A Distributed Method and System for Automatic Generation of Financial Statements Based on Channel State

Abstract

This invention discloses a method and system for automatically generating distributed financial statements based on channel state, belonging to the field of report data processing technology. The invention acquires settlement message streams, actual and standard sampling sequences, and message retransmission rates; extracts the offset variation coefficient between the actual and standard sampling sequences as the physical steady-state confidence level; calculates the differential entropy of the actual sampling sequence and weights it with the retransmission rate to obtain the asymmetric transmission entropy, and uses this as the base to perform an exponential operation on the buffer period to generate a transaction locking period; exponentially decays the ratio of the maximum differential entropy to the asymmetric transmission entropy and multiplies it by the confidence level to output a synchronization confidence index; based on this index, it determines whether to perform gain step accumulation or encapsulation to generate reports. This invention utilizes the nonlinear mutual verification of physical steady state and channel distortion to achieve accurate alignment of lending data in network timing mismatch scenarios.

Description

Technical Field

[0001] This invention relates to the field of report data processing technology, and more specifically, this application relates to a method and system for automatically generating distributed financial statements based on channel status. Background Technology

[0002] With the deepening of digital transformation in large enterprises, distributed financial settlement systems have been widely used in cross-regional business aggregation scenarios. However, existing distributed ledger systems generally rely on the assumption of an ideal strong consistency network. When faced with frequent asymmetric network jitter and underlying communication congestion in wide area networks, the static reconciliation window and single business layer verification rules of traditional systems often fail to truly reflect the causal sequence of physical events, which can easily lead to the logical collapse of financial data reports.

[0003] In high-concurrency, cross-regional financial settlement applications, the sensor sampling pulses for physical goods receipt and the upstream business settlement messages are often transmitted through different heterogeneous network links. Existing solutions typically only set a static timeout threshold at the application layer, executing the receipt and accounting as soon as a data packet is received within the specified threshold. However, when the objective communication environment experiences asymmetric link delays or local congestion at physical nodes, a significant phase difference will occur in the time it takes for physical information and financial information to reach the aggregation engine. Because existing technologies lack the ability to perceive the true state of the underlying physical channels, they cannot distinguish between normal network delays and physical deadlocks of underlying devices, nor can they dynamically adjust alignment strategies based on the deterioration trend of the objective communication links. This leads to the system frequently forcing receipts under conditions of timing mismatch, resulting in technical defects such as unilateral isolation of loans and accounts and imbalanced accounts.

[0004] In summary, how to overcome the causal break and synchronization failure of lending data caused by the lack of an underlying objective physical state and application layer time window compensation interaction mechanism in heterogeneous network asymmetric latency scenarios is a core technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] To address the aforementioned technical problems, this paper provides a method and system for automatically generating distributed financial statements based on channel state. This technical solution resolves the issues raised in the background section.

[0006] In a first aspect, embodiments of this application provide a method for automatically generating distributed financial statements based on channel state, comprising the following steps: acquiring the business settlement message stream of the alignment buffer period, the corresponding actual data sampling sequence, the corresponding message retransmission rate, and the standard sampling sequence; extracting the offset variation coefficient between the actual data sampling sequence and the standard sampling sequence; calculating the physical steady-state confidence level based on the reciprocal of the offset variation coefficient between the actual data sampling sequence and the standard sampling sequence; calculating the differential entropy of the actual data sampling sequence in each preset discrete interval and performing normalized weighted summation with the message retransmission rate to obtain the asymmetric transmission entropy; using the asymmetric transmission entropy as the base and performing exponentiation on the time scalar obtained by mapping the alignment buffer period as the exponent term to generate a transaction lock-in period; within the current transaction lock-in period, extracting the actual data sampling sequence in each preset discrete interval... The maximum differential entropy within the discrete interval is calculated, and the first ratio of the maximum differential entropy to the asymmetric transmission entropy is calculated. An exponential decay operation is performed on this first ratio using a preset damping coefficient, and then multiplied by the physical steady-state confidence level to output a synchronization confidence index characterizing the bilateral synchronization state of settlement. If the synchronization confidence index is less than a preset threshold, a historical observation sequence of the asymmetric transmission entropy is constructed, and the second-order difference value is calculated. If the second-order difference value is greater than zero, it is multiplied by a preset step size to obtain the gain step size, which is then added to the current transaction lock-up period to obtain the updated transaction lock-up period, which is applied to the next alignment buffer period. If the synchronization confidence index is greater than or equal to the preset threshold, a financial statement for the current transaction lock-up period is generated based on the business settlement message stream.

[0007] Secondly, embodiments of this application provide a distributed automatic financial statement generation system based on channel state, comprising: a data acquisition module: used to acquire the business settlement message stream of the alignment buffer period, the corresponding actual data sampling sequence, the corresponding message retransmission rate, and the standard sampling sequence; extract the offset variation coefficient between the actual data sampling sequence and the standard sampling sequence; calculate the physical steady-state confidence level based on the reciprocal of the actual data sampling sequence; calculate the differential entropy of the actual data sampling sequence in each preset discrete interval and perform normalized weighted summation with the message retransmission rate to obtain the asymmetric transmission entropy; a transaction lock-up period processing module: used to use the asymmetric transmission entropy as the base and the time scalar obtained by mapping the alignment buffer period as the exponent to perform exponentiation to generate the transaction lock-up period; and a synchronization confidence index processing module: used to extract the actual data sampling sequence within the current transaction lock-up period. The module calculates the maximum value of the differential entropy within each preset discrete interval, and then calculates the first ratio of the maximum value of the differential entropy to the asymmetric transmission entropy. This first ratio is then subjected to exponential decay using a preset damping coefficient, and multiplied by the physical steady-state confidence level to output a synchronization confidence index characterizing the bilateral synchronization state of settlement. The transaction lock-up period update module is used to construct a historical observation sequence of the asymmetric transmission entropy and calculate the second-order difference if the synchronization confidence index is less than a preset threshold. If the second-order difference is greater than zero, it is multiplied by a preset step size to obtain the gain step size, which is then added to the current transaction lock-up period to obtain the updated transaction lock-up period, which is then applied to the next alignment buffer period. The report output module is used to generate a financial report for the current transaction lock-up period based on the encapsulation of the business settlement message stream if the synchronization confidence index is greater than or equal to a preset threshold.

[0008] Thirdly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for automatically generating distributed financial statements based on channel state.

[0009] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0010] 1. To address the technical problem of data arrival time disorder caused by network jitter in distributed systems, this solution obtains the actual data sampling sequence and message retransmission rate corresponding to the alignment buffer period. It calculates the differential entropy of the actual data sampling sequence within each preset discrete interval and performs a normalized weighted summation with the message retransmission rate to obtain the asymmetric transmission entropy. This entropy is then used as the base of a power function to perform an exponential operation on the alignment buffer period to generate the transaction locking period. This technique overcomes the limitations of relying on static waiting windows and can adaptively stretch the waiting time based on the physical distortion of the underlying channel, achieving the technical effect of ensuring sufficient physical space-time margin for completing bilateral data alignment in asymmetric delay network environments.

[0011] 2. To address the technical issue that single-application-layer latency judgments can easily lead to false alarms in underlying devices, this solution extracts the coefficient of variation of the offset between the actual data sampling sequence and the standard sampling sequence as the physical steady-state confidence level. The first ratio obtained after exponential decay is then multiplied by the physical steady-state confidence level to output a synchronization confidence index characterizing the bilateral synchronization status of the settlement. This technique achieves cross-verification between the fluctuation stability at the physical hardware level and the entropy status at the communication link layer, effectively eliminating interference from false alarms in underlying acquisition devices or one-way communication congestion on accounting decisions and improving the accuracy of data synchronization status judgment.

[0012] 3. To address the technical problems of passive congestion and data processing disconnect caused by continuous network deterioration, this solution constructs a historical observation sequence of asymmetric transmission entropy and calculates the second-order difference value when the synchronization confidence index is less than a preset threshold. When the second-order difference value is greater than zero, it is multiplied by a preset step size to obtain the gain step size, which is then accumulated into the current transaction locking period. This technique overcomes the lag defect of passive response to first-order parameters, enabling early detection of accelerated channel deterioration. It achieves the technical effect of performing predictive timing step size compensation before substantial link failure, thereby improving the data synchronization fault tolerance rate of the distributed system under harsh communication conditions. Attached Figure Description

[0013] Figure 1 A schematic diagram illustrating the steps of the distributed financial statement automatic generation method based on channel state provided in this application embodiment;

[0014] Figure 2 A schematic diagram of the logic flow of the distributed automatic financial statement generation method based on channel state provided in the embodiments of this application;

[0015] Figure 3 This is a schematic diagram of the structure of a distributed financial statement automatic generation system based on channel state provided in an embodiment of this application. Detailed Implementation

[0016] This application's embodiments solve the technical problems of causal breakage and synchronization failure in lending data caused by the lack of an underlying objective physical state and application layer time window compensation interaction mechanism in the prior art through a distributed financial statement automatic generation method and system based on channel state.

[0017] In distributed cross-regional settlement scenarios, physical status information and fund messages are transmitted through heterogeneous channels. When the objective physical network experiences asymmetric delays or underlying congestion, the traditional static waiting window is prone to unilateral forced recording of loans due to timing mismatches, leading to a break in financial causality. To fundamentally solve this technical problem of application layer synchronization failure caused by a lack of underlying state awareness, this solution constructs an adaptive control mechanism that uses the physical channel state to drive the business timeline.

[0018] This solution directly acquires the service settlement message stream, actual data sampling sequence, message retransmission rate, and standard sampling sequence for the alignment buffer period. To characterize the real-time health of the physical sensing devices, this solution extracts the coefficient of variation of the offset between the actual data sampling sequence and the standard sampling sequence, quantifying it as a physical steady-state confidence level. Simultaneously, to measure the objective distortion degree of the communication link, this solution calculates the differential entropy of the actual data sampling sequence within each preset discrete interval, and performs a normalized weighted summation with the message retransmission rate to obtain the asymmetric transmission entropy. These two underlying parameters represent the physical stability of the system hardware and the transmission uncertainty of the network, respectively.

[0019] Based on the uncertainties inherent in the physical world, this scheme uses the asymmetric transmission entropy as the base of a power function to perform an exponential operation on the alignment buffer period, thereby nonlinearly generating the transaction locking period. The more chaotic the objective network environment, the longer the waiting time is adaptively stretched by the system, thus forcibly providing sufficient physical spatiotemporal margin. Within the current transaction locking period, the maximum value of the differential entropy of the actual data sampling sequence in each preset discrete interval is extracted, and its first ratio relative to the asymmetric transmission entropy is calculated. This first ratio is then subjected to exponential decay operation through a preset damping coefficient, and multiplied by the physical steady-state confidence level, finally outputting a synchronization confidence index characterizing the bilateral synchronization state of settlement. This calculation mechanism accurately reflects the true alignment state through cross-validation of hardware status and network latency.

[0020] Subsequently, this scheme performs dynamic branch control based on the synchronization confidence index. If the synchronization confidence index is less than a preset threshold, it indicates that the bilateral data has not yet met the compliant accounting conditions. This scheme constructs a historical observation sequence of asymmetric transmission entropy and calculates the second-order difference value. When the second-order difference value is greater than zero, it indicates that the physical channel has a tendency to accelerate its collapse. This scheme multiplies the second-order difference value with a preset step size to obtain the gain step size, and adds it to the current transaction lock-in period to obtain the updated transaction lock-in period, which is then applied to the next cycle to achieve advance timing step size compensation for disaster prevention. If the synchronization confidence index is greater than or equal to the preset threshold, the system determines that the bilateral causality has been substantially closed, and generates the current transaction lock-in period financial statement based on the business settlement message stream encapsulation.

[0021] This solution breaks down the isolation barrier between application-layer business reconciliation logic and the underlying physical channel state, constructing an interactive compensation system based on asymmetric transmission entropy and physical steady-state confidence. In extreme application scenarios with heterogeneous network asymmetric latency, this mechanism effectively overcomes the defect of forced data entry due to unilateral data timeout caused by static thresholds, ensuring strong causal consistency of the distributed ledger under complex network fluctuations.

[0022] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0023] like Figure 1 The diagram illustrates the steps of the distributed financial statement automatic generation method based on channel state provided in this embodiment. This embodiment enables accurate causal alignment of physical data and financial messages in heterogeneous network asymmetric latency scenarios, solving the problems of data timing disorder and financial logic failure caused by the reliance on static wait windows in traditional distributed systems. The core basic solution of this invention will be described in detail below with specific steps.

[0024] The method for automatically generating distributed financial statements based on channel state first executes the steps of acquiring the business settlement message stream, the corresponding actual data sampling sequence, the corresponding message retransmission rate, and the standard sampling sequence for the alignment buffer period. In distributed financial settlement application scenarios, the business settlement message stream refers to the fund transfer instructions and electronic voucher data transmitted via the EDI gateway; the actual data sampling sequence refers to the set of timestamps obtained by sensors deployed at the warehousing or logistics site to physically sample the physical goods receiving actions; the message retransmission rate refers to the proportion of packet loss and retransmission counted by the network protocol stack within the current alignment buffer period; and the standard sampling sequence refers to the baseline time sequence preset by the system and synchronized with the actual business rhythm under an ideal jitter-free environment. The alignment buffer period is a pre-set time window for initial reconciliation observation, with a typical engineering value set to 200ms to 500ms.

[0025] Subsequently, this scheme performs the step of extracting the offset variation coefficient between the actual data sampling sequence and the standard sampling sequence, and then calculating the physical steady-state confidence score based on the reciprocal of this coefficient. The physical steady-state confidence score is used to characterize the hardware operational stability and local clock jitter of the underlying physical acquisition device.

[0026] Next, this scheme performs a step of calculating the differential entropy of the actual data sampling sequence within each preset discrete interval and then performing a normalized weighted summation with the message retransmission rate to obtain the asymmetric transmission entropy. The preset discrete interval refers to a statistical interval divided into time intervals with a fixed step size, such as 10ms. By calculating the differential entropy to quantify the spatiotemporal complexity of the channel and combining it with the message retransmission rate—a communication stress indicator—the intensity of channel asymmetric distortion can be accurately identified.

[0027] Subsequently, this scheme executes the step of using the asymmetric transmission entropy as the base and the time scalar obtained by mapping the alignment buffer period as the exponent to perform a power operation to generate the transaction lock-in period. In this step, the asymmetric transmission entropy serves as the core variable controlling the stretching of the time axis. When the channel environment deteriorates, the power operation causes the transaction lock-in period to expand non-linearly, providing sufficient physical waiting time for the convergence of data between the lending and borrowing parties.

[0028] Within the current transaction lock-up period, this scheme performs the following steps: extracting the maximum differential entropy of the actual data sampling sequence within each preset discrete interval; calculating the first ratio of the maximum differential entropy to the asymmetric transmission entropy; performing an exponential decay operation on the first ratio using a preset damping coefficient; and then multiplying it by the physical steady-state confidence level to output a synchronization confidence index characterizing the bilateral synchronization status of settlement. The preset damping coefficient is used to adjust the sensitivity of confidence level decline, and the first ratio reflects the degree of deviation of the current jitter extreme value from the global average entropy weight.

[0029] After outputting the synchronization confidence index, this scheme enters the branch decision logic. If the synchronization confidence index is less than the preset threshold, it indicates that the current data integrity and causal certainty are insufficient. Therefore, a historical observation sequence of asymmetric transmission entropy is constructed, and the second-order difference value is calculated. If the second-order difference value is greater than zero, it indicates that channel distortion is accelerating. In this case, the second-order difference value is multiplied by a preset step size to obtain the gain step size, which is then added to the current transaction lock-in period to obtain the updated transaction lock-in period and applied to the next alignment buffer period. The preset step size is typically set to 20ms to 50ms based on the maximum reconciliation latency supported by the system.

[0030] If the synchronization confidence index is greater than or equal to a preset threshold, it indicates that the causal loop between the lending and borrowing sides has been completed at both the logical and physical levels. Then, the financial statement for the current transaction lock-up period is generated by encapsulating the business settlement message stream. The encapsulation process involves extracting key elements such as transaction amount and account code from the business settlement message stream and concatenating them into the generated encrypted ledger data structure.

[0031] Figure 2 This is a schematic diagram of the logic flow of the distributed financial statement automatic generation method based on channel state provided in this application embodiment. This embodiment constructs an adaptive control mechanism that drives the application layer's accounting timing from the underlying physical channel state, effectively solving the problem of loan timing balancing failure caused by channel asymmetric jitter in distributed architecture, and achieving strong causal consistency of financial data generation under high concurrency conditions.

[0032] Furthermore, after obtaining the service settlement message flow of the alignment buffer period, this embodiment also includes the step of calculating the descent gradient of the message retransmission rate of the service settlement message flow of two adjacent alignment buffer periods. The descent gradient is obtained by calculating the arithmetic difference between the message retransmission rate of the current period and the previous period, reflecting the intervention status of the congestion control protocol.

[0033] When the absolute value of the descent gradient is detected to be greater than a preset negative gradient threshold, and the current packet retransmission rate is less than a preset false zeroing threshold, a false zeroing state caused by the TCP backoff mechanism is determined to have occurred. At this time, the step of extracting the moving average of the historical observation sequence of asymmetric transmission entropy as a replacement weighting factor is executed. The preset negative gradient threshold is used to identify a sudden drop in the retransmission rate, and the preset false zeroing threshold is usually set to 0.01.

[0034] Subsequently, the current message retransmission rate is replaced with an alternative weighting factor, and normalized weighted summation is re-executed to obtain the compensated transmission entropy. Finally, the compensated transmission entropy is used to replace the asymmetric transmission entropy in subsequent calculations. The compensated transmission entropy, as a virtual protection parameter, maintains the system's accurate assessment of channel pressure when the actual retransmission rate fails.

[0035] This embodiment solves the problem of retransmission rate data distortion caused by deep congestion by identifying the abnormal coupling relationship between the descent gradient and the message retransmission rate. It achieves the continuity and authenticity of channel evaluation indicators in extremely harsh network environments, can accurately identify communication backoff artifacts in deep congestion scenarios, and prevents the system from misjudging network recovery due to false zeroing of the message retransmission rate. This avoids financial data errors caused by erroneous triggering of accounting logic when the channel is extremely unstable.

[0036] Furthermore, this embodiment, after replacing the asymmetric transmission entropy with compensated transmission entropy, also includes the following steps: when the compensated transmission entropy is detected to be greater than a preset extreme entropy threshold, dividing the current transaction locking period into multiple sub-time windows and calculating the average local signal energy of the actual data sampling sequence within each sub-time window. The preset extreme entropy threshold represents that the channel has entered a severely distorted state.

[0037] Subsequently, the process involves obtaining the physical steady-state confidence level, multiplying it by a preset baseline energy line, and generating an energy cutoff threshold. The preset baseline energy line refers to the system's pre-defined background white noise reference energy value. Since the physical steady-state confidence level reflects hardware stability, combining it with the baseline energy line dynamically reflects the amplification effect of the current physical environment on noise.

[0038] Next, the process iterates through each sub-time window, and when the average local signal energy is detected to be lower than the energy cutoff threshold, the actual data sampling sequence within the corresponding sub-time window is removed. This step filters out invalid random pulses. Finally, within the remaining sub-time windows after the removal operation, the maximum differential entropy is extracted again.

[0039] This embodiment solves the problem of thermal noise interference under ultra-long waiting windows by using an energy truncation mechanism based on physical steady-state confidence. It achieves the purification extraction of core business sampling features and can automatically remove the interference of high-frequency thermal noise on latency features when the transaction lock-up period is excessively extended. This ensures that the extracted differential entropy features always come from real business data sampling, greatly improving the data alignment accuracy under extreme conditions.

[0040] Furthermore, after re-extracting the maximum value of the differential entropy, this embodiment also includes the step of counting the number of remaining sub-time windows. When the number of remaining sub-time windows is detected to be less than a preset lower limit, it means that there are no sufficient reliable signals within the current locking period. The system then performs the step of extracting the preset fluctuation variance of the standard sampling sequence and multiplying the preset fluctuation variance with the current compensated transmission entropy to generate a degradation penalty factor. The preset fluctuation variance refers to the maximum allowable fluctuation range of the standard sampling sequence at the time of factory calibration.

[0041] Subsequently, the step of replacing the first ratio input with a downgrade penalty factor in the exponential decay calculation to generate a downgraded synchronous confidence index is executed. Because the downgrade penalty factor is extremely large, the generated downgraded synchronous confidence index will quickly fall below the safety threshold. If the downgraded synchronous confidence index is detected to be below the preset circuit breaker threshold, fund transfer data is extracted from the business settlement message stream, all data within the current transaction lock-up period is locked, an abnormal audit alarm instruction is generated, and relevant personnel are notified.

[0042] This embodiment solves the computational deadlock and memory overflow problems caused by the substantial disconnection of the link through the degradation circuit breaker mechanism triggered by the remaining number of sub-time windows. It realizes the smooth transition of the system from automatic accounting to manual review. When the physical link is substantially disconnected and the data becomes completely invalid, it can break the system deadlock by forcibly triggering the asynchronous circuit breaker mechanism and guide manual intervention, thus ensuring the memory security and management controllability of the financial system in extreme disaster environments.

[0043] Furthermore, this embodiment clarifies the specific process for obtaining the physical steady-state confidence level. First, the absolute value of the time deviation between each actual sampling timestamp in the actual data sampling sequence and the corresponding standard timestamp in the standard sampling sequence is calculated, generating a time deviation sequence. The time deviation sequence reflects the degree of drift of each sampling pulse relative to the theoretical beat.

[0044] The next step involves calculating the standard deviation and mean of the time deviation series, and then dividing the standard deviation by the mean to obtain the coefficient of variation of the offset. Finally, the specific formula for the physical steady-state confidence level is as follows:

[0045] ,in, This represents the coefficient of variation of the offset. This represents the physical steady-state confidence level. The larger the coefficient of variation of the offset, the more severe the hardware-side jitter, and the lower the physical steady-state confidence level.

[0046] This embodiment solves the problem that hardware fluctuations cannot be quantified for financial decision-making by using the inverse mapping logic of the coefficient of variation. It realizes the quantitative evaluation of the determinism of the physical acquisition layer, and concretizes the abstract hardware fluctuations into statistical indicators, providing a solid physical evidence foundation for subsequent synchronization indicator calculations and ensuring the algorithm's keen perception of the underlying device status.

[0047] Furthermore, firstly, the actual data sampling sequence falls into the first... The probability density of a predetermined discrete interval is obtained using the differential entropy formula: ,in, Describing differential entropy, Indicates the first The probability density is calculated for each preset discrete interval. The probability density is obtained by calculating the proportion of sampling points falling within that interval to the total number of samples.

[0048] The next step involves mapping the differential entropy using the range normalization function to obtain the normalized differential entropy; then, the asymmetric transfer entropy is obtained using the weighted summation formula, as follows:

[0049] ,in, Represents asymmetric transmission entropy. Indicates the first preset weight. This indicates the second preset weight. Represents the normalized differential entropy. This is the message retransmission rate.

[0050] This embodiment solves the problem of insufficient evaluation dimensions of a single communication indicator by normalizing and weighting the fusion of information entropy and retransmission rate, and realizes full-dimensional quantification of complex channel distortion states. By introducing differential entropy calculation, it can identify the distribution pattern hidden in the delay fluctuation, thus more accurately depicting the disorder and compression state of the channel than simple average delay.

[0051] Furthermore, the calculation formula for the synchronization confidence index is clarified as follows: ,in, For synchronous confidence indicators, For physical steady-state confidence, For the preset damping coefficient, The first ratio, It is a natural constant.

[0052] This formula reflects the multiplicative coupling relationship between physical steady state and communication timing deviation. If the first ratio increases significantly, it means that the current jitter far exceeds the channel average entropy weight, and the exponential decay term will rapidly decrease, thereby lowering the synchronization confidence index.

[0053] This embodiment solves the problem of synchronous evaluation under the superposition of multiple disturbances by multiplying the exponential decay with the physical confidence level, and realizes the accurate measurement of the bilateral causal strength of lending. The unexpected technical effect of this subdivision scheme is that it uses exponential decay to simulate the rapid collapse process of the confidence level of information propagation in nature as the deviation increases, so that the system has a non-linear penalty mechanism for the tolerance of time sequence misalignment.

[0054] Furthermore, the specific process for obtaining the second-order difference value was clarified. The historical observation sequence includes at least the current alignment buffer period, the previous alignment buffer period, and the previous second-alignment buffer period.

[0055] The calculation is performed using the second-order difference formula as follows: ,in, Represents the second-order difference value. This represents the asymmetric transfer entropy of the current alignment buffer cycle. This represents the asymmetric transfer entropy of the previous alignment buffer cycle. This represents the asymmetric transfer entropy of the upper two-aligned buffer period.

[0056] A positive second-order difference value indicates that the rate of increase in transmission entropy is increasing.

[0057] This embodiment solves the problem of lagging trend perception in traditional control algorithms by using second-order difference operations, and realizes predictive gain control for timing compensation of distributed systems. By identifying the acceleration of channel degradation through second-order difference, the system has the ability to predict ahead and can preset a longer locking window before the network is completely paralyzed.

[0058] Furthermore, financial statements for the current transaction lock-up period are generated by encapsulating the business settlement message stream, including: parsing the business settlement message stream to extract the transaction amount field, the business occurrence timestamp, and the payment account identifier; concatenating the transaction amount field, the business occurrence timestamp, and the payment account identifier to generate a business hash digest; writing the business hash digest, synchronization confidence index, and alignment buffer period into the blockchain's smart contract node, and triggering the distributed ledger's accounting logic to generate financial statements.

[0059] Figure 3This is a schematic diagram of the structure of the distributed financial statement automatic generation system based on channel state provided in this application embodiment. The distributed financial statement automatic generation system based on channel state includes: a data acquisition module: used to acquire the business settlement message stream of the alignment buffer period, the corresponding actual data sampling sequence, the corresponding message retransmission rate and the standard sampling sequence, extract the offset variation coefficient between the actual data sampling sequence and the standard sampling sequence, calculate the physical steady-state confidence based on the reciprocal of the actual data sampling sequence, calculate the differential entropy of the actual data sampling sequence in each preset discrete interval and perform normalized weighted summation with the message retransmission rate to obtain the asymmetric transmission entropy; a transaction lock-up period processing module: used to use the asymmetric transmission entropy as the base and the time scalar obtained by mapping the alignment buffer period as the exponent to perform exponentiation to generate the transaction lock-up period; a synchronization confidence index processing module: used to perform exponentiation on the current transaction lock-up period. The system extracts the maximum differential entropy of the actual data sampling sequence within each preset discrete interval, calculates the first ratio of the maximum differential entropy to the asymmetric transmission entropy, performs an exponential decay operation on the first ratio using a preset damping coefficient, and then multiplies it with the physical steady-state confidence level to output a synchronization confidence index characterizing the bilateral synchronization state of settlement. The transaction lock-up period update module is used to construct a historical observation sequence of the asymmetric transmission entropy and calculate the second-order difference if the synchronization confidence index is less than a preset threshold. If the second-order difference is greater than zero, it is multiplied by a preset step size to obtain the gain step size, which is then added to the current transaction lock-up period to obtain the updated transaction lock-up period and applied to the next alignment buffer period. The report output module is used to generate a financial report for the current transaction lock-up period based on the business settlement message stream if the synchronization confidence index is greater than or equal to a preset threshold.

[0060] This application also provides a computer-readable storage medium for storing a computer program, which, when executed by a processor, implements a method for automatically generating distributed financial statements based on channel state.

[0061] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0062] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0063] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0064] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0065] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0066] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for automatically generating distributed financial statements based on channel states, characterized in that, Includes the following steps: Obtain the business settlement message stream of the alignment buffer period, the corresponding actual data sampling sequence, the corresponding message retransmission rate and the standard sampling sequence, extract the offset variation coefficient between the actual data sampling sequence and the standard sampling sequence, calculate the physical steady-state confidence based on the reciprocal of this coefficient, calculate the differential entropy of the actual data sampling sequence in each preset discrete interval and perform normalized weighted summation with the message retransmission rate to obtain the asymmetric transmission entropy; The transaction lock-up period is generated by using the asymmetric transmission entropy as the base and the time scalar obtained by mapping the alignment buffer period as the exponent. Within the current transaction lock-up period, the maximum value of the differential entropy of the actual data sampling sequence in each preset discrete interval is extracted, the first ratio of the maximum value of the differential entropy to the asymmetric transmission entropy is calculated, the first ratio is subjected to exponential decay operation by a preset damping coefficient, and then multiplied by the physical steady-state confidence level to output the synchronization confidence index that characterizes the bilateral synchronization status of settlement. If the synchronization confidence index is less than the preset threshold, a historical observation sequence of asymmetric transmission entropy is constructed and the second-order difference value is calculated. If the second-order difference value is greater than zero, the second-order difference value is multiplied by the preset step size to obtain the gain step size, which is then added to the current transaction lock-up period to obtain the updated transaction lock-up period and applied to the next alignment buffer period. If the synchronization confidence index is greater than or equal to the preset threshold, the financial statement for the current transaction lock-up period will be generated based on the business settlement message stream.

2. The method for automatically generating distributed financial statements based on channel state according to claim 1, characterized in that, After obtaining the business settlement message stream with aligned buffer periods, the following is also included: Calculate the descent gradient of the message retransmission rate of the service settlement message stream between two adjacent alignment buffer cycles; When the absolute value of the descent gradient is detected to be greater than the preset negative gradient threshold, and the current message retransmission rate is less than the preset false zeroing threshold, the moving average of the historical observation sequence of asymmetric transmission entropy is extracted as a replacement weighting factor. The current message retransmission rate is replaced with an alternative weighting factor, and the normalized weighted summation process is re-executed to obtain the compensated transmission entropy. Compensated transmission entropy is used to replace asymmetric transmission entropy in subsequent calculations.

3. The method for automatically generating distributed financial statements based on channel state according to claim 2, characterized in that, After replacing asymmetric transmission entropy with compensated transmission entropy, it also includes: When the compensated transmission entropy is detected to be greater than the preset extreme entropy threshold, the current transaction lock-up period is divided into multiple sub-time windows, and the local signal energy average of the actual data sampling sequence in each sub-time window is calculated. Obtain the physical steady-state confidence level, multiply the physical steady-state confidence level with the preset base energy line, and generate the energy cutoff threshold. Traverse each sub-time window, and when the average local signal energy is detected to be lower than the energy cutoff threshold, remove the actual data sampling sequence within the corresponding sub-time window; Within the remaining sub-time window after the removal operation, the maximum value of the differential entropy is extracted again.

4. The method for automatically generating distributed financial statements based on channel state according to claim 3, characterized in that, After re-extracting the maximum value of the differential entropy, it also includes: Count the number of remaining sub-time windows; When the number of remaining sub-time windows is less than the preset lower limit, the preset fluctuation variance of the standard sampling sequence is extracted, and the preset fluctuation variance is multiplied by the current compensated transmission entropy to generate a degradation penalty factor. The downgrade penalty factor is used to replace the first ratio in the exponential decay calculation to generate a downgraded synchronous confidence index. If the downgrade synchronization confidence index is detected to be lower than the preset circuit breaker threshold, the fund transfer data is extracted from the business settlement message stream and all data within the current transaction lock period is locked. An abnormal audit alarm instruction is generated and relevant personnel are notified.

5. The method for automatically generating distributed financial statements based on channel state according to claim 1, characterized in that, The specific process for obtaining the physical steady-state confidence level includes: Calculate the absolute value of the time deviation between each actual sampling timestamp in the actual data sampling sequence and the corresponding standard timestamp in the standard sampling sequence, and generate a time deviation sequence; Calculate the standard deviation and mean of the time deviation series, and divide the standard deviation by the mean to obtain the coefficient of variation of the offset. The specific formula for the physical steady-state confidence level is as follows: ,in, This represents the coefficient of variation of the offset. This represents the confidence level in physical steady state.

6. The method for automatically generating distributed financial statements based on channel state according to claim 1, characterized in that, The specific process for obtaining the asymmetric transfer entropy includes: The statistical actual data sampling sequence falls into the first The probability density of a predetermined discrete interval is obtained using the differential entropy formula: ,in, Describing differential entropy, Indicates the first The probability density of a preset discrete interval; Using the range normalization function to apply differential entropy The normalized differential entropy is obtained by mapping; The asymmetric transmission entropy is obtained using the weighted summation formula, as follows: ,in, Represents asymmetric transmission entropy. Indicates the first preset weight. This indicates the second preset weight. Represents the normalized differential entropy. This is the message retransmission rate.

7. The method for automatically generating distributed financial statements based on channel state according to claim 1, characterized in that, The formula for calculating the synchronization confidence index is as follows: ,in, For synchronous confidence indicators, For physical steady-state confidence, For the preset damping coefficient, The first ratio, It is a natural constant.

8. The method for automatically generating distributed financial statements based on channel state according to claim 1, characterized in that, The specific process for obtaining the second-order difference value includes: The historical observation sequence includes at least the current alignment buffer period, the previous alignment buffer period, and the second-to-last alignment buffer period; The calculation is performed using the second-order difference formula as follows: ,in, Represents the second-order difference value. This represents the asymmetric transfer entropy of the current alignment buffer cycle. This represents the asymmetric transfer entropy of the previous alignment buffer cycle. This represents the asymmetric transfer entropy of the upper two-aligned buffer period.

9. A distributed financial statement automatic generation system based on channel state, characterized in that, include: Data acquisition module: used to acquire the business settlement message stream of the alignment buffer period, the corresponding actual data sampling sequence, the corresponding message retransmission rate and the standard sampling sequence, extract the offset variation coefficient between the actual data sampling sequence and the standard sampling sequence, calculate the physical steady state confidence based on the reciprocal of this coefficient, calculate the differential entropy of the actual data sampling sequence in each preset discrete interval and perform normalized weighted summation with the message retransmission rate to obtain the asymmetric transmission entropy; The transaction lock-up period processing module is used to generate the transaction lock-up period by using the asymmetric transmission entropy as the base and the time scalar obtained by the alignment buffer period mapping as the exponent. Synchronization confidence index processing module: It is used to extract the maximum value of the differential entropy of the actual data sampling sequence in each preset discrete interval within the current transaction lock-up period, calculate the first ratio of the maximum value of the differential entropy to the asymmetric transmission entropy, perform an exponential decay operation on the first ratio through a preset damping coefficient, and then multiply it with the physical steady-state confidence level to output a synchronization confidence index that represents the bilateral synchronization status of settlement. The transaction lock-up period update module is used to construct a historical observation sequence of asymmetric transmission entropy and calculate the second-order difference value if the synchronization confidence index is less than the preset threshold value. If the second-order difference value is greater than zero, the second-order difference value is multiplied by the preset step size to obtain the gain step size, and then accumulated to the current transaction lock-up period to obtain the updated transaction lock-up period, which is then applied to the next alignment buffer period. Report output module: If the synchronization confidence index is greater than or equal to the preset threshold, it encapsulates and generates the financial report for the current transaction lock-up period based on the business settlement message stream.

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

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