Method for realizing multi-source input self-adaptive security interface of container electricity storage equipment

Abstract

The invention provides a container power storage equipment multi-source input adaptive security interface implementation method, which comprises the following steps: monitoring electrical parameter changes of renewable energy and power grid input in real time, deploying a quantum noise filtering channel to suppress microscopic interference, and embedding a dynamic compensator based on dielectric characteristics of an insulating material; an intelligent control algorithm is combined with chaotic disturbance injection to identify overvoltage, undervoltage and overcurrent anomalies, and a three-dimensional state matrix fused with multi-dimensional risks is generated; according to a result, an ultra-fast buffer module is activated to realize transient energy buffer, and an adaptive impedance matching circuit is driven to dynamically reconstruct topology and remove residual signals; based on a modularized hardware integrated distributed thermal management system, thermal management is optimized through nanoscale temperature monitoring and intelligent heat dissipation distribution, a metastable state is embedded to eliminate trigger stable logic output, protection action and strategy time sequence are coordinated, and multi-source input seamless integration and full life cycle protection are achieved.

Description

Technical Field

[0001] This invention relates to the field of energy storage, and in particular to a method for implementing a multi-source input adaptive safety interface for containerized energy storage equipment. Background Technology

[0002] When containerized energy storage devices integrate renewable energy with grid input, traditional interfaces face monitoring distortion issues caused by differences in the electrical characteristics of multiple sources. Existing technologies lack effective filtering methods for quantum-level noise in input parameters, making high-frequency signal acquisition susceptible to microscopic interference. Dynamic changes in the dielectric properties of insulating materials can lead to accumulated dielectric losses from fixed-intensity high-frequency excitation, accelerating equipment aging. Anomaly identification relies on static threshold analysis, which struggles to capture complex fault modes masked by harmonic interference, and the rigid impedance matching topology is prone to transient energy surges when the renewable energy share changes abruptly. Thermal management strategies often employ global temperature equalization designs, failing to respond to drastic changes in local temperature gradients, while critical-state metastability issues arising from protection mechanism switching can lead to logical misjudgments. Current systems have not yet achieved synergistic optimization of multi-source fluctuation trend prediction and impedance-temperature coupling analysis, resulting in weak fault tracing capabilities and limiting the reliability and adaptability of the safety interface. Summary of the Invention

[0003] This invention proposes a method for implementing a multi-source input adaptive safety interface for containerized energy storage equipment, comprising: S1. Real-time monitoring of changes in electrical parameters from multiple sources, including renewable energy sources and grid inputs; S2. Dynamically analyze the input status based on intelligent control algorithms to identify overvoltage, undervoltage, or overcurrent abnormalities; S3. Automatically adjust the protection mechanism based on the analysis results and trigger abnormal isolation protection; S4. Achieve seamless integration of multiple input sources through modular hardware design and simultaneously optimize interface management strategies.

[0004] The real-time monitoring of changes in multi-source input electrical parameters includes: Construct an input source characteristic monitoring channel to continuously collect voltage, current, and frequency parameters; An embedded dielectric loss dynamic compensator is used to adjust the intensity of the high-frequency excitation signal according to the real-time dielectric properties of the insulating material. Establish an electrical parameter change rate tracking model to capture transient fluctuation characteristics.

[0005] The dynamic analysis of input state based on intelligent control algorithms includes: Initialize the input state analysis engine and load the impedance characteristic database of renewable energy and grid input; Perform harmonic interference spectrum scanning and eliminate inherent harmonic components through an adaptive harmonic compensation unit; Injecting chaotic disturbance signals into the analysis engine enhances the identification of potential fault modes under stable operating conditions; Generate a three-dimensional state matrix that includes overvoltage risk level, undervoltage duration and overcurrent sudden change gradient; Based on the state matrix, output isolation protection priority instructions and synchronously update protection threshold adjustment parameters.

[0006] The generation of the three-dimensional state matrix includes: The fluctuation trend prediction interval of the input source is divided, and the voltage deviation probability of the next three fluctuation cycles is calculated using an artificial intelligence prediction model. By associating historical fault tracing data, the fault tree analysis engine is enhanced with deep learning to label composite anomaly correlation factors. The signal-to-noise ratio correction coefficients from the quantum noise filtering channel are used to perform weighted calibration of the current abrupt gradient. The calibrated electrical parameters are mapped to the impedance-temperature joint coordinate system, and the state matrix with timestamps is output.

[0007] The automatic adjustment protection mechanism includes: Activate the ultra-fast response buffer module to provide millisecond-level energy buffering in the early stages of transient fluctuations in the input source; Drive the adaptive source impedance matching circuit to reconstruct the topology and dynamically match the current input source impedance characteristics; Deploy an impedance cleanup feedback loop to remove residual impedance signals after impedance matching switching; Invoke the conflict avoidance synchronization algorithm to coordinate the parallel execution timing of isolation protection actions and interface management strategies.

[0008] The topology reconstruction of the drive adaptive source impedance matching circuit includes: Real-time calculation of the phase difference of the input source impedance spectrum to generate impedance matching evaluation index; When the proportion of renewable energy input is detected to exceed the threshold, topology reconfiguration of redundant link backup paths is initiated. External noise is isolated by electromagnetic interference shielding technology, and the residual common-mode interference during the impedance matching process is collected. Based on the residual feedback activation of the adaptive common-mode noise suppression network, the output impedance matching completes the verification signal to the buffer module.

[0009] The aforementioned seamless integration of multiple input sources through modular hardware design includes: Configure a distributed thermal management system node array to monitor the surface temperature gradient of the power module in real time. Deploy an intelligent thermal distribution equalizer to dynamically adjust the heat dissipation path based on temperature gradient data; Embedded metastable elimination triggers force the logic output to be determined when the protection mechanism switches to a critical state. The runtime environment for integrating fault tracing algorithms is used to persistently store abnormal event logs and impedance matching historical trajectories.

[0010] The configured distributed thermal management system node array includes: Deploy nanoscale temperature sensor clusters in a modular hardware boundary layer; Construct a heat conduction path optimization model and calculate the optimal heat dissipation vector based on real-time thermal imaging data; When a local temperature gradient is detected to exceed the safety threshold, the intelligent thermal distribution equalizer is activated to redistribute the heat dissipation flow. The temperature weighting coefficient in the interface management strategy is updated synchronously and fed back to the protection mechanism adjustment closed loop.

[0011] The present invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-8.

[0012] This invention also proposes a multi-source input adaptive safety interface system for containerized energy storage equipment, comprising: The electrical parameter monitoring unit is used to capture real-time data on voltage, current and frequency changes of renewable energy and grid input, and integrates a quantum noise filtering channel and a dynamic dielectric loss compensator. The intelligent analysis and control unit has a built-in input state analysis engine and a chaotic disturbance injection module, and outputs a three-dimensional state matrix including overvoltage risk level, undervoltage duration and overcurrent sudden change gradient. The protection mechanism execution unit includes an ultra-fast response buffer module, an adaptive source impedance matching circuit, and an impedance cleanup feedback loop, which triggers isolation protection based on the state matrix. The hardware integrated management unit deploys a distributed thermal management system and metastability elimination triggers through a modular structure, and synchronously executes conflict avoidance synchronization algorithms to optimize interface strategies.

[0013] This invention significantly improves the monitoring purity of multi-source input electrical parameters and suppresses interference from microscopic noise on high-frequency signal acquisition through the synergistic effect of a quantum noise filtering channel and a dynamic dielectric loss compensator. Simultaneously, it optimizes the excitation intensity based on the real-time state of the insulating material to avoid equipment damage caused by dielectric loss. Based on chaotic perturbation injection and three-dimensional state matrix analysis, the system can accurately identify combined anomalies of overvoltage, undervoltage, and overcurrent under harmonic shielding, enhancing the ability to predict potential faults under stable operating conditions. An ultra-fast response buffer module, combined with an adaptive source impedance matching topology, achieves millisecond-level energy buffering and dynamic impedance reconstruction, effectively mitigating transient impacts from sudden changes in renewable energy input. The distributed thermal management system eliminates the risk of localized overheating through nanometer-level temperature gradient sensing and intelligent heat dissipation vector allocation. Metastable elimination triggers force stable logic outputs, and conflict avoidance algorithms ensure the timing coordination of protection actions and strategy execution. A deeply integrated fault tracing environment persistently stores abnormal trajectories, providing full lifecycle optimization support for multi-source input adaptive safety interfaces. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating a method for implementing a multi-source input adaptive safety interface for containerized energy storage equipment, as proposed in this invention. Detailed Implementation

[0015] refer to Figure 1 This invention proposes a method for implementing a multi-source input adaptive safety interface for containerized energy storage equipment, specifically including: S1. Real-time monitoring of changes in electrical parameters from multiple input sources, including renewable energy and grid input. Specifically, this includes: constructing an input source characteristic monitoring channel to continuously collect voltage, current, and frequency parameters; deploying a quantum noise filtering channel to suppress quantum fluctuation noise in the monitoring circuit; embedding a dynamic dielectric loss compensator to adjust the intensity of the high-frequency excitation signal based on the real-time dielectric properties of the insulating material; and establishing an electrical parameter change rate tracking model to capture transient fluctuation characteristics.

[0016] Specifically, firstly, an input source characteristic monitoring channel is constructed. This channel continuously collects voltage, current, and frequency parameters from renewable energy sources (such as wind or solar power) and grid inputs via a high-precision sensor array. These parameters are transmitted to the central processing unit in real time. Next, a quantum noise filtering channel is deployed. This channel utilizes a quantum-level signal processor to detect and filter out microscopic noise interference in the monitoring loop, ensuring data purity. Simultaneously, a dynamic dielectric loss compensator is embedded. This dynamic compensator analyzes the real-time dielectric properties of the insulating material, such as changes in dielectric constant, using a microcontroller to dynamically adjust the output intensity of the high-frequency excitation signal to adapt to the effects of material aging or environmental humidity. Finally, an electrical parameter change rate tracking model is established. This model uses algorithms to analyze parameter fluctuation rates, identify transient events such as voltage spikes, and output the data to the analysis engine. The entire process is implemented through closed-loop control, ensuring the continuity and accuracy of monitoring.

[0017] Specifically, the input source characteristic monitoring channel is an integrated sensor network device used to acquire voltage, current, and frequency data in real time; the quantum noise filtering channel includes a quantum signal processor and filtering circuitry, dedicated to eliminating microscopic noise in the monitoring system; the dielectric loss dynamic compensator is a microcontroller-based adaptive adjustment device that dynamically adjusts the signal output by analyzing the characteristics of the insulating material; and the electrical parameter change rate tracking model is an algorithm module that identifies transient events by calculating the rate of parameter change. These components work together to ensure that the monitoring process is efficient and interference-resistant.

[0018] Taking wind power input as an example: When the input voltage of a wind turbine fluctuates due to sudden changes in wind speed, the input source characteristic monitoring channel first collects voltage and current data; the quantum noise filtering channel immediately filters out quantum-level noise caused by electromagnetic interference; the dielectric loss dynamic compensator detects the increase in dielectric loss of the insulating material at high temperatures and automatically reduces the intensity of the high-frequency excitation signal to prevent signal distortion; the electrical parameter change rate tracking model captures the characteristics of voltage fluctuations and generates fluctuation reports, thereby significantly improving monitoring accuracy, reducing the impact of noise on data, and ensuring the stability of parameter acquisition. In the wind power example, due to the synergistic effect of the compensator and the filtering channel, high-frequency signal distortion is effectively suppressed, thereby avoiding false alarms and improving overall reliability.

[0019] S2. Based on intelligent control algorithms, dynamically analyze the input state to identify overvoltage, undervoltage, or overcurrent anomalies. Specifically, this includes: initializing the input state analysis engine and loading impedance characteristic databases of renewable energy and grid inputs; performing harmonic interference spectrum scanning and eliminating inherent harmonic components through an adaptive harmonic compensation unit; injecting chaotic disturbance signals into the analysis engine to enhance the identification of potential fault modes under stable operating conditions; generating a three-dimensional state matrix containing overvoltage risk level, undervoltage duration, and overcurrent abrupt gradient; the operation of generating the three-dimensional state matrix includes: dividing the input source fluctuation trend prediction interval and using an artificial intelligence prediction model to calculate the voltage deviation probability of future fluctuation cycles; associating historical fault source data and using deep learning to enhance the fault tree analysis engine to label composite anomaly correlation factors; fusing the signal-to-noise ratio correction coefficient output from the quantum noise filtering channel to perform weighted calibration of the current abrupt gradient; mapping the calibrated electrical parameters to an impedance-temperature joint coordinate system and outputting a timestamped state matrix; and outputting isolation protection priority commands based on the state matrix, synchronously updating protection threshold adjustment parameters.

[0020] Specifically, first, the input state analysis engine is initialized, which calls a pre-stored impedance feature database containing typical impedance data for renewable energy sources such as wind and solar power, as well as the power grid. Then, a harmonic interference spectrum scan is performed: the adaptive harmonic compensation unit analyzes the input signal spectrum, locates and eliminates inherent harmonic components such as the third harmonic, and uses digital filters to achieve real-time compensation. Next, a chaotic disturbance signal is injected into the analysis engine. This signal simulates random interference and activates potential fault detection during stable system operation. When generating the three-dimensional state matrix, the input source fluctuation trend prediction interval is first divided. Artificial intelligence prediction models, such as LSTM neural networks, calculate the probability of future voltage deviation based on historical data. Simultaneously, historical fault tracing data is correlated, and deep learning algorithms train the fault tree analysis engine, labeling composite anomaly correlation factors such as the correlation between overvoltage and temperature rise. The signal-to-noise ratio correction coefficient of the quantum noise filtering channel is fused to perform weighted calibration of the current mutation gradient, improving data reliability. Finally, the calibration parameters are mapped to the impedance-temperature joint coordinate system to generate a timestamped state matrix. The engine outputs isolation protection commands based on the matrix and updates protection threshold parameters to ensure dynamic response.

[0021] Specifically, the input state analysis engine serves as the core of the intelligent control system, including a database loading module and an algorithm processor; the adaptive harmonic compensation unit comprises a spectrum analyzer and a digital filter to eliminate harmonic distortion; the chaotic disturbance signal is a randomly generated test signal used to simulate fault scenarios; the three-dimensional state matrix is ​​a data structure integrating overvoltage risk, undervoltage duration, and overcurrent gradient; and the impedance-temperature joint coordinate system is a multi-dimensional mapping system that correlates electrical and thermal parameters. These elements enable dynamic analysis and enhance fault identification capabilities.

[0022] Taking a solar input scenario as an example: when the photovoltaic array experiences voltage undervoltage due to cloud cover, the input state analysis engine loads the solar impedance database; the harmonic compensation unit scans and eliminates harmonics generated by the inverter; after chaotic disturbance signals are injected, the engine identifies potential overcurrent risks; when generating a three-dimensional state matrix, the prediction model divides future fluctuation ranges and calculates the probability of voltage drop; it correlates historical fault data, such as the previous overheating event, and marks the correlation factors; it fuses the signal-to-noise ratio coefficient to calibrate the current gradient; after mapping to the coordinate system, the output matrix commands prioritize isolating undervoltage regions and adjusting protection thresholds, thereby achieving accurate identification of composite anomalies and reducing misjudgments caused by harmonic interference. In the solar example, chaotic disturbances enhance the capture of latent faults, and the state matrix provides multi-dimensional decision-making basis, making protection responses more timely.

[0023] S3. Automatically adjust the protection mechanism based on the analysis results and trigger abnormal isolation protection. Specifically, this includes: first, activating the ultra-fast response buffer module to provide millisecond-level energy buffering in the early stages of transient fluctuations in the input source; driving the adaptive source impedance matching circuit to reconstruct the topology and dynamically match the current input source impedance characteristics; the topology reconstruction operation includes: real-time calculation of the phase difference of the input source impedance spectrum to generate impedance matching degree evaluation index; when the proportion of renewable energy input is detected to exceed the threshold, initiating topology reconstruction of redundant link backup paths; isolating external noise through electromagnetic interference shielding technology and collecting the residual amount of common-mode interference during the impedance matching process; activating the adaptive common-mode noise suppression network based on the residual amount feedback and outputting the impedance matching completion verification signal to the buffer module; deploying the impedance purification feedback loop to clear the residual impedance signal after impedance matching switching; and calling the conflict avoidance synchronization algorithm to coordinate the parallel execution timing of isolation protection actions and interface management strategies.

[0024] Specifically, upon startup, the ultra-fast response buffer module is activated. This module provides energy buffering during transient fluctuations in the input source, such as the initial stage of a sudden rise in grid voltage, using supercapacitors to store or release electrical energy and stabilize the system. Next, the adaptive source impedance matching circuit is driven to reconstruct the topology: the phase difference of the input source impedance spectrum is calculated in real time, generating matching degree evaluation indicators such as the matching percentage value; if the proportion of renewable energy input exceeds the standard, such as an excessively high proportion of wind power, redundant link backup paths are activated, adding parallel circuit branches; electromagnetic interference shielding technology, such as metal shielding, is used to isolate noise, and the residual common-mode interference during the matching process is collected; based on the residual feedback, the adaptive common-mode noise suppression network is activated, outputting a verification signal to the buffer module. Simultaneously, an impedance purification feedback loop is deployed, which clears residual impedance signals after topology switching, ensuring circuit cleanliness. Finally, a conflict avoidance synchronization algorithm is invoked, which coordinates the timing of isolation protection actions, such as disconnecting fault loops, and interface management strategies to prevent execution conflicts.

[0025] Among them, the ultra-fast response buffer module is a supercapacitor-based energy storage unit that provides instantaneous buffering; the adaptive source impedance matching circuit is a reconfigurable electronic circuit used to dynamically adjust impedance; the impedance cleanup feedback loop is a signal clearing mechanism used to eliminate topology switching residues; and the conflict avoidance synchronization algorithm is a timing coordination program used to prevent protection action conflicts.

[0026] Taking a scenario with mixed input of power grid and biomass energy as an example: when a sudden increase in biomass energy input leads to impedance mismatch, the buffer module instantly absorbs excess energy; the topology reconstruction operation calculates the phase difference and generates a low matching degree index; due to the excessive proportion of renewable energy, redundant links are activated; electromagnetic shielding isolates external noise and collects residual interference; after the noise suppression network is activated, a verification signal is output; the purification loop clears residual signals; and the conflict avoidance algorithm coordinates the isolation actions, prioritizing the protection of the power grid interface, thereby achieving rapid energy buffering and accurate impedance matching, reducing the damage from transient fluctuations. In the biomass energy example, redundant paths and noise suppression ensure stable matching, while conflict avoidance improves the efficiency of parallel operations.

[0027] S4. Achieve seamless integration of multiple input sources through modular hardware design and simultaneously optimize interface management strategies. Specific optimization of interface management strategies includes: configuring a distributed thermal management system node array to monitor the surface temperature gradient of the power module in real time; configuring the node array involves: deploying a nanoscale temperature sensor cluster at the modular hardware boundary layer; constructing a heat conduction path optimization model and calculating the optimal heat dissipation vector based on real-time thermal imaging data; when a local temperature gradient exceeds a safety threshold, activating an intelligent thermal distribution equalizer to redistribute heat dissipation flow; synchronously updating the temperature weight coefficients in the interface management strategy and feeding them back to the protection mechanism adjustment loop; deploying an intelligent thermal distribution equalizer to dynamically adjust the heat dissipation path based on temperature gradient data; embedding a metastable state elimination trigger to force a determination of the logic output when the protection mechanism switches to a critical state; integrating the fault tracing algorithm runtime environment and persistently storing abnormal event logs and impedance matching historical trajectories.

[0028] Specifically, the process begins with configuring a distributed thermal management system node array. This array deploys a cluster of nanoscale temperature sensors at the modular hardware boundary layer to collect real-time surface temperature data of the power modules. A heat conduction path optimization model is constructed, which calculates the optimal heat dissipation vector, such as fan speed direction, based on thermal imaging data and transmits this data to the control unit. When a local temperature gradient exceeding the limit is detected, such as when a module overheats, an intelligent thermal distribution equalizer is activated to reallocate heat dissipation flow, such as increasing the coolant flow rate. The temperature weight coefficients of the interface management strategy are updated synchronously and fed back to the protection mechanism to form a closed-loop adjustment. Simultaneously, the intelligent thermal distribution equalizer is deployed to dynamically optimize the heat dissipation path. A metastable state elimination trigger is embedded, which forces the output of a stable logic signal when the protection mechanism switches to a critical state, such as the transition from normal to isolation. Finally, a fault tracing algorithm runtime environment is integrated, and anomaly logs and impedance matching history are persistently stored for subsequent analysis.

[0029] Specifically, the distributed thermal management system node array is a sensor network used to monitor temperature distribution; the intelligent thermal distribution equalizer is a flow regulation device used to dynamically allocate heat dissipation; the metastable elimination trigger is a logic stabilizer used to force the output of a deterministic signal; and the fault tracing algorithm runtime environment is a data storage and analysis platform.

[0030] Taking tidal energy input scenarios as an example: During the integration of tidal power generation equipment, node array sensors detect uneven temperature gradients in the power modules; the optimization model calculates the heat dissipation vector; due to local overheating, the equalizer redistributes flow to hotspot areas; the strategy weight coefficients are updated; the metastability elimination trigger maintains stable output during protection switching; and the fault tracing environment stores event logs, thereby achieving efficient thermal management and hardware integration to prevent overheating damage. In the tidal energy example, heat dissipation optimization and the equalizer improve the uniformity of heat distribution, while the triggers ensure logic stability.

[0031] The present invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described in the claims.

[0032] Specifically, storage media such as solid-state drives (SSDs) store computer program code. Once loaded by the processor, this code executes the aforementioned method steps sequentially: including monitoring multi-source input parameters, analyzing input states, adjusting protection mechanisms, and optimizing hardware integration. During program initialization, the parameter monitoring module is invoked to collect data; then, a dynamic analysis algorithm is run to identify anomalies; next, protection mechanism adjustments are triggered; and finally, hardware integration strategies are managed. All steps are implemented through processor instruction sequences, ensuring automated execution of the method.

[0033] Taking cloud computing platform deployment as an example: storage media is embedded in the server. When the program is executed by the processor, parameters are monitored in real time in the wind energy storage system, overvoltage risks are analyzed, impedance matching is adjusted, and heat dissipation strategies are optimized. No manual intervention is required throughout the process. The storage media can achieve efficient automation of the method and improve the system response speed.

[0034] This invention also proposes a multi-source input adaptive safety interface system for containerized energy storage equipment, comprising: The electrical parameter monitoring unit is used to capture real-time data on voltage, current and frequency changes of renewable energy and grid input, and integrates a quantum noise filtering channel and a dynamic dielectric loss compensator.

[0035] The intelligent analysis and control unit has a built-in input state analysis engine and a chaotic disturbance injection module, and outputs a three-dimensional state matrix including overvoltage risk level, undervoltage duration and overcurrent sudden change gradient.

[0036] The protection mechanism execution unit includes an ultra-fast response buffer module, an adaptive source impedance matching circuit, and an impedance purification feedback loop, which triggers isolation protection based on the state matrix.

[0037] The hardware integrated management unit deploys a distributed thermal management system and metastability elimination triggers through a modular structure, and synchronously executes conflict avoidance synchronization algorithms to optimize interface strategies.

[0038] Specifically, the electrical parameter monitoring unit first captures voltage, current, and frequency data, and integrates filtering channels and compensators to handle noise and signal distortion. The intelligent analysis and control unit runs the analysis engine and chaotic disturbance module to generate a three-dimensional state matrix. The protection mechanism execution unit activates the buffer module, matching circuit, and purification loop based on the matrix to perform isolation protection. The hardware integration management unit deploys the thermal management system and triggers through modular design, while simultaneously running conflict avoidance algorithm optimization strategies. All units are interconnected via a bus to achieve real-time data synchronization.

[0039] Specifically, the electrical parameter monitoring unit is a data acquisition subsystem that integrates sensors and processors; the intelligent analysis and control unit is the decision-making core that executes intelligent algorithms and is used for protection mechanism execution; and the hardware integration management unit is a hardware coordination platform that manages physical components.

[0040] Taking a containerized energy storage power station application on an island as an example: the monitoring unit captures data from the solar and diesel power grids; the analysis unit outputs a matrix indicating overvoltage risks; the execution unit triggers buffering and impedance matching; and the management unit adjusts heat dissipation and coordinates strategies to ensure stable system operation in a salt spray environment, thereby providing end-to-end protection and enhancing the reliability of multi-source integration. In the island example, each unit works together to address environmental challenges and improve fault response.

[0041] The above are merely examples of preferred embodiments of the present invention. However, the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for implementing a multi-source input adaptive safety interface for containerized energy storage equipment, characterized in that, include: S1. Real-time monitoring of changes in electrical parameters from multiple sources, including renewable energy sources and grid inputs; S2. Dynamically analyze the input status based on intelligent control algorithms to identify overvoltage, undervoltage, or overcurrent abnormalities; S3. Automatically adjust the protection mechanism based on the analysis results and trigger abnormal isolation protection; S4. Achieve seamless integration of multiple input sources through modular hardware design and simultaneously optimize interface management strategies.

2. The method for implementing a multi-source input adaptive safety interface for containerized energy storage equipment as described in claim 1, characterized in that, The real-time monitoring of changes in multi-source input electrical parameters includes: Construct an input source characteristic monitoring channel to continuously collect voltage, current, and frequency parameters; An embedded dielectric loss dynamic compensator is used to adjust the intensity of the high-frequency excitation signal according to the real-time dielectric properties of the insulating material. Establish an electrical parameter change rate tracking model to capture transient fluctuation characteristics.

3. The method for implementing a multi-source input adaptive safety interface for containerized energy storage equipment as described in claim 1, characterized in that, The dynamic analysis of input state based on intelligent control algorithms includes: Initialize the input state analysis engine and load the impedance characteristic database of renewable energy and grid input; Perform harmonic interference spectrum scanning and eliminate inherent harmonic components through an adaptive harmonic compensation unit; Injecting chaotic disturbance signals into the analysis engine enhances the identification of potential fault modes under stable operating conditions; Generate a three-dimensional state matrix that includes overvoltage risk level, undervoltage duration and overcurrent sudden change gradient; Based on the state matrix, output isolation protection priority instructions and synchronously update protection threshold adjustment parameters.

4. The method for implementing a multi-source input adaptive safety interface for containerized energy storage equipment as described in claim 3, characterized in that, The generation of the three-dimensional state matrix includes: The fluctuation trend prediction interval of the input source is divided, and the voltage deviation probability of the next three fluctuation cycles is calculated using an artificial intelligence prediction model. By associating historical fault tracing data, the fault tree analysis engine is enhanced with deep learning to label composite anomaly correlation factors. The signal-to-noise ratio correction coefficients from the quantum noise filtering channel are used to perform weighted calibration of the current abrupt gradient. The calibrated electrical parameters are mapped to the impedance-temperature joint coordinate system, and the state matrix with timestamps is output.

5. The method for implementing a multi-source input adaptive safety interface for containerized energy storage equipment as described in claim 1, characterized in that, The automatic adjustment protection mechanism includes: Activate the ultra-fast response buffer module to provide millisecond-level energy buffering in the early stages of transient fluctuations in the input source; Drive the adaptive source impedance matching circuit to reconstruct the topology and dynamically match the current input source impedance characteristics; Deploy an impedance cleanup feedback loop to remove residual impedance signals after impedance matching switching; Invoke the conflict avoidance synchronization algorithm to coordinate the parallel execution timing of isolation protection actions and interface management strategies.

6. The method for implementing a multi-source input adaptive safety interface for containerized energy storage equipment as described in claim 5, characterized in that, The topology reconstruction of the drive adaptive source impedance matching circuit includes: Real-time calculation of the phase difference of the input source impedance spectrum to generate impedance matching evaluation index; When the proportion of renewable energy input is detected to exceed the threshold, topology reconfiguration of redundant link backup paths is initiated. External noise is isolated by electromagnetic interference shielding technology, and the residual common-mode interference during the impedance matching process is collected. Based on the residual feedback activation of the adaptive common-mode noise suppression network, the output impedance matching completes the verification signal to the buffer module.

7. The method for implementing a multi-source input adaptive safety interface for containerized energy storage equipment as described in claim 1, characterized in that, The seamless integration of multiple input sources through modular hardware design includes: Configure a distributed thermal management system node array to monitor the surface temperature gradient of the power module in real time. Deploy an intelligent thermal distribution equalizer to dynamically adjust the heat dissipation path based on temperature gradient data; Embedded metastable elimination triggers force the logic output to be determined when the protection mechanism switches to a critical state. The runtime environment for integrating fault tracing algorithms is used to persistently store abnormal event logs and impedance matching historical trajectories.

8. The method for implementing a multi-source input adaptive safety interface for containerized energy storage equipment as described in claim 7, characterized in that, The configured distributed thermal management system node array includes: Deploy nanoscale temperature sensor clusters in a modular hardware boundary layer; Construct a heat conduction path optimization model and calculate the optimal heat dissipation vector based on real-time thermal imaging data; When a local temperature gradient is detected to exceed the safety threshold, the intelligent thermal distribution equalizer is activated to redistribute the heat dissipation flow. The temperature weighting coefficient in the interface management strategy is updated synchronously and fed back to the protection mechanism adjustment closed loop.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-source input adaptive safety interface implementation method for containerized energy storage equipment as described in any one of claims 1-8.

10. A multi-source input adaptive safety interface system for containerized energy storage equipment, characterized in that, include: The electrical parameter monitoring unit is used to capture real-time data on voltage, current and frequency changes of renewable energy and grid input, and integrates a quantum noise filtering channel and a dynamic dielectric loss compensator. The intelligent analysis and control unit has a built-in input state analysis engine and a chaotic disturbance injection module, and outputs a three-dimensional state matrix including overvoltage risk level, undervoltage duration and overcurrent sudden change gradient. The protection mechanism execution unit includes an ultra-fast response buffer module, an adaptive source impedance matching circuit, and an impedance cleanup feedback loop, which triggers isolation protection based on the state matrix. The hardware integrated management unit deploys a distributed thermal management system and metastability elimination triggers through a modular structure, and synchronously executes conflict avoidance synchronization algorithms to optimize interface strategies.

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