Inreversible decision-oriented scheme forced contrast and traceability evidence storage association method and device, electronic equipment, medium and system

Abstract

The invention provides an irreversible decision-oriented scheme forced contrast and traceability evidence storage association method and device, electronic equipment, a medium and a system. Monitoring a service data flow in real time to identify a predefined irreversible decision node, suspending a current service execution process by physically blocking a service execution link, and forcibly redirecting the decision data flow originally flowing to an execution end to a preset contrast interface to enable the service execution process to enter a to-be-authorized locking state; in response to the redirection instruction, calculating a difference feature vector between original decision data and a reference scheme output by the heterogeneous reference decision kernel; extracting a context snapshot and an identity element of a current service execution environment, and constructing a structured decision-making traceability evidence storage object according to the context snapshot and the identity element; and performing hash operation and distributed account book evidence storage processing on the decision traceability evidence storage object to generate an evidence storage receipt containing an evidence storage hash and a block timestamp so as to release the physical blocking of the execution link and recover the service execution process.

Description

Technical Field

[0001] This application relates to the fields of information security and decision support technology, and more specifically, to a method, apparatus, electronic device, medium, and system for mandatory comparison and traceability of solutions for irreversible decision-making. Background Technology

[0002] In fields such as high-frequency trading in finance, power dispatching, automated industrial control, and emergency medical intervention, decision-making instructions, once issued, are often physically irreversible. Erroneous decisions can lead to significant property damage or safety accidents. Therefore, introducing heterogeneous reference information for decision verification in high-risk environments has become a crucial means of ensuring production safety.

[0003] Existing high-risk decision support solutions typically employ side-by-side dashboard-based information prompting systems. This approach identifies business scenarios through pre-defined monitoring logic, then loads suggested solutions from an external expert database or reference model into an auxiliary area alongside the main user interface, recording the log timestamp of when the reference solution is retrieved. Since the reference solution exists only as suggestive information, this solution does not intervene in the underlying physical flow of the original decision-making instructions; rather, the operator chooses whether to refer to this information.

[0004] However, this traditional side-hint solution has serious technical flaws. Because the execution chain and the comparison logic are physically decoupled, in high-pressure tasks or emergency fault handling scenarios, operators are prone to directly issuing original instructions in pursuit of efficiency, causing the reference solution to be completely ignored, and the auxiliary system to fail to play its actual risk avoidance role. Simultaneously, because the system lacks mandatory attribution actions logically linked to instruction issuance, post-audit can only confirm that the reference information was displayed, but cannot determine whether the decision-maker subjectively performed prudent verification of the discrepancies through a tamper-proof evidence chain. This "soft constraint" mechanism cannot meet the technical requirements for physically closed-loop monitoring of decision-making behavior and mandatory accountability in irreversible decision-making scenarios. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides a method, apparatus, electronic device, medium, and system for mandatory comparison and traceability of irreversible decision-making, thereby at least alleviating the aforementioned technical problems.

[0006] A method for mandatory comparison and traceability evidence association of solutions for irreversible decisions includes: Step 1: Real-time monitoring of business data flow to identify predefined irreversible decision nodes; in response to the trigger signal of the irreversible decision node, physically blocking the business execution link to suspend the current business execution process, and forcibly redirecting the decision data flow originally flowing to the execution end to a preset comparison interface, so that the business execution process enters an authorized locked state; Step 2: In response to the redirection instruction, activating the comparison interface to call the heterogeneous reference decision kernel, and calculating the difference feature vector between the original decision data and the reference solution output by the heterogeneous reference decision kernel; Step 3: Obtaining attribution data for the difference feature vector input, extracting the context snapshot and identity elements of the current business execution environment, and constructing a structured decision traceability evidence object accordingly; Step 4: Performing hash operation and distributed ledger evidence processing on the decision traceability evidence object to produce an evidence receipt containing evidence hash and block timestamp; mapping the evidence receipt to an unlock token for the locked state to release the physical blockage of the execution link and restore the business execution process.

[0007] Optionally, identifying predefined irreversible decision nodes in step 1 specifically includes: extracting target operation operators and associated business object attributes from the business data stream; querying a preset decision classification mapping table to determine whether the target operation operator and the business object attribute both hit a set of sensitive operations with physical irreversibility attributes; if they hit, extracting the corresponding decision risk weight based on the hit operation type, and determining the corresponding business execution link blocking strength accordingly.

[0008] Optionally, physically blocking the service execution link in step 1 to suspend the current service execution process specifically includes: according to the blocking strength of the service execution link, calling the physical layer switching switch of the process controller to change the instruction output address of the service execution process from the execution end register to the temporary storage stack; and marking the current decision data packet with a lock flag in the temporary storage stack until the unlock token is received.

[0009] Optionally, step 2, which involves retrieving the heterogeneous reference decision kernel, specifically includes: extracting the business semantic labels and task environment parameters of the irreversible decision node; and using the adapter network to perform spatial vector matching based on the business semantic labels and task environment parameters to retrieve and call the heterogeneous reference decision kernel with the highest correlation from the candidate kernel library.

[0010] Optionally, the calculation process of the difference feature vector includes: extracting the feature dimension of the decision parameters in the original decision data, and performing corresponding dimension projection on the reference scheme to generate a dimension-aligned reference feature mapping set; using a preset feature cosine similarity algorithm, calculating the deviation magnitude of the original decision data and the reference feature mapping set in each dimension component, and generating the multi-dimensional difference feature vector.

[0011] Optionally, step 3 involves extracting identity elements, specifically including: extracting the unique identity fingerprint and digital signature of the entity currently initiating the original decision data through a trusted execution environment, and encapsulating the unique identity fingerprint and the digital signature as the identity elements.

[0012] Optionally, step 3 involves constructing a structured decision tracing and evidence storage object, specifically including: using a preset semantic mapping module to encapsulate the difference feature vector, the attribution data, the context snapshot, and the identity elements into a structured message conforming to the distributed ledger storage protocol; assigning a globally unique asset serial number to the structured message, and using the asset serial number as the index identifier of the evidence storage receipt.

[0013] Optionally, in step 4, mapping the evidence receipt to an unlock token for the locked state specifically includes: extracting the evidence hash and the asset serial number from the evidence receipt, performing secondary signature processing using a preset asymmetric encryption algorithm; associating the signature result with the unlock logic address of the irreversible decision node to generate the unlock token with time constraints.

[0014] Optionally, it also includes: real-time monitoring of the duration of the service execution link in the blocked state; if the duration of the block exceeds a preset security threshold and the unlock token is not received, an abnormal alarm command is triggered and a preset service protection action is forcibly executed.

[0015] A device for mandatory comparison and traceability evidence storage of solutions for irreversible decisions includes: a monitoring and identification module for real-time monitoring of business data flow to identify predefined irreversible decision nodes; responding to a trigger signal from the irreversible decision node, physically blocking the business execution link to suspend the current business execution process and forcibly redirecting the decision data flow originally flowing to the execution end to a preset comparison interface; a feature comparison module for activating the comparison interface in response to a redirection command to retrieve a heterogeneous reference decision kernel and calculate a difference feature vector between the original decision data and the reference solution output by the heterogeneous reference decision kernel; an object construction module for acquiring attribution data input for the difference feature vector, extracting the context snapshot and identity elements of the current execution environment, and constructing a structured decision traceability evidence storage object accordingly; and an evidence storage unlocking module for performing distributed ledger evidence storage processing on the decision traceability evidence storage object to generate an evidence storage receipt; mapping the evidence storage receipt to an unlocking token for the locked state, and driving a process controller to recognize the unlocking token to release the physical blockage of the execution link.

[0016] An electronic device includes a memory and a processor; the memory is used to store computer programs; the processor is used to execute the programs stored in the memory to implement the above-mentioned scheme mandatory comparison and traceability evidence association method for irreversible decision-making.

[0017] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned method for mandatory comparison and traceability of irreversible decision-making.

[0018] A computer program product includes a computer program that, when executed by a processor, implements the aforementioned method for mandatory comparison and traceability of irreversible decision-making schemes.

[0019] A system for mandatory comparison and traceability evidence association for irreversible decision-making includes: a business execution link for carrying the physical flow of business data; a process controller for taking over control of the business execution link through a physical switch; a comparison server for deploying heterogeneous reference decision kernels and calculating difference feature vectors; and a distributed ledger network node for performing tamper-proof evidence storage on decision traceability evidence storage objects and returning evidence storage receipts. The system achieves data interconnection between components through a system-level backplane switching bus, and utilizes the process controller to block and unlock the execution link, physically ensuring that the recovery of the business execution link is logically predicated on the evidence storage receipts.

[0020] This application's method for mandatory comparison and traceability of solutions for irreversible decisions addresses the technical shortcomings of traditional side-hint solutions, such as easily overlooked reference information, vague responsibility determination, and lack of physical closure. By physically blocking the business execution link and redirecting the decision flow, it effectively mitigates the security risks of bypassing reference solutions in high-risk decisions. Compared to traditional methods that only provide soft information references, this application, upon identifying an irreversible node, uses a process controller to forcibly cut off the physical path originally flowing to the execution end, suspending the decision flow and redirecting it to the mandatory comparison interface. This ensures, from a physical level, that any instruction must be verified from a heterogeneous perspective before taking effect.

[0021] By calculating the difference feature vector between the original decision and the reference solution and obtaining enhanced attribution data, this application alleviates the problem of opaque decision-making logic in traditional solutions. The decision tracing and evidence storage object constructed by combining the current environmental context snapshot and identity fingerprint provides highly confident physical evidence for decision-making behavior. Compared to traditional solutions that only record and display timestamps in logs, this application performs distributed ledger evidence storage on messages containing difference responses and execution environments. The resulting evidence storage receipts form undeniable proof of responsibility, improving the traceability of decision auditing.

[0022] Specifically, this application constructs a logical closed loop of "blocking-comparison-proofing-unlocking" by dynamically mapping the evidence receipt to an unlock token and driving the process controller to physically release the locked state. This mechanism physically ensures that the resumption of business execution processes must be based on the generation of the evidence receipt, making each change to the decision-making asset have a definite temporal logic and security semantics. Compared with traditional solutions, in decision-making scenarios with high frequency, large scale, and irreversible characteristics, it enhances the system's risk control accuracy and the legal certainty of automated supervision. Attached Figure Description

[0023] Figure 1 This is a flowchart of a method for mandatory comparison and traceability of irreversible decision-making in an embodiment of this application.

[0024] Figure 2 This is a structural diagram of a device for mandatory comparison and traceability of irreversible decision-making schemes, as described in an embodiment of this application.

[0025] Figure 3 This is a structural diagram of an electronic device according to an embodiment of this application.

[0026] Figure 4 This is a diagram illustrating the composition of a mandatory comparison and traceability evidence-based system for irreversible decision-making in this application. Detailed Implementation

[0027] like Figure 1The image shows a method for mandatory comparison and traceability of irreversible decision-making in an embodiment of this application, comprising: Step 1: Real-time monitoring of business data flow to identify predefined irreversible decision nodes; in response to the trigger signal of the irreversible decision node, physically blocking the business execution link to suspend the current business execution process, and forcibly redirecting the decision data flow originally flowing to the execution end to a preset comparison interface, so that the business execution process enters a locked state awaiting authorization; Step 2: In response to the redirection instruction, activating the comparison interface to retrieve the heterogeneous reference decision kernel and calculate the original... Step 3: Obtain attribution data for the input of the difference feature vector, extract the context snapshot and identity elements of the current business execution environment, and construct a structured decision tracing and evidence storage object accordingly; Step 4: Perform hash operation and distributed ledger evidence storage processing on the decision tracing and evidence storage object to produce an evidence storage receipt containing evidence storage hash and block timestamp; Map the evidence storage receipt to an unlock token for the locked state to release the physical blockage of the execution link and restore the business execution process.

[0028] Optionally, identifying predefined irreversible decision nodes in step 1 specifically includes: extracting target operation operators and associated business object attributes from the business data stream; querying a preset decision classification mapping table to determine whether the target operation operator and the business object attribute both hit a set of sensitive operations with physical irreversibility attributes; if they hit, extracting the corresponding decision risk weight based on the hit operation type, and determining the corresponding business execution link blocking strength accordingly.

[0029] Preferably, the specific technical implementation process for identifying predefined irreversible decision nodes and their associated parameters in step 1 is as follows: Utilizing a data flow feature parsing operator deployed at the front end of the business link, the target operation operator of the currently pending thread (e.g., a large stop-loss sell order in a financial scenario or a high-risk surgery execution order in a medical scenario) and associated business object attributes (e.g., the amount of a single transaction, vital sign parameters of the audience involved in the operation, etc.) are captured from the real-time input business data flow to generate a multi-dimensional decision metadata data package. Using the decision metadata data package as a joint index key, a preset decision classification mapping table stored in the trusted execution environment is retrieved. Parallel retrieval actions targeting operation type and attribute thresholds are performed in the preset decision classification mapping table to produce a logical classification identifier for the current decision action under business semantics.

[0030] Preferably, in the specific technical implementation of step 1, determining whether a sensitive operation set has been hit, the logical classification identifier generated above is obtained, and it is used to perform a bit-matching operation with a preset set of sensitive operations possessing the attribute of physical irreversibility. If it is identified that the current decision action has technical characteristics in terms of physical space or logical permissions that cannot be restored without loss once executed (e.g., the transfer amount exceeds a preset personal permission threshold, or the operation in a medical plan has irreversible side effects), it is determined that the target irreversible decision node has been hit, and a corresponding decision sensitivity feedback signal is generated. This processing action achieves precise physical interception of high-risk instructions from massive amounts of business data.

[0031] Preferably, the specific technical implementation process for determining the business execution link blocking strength in step 1 is as follows: Obtain the aforementioned decision sensitivity feedback signal; retrieve the built-in risk weight quantification operator; based on the hit operation type, task urgency, and current system resource usage, retrieve and extract decision risk weights matching the current decision environment from the weight matrix. Inject the decision risk weights as control inputs into the preset energy level calculation logic to quantify and map the suspension depth of the business execution link and the physical priority of process takeover, thereby producing a business execution link blocking strength with a defined energy level.

[0032] Preferably, in one scenario, step 1 is specifically implemented as follows: The aforementioned business execution link blocking strength is obtained and converted into a low-level physical switching instruction for the process controller. In response to the physical switching instruction, the process controller drives a physical layer switching switch to modify the instruction output address of the business execution process from the execution end register to a protected temporary stack, thereby physically severing the original execution link and forcibly redirecting the decision data stream to a preset reference interface. This processing action, through hardware-level address switching, ensures that irreversible decisions are absolutely locked until a valid proof is obtained, mitigating the technical drawback of easily overlooked auxiliary suggestions from the physical source.

[0033] Optionally, physically blocking the service execution link in step 1 to suspend the current service execution process specifically includes: according to the blocking strength of the service execution link, calling the physical layer switching switch of the process controller to change the instruction output address of the service execution process from the execution end register to the temporary storage stack; and marking the current decision data packet with a lock flag in the temporary storage stack until the unlock token is received.

[0034] Preferably, the specific implementation process of physically blocking the business execution link to suspend the current business execution process in step 1 is as follows: The business execution link blocking strength quantified by the previous steps is retrieved and mapped to an enable drive signal for a hardware logic gate in the process controller. Upon receiving the enable drive signal, the process controller drives a physical layer switching switch to perform a diversion switching operation on the data bus through which the instruction flows. This forces the instruction output address of the business execution process to be offset from the regular execution end register address space to a temporary storage stack within a secure controlled area before executing the next instruction cycle. The current decision data packet, intercepted through the bus, is completely encapsulated and written to a preset physical address of the temporary storage stack. Simultaneously, the process controller writes a lock flag to the status register bit of the temporary storage stack. When the lock flag is active, the direct data handshake between the temporary storage stack and the external execution end is physically shielded, thereby ensuring that the business execution process is in a "pending authorization lock" state at the physical layer.

[0035] Preferably, in the specific technical implementation of maintaining the locked state until the token is received in step 1: the process controller maintains the placeholder validity of the locked flag in the temporary stack by continuously monitoring the feedback status of the internal instruction bus. During this period, any unauthorized execution instruction attempting to access the current decision data packet will be redirected to the exception handling sequence by the physical layer switch to ensure that irreversible decisions cannot enter the actual execution stage before obtaining external consensus feedback. This physical suspension state will be maintained until the unlock token with asymmetric signature attributes generated by the subsequent process is sent back to the process controller through the interrupt monitoring interface. After verifying the legality of the unlock token, the process controller triggers the execution of the lock flag clearing instruction and drives the physical layer switch to perform reverse path mapping, redirecting the data stream stored in the temporary stack back to the execution end register, thereby completing the physical recovery of the business process.

[0036] Preferably, in one scenario, the aforementioned physical blocking logic based on address redirection alleviates the technical bottleneck of the cognitive comparison scheme being "easily bypassed" in actual deployment. Compared to the traditional "suspend" instruction that only executes at the software logic layer, this application constructs a locking environment with strong constraints at the underlying hardware interaction level by modifying the instruction output address and setting a hardware-level locking flag in the temporary stack. This technical implementation creates a physically necessary causal relationship between the execution of irreversible decisions and the acquisition of unlock tokens, ensuring that every decision with high-risk weight is forcibly introduced into the comparison and tracing process at the physical level. This precise technical cascading from "address redirection" to "stack locking" to "token-triggered unlocking" provides a solid physical determinism basis for the reliable execution of the overall scheme.

[0037] Optionally, step 2, which involves retrieving the heterogeneous reference decision kernel, specifically includes: extracting the business semantic labels and task environment parameters of the irreversible decision node; and using the adapter network to perform spatial vector matching based on the business semantic labels and task environment parameters to retrieve and call the heterogeneous reference decision kernel with the highest correlation from the candidate kernel library.

[0038] Preferably, the specific implementation process of retrieving the heterogeneous reference decision kernel in step 2 is as follows: From the irreversible decision nodes captured in the previous steps, a preset semantic parsing operator is retrieved to perform a deep feature scan on the metadata of the decision instruction, extracting business semantic tags that can represent the decision intent and the domain (e.g., extracting the "asset risk rating" tag in the financial transaction field or the "intervention plan compliance" tag in the medical diagnosis field), and simultaneously collecting real-time operating status indicators of the current execution system to produce task environment parameters (e.g., collecting the computational load component of the current system, the urgency level of instruction execution, and the associated risk deviation threshold). The extracted business semantic tags and the task environment parameters are concatenated and encapsulated to generate an initial feature vector carrying the decision context features, and this vector is input into a preset adapter network.

[0039] Preferably, in step 2, the specific technical implementation of spatial vector matching using an adapter network is as follows: the adapter network utilizes its internally pre-trained feature mapping layer to project the input initial feature vector into a high-dimensional feature metric space, producing a highly recognizable target search vector. Subsequently, the adapter network drives the matching engine to call the feature fingerprints of each candidate kernel stored in the candidate kernel library. By calculating the cosine similarity or correlation score between the target search vector and the feature fingerprints of each candidate kernel, multi-dimensional spatial vector matching is performed in the feature metric space. The correlation scores obtained from the matching are sorted from high to low, and the heterogeneous reference decision kernel with the highest correlation to the current decision context (e.g., the heterogeneous algorithm kernel focusing on "extremely low probability extreme risk assessment") is retrieved.

[0040] Preferably, in one scenario, step 2 is specifically implemented using the aforementioned dynamic matching mechanism based on the adapter network, effectively alleviating the technical bottlenecks of insufficient flexibility in reference kernel retrieval and difficulty in adapting to complex and ever-changing decision-making environments in traditional solutions. This application extracts business semantic tags and task environment parameters, enabling the perception of the technical background and real-time environmental pressure of decision-making behavior. By performing spatial vector matching within the feature space through the adapter network, it ensures that the retrieved heterogeneous reference decision kernel is logically complementary to the original decision logic. This precise technical closed loop from "feature extraction" to "spatial mapping" to "association retrieval" enables real-time and automatic matching of the most suitable adversarial cognitive solution for each irreversible decision node, providing logically complete kernel support for the subsequent generation of differential feature vectors with deep reference value.

[0041] Optionally, the calculation process of the difference feature vector includes: extracting the feature dimension of the decision parameters in the original decision data, and performing corresponding dimension projection on the reference scheme to generate a dimension-aligned reference feature mapping set; using a preset feature cosine similarity algorithm, calculating the deviation magnitude of the original decision data and the reference feature mapping set in each dimension component, and generating the multi-dimensional difference feature vector.

[0042] Preferably, the calculation process of the difference feature vector is specifically implemented as follows: From the original decision data obtained in the preceding steps, the feature space parsing operator is used to identify and extract the feature dimensions of the decision parameters representing the decision intention (for example, in a financial decision-making scenario, extracting multi-dimensional features including asset volatility components, trading liquidity components, and risk exposure threshold components; or in a medical decision-making scenario, extracting multi-dimensional features including intervention intensity components, expected recovery time series components, and side effect probability components). Simultaneously, the reference scheme produced by the heterogeneous reference decision kernel is retrieved, and a preset coordinate system mapping function is used to perform dimensional projection of the logical parameters in the reference scheme onto the feature dimensions of the decision parameters. This processing action maps the proposed scheme from an external heterogeneous perspective to a metric space completely consistent with the current execution end, thereby generating a dimension-aligned reference feature mapping set, ensuring the consistency of subsequent comparison actions in physical semantics.

[0043] Preferably, in the specific technical implementation of calculating the deviation amplitude: the original feature vector generated from the original decision data and the reference feature mapping set aligned with the dimension are used as input objects and injected into a preset feature measurement operator. The feature measurement operator uses feature cosine similarity logic to calculate the angle and numerical distance between the original decision tendency and the reference feature component for each independent dimension component, thereby producing a deviation amplitude that reflects the degree of logical deviation between the two. The deviation amplitudes produced for each dimension component are serialized and arranged to generate a multi-dimensional difference feature vector with spatial directionality and dimensional attributes. This processing action concretizes the originally abstract method differences into structured data that can be identified and rendered, providing an accurate logical anchor for subsequent forced attribution.

[0044] Preferably, in one scenario, the aforementioned refined logic based on dimensional projection and deviation calculation alleviates the technical bottleneck of "unintuitive suggestions and the submergence of key conflict points" in traditional decision support systems. Compared to traditional solutions that only provide simple right or wrong judgments or single scores, this application, through the generated difference feature vector, can accurately identify on which specific feature dimension the decision-maker and the heterogeneous reference perspective disagree (for example, identifying that the decision-maker is overly focused on the short-term profit dimension while ignoring the long-term volatility risk dimension emphasized by the heterogeneous reference decision kernel). This technical implementation method, from "feature dimensionality reduction projection" to "multi-dimensional deviation measurement," ensures that cognitive blind spots in decision-making can be made explicit in a physically related way, forcibly guiding the decision-maker to perform attribution for logical disagreements, thereby improving the prudence and risk penetration before irreversible decision execution.

[0045] Optionally, step 3 involves extracting identity elements, specifically including: extracting the unique identity fingerprint and digital signature of the entity currently initiating the original decision data through a trusted execution environment, and encapsulating the unique identity fingerprint and the digital signature as the identity elements.

[0046] Preferably, the specific implementation process for extracting identity elements in step 3 is as follows: In response to capturing the original decision data that triggers an irreversible decision node, the built-in trusted execution environment is activated and a hardware-isolated secure processing channel is established. Within the secure processing channel, the trusted execution environment retrieves the underlying hardware root of trust and collects the unique identity fingerprint of the entity currently initiating the original decision data (e.g., collecting an encrypted biometric feature template or an associated hardware-bound device identifier) ​​through a security-aware interface to produce an entity identity representation with high confidence attributes. Simultaneously, the trusted execution environment calls a dedicated asymmetric encryption operator stored in the secure area to perform a signature operation on the original decision data and the current real-time timestamp, thereby generating a digital signature strongly coupled with the current decision instruction. By performing a structured data encapsulation operation, the trusted execution environment maps and associates the extracted unique identity fingerprint with the generated digital signature, and encapsulates both into a unified identity element for the current decision node.

[0047] Preferably, in the specific technical implementation of extracting identity elements: the trusted execution environment writes the acquired unique identity fingerprint and the digital signature into a preset bit field of a secure message, and assigns a protected physical address to the message. This processing action, through hardware-level resource isolation, physically blocks unauthorized read / write paths for sensitive identity information by processes in non-secure domains, resulting in highly logically complete identity elements. These identity elements then serve as core input objects in subsequent traceability and evidence preservation object construction stages, achieving a deep binding of "human identity" and "machine instructions" at the physical execution level.

[0048] Preferably, in one scenario, step 3 is specifically implemented by completing identity extraction and encapsulation within a trusted execution environment, alleviating the technical bottleneck of difficulty in determining responsibility caused by "easily forged identity credentials or man-in-the-middle attacks" in traditional decision-making systems. Compared to traditional account password matching that only executes in conventional memory space, this application utilizes hardware-level security enclosures and cascading calls of hardware encryption operators to ensure that the generation process of unique identity fingerprints and digital signatures occurs in an undetectable physical space. This technical implementation imprints a legally certain physical mark on the execution path of irreversible decisions, providing highly definitive technical evidence for auditing and tracing in high-risk financial transfers or medical intervention scenarios, and achieving physical-level responsibility locking for key decision-making behaviors.

[0049] Preferably, the specific implementation process of constructing a structured decision tracing and evidence storage object in step 3 is as follows: A preset semantic mapping module is invoked to perform data alignment processing on the difference feature vectors, attribution data, context snapshots of the business execution environment, and identity elements produced by the preceding steps. Specifically, the semantic mapping module uses a preset message definition protocol to map the difference feature vectors representing decision bias attributes, the attribution data representing subjective logical interpretations, the context snapshots representing objective environmental states, and the identity elements representing ownership relationships to the corresponding data fields in the message structure. By performing byte stream serialization, the aforementioned feature data with different semantic dimensions are encapsulated into a structured message conforming to the distributed ledger storage protocol. This processing action transforms discrete decision behavior fragments into a logically related technical whole, providing a standardized data carrier for subsequent consistent storage in decentralized networks.

[0050] Preferably, in the specific technical implementation of assigning a globally unique asset serial number to the structured message in step 3: after the structured message is encapsulated, a preset unique identifier generation operator (e.g., logic for cascading operations based on the current physical timestamp component, device hardware address component, and feature hash component) is retrieved to calculate and generate a unique asset serial number across the entire network for the decision transaction. The generated asset serial number is written into the header introductory area or metadata identifier of the structured message, and simultaneously used as an index identifier for subsequently obtaining the evidence receipt. This processing action achieves a unique mapping between digital assets and physical decision-making behavior, ensuring that the corresponding decision-making panoramic retrospective data can be accurately and quickly retrieved through this index identifier from massive distributed storage records.

[0051] Preferably, in one scenario, the structured encapsulation and sequence number allocation scheme achieved through semantic mapping alleviates the technical bottleneck of "fragmented audit data dimensions and disconnect between evidence content and execution site" in high-risk decision-making scenarios. Compared to traditional solutions that simply record system text logs, this application constructs a self-explanatory chain of decision-making evidence by rigidly coupling subjective attribution data with objective context snapshots and differential feature vectors within the physical message dimension, and using globally unique asset sequence numbers for digital asset management. This technical implementation approach, from "heterogeneous semantic alignment" to "protocol-based encapsulation" and then to "unique index mapping," endows each irreversible decision with a globally searchable technical anchor, providing highly logically dense technical support for complex financial transaction audits or medical malpractice liability determination, and achieving synergistic optimization of decision transparency and the depth of liability attribution.

[0052] Optionally, step 3 involves constructing a structured decision tracing and evidence storage object, specifically including: using a preset semantic mapping module to encapsulate the difference feature vector, the attribution data, the context snapshot, and the identity elements into a structured message conforming to the distributed ledger storage protocol; assigning a globally unique asset serial number to the structured message, and using the asset serial number as the index identifier of the evidence storage receipt.

[0053] Preferably, the specific implementation process of constructing a structured decision tracing and evidence storage object in step 3 is as follows: A preset semantic mapping module is invoked to obtain the difference feature vector, attribution data, context snapshot of the business execution environment, and identity elements produced by the preceding steps. The semantic mapping module uses a preset offset mapping table to write the difference feature vector representing the logic of decision deviation, the attribution data representing subjective interpretation, the context snapshot representing the objective state of the execution site, and the identity elements representing operational authority into the corresponding bit fields of the target message structure. By performing byte stream serialization, the aforementioned feature data with heterogeneous semantic dimensions is encapsulated into a structured message conforming to the distributed ledger storage protocol. This processing action transforms the originally discrete decision process data into a protocol-based data carrier with logical coupling relationships, ensuring semantic consistency of the evidence storage content during transmission between distributed network nodes.

[0054] Preferably, in the specific technical implementation of allocating a globally unique asset serial number in step 3: after the initial encapsulation of the structured message is completed, a preset unique identifier generation operator is retrieved. The unique identifier generation operator collects the physical high-precision timestamp component of the current system, the hardware MAC address component of the process controller, and the payload hash component of the structured message. Through cascading hash operations, it generates a globally unique asset serial number across the entire network. The generated asset serial number is written to the header of the structured message, and simultaneously bound to the execution logic of the currently pending business process, serving as an index identifier for subsequent retrieval of the evidence storage receipt. This processing action achieves a unique mapping between digital asset certificates and physical decision-making behavior, providing a definite addressing anchor for accurate traceability in scenarios with massive concurrent decision-making.

[0055] Preferably, in one scenario, the structured evidence storage scheme achieved through semantic mapping and unique sequence number allocation alleviates the technical bottleneck of "fragmented audit data dimensions and disconnect between the evidence chain and the execution site" in high-risk decision-making scenarios. Compared to traditional schemes that only record simple text logs, this application constructs a decision evidence entity with self-explanatory capabilities and logical consistency by rigidly encapsulating subjective attribution and objective snapshots within the protocol message dimension and using globally unique asset sequence numbers for digital asset management. This technical implementation method, from "heterogeneous data alignment" to "protocol-based reorganization" and then to "unique asset mapping," ensures that every irreversible decision is given a globally searchable technical tag, providing highly logically dense physical evidence support for liability determination and compliance auditing in complex environments.

[0056] Optionally, in step 4, mapping the evidence receipt to an unlock token for the locked state specifically includes: extracting the evidence hash and the asset serial number from the evidence receipt, performing secondary signature processing using a preset asymmetric encryption algorithm; associating the signature result with the unlock logic address of the irreversible decision node to generate the unlock token with time constraints.

[0057] Preferably, the specific implementation process of mapping the evidence receipt to an unlock token for the locked state in step 4 is as follows: The evidence receipt fed back by the distributed ledger network node is retrieved, and the evidence hash representing data integrity and the asset serial number uniquely representing the decision transaction are extracted using a preset field extraction operator. An asymmetric encryption operator deployed in the secure isolation zone of the process controller is activated, a preset hardware-level private key is retrieved, and cascaded secondary signature processing is performed on the evidence hash and the asset serial number to produce an authorization verification signature with immutable attributes and a strong business binding relationship. This processing action, by introducing hardware root of trust confirmation at the execution end, ensures that the generation of the authorization instruction is based on genuine distributed evidence feedback, logically eliminating the technical defect of false instructions directly bypassing the evidence storage process.

[0058] Preferably, in step 4, the specific technical implementation for generating the unlock token with time constraints involves: obtaining the previously generated authorization verification signature; retrieving the unlock logic address associated with the current irreversible decision node; and using a preset mapping matrix to perform the association projection between the authorization verification signature and the unlock logic address to determine the physical execution anchor point of the instruction. Simultaneously, the real-time high-precision clock signal of the current system is collected, and a lifespan threshold is configured based on the previously determined decision risk weight (e.g., a specific value within the range of 100 milliseconds to 1000 milliseconds). The authorization verification signature, the unlock logic address, and the lifespan threshold are then reassembled and encapsulated at the instruction level to generate the unlock token with time constraints. This processing action, by embedding address and time features into the physical instruction, defines strict spatiotemporal boundaries for instruction execution.

[0059] Preferably, in one scenario, the token generation scheme implemented through secondary signature and logical address mapping alleviates the technical bottleneck of "decoupling of evidence storage data and execution terminal, and easy tampering or hijacking of unlocking instructions" in high-risk decision-making scenarios. Compared with the traditional method of restoring the process by only judging the evidence storage status at the logical layer, this application constructs a physically-oriented security gating logic by performing encrypted coupling of the evidence storage hash, the asset serial number, and the unlocking logical address at the physical layer, supplemented by the mandatory constraint of the life cycle threshold. This technical implementation method, from "data credential parsing" to "hardware signing" to "spatiotemporal dimension mapping and encapsulation," ensures that the restoration of the business execution process not only relies on distributed evidence storage as a logical premise, but also achieves instruction-level locking at the physical execution end, enhancing the ownership certainty and security anti-attack capability of the entire irreversible decision-making chain.

[0060] Optionally, it also includes: real-time monitoring of the duration of the business execution link in the blocked state; if the duration of the block exceeds a preset security threshold and the unlock token is not received, an abnormal alarm instruction is triggered and a preset business protection action is forcibly executed, while the abnormal state record is synchronized to the distributed ledger.

[0061] Preferably, the specific implementation process for real-time monitoring of the hold duration of the service execution link under the blocked state is as follows: After the service execution link is physically blocked and the service execution process enters the locked state, the process controller calls the built-in high-precision clock pulse counter to start the timing action. The process controller periodically reads the level state of the locked flag bit in the temporary stack at a preset sampling frequency (e.g., a value in the range of 10 milliseconds to 50 milliseconds), and converts the accumulated clock pulse value into the hold duration representing the current suspended time dimension. This processing action realizes high-frequency monitoring of the physical blocking state at the time domain level, providing a quantitative basis for evaluating system availability and decision-making real-time performance.

[0062] Preferably, in the specific technical implementation of triggering exception handling if the retention time exceeds a preset security threshold and the unlock token is not received: the process controller performs a numerical logic comparison between the real-time generated retention time and the security threshold (e.g., a specific value configured between 2000 milliseconds and 5000 milliseconds) stored in non-volatile memory. If the comparison result shows that the retention time exceeds the security threshold, and the interrupt listening interface does not capture the unlock token that conforms to the verification protocol, an exception alarm instruction with the highest execution priority is generated. In response to the exception alarm instruction, the process controller drives a preset business protection action, specifically by clearing and resetting the execution end register, or by forcibly switching the data stream originally redirected to the comparison interface to a preset fault-safe path. This processing action, by introducing a time-sensitive fallback mechanism, prevents resource deadlock or business logic crashes caused by prolonged locking.

[0063] Preferably, in one scenario, the specific technical implementation for synchronizing abnormal state records to the distributed ledger is as follows: A semantic encapsulation module is used to extract the abnormal code at the moment the abnormality was triggered, the retention time exceeding the threshold, the context snapshot at the blocking moment, and the associated operation node identifier to construct an abnormal state data packet. A built-in cryptographic evidence storage operator is invoked to perform digest calculation on the abnormal state data packet, and the resulting digest, along with the abnormal state data packet, is sent to the distributed ledger network node to perform distributed consensus and on-chain evidence storage, generating an immutable abnormal state record.

[0064] Preferably, the aforementioned closed-loop scheme based on clock pulse monitoring and distributed evidence storage alleviates the technical bottleneck of "indefinite business stagnation due to system failure or human delay after process suspension" in high-risk decision-making scenarios. Compared to the shortcomings of traditional solutions that lack mandatory intervention on interruption duration, this application constructs a controlled execution environment with spatiotemporal self-healing capabilities by logically coupling the retention duration with the security threshold at the hardware level and coordinating with automated business protection actions. This technical implementation method, from "pulse timing" to "threshold comparison" to "physical reset and on-chain evidence storage," ensures that even in extreme abnormal situations where no unlocking signal is received, the system can still recover to a safe state through preset protection logic and retain auditable evidence of the anomaly, enhancing the technical robustness of the entire irreversible decision-making process.

[0065] like Figure 2As shown, this is a device for mandatory comparison and traceability of irreversible decision-making schemes, which includes: a monitoring and identification module, a feature comparison module, an object construction module, and an evidence storage and unlocking module. The monitoring and identification module is used to monitor the business data flow in real time to identify predefined irreversible decision nodes. In response to the trigger signal of the irreversible decision node, it suspends the current business execution process by physically blocking the business execution link and forcibly redirects the decision data flow originally destined for the execution end to a preset comparison interface. The feature comparison module is used to activate the comparison interface in response to the redirection command to retrieve the heterogeneous reference decision kernel and calculate the difference feature vector between the original decision data and the reference scheme output by the heterogeneous reference decision kernel. The object construction module is used to acquire attribution data input for the difference feature vector, extract the context snapshot and identity elements of the current execution environment, and construct a structured decision tracing and evidence storage object accordingly. The evidence storage and unlocking module is used to perform distributed ledger evidence storage processing on the decision tracing and evidence storage object to generate an evidence storage receipt. The evidence storage receipt is mapped to an unlock token for the locked state, and the process controller is driven to recognize the unlock token to release the physical blockage of the execution link.

[0066] like Figure 3 As shown, an electronic device includes a memory and a processor; the memory is used to store computer programs; when the processor executes the programs stored in the memory, it implements the above-mentioned method for mandatory comparison and traceability of irreversible decision-making.

[0067] like Figure 4 As shown, this is a system for mandatory comparison and traceability evidence association of solutions for irreversible decisions. It takes over control of the business execution link through a physical switch. The business execution link carries the physical flow of business data; the process controller takes over control of the business execution link through the physical switch; the comparison server deploys a heterogeneous reference decision kernel and calculates difference feature vectors; and the distributed ledger network nodes perform tamper-proof evidence storage on the decision traceability evidence storage object and return evidence storage receipts. The system achieves data interconnection between components through a system-level backplane switching bus. By using the process controller to block and unlock the execution link, it physically ensures that the recovery of the business execution link is logically predicated on the evidence storage receipts.

[0068] Preferably, corresponding to the above Figure 4 This application provides a physical closed-loop solution that focuses on hardware entity architecture implementation. The specific hardware structure of the solution is described below: This includes a logic array for physical interception and redirection of instructions, serially connected between the business control plane and the execution terminal. Technically, this logic array acts as a hardware-level "physical switching switch," internally composed of a multiplexer and a high-frequency level sampling circuit. When the business data stream flows through the system bus, the level sampling circuit monitors the control word characteristics of the bits in real time. Once it identifies a characteristic level matching an irreversible decision node, the logic array generates a physical trigger signal, driving the multiplexer to perform an address switch, physically redirecting the instruction stream originally pointing to the execution terminal register to a controlled temporary stack. This processing action achieves instantaneous blocking of the original decision path at the hardware electrical signal level.

[0069] Preferably, the system further includes a secure enclosure and a physically isolated processing unit (i.e., a hardware-level trusted execution environment) interconnected with the temporary storage stack via a dedicated secure bus. Technically, the processing unit provides a physically isolated "black box" environment for verifying decision data. Technically, the temporary storage stack transfers intercepted decision metadata packets to the processing unit via a protected storage mapping path. The processing unit integrates a hardware root of trust for extracting the unique identity fingerprint of the execution entity and performing physical layer signing on the decision message. Because this unit is physically isolated from the general computing environment at the hardware level, it ensures that the identity element extraction and signing process is not maliciously probed or bypassed by external means.

[0070] Preferably, it is also equipped with a specially designed heterogeneous feature matching and vector operation hardware accelerator (e.g., a parallel processing core built on a field-programmable logic array (FPGA)). The accelerator performs a high-speed data handshake with the comparison server via a system-level backplane switching bus. Technically, the accelerator receives heterogeneous kernel parameters from the comparison server and, within a hardware instruction cycle, utilizes its internally integrated vector multiply-accumulate array to perform spatial vector matching and deviation magnitude calculation, thereby efficiently producing difference feature vectors. This hardware design utilizes a parallel computing architecture to alleviate the processing latency problem of complex heterogeneous schemes in high-concurrency decision-making scenarios, providing computational support for real-time forced comparison.

[0071] Preferably, a dedicated communication module for ledger evidence storage and a hardware encryption operator are also integrated. The communication module is responsible for establishing a highly reliable, low-latency physical link with external distributed ledger network nodes; the hardware encryption operator is specifically responsible for generating hash digests of the evidence storage objects. Technically, the decision-making traceability evidence storage objects produced by the secure enclosure are pushed to the hardware encryption operator for fast hash calculation, and the communication module broadcasts the resulting structured message to the ledger network. This hardware-based communication and encryption design ensures the immutability and timeliness of the evidence storage process.

[0072] Preferably, a hardware monitoring and token-triggered unlocking circuit is deployed at the very end. Technically, this circuit executes the final closed-loop logic of "certificate-driven physical recovery." Technically, this circuit monitors the certificate receipts from the ledger network in real time, and its internal logic comparator verifies the receipt hash. Once verification is successful, the circuit generates an unlocking physical pulse (i.e., an unlocking token). This pulse acts on the reset terminal of the aforementioned instruction physical interception and redirection logic array, thereby releasing the blockage on the execution link and remapping the data locked in the temporary stack back to the execution terminal registers.

[0073] The above Figures 2-4 For an exemplary description, please refer to the above. Figure 1 .

Claims

1. A method for mandatory comparison and traceability of solutions for irreversible decision-making, characterized in that, include: Step 1: Monitor the business data flow in real time to identify predefined irreversible decision nodes. In response to the trigger signal of the irreversible decision node, physically block the business execution link to suspend the current business execution process, and forcibly redirect the decision data flow that was originally flowing to the execution end to the preset comparison interface so that the business execution process enters the pending authorization lock state. Step 2: In response to the redirection instruction, activate the comparison interface to retrieve the heterogeneous reference decision kernel, and calculate the difference feature vector between the original decision data and the reference scheme output by the heterogeneous reference decision kernel; Step 3: Obtain attribution data for the input of the difference feature vector, extract the context snapshot and identity elements of the current business execution environment, and construct a structured decision tracing and evidence storage object accordingly; Step 4: Perform hash operation and distributed ledger notarization processing on the decision tracing notarization object to produce a notarization receipt containing notarization hash and block timestamp; map the notarization receipt to an unlock token for the locked state to release the physical blockage of the execution link and restore the business execution process.

2. The method for mandatory comparison and traceability of irreversible decision-making schemes according to claim 1, characterized in that, Step 1, identifying predefined irreversible decision nodes, specifically includes: Extract the target operation operator and associated business object attributes from the business data stream; Query the preset decision classification mapping table to determine whether the target operation operator and the business object attribute both hit the sensitive operation set with the physical irreversibility attribute; If a hit occurs, the corresponding decision risk weight is extracted based on the hit operation type, and the corresponding business execution link blocking strength is determined accordingly.

3. The method for mandatory comparison and traceability of irreversible decision-making schemes according to claim 2, characterized in that, Step 1, which physically blocks the service execution link to suspend the current service execution process, specifically includes: Based on the blocking strength of the business execution link, the physical layer switching switch of the process controller is invoked to change the instruction output address of the business execution process from the execution end register to the temporary storage stack; The current decision data packet is marked with a lock flag in the temporary stack until the unlock token is received.

4. The method for mandatory comparison and traceability of irreversible decision-making schemes according to claim 1, characterized in that, Step 2, which involves retrieving the heterogeneous reference decision kernel, specifically includes: Extract the business semantic tags and task environment parameters of the irreversible decision nodes; The adapter network is used to perform spatial vector matching based on the business semantic tags and the task environment parameters in order to retrieve and call the heterogeneous reference decision kernel with the highest correlation from the candidate kernel library.

5. The method for mandatory comparison and traceability of irreversible decision-making schemes according to claim 1, characterized in that, The calculation process of the differential feature vector includes: Extract the feature dimension of the decision parameters from the original decision data, and perform the corresponding dimension projection on the reference scheme to generate a dimension-aligned reference feature map set; Using a preset feature cosine similarity algorithm, the deviation magnitude between the original decision data and the reference feature mapping set in each dimension is calculated to generate the multi-dimensional difference feature vector.

6. A device for mandatory comparison and traceability of solutions for irreversible decision-making, characterized in that, include: The monitoring and identification module is used to monitor the business data flow in real time to identify predefined irreversible decision nodes. In response to the trigger signal of the irreversible decision node, it suspends the current business execution process by physically blocking the business execution link and forcibly redirects the decision data flow that was originally flowing to the execution end to the preset comparison interface. The feature comparison module is used to activate the comparison interface in response to the redirection command to retrieve the heterogeneous reference decision kernel and calculate the difference feature vector between the original decision data and the reference scheme output by the heterogeneous reference decision kernel. The object construction module is used to obtain attribution data for the input of the difference feature vector, extract the context snapshot and identity elements of the current execution environment, and construct a structured decision tracing and evidence storage object accordingly. The evidence storage and unlocking module is used to perform distributed ledger evidence storage processing on the decision tracing evidence storage object to generate an evidence storage receipt; The evidence receipt is mapped to an unlock token for the locked state, and the process controller is driven to recognize the unlock token to release the physical blockage of the execution link.

7. An electronic device, characterized in that, Including memory and processor; The memory is used to store computer programs; When the processor executes the program stored in the memory, it implements the steps of the method described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the method described in any one of claims 1-5.

9. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the steps of the method described in any one of claims 1-5.

10. A system for mandatory comparison and traceability of solutions for irreversible decision-making, characterized in that, include: The business execution link is used to carry the physical flow of business data. A process controller is used to take over control of the business execution chain through a physical switching switch; The counterpart server is used to deploy heterogeneous reference decision kernels and calculate differential feature vectors. Distributed ledger network nodes are used to perform tamper-proof notarization on decision traceability notarization objects and return notarization receipts; The system interconnects data between components through a system-level backplane switching bus, and uses the process controller to block and unlock the execution link, physically ensuring that the recovery of the business execution link is based on the evidence receipt as a logical premise.

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