Charging pile operation state monitoring system supporting danger prediction feedback

Abstract

The invention discloses a charging pile operation state monitoring system supporting danger prediction feedback, and particularly relates to the field of charging pile operation state monitoring of danger prediction feedback, and the system comprises a path construction module which is used for obtaining a state number sequence of a charging pile, constructing a control strategy path sequence based on timestamp order, sliding time window disturbance statistics is executed, and a control strategy path disturbance index is output; the state judgment module is used for executing control strategy path disturbance index normalization processing and generating disturbance characteristics including repetition section frequency and execution time; matching the disturbance characteristics with historical path characteristics, outputting path similarity and combining three threshold conditions for judgment to generate an oscillation state mark; a monitoring process based on path oscillation identification and trend evolution regulation is formed by constructing a control strategy path sequence, extracting path disturbance characteristics and fusing multi-dimensional feedback indexes, so that advanced identification and feedback prediction of an operation state degradation risk caused by frequent transition of a control strategy are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of charging pile operation status monitoring with hazard prediction feedback, and more specifically, to a charging pile operation status monitoring system supporting hazard prediction feedback. Background Art

[0002] In existing charging pile operation control, the system typically uses a preset strategy switching mechanism to maintain closed-loop control stability based on operating parameter status and environmental disturbance conditions. When voltage offsets, the system switches to constant voltage control mode; when temperature rise exceeds the limit, it switches to power-limited control mode; when communication anomalies occur or after repeated plugging and unplugging, the system enters power hold or soft-start protection mode. The switching rules for each strategy are driven by the controller's internal state machine, and the judgment is based on a single monitoring variable or its combined threshold. The system defaults to each strategy having clear switching conditions and a defined stability target.

[0003] However, under multivariable coupled disturbance conditions, such as simultaneous equipment temperature rise, grid fluctuations, and communication retries, the state machine may trigger different strategy switching paths multiple times in its decision logic, causing the control system to repeatedly enter different strategy states within a single charging cycle. Because each switch satisfies local rules and system parameters do not exceed alarm thresholds, existing charging pile operation status monitoring systems do not identify this behavior as an abnormal process and do not constitute any form of fault reporting.

[0004] In control logic, this repeated switching manifests as short-term oscillations in the strategy path. This means that the control system is constantly in strategy transitions and fails to converge to any stable strategy range. This in turn causes abnormal controller execution frequency, power output fluctuations, response behavior jitter, and disordered thermal load distribution. Existing monitoring methods only monitor parameter violations, hardware anomalies, or communication loss. They lack the structural recognition capability for frequent strategy path transitions and a mechanism for evaluating the stability of the strategy execution trajectory evolution.

[0005] It can be seen from this that the existing charging pile operation status monitoring system does not take into account the frequent transition behavior of the control strategy under multi-source disturbances. Although such transitions do not constitute parameter violations, they can lead to the degradation of the stability of the control system logic path, thereby evolving into control anomalies or device failures. In addition, the existing charging pile operation status monitoring system cannot effectively monitor and predict such path-level operation status degradation risks. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a charging pile operation status monitoring system that supports hazard prediction feedback. By constructing a control strategy path sequence, extracting path disturbance characteristics and fusing multi-dimensional feedback indicators, a monitoring process based on path oscillation identification and trend evolution regulation is formed to achieve early identification and feedback prediction of the risk of operation status degradation caused by frequent jumps of control strategies.

[0007] To achieve the above-mentioned object, the present invention provides the following technical solutions: a charging pile operation status monitoring system supporting 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 state number sequence of the charging pile, construct a control strategy path sequence based on the ordered timestamp, perform sliding time window disturbance statistics, and output the control strategy path disturbance index;

[0009] The state determination module is used to perform normalization processing on the control strategy path disturbance index, generating a disturbance signature that includes the frequency of repeated segments and execution time. The disturbance signature is matched with the historical path signature, and the output path similarity is combined with three threshold conditions to generate an oscillation state mark.

[0010] The reconstruction module extracts a stable path set based on the oscillation state markers and constructs a path reconstruction candidate set. It then performs number difference screening to generate a control strategy path reconstruction sequence. It also 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 summary of the control strategy path reconstruction sequence, disturbance indicators and feedback indicators; determines whether the three indicators meet the feedback abnormality conditions, and outputs feedback warning marks for updating the control strategy path evolution trend structure and hazard prediction.

[0012] In a preferred embodiment, the path construction module includes obtaining a state number sequence within a complete execution cycle of the charging pile during the charging pile operation state monitoring, wherein the state number sequence includes an operation state number for constructing a control strategy path, an adjacent jump pair for performing state transition statistics, and an end state number for reconstructing the control strategy path;

[0013] The operating state 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 the control strategy path transition frequency;

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

[0016] The control strategy path transition frequency is accumulated in 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 the control strategy path disturbance feature, wherein the control strategy path disturbance feature includes the frequency of repeated segments and the execution time;

[0018] The normalization processing includes: extracting the disturbance index values ​​within the preset control strategy disturbance index threshold range from the control strategy path disturbance index to form a normalized interval; mapping the current control strategy path disturbance index to a relative position segment within the normalized interval, executing frequency statistics of the normalized control strategy disturbance index according to a continuous numerical interval, performing exponential scaling on the numerical interval whose occurrence frequency is not less than the preset disturbance frequency threshold, and realizing a unified measurement of the frequency of repeated 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 characteristics with the historical path characteristics and calculating the path similarity;

[0020] The path similarity is judged to determine whether the path similarity is lower than the preset path similarity threshold, whether the repetition frequency of the continuous sliding time window is higher than the repetition frequency threshold of 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. These three conditions are used for judgment. If all three conditions meet their corresponding preset threshold conditions, an oscillation status mark is generated; otherwise, no oscillation status mark is generated.

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

[0022] Extract the terminal state number and stable path set in the control strategy path sequence for reconstruction, and generate a control strategy path reconstruction candidate set;

[0023] The reconstruction includes: extracting the terminal state number from the control strategy path sequence, traversing each control strategy path in the stable path set, performing an equivalence comparison on the first state number of the control strategy path and the terminal state number in turn, performing an interception operation on the control strategy paths with equal comparison results, forming a reconstructed number sequence with all state numbers after the first number, and outputting all the reconstructed number sequence sets that meet the conditions as a 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 terminal 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 collection 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, performing the difference operation between the two to generate the control strategy response time; grouping all control strategy response times according to the control strategy path sequential execution interval, and calculating the average difference of the control strategy response time and the difference of the change amplitude in the control strategy response time series of this group in each group; at the same time, 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 in the continuous call segment, performing mutation gradient extraction on the call interval sequence, and outputting the call frequency as the control strategy feedback indicator.

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

[0028] The summary includes: by matching the control strategy path reconstruction sequence, the control strategy path disturbance index and the control strategy feedback index one by one according to the execution order of the control strategy path, extracting the path number, the corresponding disturbance index value and the feedback index value for each control strategy path, and performing ternary data splicing to generate a path feature vector; all path feature vectors are cumulatively spliced ​​in the order of the control strategy path to generate a path feedback record for the complete path cycle.

[0029] In a preferred embodiment, 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, as three conditions for judgment. If the three conditions are not higher than their respective corresponding preset threshold conditions, the output is no feedback early warning mark, otherwise the feedback early warning mark is output.

[0030] Technical effects and advantages of the present invention:

[0031] 1. This solution compares disturbance indicators with path characteristics to identify oscillating states that do not exceed limits but frequently switch strategies, thereby detecting potential control degradation risks in advance.

[0032] 2. Extracting the disturbance-stabilized path from the historical archive as a reference supports anomaly identification of the current path and subsequent matching of replacement strategies;

[0033] 3. When paths fluctuate, alternative control paths are generated by reconstructing stable path candidates to improve the system's self-repair capabilities.

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

[0035] 5. Generate feedback warning marks based on three types of indicators to guide the update of path evolution trends and achieve regulation of the strategy path from oscillation to stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Schematic diagram of the system module of the present invention;

[0037] Figure 2 Constructing an execution flow chart for the control strategy path disturbance index of the system in the present invention;

[0038] Figure 3 This is a flow chart of the path similarity judgment and oscillation state mark identification execution of the system in the present invention;

[0039] Figure 4 A flowchart of the stable path set screening and path reconstruction execution of the system in the present invention;

[0040] Figure 5 This is a flowchart of the feedback indicator summary and early warning judgment execution of the system in the present invention. DETAILED DESCRIPTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0042] Refer to the instruction manual Figure 1-5 , an embodiment of the present invention provides a charging pile operation status monitoring system supporting danger prediction feedback, including a path construction module, a state determination module, a reconstruction module, and an early warning module;

[0043] The path construction module is used to obtain the state number sequence of the charging pile, construct a control strategy path sequence based on the ordered timestamp, perform sliding time window disturbance statistics, and output the control strategy path disturbance index;

[0044] The state determination module is used to perform normalization processing on the control strategy path disturbance index, generating a disturbance signature that includes the frequency of repeated segments and execution time. The disturbance signature is matched with the historical path signature, and the output path similarity is combined with three threshold conditions to generate an oscillation state mark.

[0045] The reconstruction module extracts a stable path set based on the oscillation state markers and constructs a path reconstruction candidate set. It then performs number difference screening to generate a control strategy path reconstruction sequence. It also 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 summary of the control strategy path reconstruction sequence, disturbance indicators and feedback indicators; determines whether the three indicators meet the feedback abnormality conditions, and outputs feedback warning marks for updating the control strategy path evolution trend structure and hazard prediction.

[0047] It should be noted that in the formula structure involved in this solution, dimensionless terms can serve as proportionality or structural adjustment factors. When combined with quantities with units, they only play a numerical scaling role and do not introduce new physical dimensions. Therefore, they will not change or confuse the overall unit system of expression. This combination of "dimensionless terms and units" can be understood as a composite structural expression commonly used in mathematical and physical modeling, conforming to the principle of dimensional consistency and having 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 be formed into a unified structure through function mapping, ratio combination or normalization adjustment. The units and meanings are clear, and the overall expression conforms to the principle of dimensional consistency and the common formula of engineering modeling.

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

[0050] The path construction module includes obtaining the state number sequence of the charging pile within the complete execution cycle during the charging pile operation status monitoring. The state number sequence includes the operation state number for constructing the control strategy path, the adjacent jump pair for performing state transition statistics, and the terminal state number for reconstructing the control strategy path;

[0051] Timestamp sorting is performed on the operating state numbers to generate a control strategy path sequence, where timestamp sorting means arranging the operating state numbers in order according to the time sequence of their actual occurrence to establish the execution order of the control strategy path.

[0052] The path construction module also includes executing 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 jump pair by combining any operating state number and its next state number in the control strategy path sequence, and traversing all adjacent jump pairs in chronological order in the complete control strategy path sequence, performing cumulative statistics on the number of occurrences of the same adjacent jump pairs, and outputting the occurrence frequency corresponding to each adjacent jump pair as the transition frequency of the jump in the current control strategy path;

[0054] The control strategy path transition frequency is accumulated in a sliding time window and the control strategy path disturbance index is output;

[0055] Define the control strategy path disturbance index

[0056]

[0057] Where n represents the total number of operating state numbers in the control strategy path sequence; w is the width of the sliding time window; k represents the kth sliding time window obtained by sliding operation in the control strategy path disturbance index sequence; ΔF k is the total change in the transition frequency of all adjacent hopping pairs in the kth sliding time window; ΔT k is the time span of the path segment in the kth sliding time window; Φ k It represents the third-order difference change rate of the jump interval sequence in the k-th sliding time window. The third-order difference change rate is used to measure the local disturbance trend of the path; is the cumulative number of positive transitions in the k-th sliding window; is the cumulative number of reverse jumps in the kth sliding window; ε represents the minimum positive constant; Υ k It represents the number mutation structure strength of the path segment in the k-th sliding window. The number mutation structure strength is defined as the local extreme frequency of the number 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. The control strategy path disturbance characteristics include the frequency of repeated segments and the execution time.

[0059] The normalization processing includes: extracting the disturbance index values ​​within the preset control strategy disturbance index threshold range from the control strategy path disturbance index to form a normalized interval; mapping the current control strategy path disturbance index to a relative position segment within the normalized interval, executing frequency statistics of the normalized control strategy disturbance index according to a continuous numerical interval, performing exponential scaling on the numerical interval whose occurrence frequency is not less than the preset disturbance frequency threshold, and realizing a unified measurement of the frequency of repeated segments and execution time in the control strategy path disturbance characteristics.

[0060] The state determination module also includes matching the control strategy path disturbance characteristics with historical path characteristics and calculating path similarity. The historical path characteristics refer to the disturbance characteristics, jump patterns, and response data extracted from the control strategy paths that have been executed and completed without anomalies in the past. They 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 disturbance structure of the path; T is the normalized time length of the disturbance characteristic of the control strategy path; t represents the time variable in the normalized path disturbance analysis, which is used to uniformly represent the function independent variable of the evolution of the disturbance characteristic along 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 disturbance frequency distribution function in the historical control strategy path; d represents the derivative operator of the path disturbance function with respect to time, which is used to construct the structural trend partial derivative gradient; Θ(t) represents the normalized evolution curve of the disturbance characteristic change trajectory; The second-order derivative of the path segment that continuously changes in time is used to characterize the curvature trend of time compression or extension; is the gradient sequence of the current path response function; is the gradient sequence of the historical path response function; dt represents the integral differential element of the time variable t;

[0064] The path similarity is judged to determine whether the path similarity is lower than the preset path similarity threshold, whether the repetition frequency of the continuous sliding time window is higher than the repetition frequency threshold of 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. These three conditions are used for judgment. If all three conditions meet their corresponding preset threshold conditions, an oscillation status mark is generated; otherwise, no oscillation status mark is generated.

[0065] The reconstruction module includes performing disturbance index screening statistics on archived control strategy path sequences earlier than the current execution cycle when generating an oscillation state mark, screening paths whose disturbance index stability meets the preset disturbance index stability threshold, and forming 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 has been completed and stored before the generation and execution of the current round of control strategy path sequence, which is used as a reference for stability judgment and path matching;

[0066] Defining a set of stable paths

[0067]

[0068] where Q i is the i-th historical control strategy path; A set of control strategy paths for historical archiving; Indicates the integral difference comparison of 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 of a single state number; the symbol Ξ represents the index extraction function, which is used to extract the value of the control strategy path Q from the historical control strategy path Q i Extract the corresponding disturbance index from Ω(Q i [j-1],Q i [j]) represents the adjacent state number jump structure residual function, which is used to measure the stability of number switching; the symbol Ω represents the historical control strategy path Q i The path number transition difference between adjacent state number pairs in ; θ s is the combined threshold for disturbance stability;

[0069] Extract the terminal state number and stable path set in the control strategy path sequence for reconstruction, and generate a control strategy path reconstruction candidate set;

[0070] The reconstruction includes: extracting the terminal state number from the control strategy path sequence, traversing each control strategy path in the stable path set, performing an equivalence comparison on the first state number of the control strategy path and the terminal state number in turn, performing an interception operation on the control strategy paths with equal comparison results, forming a reconstructed number sequence with all state numbers after the first number, and outputting all the reconstructed number sequence sets that meet the conditions as a control strategy path reconstruction candidate set.

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

[0072] Define the control strategy path reconstruction sequence

[0073]

[0074] where R m Reconstruct the mth path segment in the candidate set for the control strategy path; Reconstruct the candidate set for the control strategy path; Ψ(R m ) is the disturbance trend enhancement measurement function of the mth path segment in the control strategy path reconstruction candidate set. The disturbance trend enhancement measurement function represents the degree of cumulative gradient change of its internal disturbance index, which is used to characterize the "positivity" or "expansion" of the disturbance change in the path segment; θ Ψ Screening thresholds for disturbance trend enhancement; is the number jump change rate function of the mth path segment in the control strategy path reconstruction candidate set. The number jump change rate function is used to characterize the volatility of the path number jump structure; the symbol ∧ represents the connection operator; θ Ω is the acceptable change range threshold of the jump structure change rate;

[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 collection 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, performing the difference operation between the two to generate the control strategy response time; grouping all control strategy response times according to the control strategy path sequential execution interval, and calculating the average difference of the control strategy response time and the difference of the change amplitude in the control strategy response time series of this group in each group; at the same time, 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 in the continuous call segment, performing mutation gradient extraction on the call interval sequence, and outputting the call frequency as the control strategy feedback indicator.

[0077] The early warning module includes summarizing the control strategy path reconstruction sequence, the control strategy path disturbance index and the control strategy feedback index to generate a path feedback record;

[0078] The summary includes: by matching the control strategy path reconstruction sequence, the control strategy path disturbance index and the control strategy feedback index one by one according to the execution order of the control strategy path, extracting the path number, the corresponding disturbance index value and the feedback index value for each control strategy path, and performing ternary data splicing to generate a path feature vector; all path feature vectors are cumulatively spliced ​​in the order of the control strategy path to generate a path feedback record for the complete path cycle.

[0079] 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 all three conditions are not higher than their respective corresponding preset threshold conditions, the output is not to output a feedback early warning mark, and the control strategy path evolution trend structure remains unchanged. Otherwise, a feedback early warning mark is output for hazard prediction and updating of the control strategy path evolution trend structure.

[0080] It's important to note that in traditional charging pile operation control, strategy switching is driven by a state machine, relying on pre-defined judgments about single or combined variables, such as voltage, current, and temperature exceeding limits. This mechanism ensures stability under ideal static disturbances, but in dynamic environments with multiple sources of disturbance (such as grid fluctuations, temperature rise, and communication retries), 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-period oscillation path behavior neither constitutes a parameter violation nor triggers traditional abnormality detection. However, its long-term existence can lead to controller load offset, response performance degradation, and even hardware risks.

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

[0083] This solution includes the path construction phase:

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

[0085] This phase achieves structural sensing of non-parameter limit-exceeding anomalies, resolving the problem of strategy oscillation occurring despite the traditional control parameters not exceeding their limits. Its core approach is to abstract path disturbances into a time-structured change sequence, characterizing disturbance risks through the cumulative trends of continuous jump frequencies and directions.

[0086] This solution includes the status determination stage:

[0087] The disturbance index is normalized to obtain a disturbance feature, which includes two sub-variables: the frequency of repeated segments and the execution time. This feature is then matched with the historical path feature (derived from stable paths without anomalies) to calculate path similarity. The repetition frequency, execution time, and similarity are then combined to determine whether they exceed a preset threshold, generating an oscillation status flag.

[0088] This stage determines whether the current path has entered a risky oscillation state. Essentially, it measures the degree of deviation from a reference trajectory defined by historically stable behavior, using both disturbance and execution time as the basis for a "before anomaly" judgment mechanism at the path behavior level.

[0089] This solution includes the reconstruction phase:

[0090] When an oscillation occurs, a stable path set is selected from the archived paths. Path reconstruction candidates are generated based on the current path end number. A number difference calculation is used to output a control strategy path reconstruction sequence. This sequence represents a set of possible stable path candidates. During execution, 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 restoration of strategy stability after structural shocks occur. Response time and call frequency are used to dynamically assess whether the current strategy execution efficiency and stability have further degraded.

[0092] This plan includes the early warning stage:

[0093] The control strategy path reconstruction sequence, control strategy path disturbance index, and control strategy feedback index are correspondingly spliced ​​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 realizes the formation of trend evolution cognitive foundation based on path-level historical feedback and the construction of a structural risk feedback loop driven by multiple variables; this part enables the system to not only have the ability to immediately identify current anomalies, but also has the ability to adaptively adjust and structurally converge to changes in path trends.

[0095] The above description is only 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 in the scope of protection 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 state number sequence of the charging pile, construct a control strategy path sequence based on the ordered timestamp, perform sliding time window disturbance statistics, and output the control strategy path disturbance index; The state determination module is used to perform normalization processing on the control strategy path disturbance index, generating a disturbance signature that includes the frequency of repeated segments and execution time. The disturbance signature is matched with the historical path signature, and the output path similarity is combined with three threshold conditions to generate an oscillation state mark. The reconstruction module extracts a stable path set based on the oscillation state markers and constructs a path reconstruction candidate set. It then performs number difference screening to generate a control strategy path reconstruction sequence. It also 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 summary of the control strategy path reconstruction sequence, disturbance indicators and feedback indicators; determines whether the three indicators meet the feedback abnormality conditions, and outputs feedback warning marks for updating the control strategy path evolution trend structure and hazard prediction.

2. The charging pile operation status monitoring system supporting risk prediction feedback according to claim 1, characterized in that: The path construction module includes obtaining the state number sequence of the charging pile within the complete execution cycle during the charging pile operation status monitoring. The state number sequence includes the operation state number for constructing the control strategy path, the adjacent jump pair for performing state transition statistics, and the terminal state number for reconstructing the control strategy path; The operating state numbers are sorted by timestamp to generate a control strategy path sequence.

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

4. The charging pile operation status monitoring system supporting risk 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. The control strategy path disturbance characteristics include the frequency of repeated segments and the execution time. The normalization process includes: extracting disturbance index values ​​within a 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 the relative position segment within the normalized interval. The normalized control strategy disturbance index is executed according to the frequency statistics of continuous numerical intervals. The numerical intervals whose occurrence frequency is not less than the preset disturbance frequency threshold are subjected to exponential scaling to achieve a unified measurement of the frequency of repeated segments and execution time in the control strategy path disturbance characteristics.

5. The charging pile operation status monitoring system supporting risk prediction feedback according to claim 4, characterized in that: The state determination module also includes matching the control strategy path disturbance characteristics with the historical path characteristics and calculating the path similarity; The path similarity is judged to determine whether the path similarity is lower than the preset path similarity threshold, whether the repetition frequency of the continuous sliding time window is higher than the repetition frequency threshold of 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. These three conditions are used for judgment. If all three conditions meet their corresponding preset threshold conditions, an oscillation status mark is generated; otherwise, no oscillation status mark is generated.

6. The charging pile operation status monitoring system supporting risk prediction feedback according to claim 5, characterized in that: The reconstruction module includes performing disturbance index screening statistics on the archived control strategy path sequence earlier than the current execution cycle when the oscillation state mark is generated, screening the 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 in the control strategy path sequence for reconstruction, and generate a control strategy path reconstruction candidate set; The reconstruction includes: extracting the terminal state number from the control strategy path sequence, traversing each control strategy path in the stable path set, performing an equivalence comparison on the first state number of the control strategy path and the terminal state number in turn, performing an interception operation on the control strategy paths with equal comparison results, forming a reconstructed number sequence with all state numbers after the first number, and outputting all the reconstructed number sequence sets that meet the conditions as a control strategy path reconstruction candidate set.

7. The charging pile operation status monitoring system supporting risk prediction feedback according to claim 6, characterized in that: The reconstruction module also includes performing a path number difference operation on the control strategy path reconstruction candidate set and the terminal 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 collection 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, performing the difference operation between the two to generate the control strategy response time; grouping all control strategy response times according to the control strategy path sequential execution interval, and calculating the average difference of the control strategy response time and the difference of the change amplitude in the control strategy response time series of this group in each group; at the same time, 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 in the continuous call segment, performing mutation gradient extraction on the call interval sequence, and outputting the call frequency as the control strategy feedback indicator.

8. The charging pile operation status monitoring system supporting risk prediction feedback according to claim 7, characterized in that: The early warning module includes summarizing the control strategy path reconstruction sequence, the control strategy path disturbance index and the control strategy feedback index to generate a path feedback record; The summary includes: by matching the control strategy path reconstruction sequence, the control strategy path disturbance index and the control strategy feedback index one by one according to the execution order of the control strategy path, extracting the path number, the corresponding disturbance index value and the feedback index value for each control strategy path, and performing ternary data splicing to generate a path feature vector; all path feature vectors are cumulatively spliced ​​in the order of the control strategy path to generate a path feedback record for the complete path cycle.

9. The charging pile operation status monitoring system supporting risk prediction feedback according to claim 8, characterized in that: 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 all three conditions are not higher than their respective corresponding preset threshold conditions, the output is a no-feedback early warning mark, otherwise a feedback early warning mark is output.

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