A charging pile risk identification and early warning system and method thereof

Abstract

The application discloses a charging pile risk identification and early warning system and a method thereof, and the method comprises the following steps: S1, after the handshake of new energy vehicle charging is completed, the inherent physical parameters and historical charging health data of the power battery of the current access vehicle are collected, the working condition characteristics of different charging SOC stages are combined, a dynamic multi-dimensional safety benchmark model suitable for the current single charging period is constructed, and a steady-state safety interval dynamically changing with the charging process is formed; the application discloses a single parameter limit determination mode, and through high-frequency electrical waveform feature identification, temperature time sequence gradient analysis and battery thermal state multi-field coupling research and judgment, the hidden dangers such as terminal wear, loop micro-impedance anomaly and early lithium precipitation of the battery can be accurately captured, the thermal runaway early warning time window can be greatly advanced, and the industry pain points of the traditional protection mode, such as after-treatment and early warning lag, are fundamentally solved.

Description

Technical Field

[0001] This invention relates to the field of safety monitoring technology for new energy vehicle charging equipment, specifically to a charging pile risk identification and early warning system and method. Background Technology

[0002] With the rapid iteration and upgrading of the new energy vehicle industry, 800-volt high-voltage electrical platforms and high-power liquid-cooled supercharging technology have gradually become mainstream application solutions in the industry. The rated output power of charging piles continues to increase, and the high-voltage and high-current operating conditions place extremely high demands on the operational safety and fault response time of charging equipment. As the core electrical energy interaction device connecting the public power grid and the vehicle's power battery, the operational stability of charging piles directly affects the safety of charging equipment, vehicle batteries, charging sites, and personnel, making it a core control node for the safe operation and maintenance of new energy infrastructure.

[0003] Currently, most existing charging pile safety protection systems adopt fixed threshold protection mechanisms, relying on preset single extreme thresholds such as voltage, current, temperature, and leakage current to complete fault detection and power-off protection. This type of passive protection mode has significant technical defects and safety shortcomings in actual high-power supercharging engineering applications.

[0004] On the one hand, safety accidents such as thermal runaway of vehicle power batteries and aging and fire in charging circuits are not triggered instantaneously, but are nonlinear deterioration processes formed by the accumulation of small anomalies over a long period of time. Under early hidden conditions such as wear and oxidation of charging gun terminals, poor cable contact, trace lithium plating inside the battery, and slight increase in circuit impedance, the equipment will only produce latent abnormal characteristics such as microscopic electrical waveform distortion and local weak temperature rise, without obvious macroscopic parameter exceeding the limit. Traditional monitoring equipment has low sampling frequency, single monitoring dimensions, and lacks multi-physics field coupling analysis capabilities, and cannot identify such early microscopic hidden dangers. It can only passively cut off the power after parameters such as temperature and current exceed the limit threshold. At this time, the risk of battery thermal runaway and circuit fire is irreversible, and the protection is seriously lagging behind, and it is impossible to avoid safety accidents from the root.

[0005] On the other hand, existing charging piles lack physical safety linkage protection mechanisms at the high-voltage equipment level. In extreme and sudden fault scenarios such as external force dragging of charging cables, vehicle collision with charging piles, and insulation breakdown of high-voltage circuits, traditional equipment relies solely on software communication timeouts to determine faults and execute power-off commands. This response method depends on the stability of the communication link, resulting in high latency, poor reliability, and an inability to verify the actual physical working state of high-voltage relays and mechanical locking mechanisms. This can easily lead to problems such as relay sticking, mechanical structure jamming, and command execution failure, resulting in high-risk secondary risks such as abnormal exposure of high-voltage live parts, arcing breakdown, and cable detachment, posing significant safety hazards.

[0006] In summary, existing charging pile safety protection technologies suffer from problems such as delayed early warning, lack of ability to identify microscopic hidden dangers, and insufficient reliability of physical isolation for extreme faults, making them unsuitable for the full-cycle safety protection needs of high-power, high-voltage supercharging equipment. Therefore, a charging pile risk identification and early warning system and its method are proposed. Summary of the Invention

[0007] The purpose of this invention is to provide a charging pile risk identification and early warning system and method, which solves the technical problems of existing charging pile fixed threshold protection mode with delayed early warning, weak ability to identify early hidden dangers of thermal runaway, and lack of physical-level safety linkage isolation for extreme high-voltage faults. It realizes accurate identification of micro risks throughout the charging cycle, flexible safety intervention, and hard isolation protection for extreme faults, while taking into account both charging efficiency and the operational safety of high-voltage equipment.

[0008] The first aspect of this invention provides an intelligent main control system for high-power, high-voltage liquid-cooled DC charging piles, the method comprising the following steps: S1. After the new energy vehicle charging handshake is completed, the inherent physical parameters of the power battery of the currently connected vehicle and historical charging health data are collected. Combined with the operating characteristics of different charging SOC stages, a dynamic multi-dimensional safety benchmark model adapted to the current single charging cycle is constructed to form a steady-state safety range that changes dynamically with the charging process. S2. During the full-power charging operation of the charging pile, the high-frequency electrical waveform data of the charging circuit, the temperature distribution data of the charging gun and cable, and the internal thermodynamic operation data of the power battery are collected synchronously and in real time through the high-frequency electrical sampling unit, the distributed temperature monitoring unit and the vehicle battery management system. S3. Based on the preset multi-physics field time-series coupling analysis logic, the electrical waveform distortion characteristics, temperature time-series gradient changes, and battery internal thermal state parameters are fused and judged. The steady-state range of the dynamic multi-dimensional safety benchmark model is compared with that of the battery. The safety deviation of the current charging condition is determined in real time, and the battery thermal runaway risk probability index is quantified and generated. S4. Real-time comparison of the degree of safety deviation and the probability index of thermal runaway risk to see if they are within the preset dynamic adaptive safety envelope. When the monitoring data deviates from the steady-state safety range and there is a risk of thermal runaway, a dynamic flexible current reduction control command adapted to the current risk level is generated through the preset risk fuzzy adaptive control algorithm. S5. Based on the dynamic flexible current reduction control command, the charging pile power adjustment module and the active liquid cooling circulation module are driven synchronously to perform microsecond-level dynamic compensation and control on the charging output power and liquid cooling medium circulation flow rate. Through the coordinated intervention of power flexible adaptation and forced heat dissipation enhancement, the abnormal temperature rise of the charging circuit and the hidden danger of battery thermal runaway are suppressed. S6. Real-time acquisition of the underlying electromechanical operating parameters of the charging pile's high-voltage circuit and mechanical locking mechanism, and identification of extreme hardware failures such as high-voltage insulation breakdown, cable dragging and falling off, and equipment impact damage; S7. When it is determined that an extreme high-risk fault has occurred in the charging pile, determine the target safe disconnection status of the high-voltage contactor and the mechanical lock of the charging gun, verify the electrical status of the mechanical lock through the electrical detection dry contact, and at the same time verify the physical connection status of the gun head through the external image acquisition equipment. Combine the dual verification results to generate a high-voltage circuit hard cut-off control strategy and a mechanical lock protection strategy. S8. Based on the control strategy, complete the millisecond-level physical disconnection of the high-voltage circuit and the mechanical lock of the charging gun for protection, achieving seamless physical safety isolation under extreme fault conditions.

[0009] The second aspect of the present invention provides a charging pile risk identification and early warning system for implementing the aforementioned charging pile risk identification and early warning method. The system includes a benchmark modeling and parameter acquisition module, a multi-dimensional high-frequency state monitoring module, a safety deviation coupling calculation module, an early warning and vector generation module, a dynamic execution and compensation module, and an extreme fault safety linkage module. The benchmark modeling and parameter acquisition module is used to collect power battery parameters and historical health data during the charging handshake stage, construct a multi-dimensional safety benchmark model that is dynamically updated with the charging process, and delineate the steady-state safety operating condition range for each charging stage. The multi-dimensional high-frequency condition monitoring module includes a high-frequency electrical sampling unit, a distributed temperature sensing unit, and a data interaction unit, which are used to collect high-frequency electrical waveforms of the charging circuit, temperature distribution of the charging terminal, and internal thermodynamic parameters of the battery in real time, so as to realize high-frequency monitoring of the charging conditions in all dimensions. The safety deviation coupling calculation module is connected to the multi-dimensional high-frequency state monitoring module to fuse multi-physics field time-series monitoring data, compare dynamic safety benchmark models, and quantify the safety deviation and thermal runaway risk level of charging conditions. The early warning and vector generation module has a built-in fuzzy adaptive risk control algorithm, which is used to generate differentiated flexible current reduction and heat dissipation control commands according to the risk level when the operating conditions deviate from the steady state range. The dynamic execution and compensation module includes a power adjustment unit and a liquid cooling cycle control unit, which are used to respond to control commands, dynamically adapt the charging power and liquid cooling heat dissipation rate, and realize flexible intervention of potential hazards. The extreme fault safety linkage module is connected to high-voltage electromechanical equipment, electrical detection switch and image acquisition equipment respectively. It is used to identify extreme hardware faults and execute high-voltage circuit hard cut-off and mechanical lock safety protection actions through electrical and visual dual-core mechanism.

[0010] A third aspect of the present invention provides an electronic device; An electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the charging pile risk identification and early warning method described in any of the preceding claims.

[0011] A fourth aspect of the present invention provides a computer-readable storage medium; A computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the steps of the charging pile risk identification and early warning method described in any of the preceding claims.

[0012] Beneficial effects of this invention: Compared with the prior art, the present invention has the following substantial innovations and beneficial effects: 1. This invention abandons the single-parameter limit judgment mode. Through high-frequency electrical waveform feature recognition, temperature time-series gradient analysis and multi-field coupling judgment of battery thermal state, it can accurately capture hidden dangers such as terminal wear, abnormal circuit micro impedance, and early lithium plating of battery, significantly advancing the thermal runaway warning time window and fundamentally solving the industry pain points of post-event handling and delayed warning in traditional protection modes.

[0013] 2. This invention does not employ extreme protection logic of either power on or power off. Instead, it can adaptively adjust the charging power and liquid cooling intensity according to the risk level. While eliminating minor safety hazards, it maximizes the retention of effective charging power, avoids frequent power outages, and significantly improves the operational stability of high-power supercharging devices and the user charging experience.

[0014] 3. This invention draws on the concept of safety linkage protection for special equipment. It establishes a dual error prevention mechanism of electrical contact verification + visual image review for extreme high-risk faults such as high-voltage dragging, equipment impact, and insulation breakdown. This completely avoids fault loopholes such as software communication failure, hardware structure jamming, and relay sticking, and achieves physical-level hard isolation of high-voltage circuits to prevent secondary disasters such as arc discharge, high-voltage leakage, and equipment fire.

[0015] 4. This invention can be adapted to liquid-cooled supercharging equipment with different voltage platforms and power levels, and can be compatible with the charging safety protection requirements of different types of power batteries. It covers the entire process of charging start-up, constant current, constant voltage and end, and has strong engineering implementation and promotion value. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a charging pile risk identification and early warning method according to the present invention. Figure 2 This is a schematic diagram of the charging pile risk identification and early warning system module of the present invention; Figure 3 This is a schematic diagram of the structure of the electronic device of the present invention; Detailed Implementation

[0018] 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.

[0019] Figure 1 This is a flowchart illustrating the charging pile risk identification and early warning method according to an embodiment of the present invention. It should be noted that if substantially the same result is achieved, the method of this application is not necessarily identical. Figure 1 The process sequence shown is limited. Example

[0020] like Figure 1 As shown, the specific implementation process of the charging pile risk identification and early warning method of the present invention is as follows: The core execution entity of this invention is an industrial-grade microprocessor and edge control gateway deployed inside the main control compartment of the high-pressure liquid-cooled supercharging pile. The main control device is equipped with a high-speed analog-to-digital sampling channel, which can realize high-frequency acquisition of electrical signals. At the same time, it establishes low-latency bidirectional communication with the power battery management system, the underlying high-voltage relay, and the liquid-cooled circulation unit through the vehicle bus. It can independently complete the entire process of autonomous control of data acquisition, risk assessment, intelligent intervention and safety protection. It can also be connected to the cloud operation and maintenance platform to realize remote monitoring and data archiving.

[0021] After the vehicle connects to the charging equipment and completes the handshake authentication of the national standard charging protocol, the system initiates the initial modeling process. The main control system not only collects basic parameters such as the rated capacity and rated voltage of the power battery, but also retrieves the vehicle's historical charging health records stored in the cloud to analyze steady-state baseline characteristics such as changes in internal resistance, temperature rise characteristics, and voltage fluctuations during each charging process. Based on the above multi-dimensional benchmark parameters, the system constructs a dynamic multi-dimensional safety benchmark model. This model can dynamically update the safe operating condition range according to the power battery's SOC progress. Unlike traditional fixed safety thresholds, it can adapt to the differentiated characteristics of the battery at different charging stages, providing a standardized benchmark for subsequent accurate risk assessment.

[0022] After the charging pile starts high-power supercharging, the system activates a full-dimensional high-frequency monitoring mode. Compared to the low-frequency sampling monitoring method of traditional equipment, this invention uses high-frequency Hall effect sensors and high-speed isolated sampling circuits to continuously collect high-frequency electrical waveform signals of the charging circuit, accurately capturing hidden electrical anomalies such as micro-ripples in the current and waveform distortion. Simultaneously, a distributed temperature sensor array deployed across the charging gun terminals and liquid-cooled cables collects real-time data on the surface temperature distribution and temporal temperature rise gradient, accurately identifying potential localized micro-overheating hazards. At the same time, the system synchronizes thermodynamic data such as the temperature of individual battery cells, differential pressure, and internal thermal equilibrium state in real time via the vehicle bus, achieving synchronous acquisition and real-time updating of multi-physical field data including electrical, temperature, and battery status.

[0023] The system relies on multi-physics field time-series coupling analysis logic to complete risk assessment, which is the core component of this invention to achieve early warning of potential hazards. Under high-power charging conditions, latent faults such as wear and oxidation of charging terminals, poor cable contact, and micro-short circuits inside the battery will not cause macroscopic current and temperature parameters to exceed limits, but will cause high-frequency electrical waveform spectrum distortion and abnormal Joule heat accumulation at contact points. The system performs noise filtering and feature extraction on the collected high-frequency electrical waveforms, removes the inherent modulation noise of the equipment, and accurately identifies electrical characteristic quantities that characterize circuit aging and impedance abnormalities. At the same time, combined with full-domain temperature time-series data, it analyzes the deviation between the actual temperature rise state and the theoretical heat dissipation state of the equipment, and accurately identifies potential hazards of trace heat accumulation that cannot be captured by conventional monitoring. By integrating electrical abnormality characteristics and thermodynamic deviation characteristics, the system can quantify the degree of safety deviation of the current charging condition and accurately assess the probability of thermal runaway risk of the power battery, realizing a technological upgrade from "parameter over-limit alarm" to "hazard trend prediction".

[0024] When the system determines that the safety deviation and thermal runaway risk index deviate from the dynamic adaptive safety envelope, a flexible risk intervention mechanism is activated. The system's built-in fuzzy adaptive control algorithm can generate differentiated dynamic control commands based on the severity and development rate of the risk. For low-level risks such as minor heat accumulation and slight impedance anomalies, the system does not perform a power-off shutdown. Instead, it slightly and flexibly reduces the charging output power while increasing the operating speed of the liquid-cooled circulating pump and enhancing the forced heat dissipation effect of the cables and charging terminals. Through the coordinated control of power adaptation and heat dissipation enhancement, the system gradually eliminates the risk of abnormal temperature rise, maximizing charging efficiency while ensuring charging safety.

[0025] If the system detects a sustained increase in risk, that potential hazards cannot be eliminated through flexible intervention, or identifies extreme high-risk faults such as forceful cable dragging, vehicle collisions, or high-voltage insulation breakdown, it will immediately activate a special equipment-level physical safety linkage protection mechanism. This mechanism draws on the design concepts of fault safety avoidance and dual-core protection for high-risk special equipment, completely abandoning the single software communication power-off protection mode and eliminating security vulnerabilities such as command failure, equipment sticking, and false signal alarms.

[0026] The system collects real-time electromechanical operating parameters of the charging pile's high-voltage circuit and mechanical locking mechanism, accurately identifies extreme hardware fault types, and matches corresponding target safety disconnection states. To address issues such as high-voltage relay sticking, mechanical lock jamming, and software command failure, the system employs a dual physical verification mechanism: verifying the electrical operating status of the charging gun's mechanical electronic lock through the level feedback signal of the electrical detection dry contact; and capturing the physical connection image of the charging gun through an external wide-angle high-definition camera module, using edge image recognition technology to verify the actual connection and switching status of the charging head, thus achieving cross-verification of electrical signals and physical reality.

[0027] Based on the dual-core results, the system generates a dedicated high-voltage circuit control strategy and mechanical protection strategy. Through the underlying hardware drive circuit, it executes millisecond-level physical disconnection of the high-voltage contactor, while simultaneously controlling the mechanical locking mechanism to complete the interlocking protection, completely cutting off the high-voltage power output and locking the equipment into a safe state. This closed-loop protection mechanism can quickly block risks such as high-voltage arcing, leakage, and fire in extreme fault scenarios, avoiding secondary disasters such as equipment damage, electric shock, and site fires, thus constructing the ultimate safety defense line for high-power supercharging equipment.

[0028] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention shall fall within the scope of protection of the present invention. Example

[0029] like Figure 2 As shown, in order to solve the above-mentioned technical problems, based on Embodiment 1, another technical solution adopted in this application is: a charging pile risk identification and early warning system, used to implement the aforementioned charging pile risk identification and early warning method, the system includes a benchmark modeling and parameter acquisition module, a multi-dimensional high-frequency state monitoring module, a safety deviation coupling calculation module, an early warning and vector generation module, a dynamic execution and compensation module, and an extreme fault safety linkage module; The benchmark modeling and parameter acquisition module is used to collect power battery parameters and historical health data during the charging handshake stage, construct a multi-dimensional safety benchmark model that is dynamically updated with the charging process, and delineate the steady-state safety operating condition range for each charging stage. The multi-dimensional high-frequency condition monitoring module includes a high-frequency electrical sampling unit, a distributed temperature sensing unit, and a data interaction unit, which are used to collect high-frequency electrical waveforms of the charging circuit, temperature distribution of the charging terminal, and internal thermodynamic parameters of the battery in real time, so as to realize high-frequency monitoring of the charging conditions in all dimensions. The safety deviation coupling calculation module is connected to the multi-dimensional high-frequency state monitoring module to fuse multi-physics field time-series monitoring data, compare dynamic safety benchmark models, and quantify the safety deviation and thermal runaway risk level of charging conditions. The early warning and vector generation module has a built-in fuzzy adaptive risk control algorithm, which is used to generate differentiated flexible current reduction and heat dissipation control commands according to the risk level when the operating conditions deviate from the steady state range. The dynamic execution and compensation module includes a power adjustment unit and a liquid cooling cycle control unit, which are used to respond to control commands, dynamically adapt the charging power and liquid cooling heat dissipation rate, and realize flexible intervention of potential hazards. The extreme fault safety linkage module is connected to high-voltage electromechanical equipment, electrical detection switch and image acquisition equipment respectively. It is used to identify extreme hardware faults and execute high-voltage circuit hard cut-off and mechanical lock safety protection actions through electrical and visual dual-core mechanism.

[0030] For other details regarding the implementation techniques of each module in the above-described system, please refer to the description in the method of Embodiment 1 above, which will not be repeated here.

[0031] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system-type embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. Example

[0032] like Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. It illustrates a structural schematic diagram suitable for implementing the electronic device in the embodiment of the present disclosure. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0033] like Figure 3 As shown, the electronic device includes a processor, a memory, and a communication interface. The memory stores a computer program, and when the processor executes the computer program, it implements the methods of the aforementioned embodiments of this disclosure. The electronic device can exchange data with other devices or systems through the communication interface, enabling real-time updates and sharing of data information.

[0034] The processor in the aforementioned electronic device serves as its core, responsible for executing the computer program stored in the memory to implement the various functions of the method. The processor can employ a high-performance multi-core CPU or a dedicated chip to meet the demands of complex calculations and real-time processing. The memory stores the operating system, applications, data, and computer programs. In this embodiment, the memory stores the computer program of the method. The memory can be RAM, ROM, Flash memory, or other types of non-volatile memory. The communication interface connects the electronic device to other devices or networks, enabling data transmission and exchange. In this embodiment, the communication interface supports various communication protocols and interface standards, such as Wi-Fi, Bluetooth, USB, and Ethernet, to meet communication needs in different scenarios.

[0035] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here. Example

[0036] According to embodiments of the present disclosure, a computer-readable storage medium stores a computer program, which, when executed by a processor, implements the functions of the methods described in the foregoing embodiments of the present disclosure.

[0037] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0038] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0039] 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 within the protection scope of the present invention.

Claims

1. A method for risk identification and early warning of charging piles, characterized in that, The method, applied to the intelligent main control system of high-power DC charging piles, includes the following steps: Obtain the physical characteristic parameters of the power battery of the currently connected vehicle and the historical charging health benchmark file. Based on the physical characteristic parameters of the power battery and the historical charging health benchmark file, construct a dynamic multi-dimensional safety benchmark model that is adapted to the current charging cycle. The system controls the charging pile to perform charging operations according to the preset initial power output curve. During the charging process, it collects high-frequency electrical waveform data at the output end of the charging pile, temperature distribution data on the surface of the charging gun and cable, and internal thermodynamic data of the battery synchronously sent by the vehicle battery management system. Based on the high-frequency electrical waveform data, surface temperature distribution data, and internal thermodynamic data of the battery, the safety deviation value and thermal runaway probability index between the current charging condition and the dynamic multidimensional safety benchmark model are calculated in real time using a preset multiphysics field time-series coupling algorithm. When the safety deviation value and thermal runaway probability index exceed the preset dynamic adaptive safety envelope, a dynamic flexible descent vector is generated in real time through a preset risk intervention algorithm. Based on the dynamic flexible current reduction vector, the power distribution module and the active liquid cooling circulation module of the charging pile are driven to perform microsecond-level dynamic regulation and compensation on the output power of the charging pile and the flow rate of the cooling medium, thereby suppressing the potential thermal runaway risk of the power battery and the charging link.

2. The charging pile risk identification and early warning method according to claim 1, characterized in that, Based on the high-frequency electrical waveform data, surface temperature distribution data, and battery internal thermodynamic data, a preset multiphysics time-series coupling algorithm is used to calculate in real time the safety deviation value and thermal runaway probability index between the current charging condition and the dynamic multidimensional safety benchmark model. Specifically, this includes: The high-frequency electrical waveform data is subjected to fast Fourier transform to filter out the inherent mechanical and electrical noise generated by the high-frequency operation of the charging pile switching power supply IGBT, and to extract the weak arc high-frequency transient components and wideband impedance change rate in the characteristic frequency band. Based on the surface temperature distribution data of the charging gun and cable, and combined with the equivalent thermal resistance coefficients of the charging gun and cable materials, the abnormal temperature rise gradient increment caused by the Joule heat accumulation of the high current is calculated. The high-frequency transient component of the weak electric arc, the broadband impedance mutation rate, and the abnormal temperature rise gradient increment are nonlinearly fused to solve the real-time physical degradation deformation of the charging connection circuit. By substituting the physical degradation variables into the dynamic multidimensional safety benchmark model and combining the extreme values ​​of cell temperature difference in the battery's internal thermodynamic data, the safety deviation value and thermal runaway probability index, which characterize the overall health status of the power battery body and the charging physical link, are calculated.

3. The charging pile risk identification and early warning method according to claim 1, characterized in that, The process of generating a dynamic flexible descent vector in real time through a preset risk intervention algorithm specifically includes: The thermal runaway probability index is used as a feedforward control variable, and the safety deviation value is used as a feedback control variable, which is synchronously input into a preset fuzzy PID composite controller. Based on the battery's internal thermodynamic data and the corresponding battery state of charge (SOC) range, the gain coefficient of the fuzzy PID composite controller is dynamically tuned, where the proportional gain and derivative gain are amplified in the high SOC range. The risk intervention target signal is output by the calibrated fuzzy PID composite controller, and the target signal is mapped into a dynamic flexible current reduction vector that controls the slope of the DC bus output current and the speed of the liquid cooling pump.

4. The charging pile risk identification and early warning method according to claim 1, characterized in that, The method also includes safe docking and physical isolation protection logic for extreme hardware anomalies and high-voltage faults, the protection logic specifically including: The charging pile's operating parameters are collected periodically or in real time, and the charging pile is used to determine whether there is an operating fault based on the operating parameters. If a fault is found in the charging pile, determine the target safe disconnection status of the charging pile's high-voltage DC contactor and physical locking mechanism according to the fault type. By detecting the working status of the electrical detection switch, the fault status of the mechanical electronic lock of the charging gun under the target safety disconnection state is determined. At the same time, the physical image of the charging gun is collected by the camera mounted on the charging pile monitoring column to verify the actual mechanical connection status and on / off status of the charging gun. Based on the target safe disconnection state, a hard cut-off system control strategy for the charging pile power module is formulated. Combined with the fault state of the charging gun's mechanical electronic lock and the actual mechanical connection state, a physical control strategy for the charging gun to disconnect from the high-voltage relay is formulated. The system control strategy controls the safe disconnection of the charging pile power module, and simultaneously regulates the on / off state of the charging pile high-voltage physical isolation circuit and the charging gun electronic lock according to the physical control strategy.

5. The charging pile risk identification and early warning method according to claim 4, characterized in that, If a fault is determined in the charging pile, the target safe disconnection state of the charging pile's high-voltage DC contactor and physical locking mechanism is determined according to the fault type, specifically including: When the fault type is the irreversible thermal runaway alarm at the power battery cell level, the target safety disconnection state is set to instantaneously cut off the high voltage DC contactor, open the fire extinguishing medium injection valve, and forcibly unlock the mechanical and electronic lock of the charging gun, allowing external physical intervention to disengage. When the fault type is an alarm indicating abnormal pulling of the charging cable or severe damage to the insulation layer, the target safety disconnection state is set to immediately cut off the high voltage output and lock the mechanical electronic lock of the charging gun to prevent secondary fire hazards caused by arcing during live plugging and unplugging.

6. A charging pile risk identification and early warning system, characterized in that, The system is used to implement the charging pile risk identification and early warning method according to any one of claims 1-5, the system comprising: The benchmark modeling and parameter acquisition module is configured to acquire the physical characteristic parameters of the power battery of the vehicle connected to the charging pile and the historical charging health benchmark file, and build a dynamic multi-dimensional safety benchmark model that is adapted to the current charging cycle. The multi-dimensional high-frequency status monitoring module is configured to control the charging pile to perform charging operations. During the charging process, it collects high-frequency electrical waveform data at the output end of the charging pile, surface temperature distribution data of the charging gun and cables, and internal thermodynamic data of the battery issued by the vehicle battery management system in real time. The safety deviation coupling calculation module is configured to calculate the safety deviation value and thermal runaway probability index between the current charging condition and the dynamic multidimensional safety benchmark model in real time by using a preset multi-physics field time-series coupling algorithm, based on high-frequency electrical waveform data, surface temperature distribution data and internal battery thermodynamic data. The early warning and vector generation module is configured to generate a dynamic flexible descent vector in real time through a preset risk intervention algorithm when the safety deviation value and thermal runaway probability index exceed the dynamic adaptive safety envelope. The dynamic execution and compensation module is configured to drive the charging pile power distribution module and the active liquid cooling circulation module based on the dynamic flexible current reduction vector, so as to realize the microsecond-level dynamic adjustment and compensation of the charging pile output power and the flow rate of the cooling medium, and suppress the risk of charging thermal runaway.

7. The charging pile risk identification and early warning system according to claim 6, characterized in that, The safety deviation coupling calculation module includes: The characteristic frequency band extraction submodule is configured to perform fast Fourier transform on high-frequency electrical waveform data, filter out the inherent noise of the switching power supply, and extract the high-frequency transient components of weak electric arcs and the wideband impedance change rate. The temperature rise gradient calculation submodule is configured to calculate the abnormal temperature rise gradient increment of the charging link based on the surface temperature distribution data of the charging gun and cable, combined with the equivalent thermal resistance coefficient of the equipment. The degradation fusion submodule is configured to perform nonlinear vector fusion of the high-frequency transient component of the weak arc, the broadband impedance mutation rate and the abnormal temperature rise gradient increment to solve the real-time physical degradation deformation of the charging circuit. The probability analysis submodule is configured to substitute physical degradation variables into a dynamic multidimensional safety benchmark model to quantitatively calculate the charging system safety deviation value and the battery thermal runaway probability index.

8. The charging pile risk identification and early warning system according to claim 6, characterized in that, The system also includes a fault diagnosis and high-voltage safety linkage module, which is configured as follows: Real-time collection of charging pile operating parameters to accurately determine the charging pile's operating fault status and fault type; When an operational fault is detected, the target safety disconnection status of the charging pile high-voltage contactor and the charging gun electronic lock is determined according to the fault type. The fault status of the electronic lock is verified by using an electrical detection switch, and the actual connection and on / off status of the equipment is verified by combining the charging gun footage captured by the camera. Correspondingly, a high-voltage power safety disconnection system control strategy and a charging gun physical protection control strategy are generated to complete the safe disconnection of the high-voltage isolation circuit and precise control of the electronic lock status.

9. An electronic device, characterized in that, Including memory and processor; The memory stores computer programs that can run on the processor; When the processor executes the computer program, it implements the steps of the charging pile risk identification and early warning method according to any one of claims 1-5.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the steps of the charging pile risk identification and early warning method according to any one of claims 1-5.

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