FPGA program loading and updating method and system

Abstract

The invention provides an FPGA program loading and updating method and system, and relates to the technical field of FPGA configuration management. The FPGA program loading and updating method comprises the following steps: S1, based on an RS422 physical layer protocol, adopting a self-adaptive block verification algorithm, acquiring a signal-to-noise ratio parameter in real time through a channel quality monitoring module, dynamically adjusting the size of a data packet, and implementing a double-buffer alternate receiving mechanism; and verifying each data block by using cyclic redundancy check and serial number continuity detection, discarding abnormal data packets with bit error rates exceeding the limit, and generating a data block set passing verification. Through combination of an adaptive block check algorithm and channel quality dynamic monitoring, real-time optimization and adjustment of the size of a data packet in a transmission process are realized, transmission requirements in different channel environments are effectively adapted, the data retransmission probability is reduced, and through cooperative application of a double-buffer alternate receiving mechanism, cyclic redundancy check and a serial number tracking technology, the transmission efficiency is improved. And continuity and integrity of data receiving are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of FPGA configuration management, and in particular to an FPGA program loading and updating method and system. Background Art

[0002] The field of FPGA configuration management technology involves the storage, transmission, verification, and update mechanisms of configuration data during programmable logic device operation, focusing on addressing configuration data integrity protection, multi-version management, dynamic reconfiguration, and system reliability. This field encompasses key technologies such as configuration data compression algorithms, partial reconfiguration techniques, encryption verification mechanisms, and fault-tolerant recovery strategies. Specifically, an FPGA program loading and update method and system, a technical solution for securely transmitting and dynamically updating FPGA configuration data through a specific communication interface and storage architecture, aims to address the low efficiency, poor security, and lack of flexibility inherent in traditional FPGA firmware update processes.

[0003] Traditional FPGA configuration management technology uses a fixed block transmission mode, which is unable to dynamically adjust data encapsulation strategies based on channel quality. This makes it prone to packet loss or verification failures in complex electromagnetic environments, resulting in reduced transmission efficiency. Storage allocation strategies are often based on static address mapping and lack dynamic analysis of configuration version access characteristics. This makes it difficult to balance storage space utilization with media loss, causing some storage blocks to fail prematurely. Security verification mechanisms typically rely on fixed keys or a single encryption algorithm, posing a risk of key leakage and making it difficult to mitigate diverse attack vectors. The decryption process also lacks runtime feature verification, making it ineffective at identifying tampered configuration data. Summary of the Invention

[0004] To address the shortcomings of the existing technology, the present invention provides an FPGA program loading and updating method and system, which solves the problem that traditional FPGA configuration management technology adopts a fixed block transmission mode, cannot dynamically adjust the data encapsulation strategy according to channel quality, and is prone to data packet loss or verification failure in complex electromagnetic environments, resulting in reduced transmission efficiency.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for loading and updating an FPGA program, comprising the following steps: S1: Based on the RS422 physical layer protocol, it uses an adaptive block checksum algorithm, obtains signal-to-noise ratio parameters in real time through the channel quality monitoring module, dynamically adjusts the data packet size, implements a double-buffered alternating reception mechanism, verifies each data block using cyclic redundancy check and sequence number continuity detection, discards abnormal data packets with excessive bit error rates, and generates a set of verified data blocks. S2: Based on the verified data block set, an improved buddy system algorithm is used to analyze the access frequency of configuration versions through a storage heat prediction model, dynamically calculate the optimal storage location, and combine a multi-level wear leveling strategy with a dynamic address mapping table to record the correspondence between physical blocks and logical addresses, generating a storage mapping table with wear leveling tags. S3: Based on a memory mapping table with wear-leveling markings, it adopts a layered security verification architecture, generates dynamic keys through physically unclonable functions, performs digital signature verification and encryption standard decryption, implements integrity verification, extracts runtime features from the decrypted bitstream, compares it with a reference model, and generates a decrypted bitstream that passes the integrity check. S4: Based on the decrypted and integrity-checked bitstream, it employs an atomic write control algorithm, uses a double-buffered alternating write mechanism to temporarily store data, uses metadata logging technology to record operation sequences, and combines power supply protection circuits to ensure storage operation integrity. Finally, a bad block management engine avoids physically damaged areas and generates an updated data image with integrity markings. S5: Based on an updated data mirror with integrity markers, a multi-dimensional health assessment model is adopted. Key parameters are collected through a runtime monitoring agent, and a scoring algorithm is used to calculate system health. When the score is continuously below the threshold, an incremental version rollback mechanism is triggered. The historical version is selected based on the fault knowledge base, and rapid recovery is achieved through the reconfiguration interface. A system health assessment report and rollback log are generated.

[0006] Preferably, generating a verified data block set based on S1 includes the following steps: S101: Based on the characteristics of the RS422 physical layer, it uses a long short-term memory neural network algorithm to train with historical channel bit error rate data to predict future channel quality and generate a channel quality prediction report. S102: Based on the channel quality prediction report, a dynamic block adjustment algorithm is used to calculate the optimal block size using an error optimization criterion, and data packets are reassembled to generate a block-optimized data set. S103: Based on the block-based optimized data set, a double-buffered alternating receiving mechanism is adopted to complete data verification through cyclic redundancy check and sequence number continuity detection, and a verified data block set is generated.

[0007] Preferably, generating a storage mapping table with a wear leveling mark based on S2 includes the following steps: S201: Based on the verified data block set, a Markov chain prediction model is used to analyze and calculate the storage heat value through configuration version access logs to generate a storage heat distribution map; S202: Based on the storage heat distribution map, an improved buddy system algorithm is used to find the optimal storage interval through a tree search mechanism to generate a logical address mapping table; S203: Based on the logical address mapping table, a dynamic wear leveling algorithm is used to adjust the physical storage location by analyzing the number of erases to generate a balanced address mapping table; S204: Based on the balanced address mapping table, a multi-level storage strategy is adopted to implement hierarchical storage allocation through access frequency determination, and a storage mapping table with a wear leveling mark is generated.

[0008] Preferably, generating a decrypted bitstream that passes the integrity check based on S3 comprises the following steps: S301: Based on a storage mapping table with a wear leveling flag, a physical unclonable function technology is used to generate a dynamic key through hardware characteristics to generate a temporary session key; S302: Based on the temporary session key, a layered verification architecture is adopted to verify the legitimacy of the data source using the elliptic curve digital signature algorithm and generate a digital signature verification result; S303: Based on the digital signature verification result, the Advanced Encryption Standard algorithm is used to decrypt the bit stream data in stream encryption mode to generate a decrypted bit stream that passes the integrity check.

[0009] Preferably, generating an updated data image with an integrity mark based on S4 includes the following steps: S401: Based on the decrypted and integrity-checked bitstream, metadata logging technology is used to record the operation sequence through buffer management to generate an operation log record; S402: Based on the operation log records, a dual-buffer alternating write mechanism is adopted to implement data switching through a buffer control circuit to generate a cache data mirror; S403: Based on the cache data mirror, power supply protection technology is used to trigger an emergency process through voltage monitoring to generate a power failure protection data mirror; S404: Based on the power-off protection data mirror, a bad block management algorithm is used to avoid damaged areas through a spare block table and generate a physical storage mapping solution; S405: Based on the physical storage mapping solution, an atomic write protocol is adopted, and storage is completed through a synchronization control mechanism to generate an updated data image with an integrity mark.

[0010] Preferably, generating a system health assessment report and a rollback log based on S5 includes the following steps: S501: Based on the updated data image with integrity mark, using runtime monitoring technology, collect system parameters through the sensor network to generate a multi-dimensional parameter data set; S502: Based on the multi-dimensional parameter data set, a weighted scoring model is used to calculate the health index through a logical reasoning engine to generate a health assessment report; S503: Based on the health assessment report, a reinforcement learning algorithm is used to determine the rollback timing using the fault knowledge base and generate a version rollback decision instruction. S504: Based on the version rollback decision instruction, incremental reconfiguration technology is used to achieve system recovery through configuration difference comparison, and a system health assessment report and rollback log are generated.

[0011] An FPGA program loading and updating system includes the following modules: a communication interface module, an intelligent storage module, and a secure writing module; The communication interface module is based on a differential transmission protocol and adopts a dynamic block verification algorithm. It realizes data reassembly through a double-buffered receiving mechanism, and uses cyclic redundancy check and sequence tracking technology to complete data verification and generate a verified data stream. The communication interface module includes a channel monitoring submodule, a block optimization submodule, and a verification processing submodule; The intelligent storage module uses a storage allocation algorithm based on verified data streams, analyzes usage frequency through access feature prediction models, combines multi-level storage strategies, and uses a dynamic address management table to achieve intelligent allocation and generate an optimized storage solution. The intelligent storage module includes an access analysis submodule, an address allocation submodule, and a balance management submodule; The secure write module, based on an optimized storage solution, uses hardware feature encryption technology to generate dynamic keys, completes security verification through a layered verification mechanism and decryption algorithm, implements an atomic write protocol to ensure operational integrity, and generates an executable program image; The secure writing module includes a key management submodule, a security verification submodule, and a writing control submodule.

[0012] Preferably, the channel quality monitoring submodule uses a neural network algorithm to analyze historical communication quality data based on physical layer characteristics and generates a channel quality assessment report; The dynamic block submodule uses a dynamic optimization algorithm to adjust the data block strategy based on the channel quality assessment report and generate a block-optimized data set; The data verification submodule uses a redundant verification mechanism and sequence tracking technology to perform verification based on the block-optimized data set, and generates verified bit stream data.

[0013] Preferably, the heat analysis submodule uses a probability prediction model to analyze storage access characteristics based on the verified bitstream data and generates a storage heat distribution map; The address mapping submodule allocates storage intervals based on the storage heat distribution map using a space optimization algorithm to generate a logical address allocation table; The wear leveling submodule, based on the logical address allocation table, uses dynamic load analysis technology to optimize storage distribution and generate a storage solution with balanced marking.

[0014] Preferably, the key generation submodule generates encryption elements and dynamic session keys based on a balanced tag storage scheme using hardware feature extraction technology; The decryption verification submodule uses a layered verification architecture to complete security verification based on dynamic session keys and generate decryption verification pass data; The atomic write submodule uses transactional write technology to ensure storage integrity based on decrypted and verified data and generates an executable configuration image.

[0015] The present invention provides a method and system for loading and updating FPGA programs. It has the following beneficial effects: This invention uses an adaptive block checksum algorithm combined with dynamic channel quality monitoring to achieve real-time optimization and adjustment of data packet size during transmission, effectively adapting to transmission requirements in different channel environments and reducing the probability of data retransmission. The synergistic application of a double-buffered alternating reception mechanism, cyclic redundancy check, and sequence number tracking technologies ensures the continuity and integrity of data reception, avoiding transmission interruptions caused by packet loss or misordering. An improved buddy system algorithm, combined with a storage heat prediction model, dynamically analyzes configuration version access characteristics to optimize storage space allocation efficiency. Combined with a multi-level wear leveling strategy, it significantly extends the service life of storage media. A layered security verification architecture generates dynamic keys from hardware features and combines digital signature verification with runtime feature extraction to build a multi-layered security protection system, enhancing the security of the data decryption process. The application of an atomic write control algorithm and a double-buffer mechanism, combined with metadata logging and power supply protection measures, ensures the transactional nature of storage operations and effectively prevents data corruption caused by unexpected interruptions. A multi-dimensional health assessment model uses real-time parameter collection and an intelligent scoring mechanism to accurately determine system status. Combined with incremental version rollback technology, it quickly recovers to a stable state when an anomaly is detected, significantly improving the system's fault tolerance. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the main steps of the present invention; Figure 2 This is a schematic diagram of the refinement of S1 of the present invention; Figure 3 This is a schematic diagram of the refinement of S2 of the present invention; Figure 4 This is a schematic diagram of the refinement of S3 of the present invention; Figure 5 This is a schematic diagram of the refinement of S4 of the present invention; Figure 6 This is a schematic diagram of the refinement of S5 of the present invention; Figure 7 This is a system block diagram of the present invention. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0018] Example: like Figure 1-7 As shown, an embodiment of the present invention provides an FPGA program loading and updating method, comprising the following steps: S1: Based on the RS422 physical layer protocol, it uses an adaptive block checksum algorithm, obtains signal-to-noise ratio parameters in real time through the channel quality monitoring module, dynamically adjusts the data packet size, implements a double-buffered alternating reception mechanism, verifies each data block using cyclic redundancy check and sequence number continuity detection, discards abnormal data packets with excessive bit error rates, and generates a set of verified data blocks. S2: Based on the verified data block set, an improved buddy system algorithm is used to analyze the access frequency of configuration versions through a storage heat prediction model, dynamically calculate the optimal storage location, and combine a multi-level wear leveling strategy with a dynamic address mapping table to record the correspondence between physical blocks and logical addresses, generating a storage mapping table with wear leveling tags. S3: Based on a memory mapping table with wear-leveling markings, it adopts a layered security verification architecture, generates dynamic keys through physically unclonable functions, performs digital signature verification and encryption standard decryption, implements integrity verification, extracts runtime features from the decrypted bitstream, compares it with a reference model, and generates a decrypted bitstream that passes the integrity check. S4: Based on the decrypted and integrity-checked bitstream, it employs an atomic write control algorithm, uses a double-buffered alternating write mechanism to temporarily store data, uses metadata logging technology to record operation sequences, and combines power supply protection circuits to ensure storage operation integrity. Finally, a bad block management engine avoids physically damaged areas and generates an updated data image with integrity markings. S5: Based on an updated data mirror with integrity markers, a multi-dimensional health assessment model is adopted. Key parameters are collected through a runtime monitoring agent, and a scoring algorithm is used to calculate system health. When the score is continuously below the threshold, an incremental version rollback mechanism is triggered. The historical version is selected based on the fault knowledge base, and rapid recovery is achieved through the reconfiguration interface. A system health assessment report and rollback log are generated.

[0019] Generating a verified data block set based on S1 includes the following steps: S101: Based on the characteristics of the RS422 physical layer, it uses a long short-term memory neural network algorithm to train with historical channel bit error rate data to predict future channel quality and generate a channel quality prediction report. Based on the transmission characteristics of the physical layer, historical communication quality data is processed through a time series prediction model, and a training dataset is constructed in an industrial control scenario. The input features include time dimension parameters and environmental interference factors. A sliding window mechanism is used to update the model input, and a periodic retraining interval is set. The quality assessment interval is set according to the protocol specification. When the predicted value exceeds the allowable range, the early warning protocol is activated, and finally a quality prediction report containing multi-dimensional evaluation indicators is formed.

[0020] S102: Based on the channel quality prediction report, a dynamic block adjustment algorithm is used to calculate the optimal block size using an error optimization criterion, and data packets are reassembled to generate a block-optimized data set. Based on the quality prediction results, the dynamic adjustment strategy optimizes the data encapsulation method according to the real-time transmission status, sets dynamic adjustment rules for block size in long-distance communication scenarios, initializes the baseline block parameters, periodically collects channel state parameters, establishes a mapping relationship between block size and channel quality, and adopts a progressive adjustment strategy to reduce or expand the block size. Error correction coding information is added in the data reconstruction stage, and structured metadata containing block characteristics is embedded in the header. The receiving end reconstructs the data stream by parsing the metadata. After multiple rounds of parameter optimization iterations, a block data set that adapts to the channel status is generated.

[0021] S103: Based on the block-based optimized data set, a double-buffered alternating receiving mechanism is adopted to complete data verification through cyclic redundancy check and sequence number continuity detection, and a verified data block set is generated.

[0022] Based on the optimized data set, the parallel buffer architecture adopts an alternating write mechanism, sets up dual buffer areas and configures capacity redundancy. In high-speed data transmission scenarios, data packet storage locations are allocated according to predetermined rules, the verification calculation process is started and fault tolerance parameters are configured, a receiving sequence number tracking table is established, and a continuity detection tolerance range is set. When an abnormal interval is detected, a compensation mechanism is triggered, data format standardization is completed through the preprocessing process, and abnormal data is transferred to an independent processing queue. After multiple rounds of buffer switching and integrity verification, a verification data set that conforms to the protocol specification is generated.

[0023] Generating a storage mapping table with wear leveling marks based on S2 includes the following steps: S201: Based on the verified data block set, a Markov chain prediction model is used to analyze and calculate the storage heat value through configuration version access logs to generate a storage heat distribution map; Based on the verified data set, the access features of the configuration version are analyzed by the state transition model. In the smart meter firmware update scenario, the access timestamps and operation types of each version firmware in the past three months are extracted, a state transition probability matrix is constructed, the conversion rules of high-frequency access state and low-frequency access state are defined, and the state transition threshold is set. When the number of continuous accesses exceeds the set threshold, the state weight is updated. The access log is analyzed periodically in a sliding window, the window length covers the typical firmware update period, the access frequency of each storage block is calculated by the probability density function, and the heat value is corrected combined with the device operating environment parameters. Finally, the distribution map reflecting the access intensity of the storage area is formed.

[0024] S202: Based on the storage heat distribution map, an improved buddy system algorithm is used to find the optimal storage interval through a tree search mechanism, and a logical address mapping table is generated; Based on the storage access intensity distribution data, a space allocation optimization algorithm is used to search for available storage intervals. In the vehicle navigation system scenario, the storage space is divided into multiple size units, a binary tree structure index table is established, the search priority is set according to the heat value, large capacity continuous storage blocks are matched in high heat areas first, and small capacity fragmented units are allocated in low heat areas. When traversing the binary tree nodes, the branch weight is dynamically adjusted, a matching degree scoring mechanism is set, the search is terminated when the candidate interval score reaches the optimization threshold, and a mapping table containing the correspondence between logical addresses and physical blocks is generated.

[0025] S203: Based on the logical address mapping table, a dynamic wear leveling algorithm is used to adjust the physical storage location by analyzing the number of erasures, and a balanced address mapping table is generated; Based on the logical address mapping relationship, dynamic adjustment is implemented through physical block load monitoring. In the video monitoring storage system, an erasure count record table is established, a wear difference threshold is set, the number of physical block erasures is scanned periodically, the wear degree variance between blocks is calculated, and when the variance exceeds the allowed range, the address remapping process is started. Select low-wear blocks as target areas, update the mapping table entries, set migration priority rules, and migrate high-access-frequency data first. During the migration process, maintain dual-version mapping tables to ensure data continuity, and generate an updated mapping table containing wear leveling markers.

[0026] S204: Based on the balanced address mapping table, a multi-level storage strategy is used to realize hierarchical storage allocation by access frequency judgment, and a storage mapping table with wear leveling markers is generated.

[0027] Based on the equalization of address mapping relationship, the multi-level storage medium allocation strategy is implemented, in the cloud computing cache system, the storage medium performance grade division standard is defined, the migration trigger condition is set according to the data access frequency, the high-frequency access data is allocated to the high-speed storage medium, the medium-frequency data is migrated to the standard performance medium, the low-frequency data is transferred to the archive storage area, the access counter and the time attenuation factor are set, the data hotness score is dynamically updated, when the score crosses the grade threshold, the data migration task is triggered, and the final storage mapping table containing the medium type label and the access strategy is generated.

[0028] The S3-based decrypted and integrity-checked bit stream includes the following steps: S301: Based on the storage mapping table with wear leveling marks, a dynamic key is generated by using a physically unclonable function technology through hardware features, and a temporary session key is generated; Based on the storage management information, a dynamic encryption element is generated by using a hardware feature extraction technology, in the industrial control system scenario, the physical characteristic parameters of the storage medium are collected as the entropy source, a feature vector set is established, a feature screening rule is set to remove redundant parameters, the screened features are normalized, seed data is generated through a specific transformation algorithm, combined with the equalization mark information in the storage mapping table, a multi-round confusion operation is performed, the key length is set to adapt to the encryption protocol requirements, and the confusion parameters are periodically updated to prevent key solidification, and finally a temporary session key bound to the current storage state is formed.

[0029] S302: Based on the temporary session key, a layered verification architecture is adopted, and the elliptic curve digital signature algorithm is used to verify the legality of the data source to generate a digital signature verification result Based on the dynamic encryption element, a layered security verification mechanism is implemented, in the smart meter firmware update scenario, a three-level verification system of device identity layer, data source layer and operation permission layer is established, the device identity layer verifies the matching degree of the hardware identification code and the pre-stored whitelist, the data source layer analyzes the digital signature information embedded in the data packet header, the standard verification process is used to compare the signature hash value, the operation permission layer checks the compatibility of the firmware version and the device model, the verification result weight distribution rule is set, and when the weighted score of each level verification result exceeds the security threshold, a digital signature verification report containing the timestamp and verification details is generated.

[0030] S303: Based on the digital signature verification result, the Advanced Encryption Standard algorithm is adopted, and the bit stream data is decrypted through the stream encryption mode to generate a decrypted and integrity-checked bit stream.

[0031] Based on the security verification result, the data decryption and integrity verification process is performed, in the medical device data loading scene, the encrypted data is processed block by block using the streaming decryption technology, the dynamic key element is injected when initializing the decryption engine, the decryption buffer capacity is set to match the data block size, the data hash value is calculated synchronously during the decryption process, the consistency of the hash value and the original check value carried in the data packet tail is verified, when the check value deviation is detected, the processing flow is interrupted, the abnormal data block is marked and the retransmission mechanism is triggered, through multiple rounds of decryption and verification operations, the plaintext data stream that meets the protocol specification and passes the integrity verification is generated.

[0032] The generation of the update data mirror with integrity label based on S4 includes the following steps: S401: Based on the decrypted and integrity-verified bit stream, the metadata log technology is used to record the operation sequence through buffer management to generate operation log records; Based on the verified data stream, the operation sequence record technology is used to manage the buffer storage process, in the smart grid control system scenario, the operation instructions are stored in the ring buffer, the data write start position, the check state label and the timestamp information are recorded, the incremental log record mode is used, the log entry containing the operation type, the target address and the data length is generated each time the data block is written, and when the log entries accumulate to a set number, the compression archiving is triggered to generate the log record set containing the complete operation time sequence and the state label.

[0033] S402: Based on the operation log records, the double-buffer alternating write mechanism is used to realize data switching through the buffer control circuit to generate the cache data mirror; Based on the operation log records, the double-storage alternating write strategy is implemented, in the rail transit signal control scenario, two independent cache areas are configured, the cache switching trigger condition is set, when the main cache area write progress reaches the capacity threshold, the control circuit switches to the standby cache area for continuous writing, the data solidification operation of the main cache area is completed in the switching process, the check code and the version identifier are added in the solidification process, and the cache data copy containing the double version identifier and the switching label is generated.

[0034] S403: Based on the cache data mirror, the power supply protection technology is used to trigger the emergency process through voltage monitoring to generate the power failure protection data mirror; Based on the cache data copy, the power anomaly protection mechanism is implemented, in the medical device firmware update scenario, the voltage monitoring circuit is configured to collect power supply parameters in real time, the voltage fluctuation tolerance range is set, when it is detected that the voltage value continuously deviates from the normal interval, the data protection process is triggered, the cache data is quickly transferred to the non-volatile storage medium, the data compression technology is used in the transfer process to reduce the write time, and the power failure protection data copy containing the emergency transfer path label is generated.

[0035] S404: Based on the power-off protection data mirror, a bad block management algorithm is used to avoid damaged areas through a spare block table and generate a physical storage mapping solution; Based on emergency protection data, a storage medium defect avoidance strategy is implemented. In the industrial robot control system scenario, a physical block health status table is established, the storage medium bad block mark is periodically scanned, and a bad block judgment threshold is set. When the number of consecutive read failures exceeds the threshold, the corresponding physical block is marked as unavailable, and an alternative storage area is allocated from the spare block table. The address mapping relationship is updated to generate a storage allocation plan that includes bad block avoidance marks.

[0036] S405: Based on the physical storage mapping solution, an atomic write protocol is adopted, and storage is completed through a synchronization control mechanism to generate an updated data image with an integrity mark.

[0037] Based on the storage allocation plan, transactional data write control is implemented. In the aerospace electronic equipment scenario, a write operation synchronization lock mechanism is configured and operation atomicity guarantee rules are set. The write process is divided into the preparation phase to submit storage parameters, the execution phase to complete data solidification, and the confirmation phase to verify the storage results. If any phase fails, it rolls back to the pre-preparation state, and finally generates an updated data image containing the version number and integrity check mark.

[0038] Generating a system health assessment report and rollback log based on S5 includes the following steps: S501: Based on the updated data image with integrity mark, using runtime monitoring technology, collect system parameters through the sensor network to generate a multi-dimensional parameter data set; Based on integrity-tagged data, operating status parameters are collected through a multi-source sensor network. In the smart grid control system scenario, temperature sensors are deployed to monitor the chip operating temperature, voltage sensors collect power supply fluctuation data, and clock circuits record configuration loading timing. The data collection cycle is synchronized with the system heartbeat mechanism, and abnormal data filtering rules are set to eliminate outliers caused by transient interference. After aligning data from different sensors by timestamp, a parameter set with multiple dimensions such as device temperature, voltage stability, and response delay is constructed.

[0039] S502: Based on the multi-dimensional parameter data set, a weighted scoring model is used to calculate the health index through a logical reasoning engine to generate a health assessment report; Based on a multi-dimensional parameter set, a dynamic weight scoring mechanism is implemented. In the industrial robot control scenario, the temperature weight coefficient is defined to reflect the status of the cooling system, the voltage weight coefficient represents the power supply stability, and the delay weight coefficient reflects the processing performance. The mean and standard deviation of each parameter are calculated through a sliding window, and a health baseline is set. When the parameter value deviates from the baseline, the deduction items are calculated according to the deviation amplitude. The health index is generated by deducting the accumulated deductions from the total score. The index range is divided into three status intervals: normal, warning, and fault.

[0040] S503: Based on the health assessment report, a reinforcement learning algorithm is used to determine the rollback timing using the fault knowledge base and generate a version rollback decision instruction. Based on the health assessment results, an intelligent rollback decision-making strategy is implemented. In the aerospace electronic equipment scenario, a knowledge graph containing historical failure modes and repair solutions is constructed. When the health index enters the warning range, the reinforcement learning model is started to analyze recent operation logs, calculate the risk value of continuing operation and the rollback cost value, and set the decision threshold. When the risk value exceeds a specific multiple of the cost value, a decision instruction containing the target version identifier and rollback priority is generated.

[0041] S504: Based on the version rollback decision instruction, incremental reconfiguration technology is used to achieve system recovery through configuration difference comparison, and a system health assessment report and rollback log are generated.

[0042] Based on the rollback decision instructions, difference comparison and incremental recovery are implemented. In the rail transit signal system scenario, the metadata features of the current configuration and the target version are extracted, the hash value differences of the functional modules are compared, and reconfiguration operations are only performed on the difference modules. The reconfiguration process adopts a dual-stream verification mechanism. The main thread executes the write operation, and the monitoring thread verifies the integrity of the written data in real time. After the reconfiguration is completed, a log file containing the operation timestamp, recovery module list, and verification results is generated.

[0043] An FPGA program loading and updating system includes the following modules: a communication interface module, an intelligent storage module, and a secure writing module; The communication interface module is based on a differential transmission protocol and adopts a dynamic block verification algorithm. It realizes data reassembly through a double-buffered receiving mechanism, and uses cyclic redundancy check and sequence tracking technology to complete data verification and generate a verified data stream. The communication interface module includes a channel monitoring submodule, a block optimization submodule, and a verification processing submodule; The intelligent storage module uses a storage allocation algorithm based on verified data streams, analyzes usage frequency through access feature prediction models, combines multi-level storage strategies, and uses a dynamic address management table to achieve intelligent allocation and generate an optimized storage solution. The intelligent storage module includes an access analysis submodule, an address allocation submodule, and a balance management submodule; The secure write module, based on an optimized storage solution, uses hardware feature encryption technology to generate dynamic keys, completes security verification through a layered verification mechanism and decryption algorithm, implements an atomic write protocol to ensure operational integrity, and generates an executable program image; The secure writing module includes a key management submodule, a security verification submodule, and a writing control submodule.

[0044] The channel quality monitoring submodule uses a neural network algorithm to analyze historical communication quality data based on physical layer characteristics and generate a channel quality assessment report; Based on the transmission characteristics of the physical layer, historical communication quality data is processed through a timing analysis model. In the industrial wireless sensor network scenario, the signal strength, bit error rate, and environmental interference intensity within continuous working cycles are collected as input features. A sliding window dataset is constructed with the window length covering a typical communication cycle. Feature normalization rules are set to eliminate dimensional differences. Channel state features are extracted through a multi-layer perception network. The output layer generates quality assessment indicators including channel stability score, noise interference level, and available bandwidth prediction. Quality grade classification standards are set in combination with communication protocol requirements. When the evaluation indicators deviate from the normal range, a warning mark is triggered, and finally a quality report containing multi-dimensional evaluation parameters is formed.

[0045] The dynamic block submodule uses a dynamic optimization algorithm to adjust the data block strategy based on the channel quality assessment report and generate a block-optimized data set; Based on the channel quality assessment results, dynamic block strategy optimization is implemented. In the vehicle network data transmission scenario, the block benchmark parameters are initialized, the block size is adjusted according to the channel stability score, and a nonlinear mapping relationship between the block size and the channel quality is set. When the channel score drops, the block size is reduced exponentially, and when the score rises, the block size is increased linearly. Forward error correction coding is added in the data reconstruction stage, and block feature metadata is embedded in the header. The receiving end reconstructs the data stream by parsing the metadata. After multiple rounds of iterative optimization of the block parameters, a block data set that adapts to the channel status is generated.

[0046] The data verification submodule uses a redundant verification mechanism and sequence tracking technology to perform verification based on the block-optimized data set, and generates verified bit stream data.

[0047] Based on the optimized block data set, a multiple verification mechanism is implemented. In the satellite communication scenario, the cyclic redundancy check polynomial parameters are configured, the check code of each data block is calculated, the receiving sequence number tracking table is established, and the continuity detection tolerance range is set. When an abnormal sequence number interval is detected, the compensation mechanism is triggered. The verification thread processes the data blocks of the double buffer in parallel, and the consistency of the check code is quickly verified through the XOR operation. The abnormal data blocks are transferred to the retransmission queue. After three rounds of integrity verification and data reorganization, a verified data stream that complies with the transmission protocol specification is generated.

[0048] The heat analysis submodule uses a probability prediction model to analyze storage access characteristics based on verified bitstream data and generates a storage heat distribution map; Based on verified data streams, storage behavior characteristics are analyzed through access pattern modeling technology. In the smart home gateway storage scenario, the device firmware access timestamps and operation types of the past seven days are extracted, and an access frequency matrix is ​​constructed. A time decay factor is set to give more weight to recent accesses. The heat score of each storage block is calculated, and a heat level classification standard is established. The storage blocks are divided into three levels: high-frequency access area, medium-frequency access area, and low-frequency access area. The heat score is periodically updated and the area is re-divided to generate a two-dimensional distribution map reflecting the access intensity of the storage area.

[0049] The address mapping submodule allocates storage intervals based on the storage heat distribution map using a space optimization algorithm to generate a logical address allocation table; Based on the storage access intensity distribution data, a space allocation optimization strategy is implemented. In the video surveillance storage system, the storage medium is divided into performance difference areas, and an address mapping binary tree structure is established. The search priority is set according to the heat level. High-frequency access data is preferentially matched to the high-speed storage area, medium-frequency data is allocated to the standard performance area, and low-frequency data is transferred to the archive storage area. The branch weight is dynamically adjusted when traversing the binary tree nodes, and a matching score threshold is set. When the candidate area score meets the optimization requirements, the search is terminated and an allocation table containing the mapping relationship between logical addresses and physical blocks is generated.

[0050] The wear leveling submodule, based on the logical address allocation table, uses dynamic load analysis technology to optimize storage distribution and generate a storage solution with balanced marking.

[0051] Based on the logical address mapping relationship, a physical storage load balancing strategy is implemented. In the IoT terminal device scenario, a physical block erase count monitoring table is established, a wear difference tolerance threshold is set, and the statistical variance of the erase count of each block is periodically calculated. When the variance exceeds the threshold, the remapping process is started, and the low-wear block is selected as the target area. A dual-version comparison table of the new and old address mappings is established, and data migration priority rules are set. Prioritize the migration of high-frequency access data, maintain the accessibility of dual-version data during the migration process, update the mapping table version after the migration is completed, and generate a storage solution that includes balancing marks and migration records.

[0052] The key generation submodule, based on a balanced tag storage scheme, uses hardware feature extraction technology to generate encryption elements and generate dynamic session keys; Based on storage management information, dynamic encryption elements are generated through physical feature extraction technology. In the industrial control system scenario, the storage medium crystal oscillator frequency deviation, power supply ripple characteristics, and silicon wafer process difference parameters are collected as entropy sources to establish a feature vector pool. Feature screening rules are set to remove fluctuation parameters caused by environmental interference. The screened features are transformed nonlinearly and combined with the balance mark information in the storage mapping table to perform multiple rounds of displacement confusion and XOR operations. The key update cycle is linked to the storage status change event to generate a temporary session key bound to the physical hardware.

[0053] The decryption verification submodule uses a layered verification architecture to complete security verification based on dynamic session keys and generate decryption verification pass data; Based on dynamic encryption elements, a multi-level security verification mechanism is implemented. In the smart meter firmware update scenario, a device fingerprint layer is established to verify the matching degree between the hardware unique identification code and the pre-stored database. The data integrity layer parses the hash tree information carried in the data packet header and verifies the data block hash value step by step. The operation compliance layer checks the compatibility identification of the firmware version and the hardware model, and sets a comprehensive scoring rule for the verification result. When the weighted sum of the verification scores at each level reaches the security access threshold, a decryption permission certificate containing a timestamp and verification details is generated.

[0054] The atomic write submodule uses transactional write technology to ensure storage integrity based on decrypted and verified data and generates an executable configuration image.

[0055] Based on security verification credentials, transactional data storage operations are implemented. In the rail transit signal system scenario, a dual-version storage mapping table is configured. The writing process is divided into a preparation phase to lock the target storage area, an execution phase to synchronously update the primary and backup mapping tables, a confirmation phase to verify the consistency of the two tables, and an operation timeout rollback mechanism to automatically restore to the state before the operation when any phase is not completed. After the writing is completed, an integrity mark containing the operation sequence number and check code is added to generate an executable device configuration image.

[0056] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for loading and updating FPGA programs, characterized in that The following steps are involved: S1: Based on the RS422 physical layer protocol, it uses an adaptive block checksum algorithm, obtains signal-to-noise ratio parameters in real time through the channel quality monitoring module, dynamically adjusts the data packet size, implements a double-buffered alternating reception mechanism, verifies each data block using cyclic redundancy check and sequence number continuity detection, discards abnormal data packets with excessive bit error rates, and generates a set of verified data blocks. S2: Based on the verified data block set, an improved buddy system algorithm is used to analyze the access frequency of configuration versions through a storage heat prediction model, dynamically calculate the optimal storage location, and combine a multi-level wear leveling strategy with a dynamic address mapping table to record the correspondence between physical blocks and logical addresses, generating a storage mapping table with wear leveling tags. S3: Based on a memory mapping table with wear-leveling markings, it adopts a layered security verification architecture, generates dynamic keys through physically unclonable functions, performs digital signature verification and encryption standard decryption, implements integrity verification, extracts runtime features from the decrypted bitstream, compares it with a reference model, and generates a decrypted bitstream that passes the integrity check. S4: Based on the decrypted and integrity-checked bitstream, it employs an atomic write control algorithm, uses a double-buffered alternating write mechanism to temporarily store data, uses metadata logging technology to record operation sequences, and combines power supply protection circuits to ensure storage operation integrity. Finally, a bad block management engine avoids physically damaged areas and generates an updated data image with integrity markings. S5: Based on an updated data mirror with integrity markers, a multi-dimensional health assessment model is adopted. Key parameters are collected through a runtime monitoring agent, and a scoring algorithm is used to calculate system health. When the score is continuously below the threshold, an incremental version rollback mechanism is triggered. The historical version is selected based on the fault knowledge base, and rapid recovery is achieved through the reconfiguration interface. A system health assessment report and rollback log are generated.

2. The FPGA program loading and updating method according to claim 1, wherein: Generating a verified data block set based on S1 includes the following steps: S101: Based on the characteristics of the RS422 physical layer, it uses a long short-term memory neural network algorithm to train with historical channel bit error rate data to predict future channel quality and generate a channel quality prediction report. S102: Based on the channel quality prediction report, a dynamic block adjustment algorithm is used to calculate the optimal block size using an error optimization criterion, and data packets are reassembled to generate a block-optimized data set. S103: Based on the block-based optimized data set, a double-buffered alternating receiving mechanism is adopted to complete data verification through cyclic redundancy check and sequence number continuity detection, and a verified data block set is generated.

3. The FPGA program loading and updating method according to claim 1, wherein: Generating a storage mapping table with wear leveling marks based on S2 includes the following steps: S201: Based on the verified data block set, a Markov chain prediction model is used to analyze and calculate the storage heat value through configuration version access logs to generate a storage heat distribution map; S202: Based on the storage heat distribution map, an improved buddy system algorithm is used to find the optimal storage interval through a tree search mechanism to generate a logical address mapping table; S203: Based on the logical address mapping table, a dynamic wear leveling algorithm is used to adjust the physical storage location by analyzing the number of erases to generate a balanced address mapping table; S204: Based on the balanced address mapping table, a multi-level storage strategy is adopted to implement hierarchical storage allocation through access frequency determination, and a storage mapping table with a wear leveling mark is generated.

4. The FPGA program loading and updating method according to claim 1, wherein: Generating a decrypted and integrity-checked bitstream based on S3 involves the following steps: S301: Based on a storage mapping table with a wear leveling flag, a physical unclonable function technology is used to generate a dynamic key through hardware characteristics to generate a temporary session key; S302: Based on the temporary session key, a layered verification architecture is adopted to verify the legitimacy of the data source using the elliptic curve digital signature algorithm and generate a digital signature verification result; S303: Based on the digital signature verification result, the Advanced Encryption Standard algorithm is used to decrypt the bit stream data in stream encryption mode to generate a decrypted bit stream that passes the integrity check.

5. The FPGA program loading and updating method according to claim 1, wherein: Generating an updated data image with integrity markers based on S4 includes the following steps: S401: Based on the decrypted and integrity-checked bitstream, metadata logging technology is used to record the operation sequence through buffer management to generate an operation log record; S402: Based on the operation log records, a dual-buffer alternating write mechanism is adopted to implement data switching through a buffer control circuit to generate a cache data mirror; S403: Based on the cache data mirror, power supply protection technology is used to trigger an emergency process through voltage monitoring to generate a power failure protection data mirror; S404: Based on the power-off protection data mirror, a bad block management algorithm is used to avoid damaged areas through a spare block table and generate a physical storage mapping solution; S405: Based on the physical storage mapping solution, an atomic write protocol is adopted, and storage is completed through a synchronization control mechanism to generate an updated data image with an integrity mark.

6. The FPGA program loading and updating method according to claim 1, wherein: Generating a system health assessment report and rollback log based on S5 includes the following steps: S501: Based on the updated data image with integrity mark, using runtime monitoring technology, collect system parameters through the sensor network to generate a multi-dimensional parameter data set; S502: Based on the multi-dimensional parameter data set, a weighted scoring model is used to calculate the health index through a logical reasoning engine to generate a health assessment report; S503: Based on the health assessment report, a reinforcement learning algorithm is used to determine the rollback timing using the fault knowledge base and generate a version rollback decision instruction. S504: Based on the version rollback decision instruction, incremental reconfiguration technology is used to achieve system recovery through configuration difference comparison, and a system health assessment report and rollback log are generated.

7. An FPGA program loading and updating system, characterized in that Includes the following modules: communication interface module, intelligent storage module, and secure writing module; The communication interface module is based on a differential transmission protocol and adopts a dynamic block verification algorithm. It realizes data reassembly through a double-buffered receiving mechanism, and uses cyclic redundancy check and sequence tracking technology to complete data verification and generate a verified data stream. The communication interface module includes a channel monitoring submodule, a block optimization submodule, and a verification processing submodule; The intelligent storage module uses a storage allocation algorithm based on verified data streams, analyzes usage frequency through access feature prediction models, combines multi-level storage strategies, and uses a dynamic address management table to achieve intelligent allocation and generate an optimized storage solution. The intelligent storage module includes an access analysis submodule, an address allocation submodule, and a balance management submodule; The secure write module, based on an optimized storage solution, uses hardware feature encryption technology to generate dynamic keys, completes security verification through a layered verification mechanism and decryption algorithm, implements an atomic write protocol to ensure operational integrity, and generates an executable program image; The secure writing module includes a key management submodule, a security verification submodule, and a writing control submodule.

8. The FPGA program loading and updating system according to claim 7, characterized in that: The channel quality monitoring submodule uses a neural network algorithm to analyze historical communication quality data based on physical layer characteristics and generate a channel quality assessment report; The dynamic block submodule uses a dynamic optimization algorithm to adjust the data block strategy based on the channel quality assessment report and generate a block-optimized data set; The data verification submodule uses a redundant verification mechanism and sequence tracking technology to perform verification based on the block-optimized data set, and generates verified bit stream data.

9. The FPGA program loading and updating system according to claim 7, characterized in that: The heat analysis submodule uses a probability prediction model to analyze storage access characteristics based on verified bitstream data and generates a storage heat distribution map; The address mapping submodule allocates storage intervals based on the storage heat distribution map using a space optimization algorithm to generate a logical address allocation table; The wear leveling submodule, based on the logical address allocation table, uses dynamic load analysis technology to optimize storage distribution and generate a storage solution with balanced marking.

10. The FPGA program loading and updating system according to claim 7, characterized in that: The key generation submodule, based on a balanced tag storage scheme, uses hardware feature extraction technology to generate encryption elements and generate dynamic session keys; The decryption verification submodule uses a layered verification architecture to complete security verification based on dynamic session keys and generate decryption verification pass data; The atomic write submodule uses transactional write technology to ensure storage integrity based on decrypted and verified data and generates an executable configuration image.

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