Charging pile extreme environment test method based on composite stress simulation, terminal and storage medium

Abstract

The invention belongs to the technical field of charging detection, and particularly relates to a charging pile extreme environment test method based on composite stress simulation, a terminal and a storage medium, and the method comprises the steps: inputting an identifier corresponding to a target test scene through a human-computer interaction interface of a test cabin, and executing a query operation based on the input identifier, retrieving a preset parameter combination from a preset scenarized stress library by using the mapping function; in a closed test cabin, environment parameter data are collected in real time through a distributed sensor array and fed back to a central controller, and the central controller drives an execution component to cooperatively regulate and control parameters such as temperature and humidity, so that the environment parameters are stabilized in a preset range; a sequential stress scheme is generated based on a pre-stored dynamic loading algorithm, an execution component is controlled to realize stress superposition or stress alternation, and a charging load is synchronously adjusted to simulate a composite stress effect under an actual working condition; simulation of multi-factor combined stress and dynamic loading is realized, an actual use fault can be found in advance, and the matching degree of a test result and a real environment is remarkably improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of charging detection, and particularly relates to a charging pile extreme environment test method based on composite stress simulation, a terminal and a storage medium. BACKGROUND

[0002] With the global popularization of electric vehicles, as a key energy supply facility, charging piles need to adapt to diversified extreme environments such as high-cold (low temperature of-40℃+high humidity), coastal (salt spray corrosion+strong electromagnetic interference), plateau (low air pressure+ultraviolet radiation) and the like. However, the existing charging pile test technology has significant limitations: the test process is mostly static loading, the environmental parameters are fixed, and it is difficult to evaluate the response capability of the charging pile to dynamic stresses such as diurnal temperature difference fluctuation and sudden electromagnetic interference; the test dimension is limited to electrical parameters, ignoring the correlation between mechanical structure (such as interface plugging reliability), material performance (such as shell aging resistance) and environmental stress, and it is impossible to comprehensively judge the extreme environment adaptability. SUMMARY

[0003] In view of the above deficiencies of the prior art, the application provides a charging pile extreme environment test method based on composite stress simulation, a terminal and a storage medium.

[0004] In a first aspect, the application provides a charging pile extreme environment test method based on composite stress simulation, comprising: S1, inputting an identifier corresponding to a target test scene through a man-machine interface of a test cabin, performing a query operation based on the input identifier, and retrieving a preset parameter combination from a pre-stored scenario stress library by using a mapping function, the parameters including temperature, humidity, salt spray concentration, electromagnetic interference intensity, air pressure intensity, mechanical vibration frequency and charging load configuration; S2, in the closed test cabin, real-time collection of environmental parameter data is performed by using a distributed sensor array, and the data is fed back to a central controller, the central controller drives an execution component to control temperature, humidity, salt spray concentration, electromagnetic interference intensity, air pressure intensity and mechanical vibration frequency, so that the environmental parameters are stabilized in a preset range corresponding to the preset parameter combination; S3, generating a time-sequenced stress scheme based on a pre-stored dynamic loading algorithm, controlling the execution component to realize stress superposition or stress alternation, and synchronously adjusting the charging load to simulate the composite stress effect under actual working conditions; S4, real-time collection of electrical performance, mechanical structure deformation, material aging and communication state data of the charging pile is performed by using a configured multi-dimensional sensor network; S5, generating failure warning information and a comprehensive evaluation report containing an environmental adaptability score and a life prediction according to the collected electrical performance, mechanical structure deformation, material aging and communication state data of the charging pile. The further improvement of the technical solution is that step S1 comprises: S1.1, input the identifier corresponding to the target test scene through the human-computer interaction interface of the test cabin, the identifier corresponds to an extreme environment scene in the preset scenario stress library; the extreme environment scene includes but is not limited to high-cold area, coastal area, plateau area; S1.2, based on the input identifier, perform a query operation, and use a mapping function retrieve a preset parameter combination from the preset scenario stress library to form a target scene parameter vector , wherein, is the identifier corresponding to the target test scene, is the temperature, is the humidity, is the salt mist concentration, is the electromagnetic interference intensity, is the air pressure intensity, is the mechanical vibration frequency, is the charging load configuration; wherein the charging load configuration includes fast / slow charging switching and multi-car concurrent charging; S1.3, send the retrieved target scene parameter vector to the configuration register of the central controller of the test cabin.

[0005] Further improvement of the technical solution is that step S2 includes: S2.1, arranging a distributed sensor array in the closed test cabin, the sensor array includes a temperature sensor, a humidity sensor, a salt mist concentration sensor, an electromagnetic interference sensor, an air pressure sensor and a vibration sensor, for collecting environmental parameter data in real time; S2.2, feedback the collected environmental parameter data to the central controller, and the central controller calculates the deviation value according to the target scene parameter vector , and drives the execution component to perform regulation and control, so that the environmental parameter is stabilized in the preset range, and the regulation and control process is represented by the following formula: ; , wherein, is the regulation and control amount of the execution component; is the preset parameter value; is the actual measured parameter value; , , are proportional, integral and differential control parameters respectively; S2.3, the execution component includes a refrigeration unit, an ultrasonic humidifier, a salt mist generator, a signal generator, an air pressure pump and a vibration table, and the central controller drives the corresponding execution component to work cooperatively according to the deviation value, so as to realize accurate regulation and control of the environmental parameter.

[0006] The further improvement of the technical solution is that step S3 includes: S3.1, according to the target scene parameter vector , select the stress mode from the dynamic loading algorithm library: ; Wherein, is the target scene parameter vector The scene stress parameter subset; is the stress type that needs to be coordinated, wherein, , indicates different stress types; S3.2, when executing the stress superposition mode, activate the multi-stress coordination according to the time sequence: ; Wherein, is the intensity value of the i-th stress; is the unit impulse function; is the stress trigger time point, , activate the stress; is the weight coefficient; S3.3, when executing the stress alternation mode, control the electromagnetic interference to switch the frequency according to the period: ; Wherein, is the electromagnetic interference frequency at time t; is the preset frequency of the k-th period; is the current time; is the frequency switching period; is the floor function; is the modulo operation; S3.4, synchronize the adjustment of the charging load and the environmental stress: ; Wherein, is the basic vibration acceleration; is the load power change rate; is the load-vibration coupling coefficient.

[0007] The further improvement of the technical solution is that step S4 includes: S4.1, real-time collection of electrical performance data of the charging pile through the configured multi-dimensional sensor network, the electrical performance data including output voltage , output current , power factor , harmonic content , insulation resistance and leakage current ; S4.2, real-time collection of mechanical structure deformation data of the charging pile through the configured multi-dimensional sensor network, the mechanical structure deformation data including charging gun insertion and extraction force , interface wear amount , and structure deformation , the deformation data being calculated by the following formula: ; wherein, is the deformation amount of the structure; is the length after deformation; is the initial length; S4.3, real-time collection of material aging data and communication state data of the charging pile through the configured multi-dimensional sensor network, the material aging data including shell aging degree and cable weather resistance , the communication state data including communication delay , data packet loss rate , and anti-interference ability .

[0008] Further improvement of the technical solution is that step S5 includes: According to the collected electrical performance, mechanical structure deformation, material aging and communication state data of the charging pile, failure warning analysis is performed using a failure threshold model, the failure warning analysis being represented by the following formula: ; wherein, is the comprehensive warning index; is the weight of the i-th performance parameter; is the actual measured value of the i-th performance parameter; is the total number of performance parameters.

[0009] Further improvement of the technical solution is that step S5 further includes: Based on the pre-stored performance degradation model, the service life of the charging pile in the target environment is predicted, the performance degradation model being represented by the following formula: ; wherein, is the current performance level; is the initial performance level; is the decay rate; denotes time.

[0010] Further improvement of the technical solution is that step S5 further includes: A comprehensive evaluation report containing environmental adaptability score, high-risk failure points and improvement suggestions is generated, the environmental adaptability score being calculated by the following formula: ; wherein, an environmental adaptability score; a weight of the jth evaluation index; a score of the jth evaluation index; a total number of evaluation indexes.

[0011] In a second aspect, the present application provides a terminal, comprising: a processor, a memory, wherein, the memory is used to store a computer program, the processor is used to call and run the computer program from the memory, so that the terminal executes the method of the terminal described above.

[0012] In a third aspect, the present application provides a computer storage medium, the computer readable storage medium stores instructions, when the instructions are run on a computer, the computer executes the method described in the above aspects.

[0013] The beneficial effects of the present application are: Dynamic stress loading capability: through dynamic loading algorithm, stress superposition and stress alternation are realized, dynamic changes of actual working conditions such as diurnal temperature difference fluctuation and sudden electromagnetic interference are accurately simulated, and the response capability of the charging pile to dynamic stress is evaluated.

[0014] The test scene is highly close to the actual situation: breaking through the limitation of single factor test in the prior art, the simulation of "multi-factor combined stress + dynamic loading" is realized, the actual use failure can be found in advance, and the matching degree of the test result and the real environment is significantly improved.

[0015] Test dimension is comprehensively covered: breaking through the limitation of traditional electrical parameter test, mechanical structure deformation, material aging and communication state are monitored synchronously, the evaluation of "full life cycle reliability" of the charging pile is realized, and the performance failure caused by neglecting multi-dimensional correlation is avoided. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, for those skilled in the art, other drawings can also be obtained based on these drawings without creative labor.

[0017] Figure 1 It is a schematic flow chart of the method of one embodiment of the present application.

[0018] Figure 2 It is a structural schematic diagram of a terminal provided by an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the objectives, characteristics and advantages of the present application more obvious and easy to understand, the technical solutions in the present application will be clearly and completely described below in combination with the drawings in the specific embodiments. Obviously, the embodiments described below are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terminology used in the description of the present application herein only for the purpose of describing specific embodiments and is not intended to limit the present application.

[0021] Figure 1 is a schematic flow chart of a charging pile extreme environment test method based on composite stress simulation provided by the present application. In the flow chart, the order of the steps can be changed according to different requirements, and some steps can be omitted.

[0022] As shown in Figure 1 , the method comprises: S1, inputting an identifier corresponding to a target test scene through a man-machine interaction interface of a test cabin, performing a query operation based on the input identifier, and retrieving a preset parameter combination from a preset scenario stress library by using a mapping function, the parameters including temperature, humidity, salt spray concentration, electromagnetic interference intensity, air pressure intensity, mechanical vibration frequency and charging load configuration; S2, in the closed test cabin, real-time collection of environmental parameter data by a distributed sensor array and feedback to a central controller, driving and executing components by the central controller to control temperature, humidity, salt spray concentration, electromagnetic interference intensity, air pressure intensity and mechanical vibration frequency, so that the environmental parameters are stabilized in the preset range corresponding to the preset parameter combination; S3, generating a time-sequenced stress scheme based on a pre-stored dynamic loading algorithm, controlling the execution components to realize stress superposition or stress alternation, and synchronously adjusting the charging load to simulate the composite stress action under actual working conditions; S4, real-time collection of electrical performance, mechanical structure deformation, material aging and communication state data of the charging pile by the configured multi-dimensional sensor network; S5, generating failure warning information and a comprehensive evaluation report containing environmental adaptability score and life prediction based on the collected electrical performance, mechanical structure deformation, material aging and communication state data of the charging pile. To facilitate understanding of the present invention, the following description further illustrates the extreme environment testing method for charging piles based on composite stress simulation, using the principle of the present invention and the process of conducting extreme environment testing on charging piles based on composite stress simulation in the embodiments.

[0023] First, step S1 includes: S1.1 Input the identifier corresponding to the target test scenario through the human-computer interaction interface of the test cabin. This identifier corresponds to the extreme environment scenario in the pre-set scenario-based stress library. The extreme environment scenario includes, but is not limited to, high-altitude cold regions, coastal regions, and plateau regions. S1.2 Execute a query operation based on the input identifier, using a mapping function. Preset parameter combinations are retrieved from a pre-defined scenario-based stress library to form the target scenario parameter vector. ,in, The identifier corresponding to the target test scenario. For temperature, For humidity, Salt spray concentration, Electromagnetic interference intensity, For air pressure intensity, For mechanical vibration frequency, Configuration for charging loads; the charging load configuration includes fast / slow charging switching and concurrent charging of multiple vehicles; S1.3, retrieve the target scene parameter vector The configuration register is sent to the central controller of the test chamber.

[0024] Users input scene identifiers through the test chamber's human-machine interface (touchscreen / PC terminal). The system verifies the validity of the identifiers, ensuring they exist in the pre-set scene library (e.g., high-altitude cold regions, coastal regions, plateau regions). The central controller calls the mapping function: def f_map(scene_id): if scene_id == "Hainan Coast": return [35, 90, 3, 80, 101.3, 100, "Fast and slow charging cycle"]# Parameter vector B.

[0025] function Efficient retrieval is achieved through hash table or database queries (response time <100ms), and the parameter range is strictly limited (e.g., salt spray concentration 0 to 5%) to ensure the realism of the simulation.

[0026] The central controller parses parameter vector B and writes it into the configuration register: Temperature register: TW=35℃; Humidity register: RH=90%RH; Load configuration register: L=“fast-slow charging cycle”.

[0027] Register address maps to execution components (e.g. chiller unit address 0x1000 corresponds to temperature control).

[0028] Scene identifier: scene_id = “Hainan coastal”; Parameter vector B: ; Execution flow: User input: Select “Hainan coastal” on the touch screen, and the system calls f_map(“Hainan coastal”).

[0029] Parameter retrieval: The database returns preset parameters (salt spray concentration 3%, vibration frequency 100 Hz).

[0030] Register loading: Central controller configuration: Temperature target value: 35±0.5℃; Salt spray spraying mode: spray for 15 minutes every hour; Load timing: 12 times of fast charging (60kW) + 8 times of slow charging (6.6kW) per day.

[0031] Secondly, step S2 includes: S2.1, arranging a distributed sensor array in the closed test cabin, the sensor array including temperature sensors, humidity sensors, salt spray concentration sensors, electromagnetic interference sensors, air pressure sensors, and vibration sensors, for real-time collection of environmental parameter data; S2.2, feeding back the collected environmental parameter data to the central controller, and the central controller calculates the deviation value according to the target scene parameter vector and drives the execution components to regulate and control, so that the environmental parameters are stabilized within the preset range, and the regulation and control process is represented by the following formula: ; Wherein, is the control amount of the execution component; is the preset parameter value; is the actual measured parameter value; , , are proportional, integral, and differential control parameters, respectively; S2.3, the execution component includes a refrigeration unit, an ultrasonic humidifier, a salt mist generator, a signal generator, an air pressure pump, and a vibration table. The central controller drives the corresponding execution components to work cooperatively according to the deviation value, so as to realize accurate regulation and control of the environmental parameters.

[0032] Hardware configuration: Temperature sensor: platinum resistance PT100 (accuracy ±0.5℃), installed at four corners of the test cabin and the surface of the charging pile; Salt mist concentration sensor: conductivity probe (range 0~5%, accuracy ±0.1%); Vibration sensor: piezoelectric accelerometer (range 0.1~10g, frequency response 10~2000Hz).

[0033] When the threshold value (such as temperature threshold ±0.5℃) is reached, the PID calculation is triggered, and the control amount is output to drive the execution component (such as the refrigeration unit is started).

[0034] The mapping relationship between the components and the parameters is shown in Table 1: Table 1 is the component and parameter mapping table:

[0035] Coordination rules: The humidifier is paused when the salt mist is sprayed (to avoid humidity interference), and in addition, the vibration is enhanced to 3g when fast charging is started (linked with S3 load).

[0036] Test configuration: Target parameters (vector B): ; Initial state: .

[0037] Control process: 1. Temperature control: Deviation ; PID calculation: ; Execution: the refrigeration unit outputs 0.35kW of refrigeration, and the temperature rises to 34.8℃ within 10 minutes (accuracy ±0.5℃); 2. Salt mist control: Deviation ; Control: the salt mist generator is started for 900s of spraying (every hour); After 30 minutes, the concentration stabilizes to 2.9% (error <0.1%).

[0038] 3. Vibration linkage: When fast charging is started ; Vibration enhancement: .

[0039] The effects of the regulation after adjustment are shown in Table 2: Table 2 shows the regulation effect.

[0040] Next, step S3 includes: S3.1, Based on the target scene parameter vector Select stress mode from the dynamic loading algorithm library: ; in, Target scene parameter vector A subset of scene stress parameters; For the stress types that require coordination, among which, , indicating different stress types; S3.2 When executing the stress superposition mode, activate the multi-stress synergy according to the time sequence: ; in, Let be the intensity value of the i-th type of stress; It is a unit impulse function; The stress triggering time point, Activation stress; These are the weighting coefficients; S3.3 When executing the stress alternation mode, control the electromagnetic interference to switch frequencies periodically: ; in, Let be the frequency of the electromagnetic interference at time t; Preset the frequency for the k-th time period; The current time; This refers to the frequency switching cycle; To round down; Modulo operation; S3.4 Synchronously adjust the charging load and link it with environmental stress: ; in, Basic vibration acceleration; The load power change rate; This is the load-vibration coupling coefficient.

[0041] Test configuration: Target parameters: ; Load configuration: Fast charging 60kW (triggered once every 2 hours); Dynamic loading execution: 1. Mode selection (S3.1): if the analysis vector B contains , i.e. salt spray + vibration exists, therefore, select the "superposition" mode; 2. Stress superposition control (S3.2): : activate salt spray ; : superimpose vibration ; # Step function implementation if t>= t_i: stress_active = True# for example, t_i=86400s (24h) to start vibration 3. Electromagnetic frequency jump (S3.3): see Table 3; Table 3 is the electromagnetic frequency jump table:

[0042] 4. Load linkage (S3.4): Fast charging start (t=7200s): ; where, after 30 minutes, resume 2g.

[0043] Subsequently, step S4 includes: S4.1, real-time collection of electrical performance data of the charging pile through the configured multi-dimensional sensor network, the electrical performance data including output voltage , output current , power factor , harmonic content , insulation resistance and leakage current ; S4.2, real-time collection of mechanical structure deformation data of the charging pile through the configured multi-dimensional sensor network, the mechanical structure deformation data including charging gun insertion and extraction force , interface wear amount and structure deformation , the deformation data is calculated by the following formula: ; wherein, is the deformation amount of the structure; is the length after deformation; is the initial length; S4.3, real-time collection of material aging data and communication state data of the charging pile through the configured multi-dimensional sensor network, the material aging data including shell aging degree and cable weather resistance , the communication state data including communication delay , data packet loss rate and anti-interference ability .

[0044] By multi-dimensional sensor network, six types of electrical parameters such as output voltage, current, power factor, etc. (S4.1) are collected in real time to form a complete evaluation system for the energy conversion efficiency and safety of the charging pile. For example, harmonic content monitoring can capture power pollution caused by nonlinear loads (such as triggering an early warning when the total harmonic distortion rate is >5%), insulation resistance testing can detect insulation material aging in advance (such as determining failure when the resistance value is <100MΩ), and leakage current detection can prevent the risk of electric shock (such as immediately shutting down when the leakage current is >3.5mA). This full-parameter coverage monitoring solves the one-sidedness of traditional testing "only measuring voltage and current", ensuring the electrical safety of the charging pile in extreme environments.

[0045] Through devices such as tension sensors and laser displacement sensors, mechanical data such as charging gun insertion and extraction force and interface wear are collected in real time (S4.2), and the deformation of structural components is quantified using formulas. For example, when the charging gun insertion and extraction force exceeds the initial value by 50% (such as increasing from 30N to 45N), the system determines that the interface wear is excessive; when the structural component deformation is >2%, it is prompted to strengthen the support structure. This quantitative analysis method upgrades mechanical reliability evaluation from "experience judgment" to "data-driven", significantly improving fault prediction accuracy.

[0046] Through devices such as colorimeters and communication protocol analyzers, material data such as shell aging degree and cable weather resistance, as well as communication indicators such as communication delay and packet loss rate are collected in real time (S4.3), forming a dual protection for the "physical layer-data layer" of the charging pile. For example, when the shell color difference ΔE is >3 (indicating significant discoloration) or the cable tensile strength decreases by 20%, the system warns of the risk of material failure; when the communication packet loss rate is >1% or the anti-interference ability is <80dBμV / m, it is determined that the communication module needs to be optimized. This synchronous monitoring capability solves the limitations of traditional testing "focusing on electrical and ignoring materials" and "focusing on function and ignoring communication", ensuring the overall reliability of the charging pile in long-term use.

[0047] By synchronously collecting electrical, mechanical, material, and communication data (S4.1 to S4.3), a multi-dimensional feature library of the charging pile health status is constructed. For example, when monitoring "insulation resistance decrease + shell cracking + communication packet loss rate increase" composite anomalies, the system can infer that it is caused by moisture intrusion due to sealing failure, and then accurately locate the root cause of the fault. This fusion analysis method changes maintenance from "passive response" to "active prevention", significantly reducing the failure rate (according to actual measurement data, more than 30% of sudden failures can be reduced).

[0048] Technical effect progressive relationship: The electrical performance monitoring (S4.1) → mechanical deformation analysis (S4.2) → material and communication monitoring (S4.3) form a progressive evaluation system of "function-structure-endurance". Electrical data ensure immediate safety, mechanical data predict structural life, and material and communication data evaluate environmental adaptability. The three work together to solve the three major problems of "single-dimensional evaluation", "short-term behavior inference", and "static performance detection" in traditional testing, and ultimately achieve precise evaluation of the reliability of charging piles throughout their life cycle.

[0049] Then, step S5 includes: According to the collected electrical performance, mechanical structural deformation, material aging, and communication state data of the charging pile, failure threshold model is used for failure warning analysis, which is represented by the following formula: ; Wherein, is the comprehensive warning index; is the weight of the i-th performance parameter; is the actual measured value of the i-th performance parameter; is the total number of performance parameters.

[0050] Further, step S5 also includes: Based on the pre-stored performance degradation model, the service life of the charging pile in the target environment is predicted, and the performance degradation model is represented by the following formula: ; Wherein, is the current performance level; is the initial performance level; is the decay rate; denotes time.

[0051] Finally, step S5 also includes: Generate a comprehensive evaluation report containing environmental adaptability score, high-risk failure points and improvement suggestions, and the environmental adaptability score is calculated by the following formula: ; Wherein, is the environmental adaptability score; is the weight of the j-th evaluation index; is the score of the j-th evaluation index; is the total number of evaluation indexes.

[0052] Based on the performance degradation model and the failure threshold model, the service life of the charging pile in the target environment can be predicted, and a comprehensive evaluation report containing high-risk failure points and improvement suggestions can be generated, providing data support for design optimization and operation and maintenance planning, and reducing the failure rate.

[0053] This embodiment takes the simulation of the "coastal port" extreme environment as an example to illustrate the specific implementation process of the charging pile extreme environment test method based on composite stress simulation, including the implementation mode of stress superposition and stress alternation.

[0054] Step S1: scene parameter configuration S1.1, the operator selects the "coastal area" test scene through the man-machine interaction interface, and the system automatically matches the pre-stored typical parameter combination in the scene stress library.

[0055] S1.2, the preset parameter combination called is: ; Among them, the temperature T is in the range of 30 to 40℃ (accuracy ±0.5℃), and the humidity RH is in the range of 80% to 95% RH (accuracy ±2% RH); The salt spray concentration Salt is 3% (mass fraction, accuracy ±0.5%), and the intermittent spraying mode is adopted (spraying for 15 minutes per hour); The electromagnetic interference EMI is in the 2.4GHz frequency band, and the intensity is 80dBμV / m (accuracy ±5dBμV / m); The mechanical vibration frequency F is 100Hz (accuracy ±5Hz), and the acceleration is 2g (accuracy ±0.1g); The charging load L is configured as "12 times of fast charging (60kW) + 8 times of slow charging (6.6kW) per day", and the load switching interval is 1 hour.

[0056] Step S2: composite environment simulation S2.1, arrange a distributed sensor array in the closed test cabin: Temperature sensor (arranged on the surface of the charging pile and at different heights in the cabin, sampling frequency 10Hz); Humidity sensor (same position as temperature sensor, sampling frequency 10Hz); Salt spray concentration sensor (installed 30cm in front of the charging pile interface, sampling frequency 5Hz); Electromagnetic interference sensor (covering 1MHz to 6GHz frequency band, sampling frequency 1kHz); Air pressure sensor (central top of the cabin, sampling frequency 1Hz); Vibration sensor (fixed to the bottom of the charging pile, sampling frequency 1kHz).

[0057] S2.2, the central controller adjusts the environmental parameters in real time through the PID algorithm, and the adjustment formula is: ; Among them, the proportional coefficient , the integral coefficient , and the differential coefficient When the measured temperature (Preset) When ), the calculation yields The controller drives the heating device to increase its power until the temperature stabilizes at 35℃±0.5℃.

[0058] S2.3, Coordinated operation of execution components: Refrigeration units / heating systems maintain temperature; Ultrasonic humidifiers maintain humidity; The salt spray generator sprays a 3% sodium chloride solution through a nozzle (0.5 mm in diameter); The signal generator transmits a 2.4 GHz interference signal via a log-periodic antenna; The vibration table drives the charging pile to vibrate through a rigid connection.

[0059] Step S3: Dynamic stress synergistic loading (including stress superposition and alternation): S3.1 The system generates a time-series stress scheme based on a dynamic loading algorithm, specifically as follows: Stress superposition phase: Electromagnetic interference is simultaneously enhanced during salt spraying from 9:00 to 11:00 daily. Stress alternation phase: The vibration frequency is switched every hour (100Hz and 200Hz alternate).

[0060] S3.2 The control logic for stress superposition and alternation is implemented through the following formula: ; Example of stress superposition (9:00-11:00): Salt spray stress , superimposed weight Electromagnetic interference stress , superimposed weight Total stress (That is, the salt spray concentration is increased to 3.6%, and the electromagnetic interference is enhanced to 120dBμV / m).

[0061] Example of stress alternation (switching every hour): Vibration frequency At 100Hz ( ) and 200Hz ( Alternating between ) hour , .

[0062] S3.3, Synchronous adjustment of charging load, the formula is: ; in, (Slow charging), fast charging , The value is 1, therefore Furthermore, during vibration frequency switching, It is 1.2, that is (Simulated load impact).

[0063] Step S4, Multi-dimensional performance monitoring: S4.1 Electrical performance data acquisition: The Hall sensor acquires the output voltage (accuracy ±0.1V) and current (±0.01A). Power analyzer calculates power factor ( , hour, ); Harmonic analyzer measures total harmonic distortion (THD). (fundamental wave Second harmonic , ).

[0064] S4.2 Mechanical property acquisition: The tension sensor measures the insertion and extraction force of the charging gun (initially 30N, triggering an alarm when it rises to 45N after wear). Laser displacement sensor detects interface wear (accuracy ±0.01mm). Strain gauges monitor structural deformation; the formula is as follows: (Initial length) After deformation ,but ).

[0065] S4.3, Material and Communication Performance Acquisition: A colorimeter measures casing aging; a communication protocol analyzer records latency and packet loss rate.

[0066] Step S5, Data Analysis and Early Warning: S5.1 Failure Warning Analysis: The formula for the comprehensive early warning index is: ,in: Interface contact resistance (weight) ), Packet loss rate ( ); (Threshold 15, approaching warning level).

[0067] S5.2 Lifetime Prediction: Performance degradation model The initial insulation resistance attenuation rate Then after 300 days (Safety threshold 100MΩ, estimated lifespan approximately 400 days).

[0068] S5.3 Comprehensive Evaluation Report: Environmental adaptability score Among them, electrical performance ( ), mechanical properties , ), communication performance ( ),but The high-risk failure point is "accelerated interface wear caused by salt spray and vibration", and it is recommended to "replace the interface with 316 stainless steel".

[0069] Figure 2 This is a schematic diagram of a terminal 200 provided in an embodiment of the present invention. The terminal 200 can be used to execute the extreme environment testing method for charging piles based on composite stress simulation provided in an embodiment of the present invention.

[0070] The terminal 200 may include a processor 210, a memory 220, and a communication module 230. These components communicate via one or more buses. Those skilled in the art will understand that the server structure shown in the figure does not constitute a limitation of the present invention. It may be a bus topology or a star topology, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0071] The memory 220 can be used to store the execution instructions of the processor 210. The memory 220 can be implemented by any type of volatile or non-volatile storage terminal or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. When the execution instructions in the memory 220 are executed by the processor 210, the terminal 200 is able to perform some or all of the steps in the above method embodiments.

[0072] The processor 210 serves as the control center of the storage terminal, connecting various parts of the electronic terminal via various interfaces and lines. It executes software programs and / or modules stored in the memory 220, and calls data stored in the memory to perform various functions of the electronic terminal and / or process data. The processor can be composed of integrated circuits (ICs), such as a single packaged IC or multiple packaged ICs with the same or different functions connected together. For example, the processor 210 may consist only of a central processing unit (CPU). In this embodiment of the invention, the CPU may have a single processing core or include multiple processing cores.

[0073] The communication module 230 is used to establish a communication channel, enabling the storage terminal to communicate with other terminals. It receives user data sent by other terminals or sends user data to other terminals.

[0074] The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, which, when executed, may include some or all of the steps provided in the embodiments of the present invention. The storage medium may be a magnetic disk, an optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0075] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium such as a USB flash drive, mobile hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, or other media capable of storing program code. It includes several instructions to cause a computer terminal (which may be a personal computer, server, or a second terminal, network terminal, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0076] The same or similar parts between the various embodiments in this specification can be referred to mutually. In particular, the terminal embodiments are basically similar to the method embodiments, so the description is relatively simple, and the relevant parts can be referred to the description in the method embodiments.

[0077] Although the present invention has been described in detail with reference to the accompanying drawings and preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made to the embodiments of the present invention by those skilled in the art without departing from the spirit and essence of the invention, and such modifications or substitutions should all be within the scope of the present invention. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should also be covered within the protection scope of the present invention.

Claims

1. A method for testing charging piles in extreme environments based on composite stress simulation, characterized in that, include: S1. Input the identifier corresponding to the target test scenario through the human-computer interaction interface of the test chamber, perform a query operation based on the input identifier, and use the mapping function to retrieve the preset parameter combination from the preset scenario stress library. The parameters include temperature, humidity, salt spray concentration, electromagnetic interference intensity, air pressure intensity, mechanical vibration frequency and charging load configuration. S2. In the closed test chamber, environmental parameter data is collected in real time through a distributed sensor array and fed back to the central controller. The central controller drives the actuators to regulate the temperature, humidity, salt spray concentration, electromagnetic interference intensity, air pressure intensity and mechanical vibration frequency, so that the environmental parameters are stabilized within the preset range corresponding to the preset parameter combination. S3. Based on the pre-stored dynamic loading algorithm, a time-sequential stress scheme is generated, and the execution components are controlled to achieve stress superposition or stress alternation, and the charging load is adjusted synchronously to simulate the combined stress effect under actual working conditions. S4. Real-time data collection of electrical performance, mechanical structure deformation, material aging, and communication status of the charging pile is achieved through a configured multi-dimensional sensor network. S5. Generate failure warning information and a comprehensive evaluation report including environmental adaptability score and life prediction based on the collected data on the electrical performance, mechanical structure deformation, material aging and communication status of the charging pile.

2. The extreme environment testing method for charging piles based on composite stress simulation according to claim 1, characterized in that, Step S1 includes: S1.1 Input the identifier corresponding to the target test scenario through the human-computer interaction interface of the test cabin. This identifier corresponds to the extreme environment scenario in the pre-set scenario-based stress library. The extreme environment scenario includes, but is not limited to, high-altitude cold regions, coastal regions, and plateau regions. S1.2 Execute a query operation based on the input identifier, using a mapping function. Preset parameter combinations are retrieved from a pre-defined scenario-based stress library to form the target scenario parameter vector. ,in, The identifier corresponding to the target test scenario. For temperature, For humidity, Salt spray concentration, Electromagnetic interference intensity, For air pressure intensity, For mechanical vibration frequency, Configuration for charging loads; the charging load configuration includes fast / slow charging switching and concurrent charging of multiple vehicles; S1.3, retrieve the target scene parameter vector The configuration register is sent to the central controller of the test chamber.

3. The extreme environment testing method for charging piles based on composite stress simulation according to claim 2, characterized in that, Step S2 includes: S2.

1. A distributed sensor array is arranged in the closed test chamber. The sensor array includes a temperature sensor, a humidity sensor, a salt spray concentration sensor, an electromagnetic interference sensor, a barometric pressure sensor, and a vibration sensor to collect environmental parameter data in real time. S2.2 The collected environmental parameter data is fed back to the central controller, which then uses the target scene parameter vector to... The deviation value is calculated, and the actuator is driven to adjust and stabilize the environmental parameters within a preset range. The adjustment process is expressed by the following formula: ； in, The amount of control for the execution component; These are preset parameter values; These are the actual measured parameter values; , , These are the proportional, integral, and derivative control parameters, respectively. S2.3 The actuators include a refrigeration unit, an ultrasonic humidifier, a salt spray generator, a signal generator, an air pump, and a vibration table. The central controller drives the corresponding actuators to work together according to the deviation value to achieve precise control of environmental parameters.

4. The extreme environment testing method for charging piles based on composite stress simulation according to claim 2, characterized in that, Step S3 includes: S3.1, Based on the target scene parameter vector Select stress mode from the dynamic loading algorithm library: ； in, Target scene parameter vector A subset of scene stress parameters; For the stress types that require coordination, among which, , indicating different stress types; S3.2 When executing the stress superposition mode, activate the multi-stress synergy according to the time sequence: ； in, Let be the intensity value of the i-th type of stress; It is a unit impulse function; The stress triggering time point, Activation stress; These are the weighting coefficients; S3.3 When executing the stress alternation mode, control the electromagnetic interference to switch frequencies periodically: ； in, Let be the frequency of the electromagnetic interference at time t; Preset the frequency for the k-th time period; The current time; This refers to the frequency switching cycle; To round down; Modulo operation; S3.4 Synchronously adjust the charging load and link it with environmental stress: ； in, Basic vibration acceleration; The load power change rate; This is the load-vibration coupling coefficient.

5. The extreme environment testing method for charging piles based on composite stress simulation according to claim 1, characterized in that, Step S4 includes: S4.1 The electrical performance data of the charging pile is collected in real time through a configured multi-dimensional sensor network. The electrical performance data includes output voltage. Output current Power factor Harmonic content Insulation resistance and leakage current ; S4.2 Real-time acquisition of mechanical structural deformation data of the charging pile through a configured multi-dimensional sensor network, including the insertion and extraction force of the charging gun. Interface wear and structural component deformation The deformation data is calculated using the following formula: ;in, The deformation of the structural component; The length after deformation; This is the initial length; S4.3 Real-time data collection of material aging and communication status of the charging pile is achieved through a configured multi-dimensional sensor network. Material aging data includes the degree of aging of the outer casing. and cable weather resistance Communication status data includes communication delay. Data packet loss rate and anti-interference ability .

6. The extreme environment testing method for charging piles based on composite stress simulation according to claim 5, characterized in that, Step S5 includes: Based on the collected data on the electrical performance, mechanical structural deformation, material aging, and communication status of the charging piles, a failure warning analysis is performed using a failure threshold model. The failure warning analysis is expressed by the following formula: ； in, This is a comprehensive early warning index; The weight of the i-th performance parameter; This represents the actual measured value of the i-th performance parameter; This represents the total number of performance parameters.

7. The extreme environment testing method for charging piles based on composite stress simulation according to claim 5, characterized in that, Step S5 also includes: The lifespan of charging piles under target environments is predicted based on a pre-existing performance degradation model, which is expressed by the following formula: ； in, This represents the current performance level. This represents the initial performance level. The attenuation rate; Indicates time.

8. The extreme environment testing method for charging piles based on composite stress simulation according to claim 5, characterized in that, Step S5 also includes: Generate a comprehensive assessment report that includes an environmental adaptability score, high-risk failure points, and improvement recommendations. The environmental adaptability score is calculated using the following formula: ； in, Assess environmental adaptability. Let be the weight of the j-th evaluation indicator; The score for the j-th evaluation indicator; This represents the total number of evaluation indicators.

9. A terminal, characterized in that, include: processor; Memory used to store the processor's execution instructions; The processor is configured to perform the method according to any one of claims 1-8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-8.

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