean square value) of acceleration, peak acceleration, total energy of principal axis acceleration, vibration rate of change, and amplitude density of the main frequency band are calculated in each of the three directions, and the vibration intensity level within that time segment is further derived. Special attention is paid to the energy in the frequency band between 1Hz and 10Hz, as micro-vibrations in this band can easily cause slight loosening of the crimped joints. To further quantify the actual interference capability of vibration on the connection point, the angle between the principal vibration direction and the electrical connection axis also needs to be calculated; when the angle is less than 30° and the vibration amplitude exceeds twice the static mean, this time window is defined as a "strong disturbance" window, and this judgment provides a basis for subsequent weighting steps.

[0028] The vibration intensity level obtained within each time window is fused with the thermoelectric coupling joint anomaly factor obtained in the previous steps. Specifically, a vibration weight term is introduced into the thermoelectric anomaly score corresponding to each window. This weight term depends on the vibration amplitude, directional angle, energy density, and disturbance duration of the current window. Using the initial value of the thermoelectric factor as a baseline, when the vibration weight value is greater than 0.6, the thermoelectric score is linearly increased according to the disturbance level, with a maximum increase of no more than 15%. For example, if the impedance jump amplitude at the connection point is 0.28 and the temperature rise rate is 1.9℃ / min within a certain time period, and the basic thermoelectric factor score is 0.73, if the vibration intensity level in the corresponding window is "strong disturbance," the score is corrected to 0.84 to more sensitively reflect the potential contact risk triggered by micro-vibrations. It is worth noting that the vibration weighting correction is only implemented in windows that meet both direction and amplitude conditions to avoid misweighting caused by traffic background disturbances or wind loads.

[0029] After updating the vibration-weighted thermoelectric score, a fused score curve is generated, reflecting the multidimensional anomaly degree of the connection point under each time window. This curve, with time on the horizontal axis and the fused score on the vertical axis, presents the combined evolution trend of vibration and thermoelectric anomalies. When performing continuity and fluctuation analysis on this curve, the slope, maximum rise segment, maximum fall segment, continuous high-value intervals, and vibration abrupt change points of the score should be extracted as the basis for judging score anomalies. If the score value exceeds 0.85 for three consecutive windows, and at least one of these windows is a "strong disturbance" window, the connection point is marked as a "high-risk state"; if the score remains between 0.65 and 0.85 for more than five minutes, accompanied by a slow upward trend in temperature, it is judged as a "potentially deteriorated state"; when the score remains below 0.5 and the vibration intensity remains low, it is judged as a "stable state." By simultaneously identifying the score interval and vibration behavior, a high-confidence assessment mechanism integrating physical disturbance background and electrical thermal response characteristics is established.

[0030] The fusion scoring curve outputs a contact anomaly probability score for risk identification and control decisions in energy management. This score not only outputs the instantaneous risk level of the current connection point but also includes dynamic attributes such as the continuous growth trend of the score, the proportion of vibration coupling, and the degree of temperature rise matching, forming a multi-dimensional risk index table. During periods when the score exceeds a preset control threshold, the energy control logic can immediately invoke a connection point switching strategy to preemptively reduce the discharge power of the battery cluster belonging to that point or disconnect it from the operational link, placing it in observation mode. Simultaneously, this score also serves as a trigger condition for initiating subsequent short-term reverse power supply repair strategies, providing an accurate and physically clear basis for proactive repair.

[0031] Based on the probability score of abnormal contact at the connection point, a dynamic judgment threshold is set, and multiple electrical connection points are subjected to time-series backtracking verification and cross-verification under this judgment threshold. Interference abnormal points are eliminated through cross-verification, and a set of target electrical connection points with the risk of sudden increase in contact resistance is obtained. During the operation of energy storage batteries in communication base stations, it is necessary to accurately determine and verify the contact anomaly probability scoring results to effectively identify electrical connection points with a genuine risk of sudden resistance increases. To this end, based on a fusion scoring of thermoelectric and vibration parameters, a dynamic threshold setting and multi-dimensional verification method is proposed to ensure the high accuracy of the screening results. This method includes the following steps: For each electrical connection point, a dynamic judgment threshold is set based on the contact anomaly probability score sequence generated throughout the entire operating cycle, replacing the traditional fixed threshold method. The setting of the dynamic judgment threshold must fully consider factors such as the historical score variation characteristics of the connection point, the stability of electrical parameters, the intensity level of geological disturbance, and the load status during the operating period. Specifically, based on the score curve of the connection point over the most recent 72 consecutive hours, its mean, standard deviation, maximum value, minimum value, and rate of change of the score slope are calculated. The mean score plus twice the standard deviation is used as the baseline threshold, and then a disturbance correction coefficient is introduced. When the geological vibration energy in the current time period is greater than 1.8 times the average value of the previous day, the correction coefficient is set to 1.2, raising the judgment threshold; if the temperature changes slowly and it is in the low-load range at night, the correction coefficient is set to 0.9, appropriately lowering the threshold. Through these adjustments, each connection point has differentiated threshold setting standards in different time periods and operating environments, thereby improving the environmental adaptability and response sensitivity of the scoring judgment.

[0032] When the score reaches or exceeds the current dynamic threshold, the scoring sequence of the connection point is continuously backtracked to verify its trend, thus eliminating false alarms caused by occasional current surges or single-point vibrations. The specific implementation method is as follows: using the point when the score first exceeds the threshold as the boundary, backtrack for at least thirty minutes of scoring history, extracting five types of features: score change slope, total score increase, continuous score increase duration, score peak duration, and the length of the stable interval before the increase. If the score slope remains positive within the backtracking interval, the score increase exceeds 0.3, the continuous increase duration is greater than ten minutes, the high score duration is at least three minutes, and the previous stable score value is below 0.5, then the score surge can be preliminarily determined to be caused by connection point contact degradation. If there is no stable plateau before the score surge, or the score immediately decreases after the surge, it is considered an anomaly lacking sustainability and is not retained as a risk input.

[0033] After completing the time-backtracking analysis of the single-point score, cross-validation was performed on multiple electrical connection points with similar operating conditions to verify whether the score spike possessed structural consistency. The cross-validation process was as follows: Taking the score spike point as the core, all connection points connected to it in the same battery cluster and on the same busbar were selected as comparison objects. The characteristics of their score curve changes during the same time period were analyzed, including whether the scores rose synchronously, whether the peak time points of the score changes were similar, whether the score slope changes were consistent, and whether there were similar temperature rise trends or vibration amplitudes in the score rise interval. If multiple connection points showed score spikes simultaneously during the same time period, with the time difference between the maximum score values ​​less than five minutes, the difference in the score rise slope not exceeding 30%, and the temperature change trend direction consistent, then the score spike was determined to have structural correlation. Otherwise, if the connection point showed an isolated score spike in adjacent locations without resonance, it was determined to be a pseudo spike and excluded from the target connection point set.

[0034] After completing dynamic threshold determination, time-series backtracking analysis, and cross-validation, a target electrical connection point set is formed based on the retained screening results. This set is used for power supply path reconfiguration and subsequent intervention. This target set only includes connection points whose scores exceed the dynamic threshold, whose backtracking curves show a continuous upward trend, and whose scores are synchronized with surrounding connection points. To further ensure the accuracy of this set, the scoring trend of each connection point is further aggregated and analyzed. If its score remains in a high-risk state for 30 consecutive minutes, and its heat rise rate exceeds twice the historical average, and its vibration energy density is 1.5 times higher than the average, then this connection point is marked as a "target requiring active intervention," and an anomaly notification is sent to the energy dispatch layer, initiating the dynamic scheduling process for the battery cluster energy path. Furthermore, the scoring evolution trajectory, temperature response trend, and vibration co-change characteristics of each connection point in the target set are archived for subsequent anomaly classification learning and rule correction.

[0035] High-frequency impedance sampling is performed on the target electrical connection point set, and the contact impedance surge duration label of the target electrical connection point is calculated by combining the temperature change trend of the target electrical connection point with the historical response residual data, and the trigger signal is output as the basis for dynamic control. During the operation of communication base stations, target electrical connection points with a risk of sudden resistance increases have been identified. To achieve more accurate quantification of degradation status and triggering of control responses, these connection points need to be sampled continuously with fine granularity. This sampling, combined with analysis of temperature trends and historical behavior differences, forms a sudden resistance increase duration label that can be invoked by a dynamic response mechanism. This process includes the following steps: For the target set of electrical connection points, a new round of high-frequency sampling is performed to obtain higher-resolution contact state evolution data. This sampling operation should be carried out under the condition that the energy storage battery pack maintains normal power supply and stable load fluctuations to ensure the representativeness and comparability of the data. For each connection point, its positive and negative terminal voltages, current flow, and current loading rate at the corresponding time point should be recorded during sampling. All sampling signals should be driven by a unified clock and collected synchronously. The sampling frequency should be increased to no less than 50,000 times per second, with each sampling period not exceeding 20 microseconds, covering the entire change process within the complete operating cycle, including the three sub-stages of charging, resting, and discharging. During this process, the sampling voltage accuracy should be controlled within 0.01 volts, and the current accuracy should not exceed 0.05 amperes to ensure that small abrupt changes are not masked by measurement noise. The sampling data is used to construct a one-dimensional impedance change curve in a time-series structure, where each data point is the instantaneous impedance value obtained by dividing the voltage by the current at a certain sampling moment. Through these data, the short-cycle fluctuation pattern and jump points of the impedance can be observed.

[0036] After obtaining the impedance time-series data, this data is time-aligned with the temperature evolution trend at the connection point to construct a thermoelectric coupling analysis sample. Temperature data is obtained from a high-sensitivity temperature sensor deployed at the closest point to the heat conduction path of the connection point; its sampling frequency should be consistent with the impedance data or processed using linear interpolation to a uniform time scale. During processing, the temperature response delay for each impedance surge segment is calculated, i.e., the lag time between the start time of the temperature rise and the starting point of the impedance surge. Simultaneously, the temperature rise rate, total temperature rise amplitude, and local temperature extreme value change range within this segment are extracted and paired one-to-one with the impedance value increase amplitude, fluctuation frequency, and maximum surge slope. Subsequently, the impedance-temperature rise combination characteristics collected in the current cycle are compared with the historical standard characteristics of this connection point. The historical standard characteristics are extracted from past continuous operating data under the same load conditions, and after normalization and disturbance factor removal, serve as a benchmark reference. By calculating the residual between the current characteristic sequence and the historical reference characteristics, for example, if the impedance growth value exceeds the historical maximum value by more than 20%, or the temperature rise delay time is shortened to within 30% of the original delay value, the current connection point is considered to have entered a significant abnormal range.

[0037] A multi-dimensional feature vector is constructed by combining impedance jump and temperature rise response data to generate a contact impedance surge duration label. This label reflects the duration, strength stability, and thermal response linkage of the resistance degradation at the connection point. The duration label should include the following three specific numerical fields: 1) surge duration, representing the total time the impedance remains continuously in the abnormal range, in seconds; 2) surge magnitude, representing the absolute difference between the maximum impedance value and the previous stable value within the abnormal range, in ohms; and 3) thermal response lag time, representing the time difference between the impedance surge point and the temperature response point, in seconds. After standardizing these three data points to values ​​between 0 and 1, a unified surge duration score label is formed according to a set weighting rule. For example, if the duration percentage is 0.8, the surge magnitude is 0.6, and the thermal response lag time is 0.9, then the overall score is a weighted average label value of 0.76. When the tag value exceeds 0.75 and the thermal response hysteresis time is less than 2 seconds, the connection point is determined to be in a high-risk operating state, and a control trigger signal is immediately sent to the upper-level control logic, suggesting measures such as reducing discharge power, switching the connection point, or reverse micro-power supply. If the score value is between 0.5 and 0.75, it is recorded in the observation task list, and the observation period is extended for continuous monitoring; if the score is below 0.5, control is not triggered temporarily, and only the current status data is archived.

[0038] Based on the trigger signal of the contact impedance surge duration tag, a short-term reverse micro-current power supply operation is performed on the healthy energy storage battery cluster near the target electrical connection point. Reverse charging is achieved by applying a local voltage difference to guide electron migration, repair the electrical contact interface of the connection point and reduce the contact impedance level. During the operation of energy storage equipment in communication base stations, for target electrical connection points identified by contact impedance surge duration tags, a non-destructive intervention strategy is proposed to achieve online autonomous repair of the contact interface state and reduction of impedance levels. This strategy utilizes nearby healthy energy storage battery clusters to implement short-term reverse micro-power supply. By stimulating electron migration through voltage difference, electrochemical repair and restoration of the contact surface's crimped state are achieved. This process includes the following steps: Based on the score and constituent parameters of the contact impedance surge duration label, it is determined whether the target electrical connection point meets the active repair triggering conditions. The score must reach a set threshold, such as not lower than 0.75, and the score should consist of three data points: surge duration, maximum impedance surge amplitude, and temperature rise delay time. For example, if a connection point has an impedance value consistently higher than 0.3 ohms for 30 consecutive seconds, a temperature delay time of less than two seconds, and a score of 0.82, it meets the repair execution standard. After passing the judgment, a healthy battery cluster with the closest electrical path, stable load, and a historical score value consistently below 0.3 is selected from the power supply link of the battery cluster where the connection point is located as the reverse power source. The healthy battery cluster must be on the same main busbar branch as the target connection point, and there should be no power flow limiting components between them to ensure that the reverse current can fully act on the contact interface. After confirming that the power path is clear, the voltage level is matched, and the electrochemical tolerance is compliant, the reverse power supply preparation stage begins.

[0039] Set the parameters for reverse micro-energy supply and complete the energy supply path initialization. The goal of reverse energy supply is to repair the high-resistance state of the contact surface caused by oxidation, micro-cracks, surface contamination, etc., through short-term electron injection. The energy supply voltage should be set slightly higher than the current voltage of the target connection point. For example, if the current voltage is 3.2 volts, the reverse voltage should be set to 3.5 volts, with the voltage difference controlled within 0.3 volts. The current should be controlled within 3% of the recent discharge value of the target battery pack, typically between 0.1 amperes and 0.5 amperes. The energy supply duration should not exceed 5 seconds, with an initial value of 2 seconds recommended. Before execution, the temperature, voltage, and current status at both ends of the target connection point should be collected once as the energy supply reference values. Connect the reverse channel through a dedicated circuit switching device to start directional energy injection. To avoid battery cluster load imbalance, each reverse energy supply should only act on one connection point, and a cooling interval of no less than 10 minutes should be allowed after execution.

[0040] Perform reverse power supply operation and implement high-frequency monitoring throughout the process. During the application of reverse current, the sampling frequency is set to 10,000 times per second, recording the voltage change trend, instantaneous impedance value, temperature change rate, and current fluctuation curve. If the impedance curve shows a downward trend within 0.5 seconds after the start of power supply, it indicates that the contact interface has responded to the repair current; if there is no significant change in impedance within 1 second but the temperature rises slowly, it indicates that the repair has initially taken effect but has not fully recovered, and the current amplitude can be increased in the next cycle; if the temperature rises suddenly or the current fluctuation is abnormal, the operation should be terminated immediately. After the power supply ends, continue sampling for no less than 2 minutes to observe whether the impedance maintains a downward state to determine whether the repair is stable. The temperature response should remain within a fluctuation range of 1°C, and the voltage drop should not be lower than the baseline value before the operation. Compare the sampled data with historical states to form a time series analysis chart of the repair effect.

[0041] The effectiveness of the repair is calculated based on the sampling results, and the operating status label of the connection point is updated. Evaluation dimensions include impedance drop magnitude, stabilization time, temperature rebound, and whether the secondary rise is delayed. If the impedance drop exceeds 40% of the peak impedance before power supply, and the fluctuation remains below 10% within 10 minutes, while the temperature returns to its original level within 3 minutes, the repair is considered successful. The connection point status label is updated to "Repair Confirmed," and the sudden rise duration label and control trigger status are removed. If the repair magnitude is between 20% and 40%, the temperature does not rise significantly, but the impedance fluctuation remains large, the status is set to "Repair Observation," and the next power supply cycle is set. If there is no change or an anomaly occurs, it is marked as "Repair Failed," and the process is transferred to manual maintenance evaluation, with circuit bypass recommended. This mechanism not only achieves full-process management of electrical connection point contact interfaces from identification to intervention to status closed-loop confirmation, but also effectively avoids traditional maintenance methods that rely on manual inspection and disassembly, improving the operational stability and safety assurance capabilities of communication base station equipment under complex operating conditions.

[0042] This invention overcomes the limitations of existing technologies that rely on macroscopic indicators such as temperature and voltage by using a high-frequency synchronous sampling and impedance differential feature construction mechanism. It is the first to achieve real-time modeling and response identification of sudden contact resistance jumps. By introducing a weighted mechanism of temperature evolution and geological micro-vibration intensity, a three-dimensional coupled thermoelectric vibration analysis framework is established, which effectively improves the accuracy and adaptability of anomaly judgment. By setting dynamic thresholds and a backtracking cross-validation mechanism, connection points with fault precursors are accurately screened, enabling proactive location of risky locations. Furthermore, through reverse micro-power supply and differential pressure-guided electron migration strategies, contact interface self-repair can be completed without interrupting power supply or manual intervention. This significantly reduces the risk of local overheating, thermal runaway, and chain combustion caused by contact degradation, extends the service life of energy storage equipment, and provides a new technical path and practical foundation for building green, safe, and intelligent communication energy systems.

[0043] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing energy management strategies for communication base stations, characterized in that, Includes the following steps: High-frequency synchronous sampling is performed on electrical connection points to obtain full-cycle voltage and current sampling sequences. The ratio of voltage change rate to current change rate per unit time is calculated to construct an impedance jump characteristic vector to characterize the contact state. Based on the impedance jump feature vector, a time sliding window increment matrix is ​​constructed to extract the impedance mean jump amplitude, and a thermo-electric coupling joint anomaly factor is generated by combining the temperature change trend. Geological vibration intensity parameters are introduced, and weighting factors are set and superimposed on the joint anomaly factor to output a contact anomaly probability score characterizing the impact of vibration disturbance; By setting a dynamic judgment threshold through the contact anomaly probability score of the connection point, and performing backtracking and cross-validation on multiple connection points based on the contact anomaly probability score, a set of target electrical connection points with the risk of impedance surge is obtained. High-frequency impedance resampling is performed on the target electrical connection point set. Combined with temperature trends and historical response residuals, the surge duration label is calculated to generate a trigger signal for dynamic control. Based on the trigger signal, a short-term reverse micro-power supply is performed on the healthy battery clusters near the connection point. Electron migration is guided by the local voltage difference to repair the contact interface and reduce the contact impedance.

2. The energy management strategy optimization method for communication base stations according to claim 1, characterized in that, The steps for constructing the impedance transition eigenvector to characterize the contact state are as follows: The full-cycle voltage and current sampling sequences of the electrical connection points of the energy storage battery in the communication base station are obtained, and a unified clock source is used to synchronously sample at a high frequency. The voltage and current rate of change per unit time are calculated based on the voltage and current sampling sequences, respectively, and the continuous impedance change sequence is obtained by dividing the voltage rate of change by the current rate of change. The impedance change value sequence is divided into equal-width time windows. The impedance mean value in each time window is calculated, an increment matrix is ​​constructed, and the impedance jump amplitude between adjacent time windows is extracted. By combining the impedance jump amplitude with the temperature change trend, a thermoelectric coupling factor vector is constructed as the output of the impedance jump characteristic vector.

3. The energy management strategy optimization method for communication base stations according to claim 2, characterized in that, The steps for generating the thermoelectric coupling joint anomaly factor are as follows: The impedance jump eigenvector is divided into multiple time sliding windows with overlapping time ranges, and the average impedance change value within each time window is calculated. Extract the difference in the mean impedance between adjacent time windows as the jump amplitude, and mark the continuous jump trend segments; Extract the temperature data sequence within the corresponding time window, calculate the average temperature and temperature growth trend index, and construct a thermoelectric binary feature group composed of impedance jump amplitude and temperature rise trend. All thermoelectric binary characteristic groups are connected in chronological order, normalization is performed, and combined indicators of sudden impedance rise and intensified temperature rise in a short period of time are extracted to generate thermoelectric coupling joint anomaly factor.

4. The energy management strategy optimization method for communication base stations according to claim 3, characterized in that, The steps for outputting the contact anomaly probability score are as follows: Triaxial vibration acceleration sensors are deployed near the battery pack mounting bracket, base, or electrical connection point to collect geological disturbance signals; The vibration signal is segmented and processed to extract the root mean square value of acceleration, peak value, energy density and frequency characteristics, and to identify the time window of strong disturbance. The vibration intensity level is fused with the thermo-electric coupling anomaly factor, and vibration weights are introduced to weight and correct the anomaly score. A fusion score curve was constructed based on the weighted correction results, and the continuous high value intervals, slope changes, and locations of perturbation abrupt change points were analyzed. The probability score of contact anomalies is output based on the fusion scoring curve, which serves as the triggering basis for risk identification and control strategies in energy management.

5. The energy management strategy optimization method for communication base stations according to claim 4, characterized in that, The steps for screening the set of target electrical connection points that pose a risk of impedance surge are as follows: A dynamic judgment threshold is set based on the contact anomaly probability scoring sequence of the connection point, and the mean score, standard deviation, vibration energy and load state are introduced to correct the threshold. When the score exceeds the dynamic judgment threshold, time backtracking verification is performed to extract the score rise slope, duration and historical stability features to determine the validity of the score. Cross-validation was performed by selecting clustered connection points, comparing the peak time difference of scores, the rising slope and the temperature rise trend, and identifying the structural consistency of score spikes. Retain connection points that simultaneously satisfy the requirements of score surge effectiveness and structural consistency, form a target electrical connection point set, and output it to the energy dispatching stage.

6. The energy management strategy optimization method for communication base stations according to claim 5, characterized in that, When a connection point in the target electrical connection point set has a score that is higher than the dynamic judgment threshold for 30 consecutive minutes, a temperature rise rate that is more than twice the historical average, and a vibration energy density that is more than 1.5 times the average, it is marked as an object requiring active intervention, and an abnormal notification is output to the energy dispatch layer.

7. The energy management strategy optimization method for communication base stations according to claim 5, characterized in that, The steps for generating the trigger signal for dynamic control are as follows: High-frequency impedance sampling is performed on the target set of electrical connection points, with a sampling frequency of no less than 50,000 times per second. Voltage, current and temperature data are acquired simultaneously during the sampling process to construct the impedance change time-series curve of the electrical connection points during operation. The impedance change curve is time-aligned with the temperature evolution trend. The temperature rise rate, temperature rise lag time and impedance increase magnitude of the impedance surge segment are extracted and compared with historical standard features to determine the degree of anomaly. A multidimensional feature vector is constructed based on impedance jump amplitude, surge duration, and thermal response hysteresis time. A surge duration label is generated and a threshold is set. When the label value exceeds the preset upper limit, a control trigger signal is output.

8. The energy management strategy optimization method for communication base stations according to claim 7, characterized in that, The steps for performing a short-term reverse micro-power supply to repair the contact interface of an electrical connection point include: The repair triggering conditions are determined based on the score value of the contact impedance surge duration label, and healthy battery clusters with the shortest electrical path to the target electrical connection point, stable load, and long-term low historical scores are selected as reverse energy sources. Set the voltage, current and duration parameters for reverse power supply, and connect the reverse power supply path through channel switching to ensure that the voltage difference range, power supply stability and safety meet the conditions for non-destructive intervention. High-frequency sampling is performed during the reverse power supply process to record data on voltage, impedance, temperature and current changes. After the power supply is completed, monitoring continues to determine whether the contact interface has been repaired. The effectiveness of the repair is calculated based on the impedance drop magnitude, temperature change rate, and state stabilization time. The status label of the connection point is updated to repair confirmed, repair observed, or repair failed, and a decision is made on whether to enter the next round of intervention cycle accordingly.

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