B2B intelligent contract automatic execution and dispute arbitration system

Abstract

The invention relates to the technical field of smart contracts, in particular to a B2B smart contract automatic execution and dispute arbitration system, which is characterized in that performance feature information of a target smart contract and progress feature information of a target smart contract dispute arbitration process in a historical period are acquired through an acquisition module; analyzing a performance characteristic characterization value based on the performance characteristic information and analyzing a progress characteristic characterization value based on the progress characteristic information through an analysis module; through a monitoring module, whether the execution environment of the target smart contract meets the standard is judged based on the performance feature representation value, and whether the processing efficiency of the target smart contract dispute arbitration is abnormal is judged based on the progress feature representation value; and determining the cause of the abnormal processing efficiency and the corresponding processing strategy based on the difference value between the progress feature representation value and a preset progress feature representation threshold value through a diagnosis module. According to the invention, the precision of the dispute arbitration decision of the B2B intelligent contract platform is improved.

Description

Technical Field

[0001] This invention relates to the field of smart contract technology, and in particular to a B2B smart contract automated execution and dispute arbitration system. Background Technology

[0002] In complex B2B scenarios such as digital procurement of bulk commodities and supply chain finance, existing automated platforms based on smart contracts suffer from technical performance bottlenecks. Take a typical scenario as an example: a steel procurement platform executes batch procurement, in-transit cargo pledge financing, and final payment settlement through smart contracts. While existing solutions integrate IoT sensors and electronic bills of lading, they rely entirely on centralized oracles to synchronize offline warehouse temperature and GPS location data to the blockchain. When thousands of concurrent transactions trigger data uploading simultaneously, the oracle server cannot guarantee millisecond-level synchronization, leading to unpredictable data synchronization delays. In this case, a shipment delayed by only two hours due to port congestion might be mistakenly judged as out of control and subject to full confiscation due to the sensor data not being uploaded to the blockchain in time. Simultaneously, warehouse receipt pledge financing requests initiated by upstream suppliers may also be blocked in the high-concurrency arbitration queue due to off-chain verification processes, failing to complete asset freezing within the margin freeze response time, causing the funding party to miss risk control opportunities. This means that existing technology cannot distinguish between genuine defaults and misjudgments caused by data delays, and it cannot dynamically adjust resource priorities under high load.

[0003] Chinese Patent Publication No. CN113852678A discloses a method for implementing a B2B smart contract mechanism, including the following steps: First, the client and server communicate one-to-one through a message service module; second, the communication messages and records are transmitted to a database and a cloud server, and NLP in the cloud server is used to optimize and analyze the data; third, after optimization and analysis, business keywords in the chat records are identified and automatically used as elements of the transaction contract; fourth, resource orders and orders are retrieved through the database and cloud server, and orders and electronic contracts are intelligently generated; fifth, after online communication and confirmation between the client and server, online signing is completed and online payment is made. Summary of the Invention

[0004] To address this, the present invention provides a B2B smart contract automated execution and dispute arbitration system to overcome the problem in existing technologies that fail to consider scenarios involving multiple order synchronization and complex asset disposal on B2B smart contract platforms. This lack of real-time monitoring of platform technical performance makes it impossible to distinguish between user commercial defaults and insufficient platform technical performance, leading to inappropriate penalties and low accuracy in arbitration decisions.

[0005] To achieve the above objectives, the present invention provides a B2B smart contract automated execution and dispute arbitration system, comprising: The data acquisition module is used to collect performance characteristic information of the target smart contract and progress characteristic information of the dispute arbitration process of the target smart contract within a historical period. An analysis module is used to analyze performance characteristic representation values ​​based on the performance characteristic information and to analyze progress characteristic representation values ​​based on the progress characteristic information. The monitoring module is used to determine whether the execution environment of the target smart contract meets the standard based on the performance characteristic characterization value, and to determine whether the processing efficiency of the dispute arbitration of the target smart contract is abnormal based on the progress characteristic characterization value. The diagnostic module is used to determine the cause of abnormal processing efficiency and its corresponding processing strategy based on the difference between the progress feature characterization value and the predetermined progress feature characterization threshold: performing resource rebalancing based on weight offset and performing a degradation mechanism based on load. The performance characteristics include the oracle's data synchronization delay and the concurrent capacity of the execution node, while the progress characteristics include the evidence association matching time and the margin freeze response time.

[0006] Furthermore, the analysis module is used to analyze performance characteristic representation values ​​based on the performance characteristic information, wherein the performance characteristic representation values ​​are determined by summing a first performance factor and a second performance factor according to a predetermined first weighting ratio. The first performance factor is determined based on the ratio of a predetermined data synchronization delay duration threshold to the data synchronization delay duration. The second performance factor is determined based on the ratio of a predetermined concurrent load threshold to the concurrent load.

[0007] Furthermore, the monitoring module is used to determine whether the execution environment of the target smart contract meets the standard based on the performance characteristic values, including: If the performance characteristic value is less than or equal to the predetermined performance characteristic threshold, the execution environment is determined to be non-compliant with the standard. If the performance characteristic value is greater than the predetermined performance characteristic threshold, then the execution environment is determined to meet the standard.

[0008] Furthermore, the analysis module is used to analyze the progress feature characterization value based on the progress feature information, wherein, The progress characteristic value is determined based on the sum of a first progress factor and a second progress factor according to a predetermined second weighting ratio. The first progress factor is determined based on the ratio of a predetermined matching time threshold to the matching time. The second progress factor is determined based on the ratio of a predetermined freeze response duration threshold to the freeze response duration.

[0009] Furthermore, the monitoring module is used to determine whether the processing efficiency of the target smart contract dispute arbitration is abnormal based on the progress feature characterization value, including: If the progress feature characterization value is less than or equal to the predetermined progress feature characterization threshold, then the processing efficiency is determined to be abnormal. If the progress feature value is greater than the predetermined progress feature threshold, then the processing efficiency is determined to be normal.

[0010] Furthermore, the diagnostic module is used to determine the cause of the abnormal processing efficiency based on the difference, including: If the difference is less than or equal to a predetermined difference threshold, it is determined that there is a temporary shortage of computing resources, resulting in a slow response to the dispute arbitration. If the difference is greater than a predetermined difference threshold, it is determined to be a concurrent load overload, resulting in a decrease in processing efficiency.

[0011] Furthermore, the diagnostic module uses a processing strategy to determine the cause of the abnormal processing efficiency based on the difference, including: If it is determined that the slow response to dispute arbitration is due to a temporary shortage of computing resources, then a strategy of resource rebalancing based on weight offset is determined. If it is determined that the concurrent load is overloaded, resulting in a decrease in processing efficiency, then a strategy based on load degradation mechanism is determined.

[0012] Furthermore, the diagnostic module is used to execute a resource rebalancing strategy based on weight offsets, including: Efficiency deviation is calculated based on matching duration and freeze response time; Calculate the weight adjustment amount based on the efficiency deviation; New weights are constructed based on the aforementioned weight adjustment amount, and resource scheduling is performed.

[0013] Furthermore, the diagnostic module is used to implement a degradation mechanism strategy based on the load, including: The load index is calculated based on the data synchronization delay and concurrent capacity. A degradation mechanism is established to match the current task based on the comparison result between the load index and a predetermined load index threshold.

[0014] Furthermore, the acquisition module includes: a clock synchronization card, an edge computing gateway, and an SSD hard drive.

[0015] Compared with existing technologies, the beneficial effects of this invention are that it provides a B2B smart contract automated execution and dispute arbitration system, comprising: a data acquisition module, an analysis module, a monitoring module, and a diagnostic module. The data acquisition module collects performance characteristic information of the target smart contract and progress characteristic information of the target smart contract's dispute arbitration process within a historical period. The analysis module analyzes performance characteristic values ​​based on the performance characteristic information and progress characteristic values ​​based on the progress characteristic information. The monitoring module determines whether the execution environment of the target smart contract meets the standard based on the comparison result of the performance characteristic values ​​and a predetermined performance characteristic threshold. When the execution environment meets the standard, it further determines whether the processing efficiency of the target smart contract's dispute arbitration is abnormal based on the comparison result of the progress characteristic values ​​and the predetermined progress characteristic threshold. The diagnostic module, in response to abnormal processing efficiency of the target smart contract's dispute arbitration, determines the cause of the abnormal processing efficiency and its corresponding processing strategy based on the difference between the progress characteristic values ​​and the predetermined progress characteristic threshold. It overcomes the problem that existing technologies in B2B smart contract platforms, in scenarios involving multiple order synchronization and complex asset disposal, lack real-time monitoring of platform technical performance, making it impossible to distinguish between user commercial defaults and insufficient platform technical performance, thus triggering inappropriate penalties and resulting in low accuracy of arbitration decisions.

[0016] In particular, this invention obtains performance characteristic information by collecting the data synchronization delay of the oracle of the target smart contract and the concurrent capacity of the execution node within a historical period through the acquisition module. This information is used to characterize the load capacity of the smart contract's execution environment. Furthermore, by collecting the matching time of evidence association in the dispute arbitration process of the target smart contract and the response time of the margin freeze, progress characteristic information is obtained to characterize the response efficiency of the smart contract dispute arbitration. This enables real-time perception of the health of the execution environment and the efficiency of the arbitration process, thereby providing a complete data foundation for the analysis module.

[0017] In particular, this invention uses an analysis module to normalize the two original data feature information—performance feature information representing the load capacity of the smart contract execution environment and progress feature information representing the response efficiency of smart contract dispute arbitration—by weighting and summing them according to a predetermined weight ratio. The analysis yields performance feature characterization values ​​and progress feature characterization values, which can unify data of different dimensions into quantifiable criteria that reflect the stability of the target smart contract execution environment and the normality of the target smart contract dispute arbitration processing status. This provides a quantitative decision-making basis for the intelligent execution of B2B smart contract platforms.

[0018] In particular, the present invention compares the performance characteristic values ​​with predetermined performance characteristic thresholds through a monitoring module to determine whether the execution environment of the target smart contract meets the standards, and compares the progress characteristic values ​​with predetermined progress characteristic thresholds to determine whether the processing efficiency of the dispute arbitration of the target smart contract is abnormal. It can distinguish whether the problem of the smart contract exists in the execution stage or originates from the dispute arbitration stage, thereby improving the accuracy of the B2B smart contract platform in diagnosing anomalies.

[0019] In particular, the present invention, through its diagnostic module, only responds to when it determines that the processing efficiency of the target smart contract dispute arbitration is abnormal. Based on the difference between the computational progress characteristic value and the predetermined progress characteristic threshold, it distinguishes whether the abnormal processing efficiency of the target smart contract dispute arbitration is caused by a temporary shortage of computing resources leading to a slow dispute arbitration response, or by an overload of concurrent load leading to a decrease in processing efficiency. Based on this, it matches differentiated processing strategies, thereby ensuring the timeliness of the B2B smart contract platform in handling abnormal issues. Attached Figure Description

[0020] Figure 1 This is a structural block diagram of the B2B smart contract automated execution and dispute arbitration system according to an embodiment of the present invention; Figure 2 This invention provides a logical decision diagram for determining whether the execution environment of a smart contract conforms to a standard based on performance characteristic values. Figure 3 This invention provides a logical determination diagram for assessing whether the processing efficiency of dispute arbitration of a target smart contract is abnormal based on progress feature characterization values. Figure 4 The present invention provides a logical decision diagram for determining the cause of abnormal processing efficiency and the corresponding processing strategy based on the difference. Detailed Implementation

[0021] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0022] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0023] It should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the term "connected" should be interpreted broadly. For example, it can refer to a fixed connection, a detachable connection, or an integral connection; it can refer to a mechanical connection or an electrical connection; it can refer to a direct connection or an indirect connection through an intermediate medium; it can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0024] Please see Figure 1 The diagram shown is a structural block diagram of a B2B smart contract automated execution and dispute arbitration system according to an embodiment of the present invention. The present invention provides a B2B smart contract automated execution and dispute arbitration system, comprising: The data acquisition module is used to collect performance characteristic information of the target smart contract and progress characteristic information of the dispute arbitration process of the target smart contract within a historical period. An analysis module, which is connected to the acquisition module, is used to analyze performance characteristic values ​​based on the performance characteristic information and to analyze progress characteristic values ​​based on the progress characteristic information. The monitoring module, which is connected to the acquisition module and the analysis module respectively, is used to determine whether the execution environment of the target smart contract meets the standard based on the performance characteristic characterization value, and to determine whether the processing efficiency of the dispute arbitration of the target smart contract is abnormal based on the progress characteristic characterization value. The diagnostic module, which is connected to the monitoring module, is used to determine the cause of abnormal processing efficiency and its corresponding processing strategy based on the difference between the progress feature characterization value and the predetermined progress feature characterization threshold: performing resource rebalancing based on weight offset and performing a degradation mechanism based on load. The performance characteristics include the oracle's data synchronization delay and the concurrent capacity of the execution node, while the progress characteristics include the evidence association matching time and the margin freeze response time.

[0025] Specifically, embodiments of the present invention provide implementation steps for a B2B smart contract automated execution and dispute arbitration system, including: Step S1: Collect performance characteristic information of the target smart contract within the historical period; Step S2: Analyze the performance characteristic representation values ​​based on the performance characteristic information; Step S3: Determine whether the execution environment of the target smart contract meets the standard based on the performance characteristic characterization value; Step S4: When the execution environment meets the standards, collect the progress characteristic information of the target smart contract dispute arbitration process; Step S5: Analyze the progress feature representation value based on the progress feature information; Step S6: Determine whether the processing efficiency of the target smart contract dispute arbitration is abnormal based on the progress feature characterization value; Step S7: In response to abnormal processing efficiency, calculate the difference between the progress feature characterization value and the predetermined progress feature characterization threshold; S8. Based on the difference, determine the cause of the abnormal processing efficiency and the corresponding processing strategy: perform resource rebalancing based on weight offset, and perform a degradation mechanism based on load; The performance characteristics include: the data synchronization delay of the oracle and the concurrent capacity of the execution node; the progress characteristics include: the matching time of evidence association and the response time of the margin freeze.

[0026] As is understandable, data synchronization latency refers to the absolute value of the time difference between off-chain business data and on-chain synchronization. It is obtained by collecting the event timestamps of off-chain data sources and the event block timestamps of on-chain contract events using a server equipped with a high-precision clock synchronization card, and is measured in seconds. Excessive latency will cause contract execution delays, and unsynchronized receipt records will be mistakenly judged as abnormal.

[0027] As is understandable, concurrency capacity refers to the average capacity that an execution node can simultaneously process smart contract transactions or arbitration requests per unit of time, measured in TPS. It reflects the platform's ability to handle multiple contract executions and data synchronization simultaneously. In B2B scenarios, high concurrency is particularly crucial for handling the centralized execution of bulk procurement contracts.

[0028] As is understandable, matching time refers to the time taken, measured in seconds, to integrate on-chain and off-chain evidence from the moment an arbitration request is triggered and to establish a correlation mapping. A higher value indicates a longer arbitration case processing cycle.

[0029] It's understandable that the freeze response time refers to the time difference between triggering the margin freeze instruction and its completion, measured in seconds. A slow response can lead to the responsible party transferring assets and delaying the execution of the arbitration award. This timeframe is obtained by collecting timestamps of client-sent transactions and on-chain confirmation events.

[0030] In this embodiment, the single cycle is preset, and the preferred single cycle is 1 minute.

[0031] This invention provides a B2B smart contract automated execution and dispute arbitration system. The system comprises: a data acquisition module that collects performance characteristics and progress characteristics of the target smart contract's dispute arbitration process over a historical period; an analysis module that analyzes performance characteristic values ​​based on the performance characteristic information and progress characteristic values ​​based on the progress characteristic information; a monitoring module that determines whether the target smart contract's execution environment meets standards based on a comparison of the performance characteristic values ​​with predetermined performance characteristic thresholds; and, if the execution environment meets standards, further determining whether the processing efficiency of the target smart contract's dispute arbitration is abnormal based on a comparison of the progress characteristic values ​​with predetermined progress characteristic thresholds; and a diagnostic module that, in response to abnormal processing results of the target smart contract's dispute arbitration, determines the cause of the abnormal processing efficiency and its corresponding handling strategy based on the difference between the progress characteristic values ​​and the predetermined progress characteristic thresholds. This invention improves the accuracy of dispute arbitration decisions on B2B smart contract platforms.

[0032] Specifically, the analysis module is used to analyze performance characteristic representation values ​​based on the performance characteristic information, wherein the performance characteristic representation values ​​are determined by summing a first performance factor and a second performance factor according to a predetermined first weighting ratio. The first performance factor is determined based on the ratio of a predetermined data synchronization delay duration threshold to the data synchronization delay duration. The second performance factor is determined based on the ratio of a predetermined concurrent load threshold to the concurrent load.

[0033] In this embodiment, the predetermined data synchronization delay time threshold is preset. Specifically, a data synchronization delay time sample within 5 historical periods is predetermined, and the predetermined data synchronization delay time threshold is determined based on the average value of the data synchronization delay time sample. The threshold is determined within the range [6,8] and is selected based on the tolerance of 88% for the upper limit of data delay in B2B transaction business. In this embodiment, the predetermined data synchronization delay time threshold is preferably 7.

[0034] In this embodiment, the predetermined concurrent load threshold is preset, wherein concurrent load samples within 5 historical periods are predetermined, and the predetermined concurrent load threshold is determined based on the average value of the concurrent load samples, within the range [480, 520]. It is selected based on 95% of the load threshold of the execution node hardware device. In this embodiment, the predetermined concurrent load threshold is preferably 494.

[0035] In this embodiment, the predetermined first weight ratio is set in advance. Preferably, the predetermined first weight ratio is 4:6, that is, the first performance factor contributes 40% of the weight, the second performance factor contributes 60% of the weight, and the two are weighted and summed to determine the performance characteristic value.

[0036] This invention emphasizes the decisive role of overall throughput in the stability of the execution environment by assigning a higher weight to the second performance factor based on concurrent load capacity, while also taking into account the reliability of oracle data synchronization, thus ensuring that performance evaluation is more in line with actual business load pressure.

[0037] Please see Figure 2 As shown, this is a logic diagram for determining whether the execution environment of a smart contract conforms to a standard based on performance characteristic values, according to an embodiment of the present invention. The monitoring module of the present invention is used to determine whether the execution environment of the target smart contract conforms to the standard based on the performance characteristic values, including: If the performance characteristic value is less than or equal to the predetermined performance characteristic threshold, the execution environment is determined to be non-compliant with the standard. If the performance characteristic value is greater than the predetermined performance characteristic threshold, then the execution environment is determined to meet the standard.

[0038] In this embodiment, the predetermined performance characteristic representation threshold is preset, wherein the average value of the performance characteristic representation value over 8 historical periods is predetermined, and the predetermined performance characteristic representation threshold is determined based on the average value of the performance characteristic representation value, which is determined within the range [0.92, 1.09]. In order to save operation and maintenance costs, the performance upper limit is reduced by 6%, and the predetermined performance characteristic representation threshold is preferably 1.02.

[0039] This invention improves the adaptability of the judgment criteria by setting a predetermined performance characteristic characterization threshold based on a historical average as a dynamic benchmark. By optimizing the performance characteristic characterization threshold, it is determined that when the overall performance index is lower than or equal to the recent historical average, the execution environment is deemed non-compliant with the standard; conversely, when the overall performance index is higher than the recent historical average, the execution environment is deemed compliant with the standard. This provides a reliable basic environmental guarantee for subsequent arbitration procedures, thereby reducing the risk of arbitration decision errors due to environmental performance fluctuations.

[0040] Specifically, the analysis module is used to analyze the progress feature characterization value based on the progress feature information, wherein the progress feature characterization value is determined by summing a first progress factor and a second progress factor according to a predetermined second weighting ratio. The first progress factor is determined based on the ratio of a predetermined matching time threshold to the matching time. The second progress factor is determined based on the ratio of a predetermined freeze response duration threshold to the freeze response duration.

[0041] In this embodiment, the predetermined matching time threshold is preset. Specifically, matching time samples within five historical periods are predetermined, and the predetermined matching time threshold is determined based on the average value of the matching time samples. The threshold is determined within the range [60, 80]. Based on the complexity of the case and the types and quantities of on-chain and off-chain data sources involved, an optimal value of 1.2 times that increases the lower limit of matching time is selected. In this embodiment, the predetermined matching time threshold is preferably 72.

[0042] In this embodiment, the predetermined freeze response duration threshold is preset. Specifically, freeze response duration samples within five historical periods are predetermined, and the predetermined freeze response duration threshold is determined based on the average value of the freeze response duration samples. The threshold is determined within the range [28, 32]. Considering the current network congestion status, a preferred value that satisfies 95% of the upper limit of the freeze response duration is selected. In this embodiment, the predetermined freeze response duration threshold is preferably 30.

[0043] In this embodiment, the predetermined second weight ratio is pre-set. Preferably, the predetermined second weight ratio is 1:1, that is, the first progress factor contributes 50% of the weight, the second progress factor contributes 50% of the weight, and the two are weighted and summed to determine the progress feature characterization value.

[0044] This invention, by assigning equal weight to a first progress factor based on matching time and a second progress factor based on freeze response time, clarifies two equally crucial dimensions of dispute arbitration process efficiency: internal business processing efficiency and external asset preservation efficiency. This ensures that the B2B smart contract platform maintains a balanced monitoring of arbitration processing speed and enforcement effectiveness, preventing the responsible party from transferring assets before the ruling while ensuring the construction of a key chain of evidence. This, in turn, guarantees the timeliness and reliability of B2B smart contract dispute arbitration as a whole.

[0045] Please see Figure 3 As shown, this is a logic diagram illustrating how an embodiment of the present invention determines whether the processing efficiency of a target smart contract dispute arbitration is abnormal based on progress feature characterization values. The monitoring module of the present invention is used to determine whether the processing efficiency of a target smart contract dispute arbitration is abnormal based on the progress feature characterization values, including: If the progress feature characterization value is less than or equal to the predetermined progress feature characterization threshold, then the processing efficiency is determined to be abnormal. If the progress feature value is greater than the predetermined progress feature threshold, then the processing efficiency is determined to be normal.

[0046] In this embodiment, the predetermined progress feature representation threshold is preset. The average value of the progress feature representation values ​​over eight historical periods is predetermined. The predetermined progress feature representation threshold is determined based on the average value of the progress feature representation values ​​and is determined within the range [0.92, 1.14]. Based on the enterprise's expectations for arbitration efficiency and risk tolerance, a preferred value that meets 92% of the upper limit of the progress feature representation value is selected. In this embodiment, the predetermined progress feature representation threshold is preferably 1.05.

[0047] This invention, by setting the progress feature characterization threshold as a dynamic benchmark based on historical averages, reflects a monitoring orientation that actively optimizes the dispute arbitration process. Only when the current comprehensive efficiency index is higher than the recent historical average level can it be determined that the processing efficiency of the target smart contract dispute arbitration is not abnormal. This can prevent the trend of declining execution efficiency, promote the continuous optimization of the arbitration process, and thus enhance the credibility and user experience of the B2B smart contract platform.

[0048] Please see Figure 4 As shown, this is a logical decision diagram of an embodiment of the present invention for determining the cause of abnormal processing efficiency and its corresponding processing strategy based on the difference. The diagnostic module of the present invention is used to determine the cause of abnormal processing efficiency based on the difference, including: If the difference is less than or equal to a predetermined difference threshold, it is determined that there is a temporary shortage of computing resources, resulting in a slow response to the dispute arbitration. If the difference is greater than a predetermined difference threshold, it is determined to be a concurrent load overload, resulting in a decrease in processing efficiency.

[0049] In this embodiment, the predetermined difference threshold is preset. The average value of the difference between the progress feature characterization value and the predetermined progress feature characterization threshold over 12 historical periods is predetermined. The predetermined difference threshold is determined based on the product of the average value of the difference and a tolerance coefficient. The difference is determined within the range [0.09, 0.22], and the tolerance coefficient is determined within the range [1.65, 1.70]. In this embodiment, the tolerance coefficient is preferably 1.68, and the predetermined difference threshold is preferably 0.17.

[0050] This invention distinguishes between performance fluctuations caused by temporary resource contention or network latency and performance degradation caused by concurrent request overload by setting an optimal difference threshold calculated based on the historical average difference and a tolerance coefficient. This enables the diagnostic module to accurately match different processing strategies, thereby reducing interference with normal business requests.

[0051] Specifically, the diagnostic module uses a processing strategy to determine the cause of the abnormal processing efficiency based on the difference, including: If it is determined that the slow response to dispute arbitration is due to a temporary shortage of computing resources, then a strategy of rebalancing resources based on weight offset will be implemented. If it is determined that the concurrent load is too high, resulting in a decrease in processing efficiency, then a strategy based on load degradation mechanism will be implemented.

[0052] Understandably, by employing a resource rebalancing strategy based on weight offsets, computing, storage, and network bandwidth resources can be prioritized for allocation from low-load secondary arbitration tasks or non-critical backend services to core arbitration processes where the load is nearing bottlenecks, without increasing overall hardware resource investment. This optimizes the distribution of resources among internal tasks, enabling rapid and low-cost self-healing against occasional performance fluctuations.

[0053] Understandably, a load-based degradation strategy can temporarily delay some newly initiated dispute arbitration requests when the pressure of incoming requests exceeds its sustainable processing capacity, forcibly controlling the concurrent load below a safe threshold. Temporarily sacrificing the immediate response to some requests ensures that core functions do not crash due to overload, protecting ongoing arbitration cases from being completed.

[0054] This invention employs two strategies in synergy: a resource rebalancing strategy for internal optimization, focusing on improving resource utilization and processing efficiency; and a degradation strategy for external defense, focusing on ensuring the core services of the system do not crash under extreme pressure. This enables the B2B smart contract platform to maintain the stable operation of dispute arbitration services in a complex and ever-changing operating environment.

[0055] Specifically, the diagnostic module is used to execute a resource rebalancing strategy based on weight offsets, including: Step S11: Calculate the efficiency deviation based on the matching time and the freeze response time; Step S12: Calculate the weight adjustment amount based on the efficiency deviation. Step S13: Construct new weights based on the weight adjustment amount and perform resource scheduling.

[0056] In this embodiment, the first progress factor is defined as the matching service, and the second progress factor is defined as the freezing service.

[0057] In this embodiment, the formula for calculating the efficiency deviation is: , in, To match service efficiency deviations To freeze service efficiency deviation. For the current matching duration, For the predetermined matching duration threshold, This is the current freeze response time. This is the predetermined freeze response time threshold.

[0058] In this embodiment, the formula for calculating the weight adjustment is: , in, The amount of weight adjustment for the matching service. The amount of weight adjustment for the frozen service. This is the resource compensation coefficient. This refers to the resource recovery coefficient. The resource compensation coefficient is preset; in this embodiment, a preferred resource compensation coefficient is 0.15. The resource recovery coefficient is also preset, selected based on the resource compensation coefficient being greater than twice the resource recovery coefficient; in this embodiment, a preferred resource recovery coefficient is 0.05. For example, when the matching time decreases while the freeze response time remains unchanged, the deviation in matching service efficiency decreases accordingly. That is, the matching service is given a 15% weight increase and the freezing service is given a 15% weight decrease. At this time, the weight of the first progress factor is 65% and the weight of the second progress factor is 35%. The weighted sum of the two factors determines the adjusted progress feature representation value.

[0059] In this embodiment, the formula for constructing the new weights is: , in, This represents the current progress characteristic value. As the first progress factor, It is the second progress factor.

[0060] This invention introduces a dynamic weight adjustment algorithm based on real-time efficiency deviation. When the response time of any service, whether evidence matching or asset freezing, exceeds a preset threshold, the algorithm calculates the service efficiency deviation and increases its resource weight according to a set resource compensation coefficient. Simultaneously, it decreases the resource weight of another service according to a resource recovery coefficient. This allows the limited computing power and bandwidth of the platform to be prioritized for processing stages currently experiencing performance bottlenecks, ensuring that newly submitted arbitration requests and pending freezing orders can be processed in a timely manner. This shortens the average processing cycle of a single case and improves the overall task throughput and stability of the platform under high concurrency pressure.

[0061] Specifically, the diagnostic module employs a load-based degradation mechanism strategy, including: Step S21: Calculate the load index based on the data synchronization delay and concurrent capacity; Step S22: Match the current task's degradation mechanism based on the comparison result between the load index and the predetermined load index threshold.

[0062] In this embodiment, the current performance characteristic value is defined as the load index, which is determined by the weighted sum of the first performance factor (40%) and the second performance factor (60%). The load index is determined within the range [0.92, 1.09]. To reduce load pressure, the upper limit of the load performance is reduced by 10%. In this embodiment, the preferred load index threshold is 0.98.

[0063] Understandably, if the load index is less than or equal to the predetermined load index threshold, the evidence matching will be downgraded from full association to key hash comparison; if the load index is greater than the predetermined load index threshold, all non-core data archiving and reporting tasks will be suspended.

[0064] This invention, through quantitative calculation and a graded degradation mechanism, achieves accurate determination and resource scheduling of the load on the B2B smart contract platform, providing an intelligent service guarantee method that enables core business to continue to operate in an orderly manner under high load conditions, thereby overcoming the shortcomings of only being able to passively alarm or brutally shut down circuit breakers when facing load pressure.

[0065] Specifically, the acquisition module includes: a clock synchronization card, an edge computing gateway, and an SSD hard drive.

[0066] Understandably, by introducing dedicated hardware, a high-performance data acquisition module is formed from the three links of data generation source, transmission path and storage terminal, ensuring the accuracy of subsequent diagnostic module analysis of various feature information.

[0067] Through a collaborative design combining software and hardware, this invention not only achieves intelligent degradation at the algorithmic logic level, but also reduces the root causes of load generation and improves the system's carrying capacity at the physical infrastructure level, thereby comprehensively improving the stability of the B2B smart contract platform in complex high-concurrency scenarios.

[0068] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A B2B smart contract automated execution and dispute arbitration system, characterized in that, include: The data acquisition module is used to collect performance characteristic information of the target smart contract and progress characteristic information of the dispute arbitration process of the target smart contract within a historical period. An analysis module is used to analyze performance characteristic values ​​based on the performance characteristic information and to analyze progress characteristic values ​​based on the progress characteristic information. The monitoring module is used to determine whether the execution environment of the target smart contract meets the standard based on the performance characteristic characterization value, and to determine whether the processing efficiency of the dispute arbitration of the target smart contract is abnormal based on the progress characteristic characterization value. The diagnostic module is used to determine the cause of abnormal processing efficiency and its corresponding processing strategy based on the difference between the progress feature characterization value and the predetermined progress feature characterization threshold: performing resource rebalancing based on weight offset and performing a degradation mechanism based on load. The performance characteristics include the oracle's data synchronization delay and the concurrent capacity of the execution node; The progress feature information includes the matching time for evidence association and the response time for freezing the margin.

2. The B2B smart contract automated execution and dispute arbitration system according to claim 1, characterized in that, The analysis module is used to analyze performance characteristic values ​​based on the performance characteristic information, wherein, The performance characteristic value is determined by summing the first performance factor and the second performance factor according to a predetermined first weighting ratio. The first performance factor is determined based on the ratio of a predetermined data synchronization delay duration threshold to the data synchronization delay duration. The second performance factor is determined based on the ratio of a predetermined concurrent load threshold to the concurrent load.

3. The B2B smart contract automated execution and dispute arbitration system according to claim 1, characterized in that, The monitoring module is used to determine whether the execution environment of the target smart contract meets the standard based on the performance characteristic values, including: If the performance characteristic value is less than or equal to the predetermined performance characteristic threshold, the execution environment is determined to be non-compliant with the standard. If the performance characteristic value is greater than the predetermined performance characteristic threshold, then the execution environment is determined to meet the standard.

4. The B2B smart contract automated execution and dispute arbitration system according to claim 1, characterized in that, The analysis module is used to analyze the progress feature characterization values ​​based on the progress feature information, wherein, The progress characteristic value is determined based on the sum of a first progress factor and a second progress factor according to a predetermined second weighting ratio. The first progress factor is determined based on the ratio of a predetermined matching time threshold to the matching time. The second progress factor is determined based on the ratio of a predetermined freeze response duration threshold to the freeze response duration.

5. The B2B smart contract automated execution and dispute arbitration system according to claim 1, characterized in that, The monitoring module is used to determine whether the processing efficiency of the target smart contract dispute arbitration is abnormal based on the progress feature characterization value, including: If the progress feature characterization value is less than or equal to the predetermined progress feature characterization threshold, then the processing efficiency is determined to be abnormal. If the progress feature value is greater than the predetermined progress feature threshold, then the processing efficiency is determined to be normal.

6. The B2B smart contract automated execution and dispute arbitration system according to claim 1, characterized in that, The diagnostic module is used to determine the cause of the abnormal processing efficiency based on the difference, including: If the difference is less than or equal to a predetermined difference threshold, it is determined that there is a temporary shortage of computing resources, resulting in a slow response to the dispute arbitration. If the difference is greater than a predetermined difference threshold, it is determined to be a concurrent load overload, resulting in a decrease in processing efficiency.

7. The B2B smart contract automated execution and dispute arbitration system according to claim 6, characterized in that, The diagnostic module is used to determine the processing strategy for the cause of the abnormal processing efficiency based on the difference, including: If the abnormal processing efficiency is determined to be due to a temporary shortage of computing resources, resulting in a slow response to dispute arbitration, then a resource rebalancing strategy based on weight offset is determined. If the abnormal processing efficiency is determined to be caused by concurrent overload, resulting in decreased processing efficiency, then a strategy based on load degradation mechanism is determined.

8. The B2B smart contract automated execution and dispute arbitration system according to claim 7, characterized in that, The diagnostic module is used to execute a resource rebalancing strategy based on weight offsets, including: Efficiency deviation is calculated based on matching duration and freeze response time; Calculate the weight adjustment amount based on the efficiency deviation; New weights are constructed based on the aforementioned weight adjustment amount, and resource scheduling is performed.

9. The B2B smart contract automated execution and dispute arbitration system according to claim 7, characterized in that, The diagnostic module is used to implement a degradation mechanism strategy based on the load, including: The load index is calculated based on the data synchronization delay and concurrent capacity. A degradation mechanism is established to match the current task based on the comparison result between the load index and a predetermined load index threshold.

10. The B2B smart contract automated execution and dispute arbitration system according to claim 1, characterized in that, The acquisition module includes: a clock synchronization card, an edge computing gateway, and an SSD hard drive.

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