Container electricity storage equipment fault data credible evidence storage and collaborative tracing method

Abstract

The invention discloses a credible evidence storage and collaborative tracing method for fault data of container power storage equipment, which comprises the following steps of: acquiring fault multi-source heterogeneous data streams, extracting a topological relation between key characteristic values and equipment space, and constructing a two-dimensional data fingerprint; processing the key feature value to generate a privacy protection voucher, and forming a time-space associated data packet in combination with a spatial topological relation of the equipment; the time-space association data packet is written into a block chain network in a transaction form, multi-node consensus verification is executed through an intelligent contract, and a fault evidence chain with a timestamp is established in a distributed account book, so that full-link closed-loop management from acquisition to tracing is realized, and the positioning precision and the cross-mechanism tracing safety are improved.

Description

Technical Field

[0001] This invention relates to the field of energy storage equipment, and in particular to a reliable method for storing and collaboratively tracing fault data of containerized energy storage equipment. Background Technology

[0002] Containerized energy storage equipment, as a new type of energy infrastructure, is widely used in grid peak shaving and emergency power supply. Its failure can trigger chain reactions that can cause significant economic losses. Current fault management faces multiple technical bottlenecks: First, multi-source heterogeneous data streams, due to significant differences in sensor types and inconsistent formats, lead to fragmented feature extraction, making it impossible to establish a complete fault profile. Second, the complex spatial topology of equipment makes it difficult for traditional methods to dynamically analyze the correlation between physical location and connection level, resulting in inaccurate fault location. Third, in cross-institutional data collaboration, the transmission of sensitive feature values ​​is easily leaked or tampered with, lacking a protection mechanism that balances privacy and verifiability. Finally, existing evidence storage methods rely on centralized databases, posing a single point of failure risk, and the fault evidence chain is easily forged, lacking reliable support for the traceability process. These problems collectively lead to low efficiency in fault tracing and hindered collaborative analysis, urgently requiring the construction of a secure and reliable end-to-end solution. Summary of the Invention

[0003] This invention proposes a reliable method for storing and collaboratively tracing fault data of containerized energy storage equipment, comprising: S1. Collect multi-source heterogeneous data streams of faults, extract key feature values ​​and equipment spatial topology relationships, and construct a two-dimensional data fingerprint; S2. Process key feature values ​​to generate privacy protection credentials, and combine them with device spatial topology to form spatiotemporally related data packets; S3. Write the spatiotemporal related data packets into the blockchain network in the form of transactions, execute multi-node consensus verification through smart contracts, and establish a time-stamped fault evidence chain in the distributed ledger.

[0004] This also includes: parsing the spatial topology of devices in the blockchain distributed ledger, constructing an on-chain topology index based on device spatial coordinates and connection relationships, and locating the physical location of faulty devices.

[0005] This also includes: performing cross-institutional data correlation analysis based on privacy-preserving credentials through zero-knowledge proof protocols, and combining timestamps for root cause tracing.

[0006] This also includes: smart contracts parsing spatial topology relationships, triggering warnings for associated devices based on spatial proximity rules, and generating a visualized topology map of the fault propagation path.

[0007] Wherein, S1 includes: Real-time data streams of current, voltage, temperature, and vibration from containerized energy storage equipment are collected using multi-source sensors. Extract key feature values ​​from the data stream, including abnormal fluctuation amplitude, spectral characteristics, and duration thresholds; Analyze the spatial topology relationships between devices to determine the master-slave device connection hierarchy and physical coordinate mapping; By integrating key feature values ​​and spatial topological relationships, a two-dimensional data fingerprint containing a unique device identifier is generated.

[0008] The determination of the spatial topology relationship between the parsing devices, and the mapping between the master-slave device connection level and physical coordinates, includes: A three-dimensional spatial grid is constructed using equipment deployment coordinates, and a topology tree is generated based on the cable connection path. The Euclidean distance between the faulty device and its neighboring devices is calculated based on the relationship tree, and these are marked as spatially neighboring nodes.

[0009] Wherein, S2 includes: Homomorphic encryption operations are performed on key feature values ​​to generate verifiable privacy-protected credentials; Bind the privacy protection certificate to the device's spatial topology, and attach the collection time and device location coordinates; The bound data units are encapsulated to generate encrypted data packets with spatiotemporal tags.

[0010] The process of performing homomorphic encryption on key feature values ​​to generate verifiable privacy protection credentials includes: A zero-knowledge proof algorithm is used to generate proofs of the validity of feature values; Encryption keys are distributed through a distributed key management system to ensure that credentials are tamper-proof and auditable.

[0011] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method as described in any one of claims 1-8.

[0012] This invention also proposes a reliable data storage and collaborative traceability system for containerized energy storage equipment fault data, comprising: The data acquisition module is configured to acquire real-time operating data streams of the device through multi-source sensors, extract key feature values, and parse the spatial topology of the device. The data processing module is configured to perform homomorphic encryption on key feature values ​​to generate privacy protection credentials, and combine spatial topological relationships to form spatiotemporally correlated data packets; The blockchain evidence storage module is configured to convert spatiotemporally correlated data packets into blockchain transactions, execute multi-node consensus verification through smart contracts, and record a chain of fault evidence with timestamps in the distributed ledger. The collaborative tracing module is configured to perform cross-organizational data correlation analysis based on privacy protection credentials and generate a fault propagation path topology map by combining the spatial topology of equipment.

[0013] This invention achieves reliable management of fault data throughout its entire lifecycle through an innovative technical architecture. Based on a multi-source sensor network, it collects heterogeneous data streams such as current, voltage, temperature, and vibration in real time. It integrates key feature values, including abnormal fluctuation amplitude, spectral characteristics, duration, and threshold values, with the equipment's three-dimensional spatial topology to generate a two-dimensional data fingerprint containing both state and location information. This overcomes the limitations of traditional one-dimensional analysis, significantly improving the accuracy of fault source identification. Homomorphic encryption and zero-knowledge proof algorithms are used to process feature values, generating verifiable privacy-protected credentials. Distributed key management ensures the credentials are tamper-proof. Spatiotemporally correlated data packets are stored as blockchain transactions. A timestamped fault evidence chain is constructed based on smart contract multi-node consensus verification, endowing the data with immutability and complete traceability. The smart contract automatically parses the on-chain topology, triggering early warnings and generating a fault propagation path topology map based on spatial proximity rules, greatly improving fault response speed and collaborative handling efficiency. Ultimately, a closed-loop system is formed, encompassing data collection, privacy protection, blockchain storage, and collaborative traceability, providing secure and efficient full-stack fault management support for containerized energy storage equipment. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating a reliable evidence storage and collaborative traceability method for fault data of containerized energy storage equipment proposed in this invention. Detailed Implementation

[0015] refer to Figure 1 This invention proposes a reliable method for storing and collaboratively tracing fault data of containerized energy storage equipment, including: S1. Collect multi-source heterogeneous data streams of faults, extract key feature values ​​and device spatial topology relationships, and construct a two-dimensional data fingerprint.

[0016] Specifically, the sensor network deployed on the device systematically collects fault data, capturing diverse data streams from different sources in real time. These diverse data streams include current readings, voltage fluctuations, temperature changes, and vibration signals, which together constitute a multi-source heterogeneous data stream. Key feature values ​​representing fault modes are filtered from these diverse data streams, such as identifying abnormal fluctuation amplitudes, spectral characteristics, and duration thresholds of data peaks that deviate from the normal range. The spatial topology relationship between devices is analyzed to determine the master-slave device connection hierarchy, such as the hierarchical structure of a master device controlling multiple slave devices, and the physical coordinate mapping, i.e., the position of the device in three-dimensional space. By fusing key feature values ​​and spatial topology relationships, a two-dimensional data fingerprint containing a unique identifier for each device is generated.

[0017] Specifically, the multi-source heterogeneous data stream refers to a collection of real-time data with different formats and attributes collected from different types of sensors, such as current sensors, voltage sensors, temperature sensors, and vibration sensors; the key feature values ​​specifically include quantitative indicators used to characterize faults, such as abnormal fluctuation amplitude indicating the magnitude of data deviation from the baseline, spectral characteristics indicating the analysis results of signal frequency components, and duration threshold indicating the shortest time limit for the abnormal state; the device spatial topology relationship specifically refers to the hierarchical connection structure and physical location mapping between master and slave devices, used to describe the spatial layout between devices; and the two-dimensional data fingerprint specifically refers to a unique device identifier formed by fusing key feature values ​​and spatial topology relationships, used for fault tracing.

[0018] Taking a port energy storage system as an example, multi-source sensors are installed on the energy storage units in the port's container cluster. When a unit experiences a current overload, the sensors collect relevant data streams in real time and extract key feature values, such as the current abnormal fluctuation amplitude reaching the warning level, the spectrum characteristics showing high-frequency noise, and the duration threshold exceeding the set value. A three-dimensional spatial grid is constructed based on the equipment deployment coordinates to simulate the container layout, and a topology tree is generated based on the cable connection path. Based on the topology tree, the Euclidean distance (i.e., the straight-line spatial distance) between the faulty equipment and its adjacent equipment is calculated, and these adjacent equipment are marked as spatially nearby nodes. For example, if the Euclidean distance between the faulty equipment A and its neighboring equipment B is close, it indicates a potential risk of fault propagation. Under the premise of comprehensive data collection and accurate feature extraction, fault information can be avoided, and the fault source can be quickly located, reducing manual inspection time.

[0019] S1 specifically includes: real-time acquisition of current, voltage, temperature and vibration data streams of containerized energy storage equipment through multi-source sensors, extraction of key feature values ​​from the data stream, including abnormal fluctuation amplitude, spectral characteristics and duration thresholds, analysis of spatial topological relationships between devices, and determination of master-slave device connection levels and physical coordinate mapping.

[0020] Specifically, a multi-source sensor network continuously monitors energy storage devices. Sensor types include current probes, voltmeters, thermometers, and accelerometers, installed on or inside the devices to collect data streams in real time. Current data reflects the state of charge flow, voltage data indicates potential difference, temperature data monitors heat dissipation, and vibration data captures mechanical anomalies. This data is transmitted to the processing center in the form of a stream. Key feature values ​​are extracted from the data stream. The amplitude of abnormal fluctuations is calculated by algorithms to determine the magnitude of the deviation of data points from the average value. The spectral characteristics are converted from time-domain signals to frequency-domain distributions using Fourier transform. The duration threshold is set based on time series analysis to determine the critical point for the persistence of anomalies. The spatial topology of the devices is analyzed, and a connection model between devices is established based on physical coordinates to determine the master-slave device connection hierarchy and map the physical coordinates.

[0021] Among them, multi-source sensors specifically refer to various detection devices installed on energy storage equipment, including Hall effect sensors for measuring current, differential probes for voltage detection, thermocouples for temperature monitoring, and piezoelectric sensors for vibration sensing; abnormal fluctuation amplitude specifically refers to the deviation between the peak value and the reference value in the data sequence; spectral characteristics specifically refer to the energy distribution spectrum of the signal in the frequency domain; duration threshold specifically refers to the minimum duration that the abnormal state must last, used to filter out brief interference; master-slave device connection hierarchy specifically refers to the tree-like structure relationship between the control device and the controlled device; physical coordinate mapping specifically refers to the system process of converting the device position into three-dimensional spatial coordinates.

[0022] For example, when a storage container experiences a sudden temperature rise, multiple sensors are activated. Current sensors detect abnormal current, voltage sensors capture voltage drops, temperature sensors read out of safe range, and vibration sensors record abnormal vibrations. Key feature values ​​are extracted, such as abnormal current fluctuations exceeding normal values, and spectral characteristics displaying high-frequency harmonics. Spatial topology relationships are analyzed, and equipment deployment coordinates form a three-dimensional grid covering the container yard. The master-slave device connection hierarchy is constructed based on the cable wiring diagram. For example, the master device controls three slave devices, and the physical coordinate mapping uses GPS coordinates to locate each device. In this way, the connection hierarchy and location of the faulty device are clearly presented, ensuring early warning of faults. By comprehensively considering multiple parameters, the severity of the fault can be judged, avoiding misjudgments.

[0023] Furthermore, the process of resolving the spatial topology relationship between devices and determining the connection level and physical coordinate mapping of master and slave devices includes: constructing a three-dimensional spatial grid through device deployment coordinates, generating a topology relationship tree based on cable connection paths; calculating the Euclidean distance between the faulty device and adjacent devices based on the relationship tree, marking them as spatially adjacent nodes, and fusing key feature values ​​and spatial topology relationships to generate a two-dimensional data fingerprint containing a unique device identifier.

[0024] Specifically, a three-dimensional spatial grid is constructed using equipment deployment coordinates such as latitude and longitude or local coordinate coefficients. The grid cells are divided based on equipment density to ensure that each device occupies one grid point. When generating a topology tree based on cable connection paths, the cable paths serve as edges connecting equipment nodes, forming a tree structure where the root node represents the master device and branches represent slave devices. Based on the topology tree, the Euclidean distance (i.e., the rectangular distance in three-dimensional space) between the faulty device and its neighboring devices is calculated. The algorithm traverses the tree structure and identifies the nearest device as a spatially adjacent node.

[0025] Specifically, the three-dimensional spatial grid is a data model that divides physical space into cubic units, used to locate equipment positions; the topology tree is a hierarchical data structure generated based on cable connections, representing the master-slave dependency between devices; the Euclidean distance is the formula for calculating the straight-line distance between two points in three-dimensional space; and the spatial neighbor node is the identifier of the device closest to the faulty device in the topology tree.

[0026] Here's an example: When equipment failure occurs, a 3D mesh is constructed using the equipment's deployment coordinates: the X-axis represents length, the Y-axis width, and the Z-axis height, covering the terminal container array. Based on the cable connection paths, a topology tree is generated starting from the main distribution box, with branches connecting each energy storage unit. For example, if the faulty equipment is located at grid point A, its Euclidean distance to neighboring equipment B is calculated. If the grid coordinate difference is small, B is marked as a spatially adjacent node, indicating the potential impact range of the fault. Distance calculation helps identify high-risk areas. Spatial topology analysis is accurate and fast, improving fault location efficiency, effectively narrowing the fault investigation scope, and reducing maintenance costs.

[0027] The following is a further description of "integrating key feature values ​​and spatial topological relationships to generate a two-dimensional data fingerprint containing a unique device identifier": Specifically, the core of the process is data fusion, which combines extracted key features such as abnormal fluctuation amplitude and spectral characteristics with spatial topological relationships such as master-slave hierarchy and neighboring node information, integrating them into a single data structure through algorithms. Unique device identifiers, such as MAC addresses or serial numbers, are embedded within this structure, forming a two-dimensional data fingerprint. The fusion process includes feature value normalization (standardizing the numerical range) and topological relationship encoding (converting spatial information into vectors), ultimately generating a unique fingerprint.

[0028] Specifically, fusion refers to the process of combining feature values ​​with topological relationships through an algorithm; the device unique identifier is a unique code assigned to each device; and the dual-dimensional data fingerprint is a unique data identifier that integrates the feature dimension and the spatial dimension.

[0029] Here's an example: When an anomaly in a key temperature characteristic value is detected, spatial topological relationships are integrated: for example, a unique identifier for the equipment is bound to a specific container, and spatial proximity node information indicates the coordinates of nearby equipment. The resulting two-dimensional data fingerprint is used to uniquely identify the equipment's failure mode. This data fingerprint possesses both uniqueness and comprehensiveness, enhancing fault tracing capabilities. In multi-device scenarios, it can avoid fingerprint conflicts and improve system reliability.

[0030] S2. Process key feature values ​​to generate privacy protection credentials, and combine them with device spatial topology to form a spatiotemporally associated data packet. Specifically, this includes: performing homomorphic encryption operations on key feature values ​​to generate verifiable privacy protection credentials; binding the privacy protection credentials with device spatial topology, and attaching the acquisition time and device location coordinates; encapsulating the bound data unit to generate an encrypted data packet with spatiotemporal tags.

[0031] Specifically, encryption operations are performed on key feature values ​​to generate privacy protection credentials to ensure data security. At the same time, combined with the spatial topology of the device, such as connection level and coordinates, the data is encapsulated into a spatiotemporally associated data packet. The process includes feature value encryption, relationship binding, and time and location appending.

[0032] Among them, the privacy protection credential is specifically an encrypted data verification token; the spatiotemporal associated data packet is specifically a data unit that binds time and space information.

[0033] Here's an example: When feature values ​​such as vibration spectra are processed, after generating privacy protection credentials, they are combined with topological relationships to form data packets, along with the collection time and location. For example, these data packets are used for cross-border collaboration to protect sensitive information, enhance data security, and ensure worry-free data sharing in cross-border scenarios.

[0034] The process of performing homomorphic encryption on key feature values ​​to generate verifiable privacy-protected credentials includes: generating a feature value validity proof using a zero-knowledge proof algorithm; and distributing encryption keys through a distributed key management system to ensure that the credentials are tamper-proof and auditable.

[0035] Specifically, the zero-knowledge proof algorithm is a protocol for verifying the authenticity of data without revealing its contents; the distributed key management system is a multi-node collaborative key distribution framework. The zero-knowledge proof algorithm generates validity proofs without exposing the original data. The distributed key management system distributes keys to ensure the security of credentials, prevent single points of failure, and the credentials can be used for multi-party auditing, resulting in high auditing efficiency.

[0036] The following further explains how to generate encrypted data packets with spatiotemporal tags from the encapsulated and bound data units: Specifically, homomorphic encryption allows for the processing of feature values ​​in an encrypted state, generating verifiable credentials. The binding phase combines the credentials with topological relationships, adding timestamps and location coordinates. Finally, it is encapsulated into an encrypted data packet with added spatiotemporal tags. Feature values ​​such as voltage fluctuations are homomorphically encrypted, generating credentials that are bound to topological relationships, and encapsulated after adding time and location information. The data packet is used for remote monitoring, and the tag ensures timely tracking. Encryption enhances data integrity and auditability. Specifically, homomorphic encryption is an encryption method that supports ciphertext computation; verifiable privacy-protected credentials are encrypted data proofs that can be verified by a third party; and spatiotemporal tags are data identifiers containing time and location information.

[0037] Step S2 is followed by: performing cross-institutional data correlation analysis based on privacy-preserving credentials using a zero-knowledge proof protocol, and performing root cause analysis using timestamps. This step utilizes the privacy-preserving credentials generated in S2 to achieve cross-institutional data collaboration, ensuring that root cause analysis is initiated under privacy protection, and providing preprocessing support for subsequent blockchain evidence storage in S3.

[0038] Specifically, cross-organizational data correlation analysis involves the data matching process between different organizations; root cause tracing is the mechanism for tracking the root cause of failures. Specifically, zero-knowledge proof protocols are used to analyze data correlations, and timestamps are used to trace the root cause of failures, ensuring privacy protection. Credentials are used for data sharing between organizations, zero-knowledge proofs verify correlations, and timestamps trace the origin of failures, preparing for blockchain development.

[0039] S3. Write the spatiotemporal related data packets into the blockchain network in the form of transactions, execute multi-node consensus verification through smart contracts, and establish a time-stamped fault evidence chain in the distributed ledger.

[0040] Specifically, data packets are written to the blockchain as transactions, smart contracts invoke the consensus mechanism for verification, and the distributed ledger records a timestamped chain of evidence. Once the data packets are on the blockchain, the smart contract verifies consistency across nodes, the chain of evidence is used for auditing, and the data is immutable and traceable. In large-scale power grids, the chain of evidence enhances credibility.

[0041] Specifically, the blockchain network is a decentralized distributed database; the smart contract is an automatically executed programmatic protocol; the distributed ledger is a data record shared by multiple nodes; and the fault evidence chain is a sequence of fault events with timestamps.

[0042] Step S3 is followed by: parsing the spatial topology of devices in the blockchain distributed ledger, constructing an on-chain topology index based on device spatial coordinates and connection relationships, and locating the physical location of the faulty device.

[0043] This step is based on the fault evidence chain established by S3, which uses its distributed ledger data to achieve precise physical location.

[0044] Specifically, the topological relationships are parsed using on-chain data to build an index to locate the fault location. The topological relationships in the ledger are parsed, and the coordinates of the faulty container are located after the index is built. The location is fast and accurate. The on-chain topological index is specifically a spatial relationship data structure stored in the blockchain.

[0045] Step S3 is followed by: the smart contract parsing spatial topology relationships, triggering warnings for associated devices based on spatial proximity rules, and generating a visualized topology map of the fault propagation path. This step utilizes the smart contract mechanism of S3, combined with spatial topology relationships, to predict and visualize fault propagation, enhancing traceability capabilities.

[0046] Specifically, based on proximity rules, early warnings are generated and a visual topology map is constructed. Contracts trigger alarms for nearby devices, and the topology map displays the propagation path, enhancing fault management. Early warnings are timely and visually intuitive, and propagation prediction reduces downtime. The spatial proximity rule is specifically an algorithm that triggers early warnings based on distance thresholds; the visual topology map is a graphical representation of the fault path.

[0047] The present invention proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0048] This invention also proposes a reliable data storage and collaborative traceability system for containerized energy storage equipment fault data, comprising: The data acquisition module is configured to acquire real-time operating data streams of the device through multi-source sensors, extract key feature values, and parse the spatial topology of the device. The data processing module is configured to perform homomorphic encryption on key feature values ​​to generate privacy protection credentials, and combine spatial topological relationships to form spatiotemporally correlated data packets; The blockchain evidence storage module is configured to convert spatiotemporally correlated data packets into blockchain transactions, execute multi-node consensus verification through smart contracts, and record a chain of fault evidence with timestamps in the distributed ledger. The collaborative tracing module is configured to perform cross-organizational data correlation analysis based on privacy protection credentials and generate a fault propagation path topology map by combining the spatial topology of equipment.

[0049] The above are merely preferred embodiments of the present invention, but 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 reliable storage and collaborative traceability of fault data in containerized energy storage equipment, characterized in that, include: S1. Collect multi-source heterogeneous data streams of faults, extract key feature values ​​and equipment spatial topology relationships, and construct a two-dimensional data fingerprint; S2. Process key feature values ​​to generate privacy protection credentials, and combine them with device spatial topology to form spatiotemporally related data packets; S3. Write the spatiotemporal related data packets into the blockchain network in the form of transactions, execute multi-node consensus verification through smart contracts, and establish a time-stamped fault evidence chain in the distributed ledger.

2. The method for reliable storage and collaborative traceability of fault data of containerized energy storage equipment as described in claim 1, characterized in that, Also includes: The system analyzes the spatial topology of devices in the blockchain distributed ledger, constructs an on-chain topology index based on device spatial coordinates and connection relationships, and locates the physical location of faulty devices.

3. The method for reliable storage and collaborative traceability of fault data of containerized energy storage equipment as described in claim 1, characterized in that, It also includes: performing cross-institutional data correlation analysis based on privacy-preserving credentials and using zero-knowledge proof protocols to perform root cause analysis in conjunction with timestamps.

4. The method for reliable storage and collaborative traceability of fault data of containerized energy storage equipment as described in claim 1, characterized in that, Also includes: The smart contract parses spatial topology relationships, triggers warnings for associated devices based on spatial proximity rules, and generates a visualized topology map of the fault propagation path.

5. The method for reliable storage and collaborative traceability of fault data of containerized energy storage equipment as described in claim 1, characterized in that, S1 includes: Real-time data streams of current, voltage, temperature, and vibration from containerized energy storage equipment are collected using multi-source sensors. Extract key feature values ​​from the data stream, including abnormal fluctuation amplitude, spectral characteristics, and duration thresholds; Analyze the spatial topology relationships between devices to determine the master-slave device connection hierarchy and physical coordinate mapping; By integrating key feature values ​​and spatial topological relationships, a two-dimensional data fingerprint containing a unique device identifier is generated.

6. The method for reliable storage and collaborative traceability of fault data of containerized energy storage equipment as described in claim 5, characterized in that, The determination of the spatial topology relationship between the parsing devices, and the mapping between the master-slave device connection level and physical coordinates, includes: A three-dimensional spatial grid is constructed using equipment deployment coordinates, and a topology tree is generated based on the cable connection path. The Euclidean distance between the faulty device and its neighboring devices is calculated based on the relationship tree, and these are marked as spatially neighboring nodes.

7. The method for reliable storage and collaborative traceability of fault data of containerized energy storage equipment as described in claim 1, characterized in that, S2 includes: Homomorphic encryption operations are performed on key feature values ​​to generate verifiable privacy-protected credentials; Bind the privacy protection certificate to the device's spatial topology, and attach the collection time and device location coordinates; The bound data units are encapsulated to generate encrypted data packets with spatiotemporal tags.

8. The method for reliable storage and collaborative traceability of fault data of containerized energy storage equipment as described in claim 7, characterized in that, The process of performing homomorphic encryption on key feature values ​​to generate verifiable privacy protection credentials includes: A zero-knowledge proof algorithm is used to generate proofs of the validity of feature values; Encryption keys are distributed through a distributed key management system to ensure that credentials are tamper-proof and auditable.

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

10. A reliable data storage and collaborative traceability system for containerized energy storage equipment fault data, characterized in that, include: The data acquisition module is configured to acquire real-time operating data streams of the device through multi-source sensors, extract key feature values, and parse the spatial topology of the device. The data processing module is configured to perform homomorphic encryption on key feature values ​​to generate privacy protection credentials, and combine spatial topological relationships to form spatiotemporally correlated data packets; The blockchain evidence storage module is configured to convert spatiotemporally correlated data packets into blockchain transactions, execute multi-node consensus verification through smart contracts, and record a chain of fault evidence with timestamps in the distributed ledger. The collaborative tracing module is configured to perform cross-organizational data correlation analysis based on privacy protection credentials and generate a fault propagation path topology map by combining the spatial topology of equipment.

Images