Base station energy coordination optimization method and system based on big data analysis

Abstract

The present application relates to the technical field of base station energy management, and particularly relates to a base station energy collaborative optimization method and system based on big data analysis, comprising the following steps: S1: taking a candidate energy-saving action set of a target base station as input, identifying a post-release energy-supplementing trajectory, and outputting a back-jump representation result; S2: determining whether it triggers at least two types of linkage superposition amplification within the same linkage window; S3: classifying the candidate energy-saving action triggering the collaborative amplification into a frozen action set, and combining the candidate energy-saving action not triggering the collaborative amplification into a collaborative execution sequence according to an energy-supplementing window avoidance relationship; and S4: generating an energy collaborative optimization instruction. According to the present application, the post-release energy-supplementing back-jump after the energy-saving action is withdrawn is identified, and the action with the risk of collaborative amplification is frozen before scheduling, and the remaining actions are arranged to avoid the energy-supplementing window, so that the energy-supplementing superposition interference is effectively reduced, and the stability and energy-saving effect of the base station energy scheduling are improved.

Description

Technical Field

[0001] This invention relates to the field of base station energy management technology, and in particular to a method and system for collaborative optimization of base station energy based on big data analysis. Background Technology

[0002] With the continuous expansion of communication networks and the increasing number of base stations, energy consumption during base station operation has gradually become a key factor in controlling operating costs and developing green communications. Existing technologies typically employ energy-saving strategies, such as reducing power, shutting down some carrier frequencies, adjusting air conditioning operation, and scheduling energy storage devices during low load periods or specific times to lower overall energy consumption. Furthermore, in multi-base station collaborative operation scenarios, mechanisms such as cross-site load scheduling, temperature control linkage, and energy storage collaborative control are introduced to create a certain degree of energy interaction between different base stations. While these technologies can achieve certain energy-saving effects during the execution of a single action, in actual operation, various energy-saving actions often exhibit characteristics such as periodic start-stop, dynamic adjustment, and cross-site linkage, resulting in significant phased changes in the operating status of base stations over time.

[0003] However, in existing technologies, most energy-saving optimization methods primarily focus on the energy reduction effect during the execution of energy-saving actions. They typically use real-time energy consumption indicators or short-term average energy consumption as optimization targets to screen and schedule energy-saving actions, lacking in-depth analysis of the system's response behavior after the energy-saving actions are completed. Actual operation shows that some energy-saving actions, after being withdrawn, can trigger subsequent energy replenishment phenomena such as power recovery, temperature-controlled energy replenishment, energy storage replenishment, and cross-site takeover replenishment. These replenishment behaviors are temporally coupled, and when multiple replenishment processes overlap within the same time window, a significant energy replenishment amplification effect is formed, causing the initial energy-saving benefits to be quickly offset, and even triggering new energy consumption fluctuations. Furthermore, existing scheduling methods typically perform global optimization and orchestration of all candidate energy-saving actions without prior isolation of actions with potential energy replenishment amplification risks. This allows these high-risk actions to influence each other in the execution sequence, further exacerbating the energy replenishment superposition problem. At the same time, existing technologies also lack avoidance mechanisms for conflicts in energy replenishment sensitive intervals between successive actions, making it difficult to ensure that adjacent energy-saving actions do not experience overlapping energy replenishment windows in time. Based on the above problems, there is an urgent need for a base station energy collaborative optimization method based on big data analysis, so as to reduce the risk of energy replenishment superposition from the source and improve the overall stability and energy-saving effectiveness of base station energy scheduling. Summary of the Invention

[0004] To achieve the above objectives, this invention provides a method and system for coordinated optimization of base station energy based on big data analysis.

[0005] The base station energy collaborative optimization method based on big data analysis includes the following steps: S1: Taking the set of candidate energy-saving actions of the target base station as input, the historical execution segments corresponding to each candidate energy-saving action are recalled and traced back, and the subsequent energy replenishment trajectory that appears in the target base station and its associated base stations after the candidate energy-saving action ends is identified, and the bounce characterization result corresponding to each candidate energy-saving action is output. S2: Using the results of each bounce characterization as input, determine whether they trigger at least two types of linkage superposition amplification in the same linkage window, such as cross-site takeover energy replenishment, temperature control energy replenishment, and energy storage energy replenishment, and output the corresponding collaborative amplification flag for each candidate energy-saving action; S3: Using each collaborative amplification flag as input, the candidate energy-saving actions that trigger collaborative amplification are included in the frozen action set, and the candidate energy-saving actions that do not trigger collaborative amplification are combined into a collaborative execution sequence according to the energy replenishment window avoidance relationship. S4: Taking the collaborative execution sequence as input, and according to the avoidance relationship between the impact of subsequent energy replenishment after the withdrawal of the previous candidate energy-saving action and the energy replenishment window of the next candidate energy-saving action, generate energy collaborative optimization instructions for the target base station and its associated base stations.

[0006] Optionally, S1 specifically includes: S11: Extract the historical execution records corresponding to each candidate energy-saving action from the candidate energy-saving action set of the target base station, and extract the historical execution segments corresponding to each candidate energy-saving action based on the start time, end time and withdrawal time of each candidate energy-saving action. S12: Using the end time of each candidate energy-saving action as the starting point for tracing, extract the power supply change data, air conditioning load change data, energy storage charging and discharging status change data, and inter-station load transfer data of the target base station and its associated base stations within the preset tracing time window to form the observation data segment after the withdrawal of the corresponding candidate energy-saving action. S13: Perform time-series alignment processing on each withdrawn observation data segment, identify the delayed occurrence interval of various energy responses after the candidate energy-saving actions are completed, and extract the power recovery segment, temperature control compensation segment, energy storage replenishment segment and cross-station takeover segment to form the subsequent energy replenishment trajectory of the corresponding candidate energy-saving actions. S14: Collect the subsequent energy replenishment trajectories corresponding to each candidate energy-saving action, establish a one-to-one correspondence between the candidate energy-saving action identifier and the subsequent energy replenishment trajectory, and output the bounce characterization results corresponding to each candidate energy-saving action.

[0007] Optionally, the bounce characterization results include candidate energy-saving action identifiers, bounce start time, bounce duration, bounce peak amplitude, cumulative energy replenishment, set of base stations involved, and bounce type identifiers.

[0008] Optionally, S2 specifically includes: S21: Read the bounce characterization results corresponding to each energy-saving action, and extract the bounce start time, bounce duration, set of base stations involved, and bounce type identifier. S22: Using the jump start time of each candidate energy-saving action as the starting point of the linkage analysis, and using the preset linkage judgment duration as the time boundary, construct the linkage window corresponding to each candidate energy-saving action; S23: Within each linkage window, joint detection is performed on the target base station and each associated base station in the set of base stations involved to determine whether at least two types of energy replenishment responses among cross-site takeover energy replenishment, temperature-controlled energy replenishment, and energy storage recovery energy replenishment overlap, connect, or continuously transmit in time; S24: When at least two types of energy replenishment responses within the linkage window meet the preset linkage association conditions, determine that the subsequent energy replenishment trajectory corresponding to the candidate energy-saving action forms a linkage superposition and amplification within the linkage window, and determine this linkage window as the effective linkage window corresponding to the candidate energy-saving action.

[0009] Optionally, S2 further includes: S25: Perform intensity statistics and time-series correlation analysis on cross-site takeover energy replenishment, temperature control energy replenishment, and energy storage recovery energy within each effective linkage window to obtain the corresponding linkage energy replenishment intensity value, linkage duration, and number of linkage types. S26: Compare the number of linkage types with the preset type threshold, compare the linkage energy replenishment intensity value with the preset intensity threshold, and compare the linkage duration with the preset duration threshold. When the number of linkage types is greater than or equal to 2, the linkage energy replenishment intensity value is greater than or equal to a preset intensity threshold, and the linkage duration is greater than or equal to a preset duration threshold, it is determined that the corresponding candidate energy-saving action triggers collaborative amplification, and a collaborative amplification flag is output. When the number of linkage types is less than 2, or the linkage energy replenishment intensity value is less than the preset intensity threshold, or the linkage duration is less than the preset duration threshold, it is determined that the corresponding candidate energy-saving action has not triggered collaborative amplification, and a non-collaborative amplification flag is output.

[0010] Optionally, S3 specifically includes: S31: Read the collaborative amplification markers corresponding to each candidate energy-saving action, and establish the correspondence between the candidate energy-saving actions and the collaborative amplification markers according to the candidate energy-saving action identifiers; S32: Filter candidate energy-saving actions marked as triggering collaborative amplification, and add the filtered candidate energy-saving actions to the frozen action set; S33: Write the freeze status identifier, freeze start time and corresponding linkage risk type to each candidate energy-saving action in the freeze action set; S34: Filter candidate energy-saving actions marked as not having triggered collaborative amplification, and determine the selected candidate energy-saving actions as the set of actions to be programmed.

[0011] Optionally, S3 further includes: S35: Read the bounce characterization results corresponding to each candidate energy-saving action in the action set to be arranged, and extract the bounce start time, bounce duration, bounce peak amplitude and cumulative energy replenishment. S36: Determine the corresponding impact range of subsequent energy replenishment based on the rebound start time and rebound duration of each candidate energy-saving action, and determine the corresponding candidate execution range based on the historical execution period and schedulable period of each candidate energy-saving action; S37: Perform a timing avoidance analysis on any two candidate energy-saving actions in the set of actions to be arranged. When the energy replenishment effect range of the candidate energy-saving action in the first position overlaps with the candidate execution range of the candidate energy-saving action in the second position, the candidate energy-saving action in the second position will be postponed until the two no longer overlap. S38: Based on the sequential execution relationship formed after the timing avoidance analysis, sort the candidate energy-saving actions in the action set to be arranged and generate a collaborative execution sequence.

[0012] Optionally, S4 specifically includes: S41: Read the sorting position, corresponding base station identifier, candidate execution interval, and subsequent power replenishment impact interval of each candidate energy-saving action in the collaborative execution sequence; S42: Based on the order of each candidate energy-saving action in the collaborative execution sequence, determine the plan start time, plan duration, and plan withdrawal time corresponding to each candidate energy-saving action; S43: Generate local energy-saving control instructions for candidate energy-saving actions in the target base station, including action type, planned start time, planned duration and planned withdrawal time; S44: Generate coordinated control commands for associated base stations, including takeover waiting period, temperature control adjustment period, and energy storage replenishment delay period; S45: Combine and encapsulate local energy-saving control instructions with cooperative control instructions to form energy cooperative optimization instructions for the target base station and its associated base stations.

[0013] Optionally, S4 further includes: S46: Read two adjacent candidate energy-saving actions in the collaborative execution sequence, and denot them as the preceding candidate energy-saving action and the following candidate energy-saving action, respectively. Obtain the subsequent energy replenishment influence interval corresponding to the preceding candidate energy-saving action and the energy replenishment window interval corresponding to the following candidate energy-saving action. S47: Determine whether there is a time overlap between the impact range of subsequent energy replenishment and the energy replenishment window range. If there is a time overlap, calculate the overlap duration and use the overlap duration as the postponement correction amount for subsequent candidate energy-saving actions. S48: Based on the postponement correction amount, the planned start time and planned withdrawal time of subsequent candidate energy-saving actions are postponed, and the associated base station takeover waiting period, temperature control adjustment period or energy storage replenishment delay period associated with subsequent candidate energy-saving actions are also corrected simultaneously. S49: Repeat S46 to S48 until any two adjacent candidate energy-saving actions in the collaborative execution sequence satisfy the condition that the subsequent energy replenishment influence interval of the preceding candidate energy-saving action and the energy replenishment window interval of the subsequent candidate energy-saving action do not overlap in time; finally, output the energy collaborative optimization instruction after time-correction.

[0014] The base station energy collaborative optimization system based on big data analysis is used to implement the aforementioned base station energy collaborative optimization method based on big data analysis, and includes the following modules: Back-off identification module: It is used to take the candidate energy-saving action set of the target base station as input, trace back the historical execution segment corresponding to each candidate energy-saving action after withdrawal, identify the subsequent energy replenishment trajectory that appears in the target base station and its associated base stations after the candidate energy-saving action ends, and output the back-off characterization result corresponding to each candidate energy-saving action. Amplification and discrimination module: used to receive the bounce characterization results output by the bounce recognition module, determine whether the subsequent energy replenishment trajectory corresponding to each candidate energy-saving action triggers at least two types of linkage superposition amplification in the same linkage window, namely cross-site takeover energy replenishment, temperature control follow-up energy replenishment and energy storage recovery energy replenishment, and output the corresponding collaborative amplification mark for each candidate energy-saving action; Action filtering module: Used to receive the collaborative amplification flag output by the amplification discrimination module, classify the candidate energy-saving actions that trigger collaborative amplification into the frozen action set, determine the candidate energy-saving actions that do not trigger collaborative amplification as the action set to be arranged, and form a collaborative execution sequence based on the action set to be arranged. The collaborative instruction generation module receives the collaborative execution sequence output by the action filtering module, performs timing correction on each candidate energy-saving action in the collaborative execution sequence according to the avoidance relationship between the subsequent energy replenishment impact after the withdrawal of the previous candidate energy-saving action and the energy replenishment window of the next candidate energy-saving action, and generates energy collaborative optimization instructions for the target base station and its associated base stations.

[0015] The beneficial effects of this invention are: This invention, by tracing and analyzing the operation process of candidate energy-saving actions after withdrawal, constructs a subsequent energy replenishment trajectory and generates a rebound characterization result based on parameters such as the rebound start time, duration, and energy replenishment intensity. This allows for a structured characterization of the energy replenishment behavior triggered after the energy-saving action is executed. Furthermore, by identifying the temporal coupling relationship between cross-site takeover energy replenishment, temperature-controlled energy replenishment, and energy storage energy replenishment within a unified linkage window, and by combining energy replenishment intensity, duration, and the number of participating types for joint discrimination, accurate identification of synergistic amplification phenomena is achieved. Through these technical means, the energy-saving optimization process is transformed from traditional energy consumption assessment during execution to rebound risk identification after withdrawal. Potentially high-risk actions can be identified before scheduling, thus avoiding misjudgments caused by relying solely on real-time energy consumption indicators and improving the comprehensiveness and accuracy of energy-saving strategy evaluation.

[0016] This invention freezes candidate energy-saving actions that trigger coordinated amplification and only schedules actions that do not trigger coordinated amplification. Simultaneously, it performs avoidance correction based on the relationship between the subsequent energy replenishment impact interval of the preceding action and the energy replenishment window interval of the following action, ensuring that the energy-saving actions do not overlap in their energy replenishment sensitive intervals in time. Based on this, it generates energy coordination optimization instructions containing local control parameters and coordination parameters, ensuring that energy-saving actions and energy replenishment behaviors among multiple base stations remain coordinated in time. Through these technical means, the superposition effect of energy replenishment between energy-saving actions can be weakened from the source, reducing the risk of energy consumption rebound caused by bounce amplification, thereby improving the stability, continuity, and overall energy-saving effect of base station energy scheduling. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the base station energy collaborative optimization method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a base station energy collaborative optimization system according to an embodiment of the present invention. Detailed Implementation

[0019] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0020] It should be noted that the use of terms such as "an embodiment," "an embodiment," "an exemplary embodiment," and "some embodiments" in the specification indicates that the described embodiment may include a specific feature, structure, or characteristic, but not every embodiment necessarily includes that specific feature, structure, or characteristic. Furthermore, when a specific feature, structure, or characteristic is described in connection with an embodiment, implementing such a feature, structure, or characteristic in conjunction with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the art.

[0021] Generally, terms can be understood at least partly from their use in context. For example, depending at least partly on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in a singular sense, or a combination of features, structures, or characteristics in a plural sense. Additionally, the term "based on" can be understood not necessarily to convey an exclusive set of factors, but rather, alternatively, depending at least partly on the context, to allow for the presence of other factors that are not necessarily explicitly described.

[0022] like Figure 1 As shown, the base station energy collaborative optimization method based on big data analysis includes the following steps: S1: Taking the set of candidate energy-saving actions of the target base station as input, the historical execution segments corresponding to each candidate energy-saving action are recalled and traced back, and the subsequent energy replenishment trajectory that appears in the target base station and its associated base stations after the candidate energy-saving action ends is identified, and the bounce characterization result corresponding to each candidate energy-saving action is output. S1 specifically includes: S11: Read the action identifier, action type, start time, end time, and withdrawal time of candidate energy-saving actions from the base station operation log; then, taking each candidate energy-saving action as a unit, extract the historical execution segment corresponding to that candidate energy-saving action in chronological order. The starting boundary of the historical execution segment is taken as the start time of the candidate energy-saving action, and the ending boundary is taken as the withdrawal time of the candidate energy-saving action; when the end time and the withdrawal time coincide, that time is taken as the end point of the action execution; the length of the historical execution segment can be expressed as: ,in, Indicates the first The length of the historical execution segment corresponding to each candidate energy-saving action; Indicates the first The timing of withdrawing a candidate energy-saving action; Indicates the first The start time of each candidate energy-saving action; when it is necessary to distinguish between the natural end of an action and manual withdrawal, the termination time of the action execution can be defined as: ,in, Indicates the first The actual termination time of each candidate energy-saving action; Indicates the first The end time of each candidate energy-saving action; Indicates the first The timing of withdrawing a candidate energy-saving action; This indicates the earlier of the two times. This step allows us to segment the original operating process corresponding to the candidate energy-saving action according to the action granularity, ensuring that the subsequent traceability objects are clear and the time boundaries are consistent, thereby improving the basic accuracy of identifying subsequent energy replenishment.

[0023] S12: Using the end time of each candidate energy-saving action as the starting point for tracing, extract the power supply change data, air conditioning load change data, energy storage charging and discharging status change data, and inter-station load transfer data of the target base station and its associated base stations within the preset tracing time window, forming the post-withdrawal observation data segment for the corresponding candidate energy-saving action. The associated base station refers to a base station that has energy interaction with the target base station in load switching, coverage takeover, temperature control coupling, energy storage coordination, or energy supply linkage. In practice, all extracted data are accompanied by a unified timestamp and organized according to the same sampling period to form the post-withdrawal observation data segment for each individual candidate energy-saving action. The purpose of this processing is to ensure that all types of energy replenishment responses after the action are analyzed within a unified time frame, avoiding subsequent response identification bias caused by inconsistent sampling starting points from different data sources. Specifically, the preset tracing time window is represented as follows: ,in, Indicates the first Preset traceability time windows corresponding to each candidate energy-saving action; Indicates the first The end time of each candidate energy-saving action; The preset traceability window length is 30 minutes; [] indicates the time range of the closed interval. The preset traceability window length is fixed at 30 minutes because the power recovery, temperature control replenishment, energy storage replenishment, and neighbor station takeover replenishment generated after the base station energy-saving action usually occur in a short period of time after the action ends. 30 minutes can fully cover the main subsequent replenishment process without introducing irrelevant operational disturbances over a long period of time.

[0024] S13: Perform time-series alignment processing on each withdrawn observation data segment, identify the delayed occurrence intervals of various energy responses after the candidate energy-saving actions, and extract the power recovery segment, temperature control compensation segment, energy storage replenishment segment, and inter-station takeover segment to form the subsequent energy replenishment trajectory of the corresponding candidate energy-saving actions; in specific implementation, first align the power supply change data, air conditioning load change data, energy storage charging and discharging status change data, and inter-station load transfer data according to a unified timestamp to ensure that various types of data can be directly compared at the same sampling time; specifically, set the unified sampling period as... Then the first Each sampling time can be represented as: ,in, Indicates the first A unified sampling time; Indicates the first The end time of each candidate energy-saving action; Indicates the sampling sequence number. ; Indicates a uniform sampling period; This indicates the total number of sampling points within the tracing window.

[0025] Then, using the operating state corresponding to the end time of the candidate energy-saving action as the baseline state, the increments of various data within the traceability window relative to the baseline state are calculated. Specifically, using the state at the end time of the action as the baseline value, the four types of increments are as follows: ; ; ; ;in, Indicates the first The candidate energy-saving actions in time The increase in power supply; Indicates the first Candidate energy-saving actions in time The increase in air conditioning load; Indicates the first The candidate energy-saving actions in time The increment of energy storage status; Indicates the first The candidate energy-saving actions in time Inter-station load transfer increment; These represent the baseline values ​​corresponding to the end time of the action. When the increment of a certain type of data continuously exceeds the corresponding threshold and remains for a set duration, this continuous period is identified as the delayed occurrence interval of that type of energy response after the candidate energy-saving action ends. The delayed occurrence interval is specifically defined as the time interval during which the increment first exceeds the threshold and remains continuously after the action ends. Taking power recovery as an example, the identification condition is: ,satisfy: and ,in, Indicates the first The power recovery delay range corresponding to each candidate energy-saving action; Indicates the starting time of the power recovery interval; Indicates the end time of the power recovery interval; This represents the threshold for incremental power supply, with a value of [value missing]. ; This indicates the minimum duration of power recovery, and its value is [value missing]. Similarly, the identification thresholds for the temperature control compensation section, energy storage replenishment section, and cross-site takeover section are set as follows: , ; , ; , ;in, Indicates the threshold for incremental air conditioning load; Indicates the minimum duration of temperature control compensation; Indicates the incremental threshold for energy storage replenishment; Indicates the minimum hold duration for energy storage replenishment; Indicates the threshold for incremental load transfer between stations; Indicates the minimum duration for cross-site takeover; This represents absolute value operations.

[0026] Finally, based on the data type, the power recovery segment, temperature control compensation segment, energy storage replenishment segment, and cross-site takeover segment are extracted respectively, and spliced ​​together in chronological order to form the subsequent energy replenishment trajectory of the corresponding candidate energy-saving actions; its expression is: ,in, and take ,in, Indicates the first The subsequent energy replenishment trajectory corresponding to each candidate energy-saving action; Indicates time The overall energy replenishment response intensity; Indicates time Corresponding energy replenishment type identifier; Indicates the power supply response weight; Indicates the weighting of the air conditioning load response; Indicates the weight of the energy storage replenishment response; The weights for cross-site takeover response are indicated. Among these weights, power supply directly reflects the recovery of main power supply, so it is assigned 0.35; air conditioning load is the most common source of supplementary energy, so it is assigned 0.25; energy storage replenishment and cross-site takeover are both collaborative energy replenishment components, each assigned 0.20. The resulting comprehensive energy replenishment response intensity can be used as a unified quantitative expression for a single subsequent energy replenishment trajectory. Through these steps, the dispersed energy response after the action ends can be transformed into a continuous energy replenishment trajectory with unified time logic, thereby accurately characterizing the actual occurrence process of energy replenishment rebound after withdrawal.

[0027] S14: Collect the subsequent energy replenishment trajectories corresponding to each candidate energy-saving action, establish a one-to-one correspondence between candidate energy-saving action identifiers and subsequent energy replenishment trajectories, and output the bounce representation results for each candidate energy-saving action. In specific implementation, extract the unique action identifier for each candidate energy-saving action and bind and store this action identifier with the corresponding subsequent energy replenishment trajectory, forming a bounce result table organized by action granularity. Each record in the bounce result table corresponds to only one candidate energy-saving action, and each candidate energy-saving action corresponds to only one collected subsequent energy replenishment trajectory, thus ensuring accurate invocation by action during subsequent processing. The bounce representation result expression is: ,in, Indicates the first The bounce characterization results corresponding to each candidate energy-saving action; Indicates the first One candidate energy-saving action label; Indicates the first The subsequent energy replenishment action corresponds to each candidate energy-saving action. The subsequent energy replenishment trajectory refers to the continuous energy response path formed by merging the power recovery segment, temperature control compensation segment, energy storage replenishment segment, and cross-site takeover segment identified within a preset tracing time window after the candidate energy-saving action ends, in a unified time sequence. This trajectory is not a single energy replenishment value, but rather describes the entire process of how various energy replenishment responses appear, intensify, continue, and decay sequentially over time after the candidate energy-saving action ends. From a data representation perspective, the subsequent energy replenishment trajectory includes not only the time dimension, but also the response intensity dimension and the energy replenishment type dimension, thus fully reflecting the energy replenishment rebound process after the action is withdrawn. It can integrate multiple discrete energy replenishment events after the action ends into a continuous time-series trajectory, providing a unified carrier for the subsequent rebound characterization results output.

[0028] The bounce characterization results include candidate energy-saving action identifiers, bounce start time, bounce duration, bounce peak amplitude, cumulative energy replenishment, set of base stations involved, and bounce type identifier.

[0029] The candidate energy-saving action identifier is used to uniquely identify the relationship between a single candidate energy-saving action and its corresponding bounce representation result. In specific implementation, the base station number, action type number, and action execution sequence number of the candidate energy-saving action can be concatenated to form the unique identifier of the candidate energy-saving action. The purpose of using a unique identifier is to ensure that each bounce representation result can accurately correspond to its source action and avoid data confusion between different candidate energy-saving actions.

[0030] The bounce-back start time is used to characterize the time position at which the subsequent energy replenishment response first reaches the effective identification condition after the candidate energy-saving action ends. In specific implementation, based on the comprehensive energy replenishment response intensity sequence, the system scans point by point from the end time of the candidate energy-saving action. When the comprehensive energy replenishment response intensity first exceeds or equals the bounce trigger threshold and remains continuously for the minimum duration, that moment is determined as the bounce-back start time. The purpose of this process is to ensure that the bounce-back start time corresponds not to instantaneous noise fluctuations, but to a truly continuous starting point for subsequent energy replenishment.

[0031] The bounce duration is used to characterize the continuous duration from the bounce start time to the point where the overall energy replenishment response intensity falls back to the invalid response range. In specific implementation, after determining the bounce start time, the search continues to move backward along the time axis. When the overall energy replenishment response intensity is continuously lower than the fall-off threshold and remains below the end judgment time, the last valid response time before that time is determined as the bounce termination time. The bounce duration is then obtained by subtracting the bounce start time from the termination time.

[0032] The rebound peak amplitude is used to characterize the maximum comprehensive energy replenishment response intensity that occurs within the entire rebound duration after the candidate energy-saving action ends. In practice, the comprehensive energy replenishment response intensity sequence is compared point by point between the rebound start time and the rebound end time, and the maximum value is taken as the rebound peak amplitude. The rebound peak amplitude reflects the maximum energy replenishment impact during a single rebound process, rather than the cumulative effect.

[0033] The cumulative energy replenishment is used to characterize the total energy replenishment increment of the target base station and associated base stations due to subsequent energy replenishment within the entire back-loop duration interval. In specific implementation, the comprehensive energy replenishment response intensity at each sampling time within the back-loop duration interval is accumulated in a time-integrated manner to obtain the cumulative energy replenishment of the candidate energy-saving action. Since the comprehensive energy replenishment response intensity has already comprehensively reflected the energy replenishment components such as power supply, air conditioning load, energy storage replenishment, and inter-station load transfer, integrating it in the time dimension can yield the total energy replenishment scale of a single back-loop process.

[0034] The set of base stations involved is used to characterize the target base stations and associated base stations that actually participate in the subsequent power replenishment response during the bounce process after the candidate energy-saving action ends. In specific implementation, all base stations that have effective power replenishment responses within the bounce duration interval are traversed. When the local power replenishment response intensity of a base station in the interval reaches the base station participation threshold, the base station is included in the set of base stations involved. In this way, it can be clarified that a bounce not only affects the target base station itself, but may also extend to multiple associated base stations.

[0035] The bounce type identifier is used to characterize the subsequent energy replenishment trajectory formed after the candidate energy-saving action ends, which belongs to one or more of the following: cross-station takeover energy replenishment, temperature control follow-up energy replenishment, and energy storage recovery energy replenishment. In specific implementation, the types of effective energy replenishment segments that appear within the bounce duration interval are classified as follows: when the inter-station load transfer increment is detected to exceed the corresponding threshold, it is recorded as cross-station takeover energy replenishment; when the air conditioning load increment is detected to exceed the corresponding threshold, it is recorded as temperature control follow-up energy replenishment; when the energy storage recovery increment is detected to exceed the corresponding threshold, it is recorded as energy storage recovery energy replenishment. Finally, the actual types are combined to form the bounce type identifier.

[0036] S2: Using the results of each bounce characterization as input, determine whether they trigger at least two types of linkage superposition amplification in the same linkage window, such as cross-site takeover energy replenishment, temperature control energy replenishment, and energy storage energy replenishment, and output the corresponding collaborative amplification flag for each candidate energy-saving action; S2 specifically includes: S21: Read the bounce characterization results corresponding to each energy-saving action, and extract the bounce start time, bounce duration, set of base stations involved, and bounce type identifier. S22: Using the rebound start time of each candidate energy-saving action as the starting point of the linkage analysis, and using the preset linkage judgment duration as the time boundary, a linkage window is constructed for each candidate energy-saving action. The linkage window refers to a time interval constructed within the preset linkage judgment duration, starting from the rebound start time, used to uniformly analyze the temporal coupling relationship between different energy replenishment types. The function of this window is to compare cross-site takeover energy replenishment, temperature-controlled replenishment, and energy storage recovery energy replenishment within the same time scale, thereby determining whether a linkage relationship exists. Specifically, the linkage window is represented as follows: ,in, Indicates the first A linked window for each candidate energy-saving action; Indicates the first The jump start time of each candidate energy-saving action; The linkage judgment duration is 15 minutes; [] indicates the time range of the closed interval. The linkage judgment duration is set to 15 minutes because different energy replenishment types have obvious short-term linkage characteristics after the rebound starts. This duration can cover the main response coupling stages between cross-station takeover, temperature control regulation and energy storage replenishment, while avoiding the interference of long-term operating fluctuations on the linkage relationship judgment.

[0037] S23: Within each linkage window, joint detection is performed on the target base station and all associated base stations in the set of involved base stations to determine whether at least two types of energy replenishment responses—cross-site takeover energy replenishment, temperature-controlled energy replenishment, and energy storage recovery energy replenishment—overlap, connect, or continuously transmit in time; specifically, to unify the time relationship between different energy replenishment types, the intervals of the three types of energy replenishment responses are represented as follows: ; ; ;in, Indicates the first The cross-site takeover and energy replenishment intervals corresponding to each candidate energy-saving action; Indicates the first The temperature control supplementary energy range corresponding to each candidate energy-saving action; Indicates the first The energy storage replenishment range corresponding to each candidate energy-saving action; These represent the start and end times of the cross-station takeover and energy replenishment interval, respectively. These represent the start and end times of the temperature control compensation interval, respectively. These represent the start and end times of the energy storage replenishment interval, respectively. The time relationship function between any two types of replenishment intervals is: ,in, Indicates the linkage relationship between the two types of energy replenishment intervals; They represent any two types of energy replenishment intervals; This represents the interval intersection operation; Representing an interval End time and interval The time interval between start times; This represents the maximum linkage interval threshold, with a value of 2 minutes. When the two types of energy replenishment intervals overlap in time or the interval is less than or equal to 2 minutes, it is considered that there is a linkage relationship between them.

[0038] S24: When at least two types of energy replenishment responses within the linkage window meet the preset linkage association conditions, it is determined that the subsequent energy replenishment trajectory corresponding to the candidate energy-saving action forms a linkage superposition amplification within the linkage window, and this linkage window is determined as the valid linkage window corresponding to the candidate energy-saving action. In specific implementation, the linkage relationship between the three types of energy replenishment intervals is determined pairwise within the linkage window, and the number of energy replenishment type pairs with established linkage relationships is counted; when at least two types of energy replenishment responses meet the linkage relationship, the candidate energy-saving action can be identified as forming a linkage superposition structure within the linkage window, and this linkage window is marked as a valid linkage window; the linkage relationship count is expressed as: ,in, Indicates the first The number of energy replenishment linkage relationships for each candidate energy-saving action within the linkage window; This represents the linkage determination function between two types of energy replenishment intervals; when: If at that time, it is considered that there is at least one set of linkage relationships between energy replenishment types, and thus the linkage window is determined to be a valid linkage window.

[0039] S2 also includes: S25: Perform intensity statistics and time-series correlation analysis on cross-site takeover energy replenishment, temperature-controlled energy replenishment, and energy storage recovery energy within each effective linkage window to obtain the corresponding linkage energy replenishment intensity value, linkage duration, and number of linkage types; in specific implementation, within the determined effective linkage window... Within the system, the intensity of the three types of energy replenishment responses is extracted separately, and the results are superimposed based on the time alignment to form a unified linkage energy replenishment intensity sequence. This can transform multi-source energy replenishment responses into a comprehensive intensity expression with unified dimensions, providing a consistent basis for subsequent threshold discrimination. Calculate the linkage energy replenishment intensity value. Within the effective linkage window, define the comprehensive linkage energy replenishment intensity as: and take the weight ;in, Indicates the first The candidate energy-saving actions in time The intensity of the coordinated energy replenishment; This indicates the increase in energy replenishment through cross-site takeover; This indicates the increase in energy supply due to temperature control replenishment; This indicates that energy storage is used to replenish incremental energy. These represent the weights of the three types of power replenishment responses. The weights are set based on the following: cross-site takeover power replenishment directly alters the base station load distribution, having the greatest impact on the system; temperature-controlled power replenishment is second; and energy storage replenishment typically serves as a buffer mechanism, with a relatively weaker impact. The coordinated power replenishment intensity is further defined as the average intensity within the window. The discrete form is: ,in, Indicates the first The linkage energy replenishment intensity value of each candidate energy-saving action; Indicates the length of the linked window; Indicates the number of sampling points.

[0040] Calculate the duration of the linkage, identify the set of time points within the linkage window that satisfy the linkage relationship, and calculate their continuous duration; let the linkage relationship indicator function be: The duration of the linkage can then be expressed as: The discrete form is: ,in, Indicates the first The duration of the linkage between each candidate energy-saving action; Indicates time Is it in a linked state at the time? This indicates the sampling period, with a value of 1 minute.

[0041] The number of linkage types is calculated by counting the number of types that meet the effective response conditions among the three types of energy replenishment responses. The calculation expression is as follows: ,in, Indicates the number of linkage types; These indicate whether cross-site takeover energy replenishment, temperature-controlled energy replenishment, and energy storage recovery energy replenishment have occurred, respectively. When a certain type of energy replenishment appears in a valid range within the linkage window, the corresponding identifier is set to 1; otherwise, it is 0.

[0042] S26: Compare the number of linkage types with the preset type threshold, compare the linkage energy replenishment intensity value with the preset intensity threshold, and compare the linkage duration with the preset duration threshold. When the number of linkage types is greater than or equal to 2, the linkage energy replenishment intensity value is greater than or equal to the preset intensity threshold, and the linkage duration is greater than or equal to the preset duration threshold, it is determined that the corresponding candidate energy-saving action triggers collaborative amplification and outputs a collaborative amplification flag. When the number of linkage types is less than 2, or the linkage energy replenishment intensity value is less than a preset intensity threshold, or the linkage duration is less than a preset duration threshold, it is determined that the corresponding candidate energy-saving action has not triggered collaborative amplification, and a non-collaborative amplification flag is output. In specific implementation, collaborative amplification is determined to be triggered when the following three conditions are met simultaneously: ; ; ,in, The threshold value represents the preset type. The essence of synergistic amplification is the superposition of multiple mechanisms, and at least two types of energy compensation mechanisms need to participate simultaneously to form a synergistic effect. If only a single type of energy compensation exists, it belongs to single-source compensation behavior and does not constitute synergistic amplification. Therefore, the type threshold value is set to 2. Indicates the preset intensity threshold; This indicates a preset duration threshold, with a value of 5 minutes; when all three conditions above are met simultaneously, a collaborative amplification flag is output. ,in, Indicates the first The collaborative amplification markers of candidate energy-saving actions are used. Through this multi-dimensional joint discrimination mechanism, the linkage process can be constrained from three dimensions: the number of energy sources, the intensity of energy replenishment, and the duration, thereby effectively distinguishing between ordinary energy replenishment fluctuations and real collaborative amplification phenomena.

[0043] S3: Using each collaborative amplification flag as input, the candidate energy-saving actions that trigger collaborative amplification are included in the frozen action set, and the candidate energy-saving actions that do not trigger collaborative amplification are combined into a collaborative execution sequence according to the energy replenishment window avoidance relationship. S3 specifically includes: S31: Read the collaborative amplification markers corresponding to each candidate energy-saving action, and establish the correspondence between candidate energy-saving actions and collaborative amplification markers according to the candidate energy-saving action identifiers; in specific implementation, read the action identifier and collaborative amplification marker of each candidate energy-saving action item by item, and establish an index mapping table according to the action identifiers so that each candidate energy-saving action corresponds to a unique collaborative amplification marker; through this processing, the judgment results output by the previous steps can be bound to the specific candidate energy-saving actions, avoiding mismatch between actions and markers in the subsequent screening process.

[0044] S32: Select candidate energy-saving actions marked as triggering collaborative amplification and add them to the frozen action set. In practice, each collaborative amplification marker in the mapping set is evaluated. When a candidate energy-saving action's corresponding collaborative amplification marker is in a triggered state, the candidate energy-saving action is extracted and written into the frozen action set. Actions in the frozen action set no longer participate in the execution orchestration of the current scheduling cycle; they are only retained as risk isolation objects to prevent high-risk actions from interfering with adjacent actions after entering the execution sequence. The frozen action set can be represented as: ;in, Indicates the set of actions to freeze; Indicates the first One candidate energy-saving action; Indicates the first The collaborative amplification flags for each candidate energy-saving action. The number of frozen actions is represented as: ,in, Indicates the number of actions in the freeze action set; This represents the total number of candidate energy-saving actions; in the above expression, since Only 0 or 1 is used, so the number of frozen actions can be obtained by summing all the co-amplification flags.

[0045] S33: Write a freeze status identifier, freeze start time, and corresponding linkage risk type to each candidate energy-saving action in the freeze action set. This is used to indicate that the candidate energy-saving action is prohibited from entering the execution orchestration process during the current scheduling cycle. Specifically: The freeze status flag indicates that the action cannot participate in the execution sequence generation during the current scheduling cycle; the freeze status flag is defined for each candidate energy-saving action in the frozen action set as follows: ,in, Indicates the first The frozen status indicator of each candidate energy-saving action; when When, it indicates that the candidate energy-saving action is in a frozen state; when When this occurs, it indicates that the candidate energy-saving action is not in a frozen state. Since the frozen action set consists of actions with a cooperative amplification flag of 1, therefore, in the current step... ,in, Indicates the first Synergistic amplification markers for candidate energy-saving actions.

[0046] The freeze start time is used to characterize the time position at which the freeze action begins to be prohibited from orchestration within the current scheduling cycle; specifically, the start time of the current scheduling cycle is taken as the freeze effective time, so that the freeze action is excluded from execution orchestration throughout the current scheduling cycle; this can ensure that the freeze action is no longer mistakenly included in the orchestration sequence within the current scheduling cycle, thereby enhancing the certainty of risk isolation.

[0047] The linkage risk type is used to characterize the source category of the collaborative amplification triggered by the freeze action; in specific implementation, the above-mentioned bounce type identifier is directly called, and combined with the analysis results of the effective linkage window, to form the linkage risk type record of the corresponding action.

[0048] S34: Select candidate energy-saving actions marked as not having triggered collaborative amplification, and determine the selected candidate energy-saving actions as the set of actions to be orchestrated. In specific implementation, all candidate energy-saving actions are traversed again. When the collaborative amplification mark of a candidate energy-saving action is not triggered, the candidate energy-saving action is extracted and written into the set of actions to be orchestrated. Actions in the set of actions to be orchestrated do not have the risk of collaborative amplification and can continue to enter the subsequent timing avoidance and execution sequence generation process. In this way, after freezing high-risk actions, low-risk actions that do not trigger collaborative amplification are retained as subsequent scheduling and orchestration objects, thus forming a hierarchical processing path of first isolating risky actions and then processing executable actions.

[0049] S3 also includes: S35: Read the bounce characterization results corresponding to each candidate energy-saving action in the action set to be arranged, and extract the bounce start time, bounce duration, bounce peak amplitude and cumulative energy replenishment. S36: Determine the corresponding impact interval for subsequent power replenishment based on the bounce start time and bounce duration of each candidate energy-saving action, and determine the corresponding candidate execution interval based on the historical execution period and schedulable period of each candidate energy-saving action; in specific implementation, the bounce start time is used as the starting point of the impact interval, and the bounce duration is used as the interval length, thus obtaining the impact interval for subsequent power replenishment; simultaneously, based on the base station scheduling strategy, action execution constraints, and time availability range, determine the corresponding candidate execution interval for each candidate energy-saving action; the impact interval for subsequent power replenishment is expressed as: ,in, Indicates the first The impact range of subsequent energy replenishment for each candidate energy-saving action; the candidate execution range is defined as: ;in, Indicates the first Candidate execution intervals for each candidate energy-saving action; Indicates the start time of candidate execution; Indicates the duration of action execution; the candidate execution start time satisfies ,in: Indicates the start time of the current scheduling cycle; Indicates the scheduling cycle length, with a value of This allows the time conflict problem to be transformed into a judgment on the relationship between intervals, thereby providing a unified calculation model for subsequent time sequence avoidance.

[0050] S37: Perform a timing avoidance analysis on any two candidate energy-saving actions in the set of actions to be arranged. When the energy replenishment effect range of the candidate energy-saving action in the first position overlaps with the candidate execution range of the candidate energy-saving action in the second position, the candidate energy-saving action in the second position will be postponed until the two no longer overlap. S38: Based on the sequential execution relationship formed after time-sequence avoidance analysis, sort the candidate energy-saving actions in the action set to be arranged to generate a cooperative execution sequence. The cooperative execution sequence refers to the ordered execution list of candidate energy-saving actions formed after time-sequence avoidance processing; this sequence ensures that there is no energy replenishment conflict between any adjacent actions; through the above processing, the energy replenishment interference problem between candidate energy-saving actions can be transformed into an interval conflict resolution problem.

[0051] S4: Taking the collaborative execution sequence as input, and according to the avoidance relationship between the impact of the subsequent energy replenishment after the withdrawal of the previous candidate energy-saving action and the energy replenishment window of the next candidate energy-saving action, generate energy collaborative optimization instructions for the target base station and its associated base stations. S4 specifically includes: S41: Read the sorting position, corresponding base station identifier, candidate execution interval, and subsequent power replenishment impact interval of each candidate energy-saving action in the collaborative execution sequence; S42: Based on the order of each candidate energy-saving action in the collaborative execution sequence, determine the plan start time, plan duration, and plan withdrawal time corresponding to each candidate energy-saving action; specifically, take the start time of the candidate execution interval as the plan start time, take the length of the execution interval as the plan duration, and add these two to calculate the plan withdrawal time. S43: Generate local energy-saving control instructions for candidate energy-saving actions in the target base station, including action type, planned start time, planned duration and planned withdrawal time; S44: Generate coordinated control commands for associated base stations, including takeover waiting period, temperature control adjustment period, and energy storage replenishment delay period; S45: Combine and encapsulate local energy-saving control instructions with cooperative control instructions to form energy cooperative optimization instructions for the target base station and its associated base stations.

[0052] S4 also includes: S46: Read two adjacent candidate energy-saving actions in the collaborative execution sequence, denoted as the preceding candidate energy-saving action and the following candidate energy-saving action, respectively, and obtain the subsequent energy replenishment influence interval corresponding to the preceding candidate energy-saving action and the energy replenishment window interval corresponding to the following candidate energy-saving action; in specific implementation, according to the sorting order in the collaborative execution sequence, select two adjacent candidate energy-saving actions in sequence, denoted as the preceding candidate energy-saving action and the following candidate energy-saving action, respectively; then read the subsequent energy replenishment influence interval corresponding to the preceding candidate energy-saving action and the energy replenishment window interval corresponding to the following candidate energy-saving action as the input objects for overlap detection; through this processing, the preceding and following relationships in the action sequence can be specifically mapped to the relationship judgment between two time intervals, thereby providing a unified analysis basis for subsequent time series correction. Specifically, let the collaborative execution sequence be: For any two adjacent candidate energy-saving actions and ,definition: ; ,in, Indicates the preceding candidate energy-saving action The range of influence of subsequent energy replenishment; Indicates the starting time of the interval affected by the subsequent energy replenishment of the preceding candidate energy-saving action; This indicates the end time of the interval affected by the subsequent energy replenishment of the preceding candidate energy-saving action; Indicates subsequent candidate energy-saving actions The energy replenishment window interval; Indicates the start time of the energy replenishment window interval for subsequent candidate energy-saving actions; This indicates the end time of the energy replenishment window interval for subsequent candidate energy-saving actions. The energy replenishment window interval for subsequent candidate energy-saving actions can be determined based on their planned start time and preset energy replenishment window duration: ,in, Indicates the planned start time of subsequent candidate energy-saving actions; This indicates the duration of the energy replenishment window for subsequent candidate energy-saving actions, and its value is [value missing]. The duration of the power replenishment window is set to 8 minutes because after a candidate energy-saving action is initiated, the local load of the base station, temperature control adjustment, and energy storage switching are usually most likely to be in a power replenishment sensitive phase within a short period of time. 8 minutes can cover the main power replenishment sensitive range after the latter action is initiated.

[0053] S47: Determine whether there is a temporal overlap between the subsequent energy replenishment impact interval and the energy replenishment window interval. If there is a temporal overlap, calculate the overlap duration and use it as the extension correction amount for subsequent candidate energy-saving actions. In specific implementation, perform an intersection operation on the two intervals. If the intersection is not empty, it means that the tail energy replenishment impact of the preceding action falls into the energy replenishment window of the subsequent action, and the two conflict in time. At this time, calculate the overlap length of the two intervals and use the overlap length as the basic extension amount. Through this process, whether avoidance is required and how much avoidance is required can be uniformly converted into an explicit time amount.

[0054] The time overlap determination function is: ;in, Indicates the time overlap between preceding and subsequent candidate energy-saving actions; This represents the interval intersection operation; Represents the empty set; when When the overlap duration is defined as: ,in, This indicates the time overlap between the preceding and subsequent candidate energy-saving actions. This indicates taking the earlier of the two termination times; This indicates that the later of the two starting times is selected; to avoid new energy replenishment interference even when the interval boundaries meet exactly, a safety margin is further set. Then the adjustment amount for the subsequent candidate energy-saving action is: ,in, This indicates the amount of subsequent candidate energy-saving actions to be adjusted accordingly. This represents a safety margin for avoidance, with a value of 2 minutes. The purpose of setting a 2-minute safety margin for avoidance is to prevent subsequent actions from entering the energy replenishment sensitive window before the energy replenishment tail section of the preceding action has fully decayed.

[0055] S48: Based on the postponement correction amount, the planned start time and planned withdrawal time of subsequent candidate energy-saving actions are adjusted backward, and the associated base station takeover waiting period, temperature control adjustment period, or energy storage replenishment delay period related to subsequent candidate energy-saving actions are adjusted simultaneously; in specific implementation, after the overlap duration is calculated, the planned start time of subsequent candidate energy-saving actions is shifted backward as a whole, while keeping its execution duration unchanged, thus obtaining the corrected planned withdrawal time; specifically, the corrected planned start time is: The revised withdrawal time for the plan is: ;in, Indicates the planned start time after the subsequent candidate energy-saving actions have been revised; Indicates the time when the plan is withdrawn after the subsequent candidate energy-saving action is revised; This indicates the time when the plan was withdrawn before the subsequent candidate energy-saving action was revised; since the duration of the action remains unchanged, therefore... ,in, Indicates the duration of the corrected action; This indicates the duration of the action before the correction.

[0056] Then, the associated base station takeover waiting period, temperature control adjustment period, and energy storage replenishment delay period corresponding to the subsequent candidate energy-saving action are simultaneously shifted backward; the corresponding shift correction expression is: ; ; ;in, This indicates the revised takeover waiting period; This indicates the revised temperature control adjustment period; This indicates the revised timeframe for energy storage replenishment. These represent the corresponding time periods before the correction. The above correction methods all use the same delay amount because after the main action time is shifted backward, its associated control actions, temperature control, and energy storage control actions must also be shifted backward to maintain consistent control relationships. Through this process, it can be ensured that the execution sequence of the action itself and the associated control sequence remain consistent, preventing mismatch of associated commands due to only correcting the main action time.

[0057] S49: Repeat S46 to S48 until any two adjacent candidate energy-saving actions in the collaborative execution sequence satisfy the condition that the subsequent energy replenishment influence interval of the preceding candidate energy-saving action and the energy replenishment window interval of the subsequent candidate energy-saving action do not overlap in time; finally, output the energy collaborative optimization instruction after time-correction.

[0058] like Figure 2 As shown, the base station energy collaborative optimization system based on big data analysis is used to implement the above-mentioned base station energy collaborative optimization method based on big data analysis, and includes the following modules: Back-off identification module: It is used to take the candidate energy-saving action set of the target base station as input, trace back the historical execution segment corresponding to each candidate energy-saving action after withdrawal, identify the subsequent energy replenishment trajectory that appears in the target base station and its associated base stations after the candidate energy-saving action ends, and output the back-off characterization result corresponding to each candidate energy-saving action. Amplification and discrimination module: used to receive the bounce characterization results output by the bounce recognition module, determine whether the subsequent energy replenishment trajectory corresponding to each candidate energy-saving action triggers at least two types of linkage superposition amplification in the same linkage window, namely cross-site takeover energy replenishment, temperature control follow-up energy replenishment and energy storage recovery energy replenishment, and output the corresponding collaborative amplification mark for each candidate energy-saving action; Action filtering module: It receives the collaborative amplification flag output by the amplification discrimination module, adds the candidate energy-saving actions that trigger collaborative amplification to the frozen action set, determines the candidate energy-saving actions that do not trigger collaborative amplification as the action set to be arranged, and forms a collaborative execution sequence based on the action set to be arranged. The collaborative instruction generation module receives the collaborative execution sequence output by the action filtering module, performs timing correction on each candidate energy-saving action in the collaborative execution sequence according to the avoidance relationship between the subsequent energy replenishment impact after the withdrawal of the previous candidate energy-saving action and the energy replenishment window of the next candidate energy-saving action, and generates energy collaborative optimization instructions for the target base station and its associated base stations.

[0059] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0060] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A base station energy collaborative optimization method based on big data analysis, characterized in that, Includes the following steps: S1: Taking the set of candidate energy-saving actions of the target base station as input, the historical execution segments corresponding to each candidate energy-saving action are recalled and traced back, and the subsequent energy replenishment trajectory that appears in the target base station and its associated base stations after the candidate energy-saving action ends is identified, and the bounce characterization result corresponding to each candidate energy-saving action is output. S2: Using the results of each bounce characterization as input, determine whether they trigger at least two types of linkage superposition amplification in the same linkage window, such as cross-site takeover energy replenishment, temperature control energy replenishment, and energy storage energy replenishment, and output the corresponding collaborative amplification flag for each candidate energy-saving action; S3: Using each collaborative amplification flag as input, the candidate energy-saving actions that trigger collaborative amplification are included in the frozen action set, and the candidate energy-saving actions that do not trigger collaborative amplification are combined into a collaborative execution sequence according to the energy replenishment window avoidance relationship. S4: Taking the collaborative execution sequence as input, and according to the avoidance relationship between the impact of subsequent energy replenishment after the withdrawal of the previous candidate energy-saving action and the energy replenishment window of the next candidate energy-saving action, generate energy collaborative optimization instructions for the target base station and its associated base stations.

2. The base station energy collaborative optimization method based on big data analysis according to claim 1, characterized in that, S1 specifically includes: S11: Extract the historical execution records corresponding to each candidate energy-saving action from the candidate energy-saving action set of the target base station, and extract the historical execution segments corresponding to each candidate energy-saving action based on the start time, end time and withdrawal time of each candidate energy-saving action. S12: Using the end time of each candidate energy-saving action as the starting point for tracing, extract the power supply change data, air conditioning load change data, energy storage charging and discharging status change data, and inter-station load transfer data of the target base station and its associated base stations within the preset tracing time window to form the observation data segment after the withdrawal of the corresponding candidate energy-saving action. S13: Perform time-series alignment processing on each withdrawn observation data segment, identify the delayed occurrence interval of various energy responses after the candidate energy-saving actions are completed, and extract the power recovery segment, temperature control compensation segment, energy storage replenishment segment and cross-station takeover segment to form the subsequent energy replenishment trajectory of the corresponding candidate energy-saving actions. S14: Collect the subsequent energy replenishment trajectories corresponding to each candidate energy-saving action, establish a one-to-one correspondence between the candidate energy-saving action identifier and the subsequent energy replenishment trajectory, and output the bounce characterization results corresponding to each candidate energy-saving action.

3. The base station energy collaborative optimization method based on big data analysis according to claim 2, characterized in that, The bounce characterization results include candidate energy-saving action identifiers, bounce start time, bounce duration, bounce peak amplitude, cumulative energy replenishment, set of base stations involved, and bounce type identifiers.

4. The base station energy collaborative optimization method based on big data analysis according to claim 1, characterized in that, S2 specifically includes: S21: Read the bounce characterization results corresponding to each energy-saving action, and extract the bounce start time, bounce duration, set of base stations involved, and bounce type identifier. S22: Using the jump start time of each candidate energy-saving action as the starting point of the linkage analysis, and using the preset linkage judgment duration as the time boundary, construct the linkage window corresponding to each candidate energy-saving action; S23: Within each linkage window, joint detection is performed on the target base station and each associated base station in the set of base stations involved to determine whether at least two types of energy replenishment responses among cross-site takeover energy replenishment, temperature-controlled energy replenishment, and energy storage recovery energy replenishment overlap, connect, or continuously transmit in time; S24: When at least two types of energy replenishment responses within the linkage window meet the preset linkage association conditions, determine that the subsequent energy replenishment trajectory corresponding to the candidate energy-saving action forms a linkage superposition and amplification within the linkage window, and determine this linkage window as the effective linkage window corresponding to the candidate energy-saving action.

5. The base station energy collaborative optimization method based on big data analysis according to claim 4, characterized in that, S2 further includes: S25: Perform intensity statistics and time-series correlation analysis on cross-site takeover energy replenishment, temperature-controlled energy replenishment, and energy storage recovery energy within each effective linkage window to obtain the corresponding linkage energy replenishment intensity value, linkage duration, and number of linkage types. S26: Compare the number of linkage types with the preset type threshold, compare the linkage energy replenishment intensity value with the preset intensity threshold, and compare the linkage duration with the preset duration threshold. When the number of linkage types is greater than or equal to 2, the linkage energy replenishment intensity value is greater than or equal to a preset intensity threshold, and the linkage duration is greater than or equal to a preset duration threshold, it is determined that the corresponding candidate energy-saving action triggers collaborative amplification, and a collaborative amplification flag is output. When the number of linkage types is less than 2, or the linkage energy replenishment intensity value is less than the preset intensity threshold, or the linkage duration is less than the preset duration threshold, it is determined that the corresponding candidate energy-saving action has not triggered collaborative amplification, and a non-collaborative amplification flag is output.

6. The base station energy collaborative optimization method based on big data analysis according to claim 5, characterized in that, S3 specifically includes: S31: Read the collaborative amplification markers corresponding to each candidate energy-saving action, and establish the correspondence between the candidate energy-saving actions and the collaborative amplification markers according to the candidate energy-saving action identifiers; S32: Filter candidate energy-saving actions marked as triggering collaborative amplification, and add the filtered candidate energy-saving actions to the frozen action set; S33: Write the freeze status identifier, freeze start time and corresponding linkage risk type to each candidate energy-saving action in the freeze action set; S34: Filter candidate energy-saving actions marked as not having triggered collaborative amplification, and determine the selected candidate energy-saving actions as the set of actions to be programmed.

7. The base station energy collaborative optimization method based on big data analysis according to claim 6, characterized in that, S3 further includes: S35: Read the bounce characterization results corresponding to each candidate energy-saving action in the action set to be arranged, and extract the bounce start time, bounce duration, bounce peak amplitude and cumulative energy replenishment. S36: Determine the corresponding impact range of subsequent energy replenishment based on the rebound start time and rebound duration of each candidate energy-saving action, and determine the corresponding candidate execution range based on the historical execution period and schedulable period of each candidate energy-saving action; S37: Perform a timing avoidance analysis on any two candidate energy-saving actions in the set of actions to be arranged. When the energy replenishment effect range of the candidate energy-saving action in the first position overlaps with the candidate execution range of the candidate energy-saving action in the second position, the candidate energy-saving action in the second position will be postponed until the two no longer overlap. S38: Based on the sequential execution relationship formed after the timing avoidance analysis, sort the candidate energy-saving actions in the action set to be arranged and generate a collaborative execution sequence.

8. The base station energy collaborative optimization method based on big data analysis according to claim 1, characterized in that, S4 specifically includes: S41: Read the sorting position, corresponding base station identifier, candidate execution interval, and subsequent power replenishment impact interval of each candidate energy-saving action in the collaborative execution sequence; S42: Based on the order of each candidate energy-saving action in the collaborative execution sequence, determine the plan start time, plan duration, and plan withdrawal time corresponding to each candidate energy-saving action; S43: Generate local energy-saving control instructions for candidate energy-saving actions in the target base station, including action type, planned start time, planned duration and planned withdrawal time; S44: Generate coordinated control commands for associated base stations, including takeover waiting period, temperature control adjustment period, and energy storage replenishment delay period; S45: Combine and encapsulate local energy-saving control instructions with cooperative control instructions to form energy cooperative optimization instructions for the target base station and its associated base stations.

9. The base station energy collaborative optimization method based on big data analysis according to claim 8, characterized in that, S4 further includes: S46: Read two adjacent candidate energy-saving actions in the collaborative execution sequence, and denot them as the preceding candidate energy-saving action and the following candidate energy-saving action, respectively. Obtain the subsequent energy replenishment influence interval corresponding to the preceding candidate energy-saving action and the energy replenishment window interval corresponding to the following candidate energy-saving action. S47: Determine whether there is a time overlap between the impact range of subsequent energy replenishment and the energy replenishment window range. If there is a time overlap, calculate the overlap duration and use the overlap duration as the postponement correction amount for subsequent candidate energy-saving actions. S48: Based on the postponement correction amount, the planned start time and planned withdrawal time of subsequent candidate energy-saving actions are postponed, and the associated base station takeover waiting period, temperature control adjustment period or energy storage replenishment delay period associated with subsequent candidate energy-saving actions are also corrected simultaneously. S49: Repeat S46 to S48 until any two adjacent candidate energy-saving actions in the collaborative execution sequence satisfy the condition that the subsequent energy replenishment influence interval of the preceding candidate energy-saving action and the energy replenishment window interval of the subsequent candidate energy-saving action do not overlap in time; finally, output the energy collaborative optimization instruction after time-correction.

10. A base station energy collaborative optimization system based on big data analysis, used to implement the base station energy collaborative optimization method based on big data analysis as described in any one of claims 1-9, characterized in that, Includes the following modules: Back-off identification module: It is used to take the candidate energy-saving action set of the target base station as input, trace back the historical execution segment corresponding to each candidate energy-saving action after withdrawal, identify the subsequent energy replenishment trajectory that appears in the target base station and its associated base stations after the candidate energy-saving action ends, and output the back-off characterization result corresponding to each candidate energy-saving action. Amplification and discrimination module: used to receive the bounce characterization results output by the bounce recognition module, determine whether the subsequent energy replenishment trajectory corresponding to each candidate energy-saving action triggers at least two types of linkage superposition amplification in the same linkage window, namely cross-site takeover energy replenishment, temperature control follow-up energy replenishment and energy storage recovery energy replenishment, and output the corresponding collaborative amplification mark for each candidate energy-saving action; Action filtering module: Used to receive the collaborative amplification flag output by the amplification discrimination module, classify the candidate energy-saving actions that trigger collaborative amplification into the frozen action set, determine the candidate energy-saving actions that do not trigger collaborative amplification as the action set to be arranged, and form a collaborative execution sequence based on the action set to be arranged. The collaborative instruction generation module receives the collaborative execution sequence output by the action filtering module, performs timing correction on each candidate energy-saving action in the collaborative execution sequence according to the avoidance relationship between the subsequent energy replenishment impact after the withdrawal of the previous candidate energy-saving action and the energy replenishment window of the next candidate energy-saving action, and generates energy collaborative optimization instructions for the target base station and its associated base stations.

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