A charging pile operation state monitoring system supporting dangerous prediction feedback

Abstract

The application discloses a charging pile operation state monitoring system supporting danger prediction feedback, and particularly relates to the field of charging pile operation state monitoring, and comprises a path construction module, a state judgment module and the like. The path construction module is used for acquiring a state number sequence of a charging pile, constructing a control strategy path sequence based on time stamp order, executing sliding time window disturbance statistics, and outputting a control strategy path disturbance index. The state judgment module is used for executing control strategy path disturbance index normalization processing, generating disturbance features containing repetition segment frequency and execution time, matching the disturbance features with historical path features, outputting path similarity, combining three threshold condition judgments, and generating a shock state marker. Through construction of a control strategy path sequence, extraction of path disturbance features, and fusion of multi-dimensional feedback indexes, a monitoring process based on path shock identification and trend evolution regulation is formed, so that early identification and feedback prediction of operation state degradation risks caused by frequent transition of control strategies are realized.

Description

Technical Field

[0001] This invention relates to the field of charging pile operation status monitoring technology with hazard prediction feedback, and more specifically, to a charging pile operation status monitoring system that supports hazard prediction feedback. Background Technology

[0002] In existing charging pile operation control, the system typically adopts a preset strategy switching mechanism to maintain the stability of the control closed loop based on the operating parameter status and environmental disturbance conditions. When the voltage deviates, it switches to constant voltage control mode; when the temperature rise exceeds the limit, it switches to power limiting control mode; when communication is abnormal or repeated plugging and unplugging occurs, it enters power holding or soft start protection state. The switching rules of each strategy are driven by the controller's internal state machine, and the judgment is based on a single monitored variable or its combination threshold. The system assumes that each strategy has clear switching conditions and a definite stability target.

[0003] However, under multivariate coupled disturbance conditions, such as simultaneous occurrence of equipment temperature rise, power grid fluctuation and communication retry, the state machine may trigger different strategy switching paths multiple times in the decision logic, causing the control system to repeatedly enter different strategy states within a charging cycle. Since each switch satisfies local rules and the system parameters do not exceed the alarm threshold, the existing charging pile operation status monitoring system will not identify this behavior as an abnormal process, nor will it constitute any form of fault reporting.

[0004] Such repeated switching manifests in the control logic as short-cycle oscillations in the strategy path, meaning that the control system is constantly in a strategy transition without converging to any stable strategy range, which in turn causes abnormal controller execution frequency, power output fluctuations, response behavior jitter, and disordered thermal load distribution. Existing monitoring methods only monitor parameter overruns, hardware anomalies, or communication failures, and do not have the ability to structurally identify frequent transitions in the strategy path, nor do they have an evaluation mechanism for the stability of the strategy execution trajectory evolution.

[0005] It is evident that existing charging pile operation status monitoring systems do not consider the frequent transition behavior of control strategies under multi-source disturbances. Although such transitions do not constitute parameter overruns, they can lead to the degradation of the stability of the control system's logic path, thus evolving into control anomalies or device failures. Furthermore, existing charging pile operation status monitoring systems cannot effectively monitor and predict this type of path-level operation status degradation risk. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a charging pile operation status monitoring system that supports hazard prediction feedback. By constructing a control strategy path sequence, extracting path disturbance features, and integrating multi-dimensional feedback indicators, a monitoring process based on path oscillation identification and trend evolution regulation is formed, so as to realize early identification and feedback prediction of the risk of operation status degradation caused by frequent transitions in control strategies.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a charging pile operation status monitoring system that supports hazard prediction feedback, comprising a path construction module, a status determination module, a reconstruction module, and an early warning module;

[0008] The path construction module is used to obtain the status number sequence of charging piles, construct the control strategy path sequence based on timestamps, perform sliding time window disturbance statistics, and output the control strategy path disturbance index.

[0009] The state determination module is used to normalize the path disturbance index of the execution control strategy, generate disturbance features including the frequency of repeated segments and execution time; match the disturbance features with historical path features, output path similarity and combine it with three threshold conditions to generate oscillation state labels.

[0010] The reconstruction module extracts a set of stable paths based on oscillation state markers and constructs a candidate set of path reconstruction. It then performs number difference filtering to generate a control strategy path reconstruction sequence. The module collects the control response and call frequency of the reconstruction sequence and outputs control strategy feedback indicators.

[0011] The early warning module generates path feedback records based on the summarized control strategy path reconstruction sequence, disturbance indicators, and feedback indicators; it determines whether the three indicators meet the feedback anomaly conditions and outputs feedback early warning flags, which are used to update the control strategy path evolution trend structure and predict risks.

[0012] In a preferred embodiment, the path construction module includes obtaining a sequence of state numbers within the complete execution cycle of the charging pile from the charging pile operation status monitoring. The sequence of state numbers includes an operation state number for constructing the control strategy path, adjacent jump pairs for performing state transition statistics, and an end state number for reconstructing the control strategy path.

[0013] The running status numbers are sorted by timestamp to generate a control strategy path sequence.

[0014] In a preferred embodiment, the path construction module further includes performing state transition statistics on the control strategy path sequence to generate control strategy path transition frequencies.

[0015] The state transition statistics include: forming an adjacent transition pair by any running state number in the control strategy path sequence and its next state number, and traversing all adjacent transition pairs in the complete control strategy path sequence in chronological order, performing cumulative statistics on the occurrence of the same adjacent transition pair, and outputting the occurrence frequency corresponding to each type of adjacent transition pair as the transition frequency of that transition in the current control strategy path.

[0016] The frequency of control strategy path transitions is accumulated within a sliding time window, and the control strategy path disturbance index is output.

[0017] In a preferred embodiment, the state determination module includes performing normalization processing on the control strategy path disturbance index and outputting control strategy path disturbance characteristics, which include the frequency of repeated segments and execution time.

[0018] The normalization process includes: extracting disturbance index values ​​within the preset control strategy disturbance index threshold range from the control strategy path disturbance index to form a normalization interval; mapping the current control strategy path disturbance index to the relative position segment within the normalization interval; performing frequency statistics on the normalized control strategy disturbance index according to continuous value intervals; and performing exponential scaling on value intervals with an occurrence frequency not lower than the preset disturbance frequency threshold to achieve a unified measurement of the frequency of repetitive segments and execution time in the control strategy path disturbance characteristics.

[0019] In a preferred embodiment, the state determination module further includes matching the control strategy path disturbance features with historical path features to calculate path similarity.

[0020] The path similarity is judged based on three conditions: whether the path similarity is lower than a preset path similarity threshold, whether the frequency of repeated segments in the continuous sliding time window is higher than the frequency threshold of repeated segments in the continuous sliding time window, and whether the execution time of the continuous control strategy path is lower than a preset execution time threshold for the continuous control strategy path. If all three conditions meet their respective preset threshold conditions, an oscillation state label is generated; otherwise, no oscillation state label is generated.

[0021] In a preferred embodiment, the reconstruction module includes, when generating an oscillation state marker, performing disturbance index screening and statistics on archived control strategy path sequences earlier than the current execution cycle, screening paths whose disturbance index stability meets a preset disturbance index stability threshold, and forming a stable path set;

[0022] Extract the terminal state number and stable path set from the control strategy path sequence and reconstruct them to generate a candidate set for control strategy path reconstruction.

[0023] The reconstruction process includes: extracting the terminal state number from the control strategy path sequence, traversing each control strategy path in the stable path set, performing an equality comparison between the first state number of the control strategy path and the terminal state number, performing a truncation operation on the control strategy path whose comparison result is equal, forming a reconstruction number sequence from all the state numbers after its first number, and outputting the set of all reconstruction number sequences that meet the conditions as the control strategy path reconstruction candidate set.

[0024] In a preferred embodiment, the reconstruction module further includes performing a path number difference operation on the control strategy path reconstruction candidate set and the end state number in the control strategy path sequence, and outputting the control strategy path reconstruction sequence.

[0025] During the execution of the control strategy path reconstruction sequence, the control strategy response time and control strategy call frequency are collected to generate control strategy feedback indicators.

[0026] The acquisition of control strategy response time and control strategy call frequency includes: recording the trigger time and completion time of each control strategy path in the control strategy path reconstruction sequence, and performing a difference calculation between the two to generate the control strategy response time; grouping all control strategy response times into intervals according to the control strategy path sequence, and calculating the difference between the average difference of the control strategy response time and the change amplitude in the control strategy response time sequence of that group within each group; simultaneously performing continuous matching statistics on each control strategy path in the control strategy path reconstruction sequence, calculating the number of calls per unit time, extracting the call interval sequence from the continuous call segments, performing abrupt gradient extraction on the call interval sequence, and outputting the call frequency as a control strategy feedback indicator.

[0027] In a preferred embodiment, the early warning module includes summarizing the control strategy path reconstruction sequence, control strategy path disturbance index and control strategy feedback index to generate a path feedback record.

[0028] The summary includes: matching the control strategy path reconstruction sequence, control strategy path disturbance index, and control strategy feedback index one-to-one according to the execution order of the control strategy path; extracting the path number, corresponding disturbance index value, and feedback index value for each control strategy path; performing three-data concatenation to generate a path feature vector; and performing cumulative concatenation of all path feature vectors according to the control strategy path order to generate a path feedback record for the complete path cycle.

[0029] In a preferred embodiment, the early warning module further includes judging the path feedback record, judging whether the control strategy path disturbance index is higher than the preset control strategy path disturbance index threshold, whether the control strategy response time is higher than the preset control strategy response time threshold, and whether the control strategy call frequency is higher than the preset control strategy call frequency threshold, as three conditions for judgment. If none of the three conditions are higher than their respective preset threshold conditions, the output is no feedback early warning flag; otherwise, the output is a feedback early warning flag.

[0030] The technical effects and advantages of this invention are as follows:

[0031] 1. This solution identifies oscillating states that do not exceed limits but exhibit frequent strategy switching by comparing disturbance indicators with path characteristics, thus detecting potential control degradation risks in advance;

[0032] 2. By extracting disturbed stable paths from historical archives as a reference, the system supports the identification of anomalies in the current path and the matching of subsequent alternative strategies.

[0033] 3. When path oscillation occurs, alternative control paths are generated by reconstructing candidates from stable paths to improve the system's self-healing capability;

[0034] 4. Collect control strategy response time and call frequency, combine with disturbance characteristics to generate feedback indicators, and realize dynamic monitoring of control behavior;

[0035] 5. Based on three types of indicators, feedback warning markers are generated to guide the update of the path evolution trend and realize the regulation of the strategy path from oscillation to stability. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the system modules of the present invention;

[0037] Figure 2 Construct an execution flowchart for the control strategy path disturbance index of the system in this invention;

[0038] Figure 3 This is a flowchart illustrating the path similarity judgment and oscillation state marker recognition process of the system in this invention.

[0039] Figure 4 This is a flowchart of the stable path set selection and path reconstruction execution process in the system of this invention;

[0040] Figure 5 This is a flowchart of the feedback indicator summary and early warning judgment execution process in the system of this invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Refer to the instruction manual appendix Figure 1-5 An embodiment of the present invention provides a charging pile operation status monitoring system that supports hazard prediction feedback, comprising a path construction module, a status determination module, a reconstruction module, and an early warning module.

[0043] The path construction module is used to obtain the status number sequence of charging piles, construct the control strategy path sequence based on timestamps, perform sliding time window disturbance statistics, and output the control strategy path disturbance index.

[0044] The state determination module is used to normalize the path disturbance index of the execution control strategy, generate disturbance features including the frequency of repeated segments and execution time; match the disturbance features with historical path features, output path similarity and combine it with three threshold conditions to generate oscillation state labels.

[0045] The reconstruction module extracts a set of stable paths based on oscillation state markers and constructs a candidate set of path reconstruction. It then performs number difference filtering to generate a control strategy path reconstruction sequence. The module collects the control response and call frequency of the reconstruction sequence and outputs control strategy feedback indicators.

[0046] The early warning module generates path feedback records based on the summarized control strategy path reconstruction sequence, disturbance indicators, and feedback indicators; it determines whether the three indicators meet the feedback anomaly conditions and outputs feedback early warning flags, which are used to update the control strategy path evolution trend structure and predict risks.

[0047] It should be noted that in the formula structure involved in this scheme, dimensionless terms can be used as proportional or structural adjustment factors. When combined with quantities with units, they only play a role in numerical scaling and do not introduce new physical dimensions. Therefore, they will not change or confuse the overall unit system. This combination of "dimensionless terms and terms with units" can be understood as a composite structural expression commonly used in mathematical physics modeling. It conforms to the principle of dimensional consistency and has a clear physical interpretation basis.

[0048] Secondly, in the formula structure of this scheme, if multiple variables with different physical units are involved, including but not limited to time, mass or energy variables, their joint appearance is to express the collaborative modeling relationship of multiple physical mechanisms. Each variable can form a unified structure through function mapping, ratio combination or normalization adjustment, with clear units and clear meaning. The overall expression conforms to the principle of dimensional consistency and the conventional formula of engineering modeling.

[0049] In this scheme, constants, weights, adjustment factors, threshold parameters, proportional coefficients, etc., are all adjustable control parameters for different application environments. Their values ​​depend on the target equipment configuration, data input characteristics, and performance optimization goals. During the implementation phase, they are converged within a reasonable range through model verification, performance constraints, or engineering calibration. Although these parameters do not have a unique preset value, they have clear adjustment logic and calculation paths. They belong to the deterministic setting process in engineering implementation. The purpose of this setting is to ensure that the scheme is both universally adaptable and reproducible and operable, without affecting its technical clarity and feasibility.

[0050] The path construction module includes obtaining the state number sequence within the complete execution cycle of the charging pile from the charging pile operation status monitoring. The state number sequence includes the operation state number used to construct the control strategy path, the adjacent jump pair used to perform state transition statistics, and the end state number used to reconstruct the control strategy path.

[0051] The operation status numbers are timestamped and sorted to generate a control strategy path sequence. The timestamping sorting refers to arranging the operation status numbers in order of their actual occurrence time to establish the execution order of the control strategy path.

[0052] The path construction module also includes performing state transition statistics on the control strategy path sequence to generate the control strategy path transition frequency;

[0053] The state transition statistics include: forming an adjacent transition pair by any running state number in the control strategy path sequence and its next state number, and traversing all adjacent transition pairs in the complete control strategy path sequence in chronological order, performing cumulative statistics on the occurrence of the same adjacent transition pair, and outputting the occurrence frequency corresponding to each type of adjacent transition pair as the transition frequency of that transition in the current control strategy path.

[0054] The frequency of control strategy path transitions is accumulated within a sliding time window, and the control strategy path perturbation index is output.

[0055] Define control strategy path disturbance index

[0056]

[0057] Where n represents the total number of running state numbers in the control strategy path sequence; w is the width of the sliding time window; k represents the k-th sliding time window obtained by the sliding operation in the control strategy path disturbance index sequence; ΔF k ΔT represents the total change in the transition frequency of all adjacent transition pairs within the k-th sliding time window. k Φ represents the time span of the path segment within the k-th sliding time window; k This represents the third-order difference rate of change of the jump interval sequence in the k-th sliding time window. The third-order difference rate of change is used to measure the local disturbance trend of the path. This represents the cumulative number of positive transitions within the k-th sliding window; ε represents the cumulative number of reverse transitions within the k-th sliding window; ε represents a minimum positive constant; Υ k This represents the numbered abrupt change structure strength of the path segment within the k-th sliding window. The numbered abrupt change structure strength is defined as the local range frequency of the numbered difference sequence of adjacent jump pairs.

[0058] The state determination module includes normalizing the control strategy path disturbance index and outputting the control strategy path disturbance characteristics, which include the frequency of repeated segments and the execution time.

[0059] The normalization process includes: extracting disturbance index values ​​within the preset control strategy disturbance index threshold range from the control strategy path disturbance index to form a normalization interval; mapping the current control strategy path disturbance index to the relative position segment within the normalization interval; performing frequency statistics on the normalized control strategy disturbance index according to continuous value intervals; and performing exponential scaling on value intervals with an occurrence frequency not lower than the preset disturbance frequency threshold to achieve a unified measurement of the frequency of repetitive segments and execution time in the control strategy path disturbance characteristics.

[0060] The status determination module also includes matching the control strategy path disturbance features with historical path features to calculate path similarity. The historical path features refer to the disturbance features, transition patterns and response data extracted from control strategy paths that have been executed in the past without any anomalies, which are used as a reference standard for evaluating the current path status.

[0061] Define path similarity Ψ:

[0062]

[0063] Where Λ represents the normalization factor, which is the integral strength of the total path disturbance structure; T is the normalized time length of the path disturbance characteristics of the control strategy; t represents the time variable in the normalized path disturbance analysis, which is used to uniformly represent the function independent variable of the disturbance characteristics evolving with the normalized time dimension; Δ r (t) is the normalized disturbance frequency distribution function in the current control strategy path; Δ h (t) is the normalized perturbation frequency distribution function in the historical control strategy path; d represents the derivative operator of the path perturbation function with respect to time, and the derivative operator is used to construct the partial derivative gradient of the structural trend; Θ(t) represents the normalized evolution curve of the perturbation characteristic change trajectory. The second derivative represents the continuous change in time of a path segment. The continuous change in time of a path segment is used to characterize the curvature trend of time compression or extension. This is the gradient sequence of the current path response function; dt represents the gradient sequence of the historical path response function; dt represents the integral infinitesimal element with respect to the time variable t.

[0064] The path similarity is judged based on three conditions: whether the path similarity is lower than a preset path similarity threshold, whether the frequency of repeated segments in the continuous sliding time window is higher than the frequency threshold of repeated segments in the continuous sliding time window, and whether the execution time of the continuous control strategy path is lower than a preset execution time threshold for the continuous control strategy path. If all three conditions meet their respective preset threshold conditions, an oscillation state label is generated; otherwise, no oscillation state label is generated.

[0065] The reconstruction module includes performing disturbance index screening and statistics on archived control strategy path sequences earlier than the current execution cycle when generating oscillation state markers. Paths whose disturbance index stability meets the preset disturbance index stability threshold are selected to form a stable path set. The archived control strategy path sequences earlier than the current execution cycle refer to historical control strategy path sequence data that have been executed and stored before the generation and execution of the current round of control strategy path sequences, and are used as a reference for stability judgment and path matching.

[0066] Define a set of stable paths

[0067]

[0068] Q i This represents the i-th historical control strategy path; This is a set of paths for historical archiving control strategies. This indicates that the integral difference comparison is performed on all candidate historical path sequences; j represents the index number of the historical control strategy path feature sequence participating in the path disturbance feature comparison in the current execution cycle; Ξ(Q i [j]) is the disturbance index stability function with a single state number; the symbol Ξ represents the index extraction function, which is used to extract data from the historical control strategy path Q. i Extract the corresponding perturbation index; Ω(Q) i [j-1],Q i [j]) represents the adjacent state numbering jump structure residual function, which is used to measure the stability of numbering switching; the symbol Ω represents the historical control strategy path Q. i The path number transition difference between adjacent state number pairs; θ s The combined threshold for perturbation stability;

[0069] Extract the terminal state number and stable path set from the control strategy path sequence and reconstruct them to generate a candidate set for control strategy path reconstruction.

[0070] The reconstruction process includes: extracting the terminal state number from the control strategy path sequence, traversing each control strategy path in the stable path set, performing an equality comparison between the first state number of the control strategy path and the terminal state number, performing a truncation operation on the control strategy path whose comparison result is equal, forming a reconstruction number sequence from all the state numbers after its first number, and outputting the set of all reconstruction number sequences that meet the conditions as the control strategy path reconstruction candidate set.

[0071] The reconstruction module also includes performing path number difference calculation on the candidate set of control strategy path reconstruction and the end state number in the control strategy path sequence, and outputting the control strategy path reconstruction sequence;

[0072] Define control policy path reconstruction sequence

[0073]

[0074] Where R m To reconstruct the m-th path segment in the candidate set for the control strategy path; Reconstruct the candidate set for control policy paths; Ψ(R) m θ is the perturbation trend enhancement measure function for the m-th path segment in the candidate set for path reconstruction of the control strategy. The perturbation trend enhancement measure function represents the degree of cumulative gradient change of its internal perturbation index, and is used to characterize the "proactive" or "expansive" nature of the perturbation change in the path segment; Ψ The threshold for enhancing the perturbation trend; To control the rate of change of the path number jump in the candidate set of path reconstruction, the rate of change function of the path number jump is used to characterize the volatility of the path number jump structure; the symbol ∧ represents the join operator; θ Ω This is the threshold for the acceptable range of change in the rate of change of the jump structure;

[0075] During the execution of the control strategy path reconstruction sequence, the control strategy response time and control strategy call frequency are collected to generate control strategy feedback indicators.

[0076] The acquisition of control strategy response time and control strategy call frequency includes: recording the trigger time and completion time of each control strategy path in the control strategy path reconstruction sequence, and performing a difference calculation between the two to generate the control strategy response time; grouping all control strategy response times into intervals according to the control strategy path sequence, and calculating the difference between the average difference of the control strategy response time and the change amplitude in the control strategy response time sequence of that group within each group; simultaneously performing continuous matching statistics on each control strategy path in the control strategy path reconstruction sequence, calculating the number of calls per unit time, extracting the call interval sequence from the continuous call segments, performing abrupt gradient extraction on the call interval sequence, and outputting the call frequency as a control strategy feedback indicator.

[0077] The early warning module includes summarizing the control strategy path reconstruction sequence, control strategy path disturbance indicators, and control strategy feedback indicators to generate path feedback records.

[0078] The summary includes: matching the control strategy path reconstruction sequence, control strategy path disturbance index, and control strategy feedback index one-to-one according to the execution order of the control strategy path; extracting the path number, corresponding disturbance index value, and feedback index value for each control strategy path; performing three-data concatenation to generate a path feature vector; and performing cumulative concatenation of all path feature vectors according to the control strategy path order to generate a path feedback record for the complete path cycle.

[0079] The early warning module also includes judging the path feedback records. It judges whether the control strategy path disturbance index is higher than the preset control strategy path disturbance index threshold, whether the control strategy response time is higher than the preset control strategy response time threshold, and whether the control strategy call frequency is higher than the preset control strategy call frequency threshold. These three conditions are used for judgment. If none of the three conditions are higher than their respective preset threshold conditions, the output is no feedback early warning flag, and the control strategy path evolution trend structure remains unchanged. Otherwise, the feedback early warning flag is output for hazard prediction and updating the control strategy path evolution trend structure.

[0080] It should be noted that in the traditional operation and control of charging piles, the strategy switching is dominated by the state machine, which relies on the preset judgment of single or combined variables, such as voltage, current, temperature and other limits. This mechanism can ensure stability under ideal static disturbances, but in the dynamic environment of multiple disturbances (such as grid fluctuations, temperature rise, communication retry) superimposed, the controller may repeatedly jump between local strategies and fail to converge to a stable path.

[0081] The key issue is that this type of short-cycle oscillation path behavior neither constitutes parameter overrun nor triggers traditional anomaly detection, but its long-term existence can lead to controller load shift, degraded response performance, and even hardware risks.

[0082] Therefore, it is necessary to design a monitoring scheme centered on path structure with evolution perception and hazard prediction capabilities to make up for the shortcomings of existing monitoring methods based on parameter thresholds in the identification of logical layer failure paths.

[0083] This solution includes a path building phase:

[0084] By collecting the running status numbers within the complete execution cycle, a control strategy path sequence sorted by timestamp is formed; the frequency of occurrence of "adjacent jump pairs" is calculated through state transition statistics, and disturbance accumulation is carried out in combination with the sliding time window to form a quantitative description of the instability of the local strategy path, and the output is the control strategy path disturbance index.

[0085] This stage enables the perception of nonparametric limit-breaking anomalies in a structural manner, solving the problem of strategy oscillations occurring even when traditional control parameters do not exceed limits; its core is to abstract path disturbances into a time structure change sequence, and characterize disturbance risk through the cumulative trend of continuous jump frequency and direction.

[0086] This solution includes a state determination phase:

[0087] The disturbance index is normalized to obtain the disturbance feature, which includes two sub-variables: the frequency of repeated segments and the execution time. Then, this feature is matched with the historical path feature (derived from stable paths that have not experienced anomalies) to calculate the path similarity. The frequency of repeated segments, the execution time and the similarity are jointly judged to determine whether they exceed the preset threshold, and an oscillation state label is generated.

[0088] This stage is used to determine whether the current path has entered a state of risk fluctuation; in essence, it is a reference trajectory defined by "historical stable behavior" and measures the degree of deviation from two dimensions: disturbance and execution time, so as to realize a "preceding anomaly" discrimination mechanism at the path behavior level.

[0089] This plan includes a refactoring phase:

[0090] When an oscillation occurs, a set of stable paths is selected from the archived paths, and path reconstruction candidate generation is performed based on the current path end number. The path reconstruction sequence of the control strategy is output using the number difference operation. This sequence represents the possible stable path candidate set. During the execution process, the response time and call frequency are collected in real time and output as control strategy feedback indicators.

[0091] This stage is used to introduce a "path reconstruction" mechanism to assist in the return of strategy stability after structural oscillations occur; response time and call frequency are used to dynamically evaluate whether the current strategy execution efficiency and stability have further degraded;

[0092] This plan includes an early warning phase:

[0093] The control strategy path reconstruction sequence, control strategy path disturbance index and control strategy feedback index are concatenated to generate a path feedback record. The disturbance index, response time and call frequency in the path feedback record are judged against their respective preset thresholds. If any index exceeds the threshold range, a feedback warning mark is generated and the control strategy path evolution trend structure is updated. Otherwise, no feedback warning mark is generated and the evolution trend structure remains unchanged.

[0094] This stage establishes a foundation for understanding trend evolution based on path-level historical feedback, and constructs a structural risk feedback loop driven by multiple variables. This part enables the system not only to have the ability to instantly identify current anomalies, but also to have the ability to adaptively adjust to changes in path trends and achieve structural convergence.

[0095] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A charging pile operation status monitoring system supporting hazard prediction and feedback, comprising a path construction module, a status determination module, a reconstruction module, and an early warning module, characterized in that: The path construction module is used to obtain the status number sequence of charging piles, construct the control strategy path sequence based on timestamps, perform sliding time window disturbance statistics, and output the control strategy path disturbance index. The state determination module is used to normalize the path disturbance index of the execution control strategy, generate disturbance features including the frequency of repeated segments and execution time; match the disturbance features with historical path features, output path similarity and combine it with three threshold conditions to generate oscillation state labels. The reconstruction module extracts a set of stable paths based on oscillation state markers and constructs a candidate set of path reconstruction. It then performs number difference filtering to generate a control strategy path reconstruction sequence. The module collects the control response and call frequency of the reconstruction sequence and outputs control strategy feedback indicators. The early warning module generates path feedback records based on the summarized control strategy path reconstruction sequence, disturbance indicators, and feedback indicators; it determines whether the three indicators meet the feedback anomaly conditions and outputs feedback early warning flags, which are used for updating the control strategy path evolution trend structure and predicting risks. The state determination module also includes matching the control strategy path disturbance features with historical path features to calculate path similarity; The path similarity is judged based on three conditions: whether the path similarity is lower than the preset path similarity threshold, whether the frequency of repeated segments in the continuous sliding time window is higher than the frequency threshold of repeated segments in the continuous sliding time window, and whether the execution time of the continuous control strategy path is lower than the preset execution time threshold of the continuous control strategy path. If all three conditions meet their respective preset threshold conditions, an oscillation state mark is generated; otherwise, no oscillation state mark is generated. The early warning module also includes judging the path feedback records, judging whether the control strategy path disturbance index is higher than the preset control strategy path disturbance index threshold, whether the control strategy response time is higher than the preset control strategy response time threshold, and whether the control strategy call frequency is higher than the preset control strategy call frequency threshold. These three conditions are used for judgment. If none of the three conditions are higher than their respective preset threshold conditions, the output is no feedback early warning flag; otherwise, the feedback early warning flag is output.

2. The charging pile operation status monitoring system supporting hazard prediction feedback according to claim 1, characterized in that: The path construction module includes obtaining the state number sequence within the complete execution cycle of the charging pile from the charging pile operation status monitoring. The state number sequence includes the operation state number used to construct the control strategy path, the adjacent jump pair used to perform state transition statistics, and the end state number used to reconstruct the control strategy path. The running status numbers are sorted by timestamp to generate a control strategy path sequence.

3. The charging pile operation status monitoring system supporting hazard prediction feedback according to claim 2, characterized in that: The path construction module also includes performing state transition statistics on the control strategy path sequence to generate the control strategy path transition frequency; The state transition statistics include: forming an adjacent transition pair by any running state number in the control strategy path sequence and its next state number, and traversing all adjacent transition pairs in the complete control strategy path sequence in chronological order, performing cumulative statistics on the occurrence of the same adjacent transition pair, and outputting the occurrence frequency corresponding to each type of adjacent transition pair as the transition frequency of that transition in the current control strategy path. The frequency of control strategy path transitions is accumulated within a sliding time window, and the control strategy path disturbance index is output.

4. A charging pile operation status monitoring system supporting hazard prediction feedback according to claim 3, characterized in that: The state determination module includes normalizing the control strategy path disturbance index and outputting the control strategy path disturbance characteristics, which include the frequency of repeated segments and the execution time. The normalization process includes: extracting disturbance index values ​​that fall within the preset control strategy disturbance index threshold range from the control strategy path disturbance index to form a normalization interval; The current control strategy path disturbance index is mapped to a relative position segment within the normalized interval. The normalized control strategy disturbance index is then statistically analyzed according to the frequency of continuous numerical intervals. For numerical intervals with an occurrence frequency not lower than the preset disturbance frequency threshold, exponential scaling is performed to achieve a unified measurement of the frequency of repetitive segments and execution time in the control strategy path disturbance characteristics.

5. A charging pile operation status monitoring system supporting hazard prediction feedback according to claim 4, characterized in that: The reconstruction module includes performing disturbance index screening and statistics on archived control strategy path sequences earlier than the current execution cycle when generating oscillation state markers, screening paths whose disturbance index stability meets the preset disturbance index stability threshold, and forming a stable path set; Extract the terminal state number and stable path set from the control strategy path sequence and reconstruct them to generate a candidate set for control strategy path reconstruction. The reconstruction process includes: extracting the terminal state number from the control strategy path sequence, traversing each control strategy path in the stable path set, performing an equality comparison between the first state number of the control strategy path and the terminal state number, performing a truncation operation on the control strategy path whose comparison result is equal, forming a reconstruction number sequence from all the state numbers after its first number, and outputting the set of all reconstruction number sequences that meet the conditions as the control strategy path reconstruction candidate set.

6. A charging pile operation status monitoring system supporting hazard prediction feedback according to claim 5, characterized in that: The reconstruction module also includes performing path number difference calculation on the candidate set of control strategy path reconstruction and the end state number in the control strategy path sequence, and outputting the control strategy path reconstruction sequence; During the execution of the control strategy path reconstruction sequence, the control strategy response time and control strategy call frequency are collected to generate control strategy feedback indicators. The acquisition of control strategy response time and control strategy call frequency includes: recording the trigger time and completion time of each control strategy path in the control strategy path reconstruction sequence, and performing a difference calculation between the two to generate the control strategy response time; grouping all control strategy response times into intervals according to the control strategy path sequence, and calculating the difference between the average difference of the control strategy response time and the change amplitude in the control strategy response time sequence of that group within each group; simultaneously performing continuous matching statistics on each control strategy path in the control strategy path reconstruction sequence, calculating the number of calls per unit time, extracting the call interval sequence from the continuous call segments, performing abrupt gradient extraction on the call interval sequence, and outputting the call frequency as a control strategy feedback indicator.

7. A charging pile operation status monitoring system supporting hazard prediction feedback according to claim 6, characterized in that: The early warning module includes summarizing the control strategy path reconstruction sequence, control strategy path disturbance indicators, and control strategy feedback indicators to generate path feedback records. The summary includes: matching the control strategy path reconstruction sequence, control strategy path disturbance index, and control strategy feedback index one-to-one according to the execution order of the control strategy path; extracting the path number, corresponding disturbance index value, and feedback index value for each control strategy path; performing three-data concatenation to generate a path feature vector; and performing cumulative concatenation of all path feature vectors according to the control strategy path order to generate a path feedback record for the complete path cycle.

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