Novel equivalence device structure closing and single-step repairing method and system in medical diagnosis, drug discovery and physiological status modeling

Abstract

The invention discloses a novel equivalence device structure closing and single-step repairing method and system in medical diagnosis, drug discovery and physiological status modeling, belongs to the field of medical diagnosis analysis, biological information processing and model verification, and ensures the reliability and traceability of the process through structural equivalence device detection and receipt track recording. The method monitors sequential equivalents (ABBA rectangles for checking consistency of operational orders) and conservation equivalents (triangles for checking information conservation and energy balance) during operation to find anomalies in diagnostic inference chains, drug action pathways, or physiological closed loops (such as predictor deviations, energy conservation imbalances, or model inconsistencies). When a non-equivalence condition is detected, the system selects a unique repair adapter from a geometric layer, a frame layer and a field layer for correction according to a preset priority, and the deviation metric mu is strictly reduced by one (delta mu = 1) during each repair, and is gradually converged to an equivalence closed state.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical diagnostic analysis, bio-information processing and model verification, and in particular to a novel equivalent structure closure and single-step repair method and system in medical diagnosis, drug discovery and physiological state modeling. BACKGROUND

[0002] In existing medical diagnosis, drug development and physiological simulation systems, the following common problems exist:

[0003] Sequence sensitivity: The order of steps in a diagnostic process or model calculation can affect the final result. For example, performing one test before another may produce different diagnostic conclusions than the reverse order; for example, in a drug screening process, swapping the order of two experimental steps may result in different model outputs. This sequence dependence can cause process risks and disputes, reducing the reliability of the results.

[0004] Conservation violation: If the input and output of each step in a multi-step physiological or pharmacological process are not strictly conserved (such as the sum of mass, energy or information is not balanced), there may be unexplained missing or loss. For example, in drug metabolism simulation, if the difference between the total amount of drug administered and the amount of metabolic output and elimination is not zero, there is a loophole in the whereabouts of the drug; if the energy calculation in the physiological feedback loop does not match the profit and loss, the model cannot be closed. Such conservation imbalance makes the diagnosis or model result lack of credibility and needs additional analysis to account.

[0005] Overcorrection: When an anomaly is found, existing systems often allow multiple repeated adjustments of model parameters or diagnostic criteria. Due to the lack of fine control, each adjustment may introduce new bias, which cannot guarantee that the problem is solved with the least change, but may lead to model overfitting or diagnostic criteria swinging, increasing the risk of operation.

[0006] Logs cannot be directly verified: The logs generated by many medical AI systems or diagnostic devices are in black box form or rely on complex encryption signatures, making it difficult for regulators, medical personnel to directly verify the entire process without the aid of specialized tools. This reduces the efficiency and transparency of audit and result verification, which is not conducive to the reliable deployment of medical AI.

[0007] Threshold judgment subjectivity: There are rules based on numerical thresholds in diagnosis and treatment decisions (such as a biomarker higher than a certain value is determined to be positive). These hard thresholds have a certain subjectivity and arbitrariness, and the boundary effect may lead to completely different decisions, and the threshold is easy to be bypassed by strategic adjustment, making it difficult to ensure the consistency and transparency of the decision logic.

[0008] Multi-system coordination difficulties: In multi-center clinical trials or multi-system collaborative diagnostics, different teams or devices generate their own decisions and logs, which are fragmented. The lack of a structured mechanism that allows parties to complete consistency checks and collaborative corrections without sharing all raw data makes it difficult to trace and assign responsibility across institutional processes.

[0009] In summary, existing technologies lack a universal core mechanism that combines structural equivalence testing, single-step minimal repair, and Boolean conditional judgment to ensure the order independence, conservation consistency, and auditability and interpretability of the entire medical diagnosis and modeling process. Summary of the Invention

[0010] This invention aims to provide an integrated governance method for medical diagnosis and physiological modeling processes without relying on traditional subjective threshold judgments, complex consensus algorithms, or strong encryption methods. Specifically, this invention addresses the following technical problems: how to detect and correct structural inconsistencies in diagnostic reasoning or simulation processes in real time (such as differences in results caused by the order of operations, non-conservation of information or energy, model structural variations, etc.); how to ensure that the system gradually converges to a stable equilibrium state with minimal single-step repair; and how to record deterministic and manually verifiable receipt trajectories, thereby achieving credible auditing, causal chain interpretability, and non-repudiation of medical processes in cross-system and multi-stage scenarios.

[0011] Technical Solution: To solve the above-mentioned technical problems, according to one aspect of the present invention, more specifically, a novel method and system for sealing and single-step repairing equivalence structures in medical diagnosis, drug discovery, and physiological state modeling, the core steps and components include:

[0012] 1. Equivalence Detection: During diagnostic or simulation process execution, a structural equivalence checking mechanism is introduced, including sequential equivalence (ABBA rectangle) detection and conservation equivalence (ABBA rectangle) detection. Triangle detection. The former is used to determine whether the order of any two critical operations (such as diagnostic steps or simulation calculations) can be interchanged: that is, if operations op1 and op2 are applied to the same initial state, if... and Inconsistent results indicate a sequence tear, signifying a risk of sequence dependency in the process that requires attention. The latter is used to monitor the system's conservation balance: for example, calculating the conservation deviation ρ of a closed-loop process (e.g., ρ = Σ_in - Σ_out - Δ_store - loss); if ρ ≠ 0, it indicates information or energy leakage or incomplete closure, triggering a conservation tear alarm and alerting the system to an imbalance requiring correction. This is achieved through ABBA and... Real-time detection of the two types of equivalences can promptly identify problems such as excessive prediction errors, metabolic energy imbalances, or model drift in the medical process, thereby providing early warnings.

[0013] 2. Single-step repair mechanism: Once a broken equivalence is detected (sequential non-equivalence or conservation imbalance, etc.), the system selects only one appropriate repair adapter according to the predefined priority to correct it. The repair adapter selection follows the priority order of geometry layer > frame layer > field layer:

[0014] Geometry layer (Geometry) repair mainly involves the adjustment of structural topology, such as adding missing loops or connections in physiological / pharmacological models. For example, when a key feedback pathway is found to be missing in an organ interaction model, an "intermediate reaction node" or "feedback bridge" is added to connect the modules that are not directly related, structurally bridging the logical gap. Geometry layer repair corresponds to adding or adjusting participating elements and relationship networks, perfecting the topology of the diagnosis or model (such as introducing additional metabolic pathways to ensure material circulation closure in drug action path analysis), strengthening the integrity of the overall framework, and reducing the risk of abnormalities.

[0015] Frame layer (Frame) repair involves the adjustment of parameter reference systems or protocol standards, such as using "unit affine transformation" or "label unification" adapters to align data formats, measurement scales, and index caliber between different systems or different steps. In medical processes, this corresponds to unifying the measurement units of different devices (such as blood glucose values unified to mmol / L), standardizing the index ranges used by different experiments or hospitals, and specifying the meanings of diagnostic labels / codes, ensuring that each link follows a consistent reference framework and reducing bias and unclear responsibility caused by inconsistent definitions or measurements.

[0016] Field layer (Field) repair targets the adjustment of operating environment variables, such as the calibration of time rhythm and scale parameters, including "retiming adaptation" or "scale calibration" operations. This is equivalent to adjusting the time window or stage synchronization of the process in the medical scene, or recalibrating the scale factor of the model to match the real conditions. For example, by changing the data acquisition frequency or aligning the administration period of treatment (retiming), or adjusting the scale of certain parameters in the simulation model (scale calibration) to reflect the real physiological ratio, the scene conditions can be matched, avoiding inconsistencies caused by stage misalignment or scale imbalance.

[0017] Through the above single-step repair mechanism, only the minimum granularity of modification is allowed each time, strictly controlling the modification range and impact. After each repair is completed, the system recalculates the equivalence state to avoid introducing too many adjustments at once, leading to new uncertainties. This single-step repair strategy ensures the fine controllability and transparency of the abnormal correction process - for each broken equivalence, only one targeted remedy is performed, without over-repairing or repeated trial and error, which helps to limit the impact of abnormalities to the minimum range and reduce the risk of diagnosis / simulation processes.

[0018] 3. Convergence Verification with Δμ = 1: The deviation metric μ represents the degree to which the system deviates from its equivalent closed state. It can be obtained by combining all currently detected tear indicators (e.g., μ can be set as the number of sequential tears plus the sum of all conserved deviations |ρ| after dimensional normalization). After each repair operation, the system calculates the μ values ​​before and after the repair. The system must satisfy μ_after = μ_before - 1, meaning each operation reduces the imbalance by exactly one unit before accepting the repair step. If an adjustment fails to effectively reduce μ (or even keeps μ unchanged or increases it), it is considered an invalid operation and is undone. This constraint ensures the monotonicity of the process convergence: through the strict Δμ = 1 criterion, the system ensures that each step progresses towards complete equivalence and closure, preventing infinite oscillations or invalid loops. For medical diagnosis and simulation, this characteristic means that contradictions can be eliminated and energy and material budgets balanced within a finite number of steps, improving the guarantee of clear convergence.

[0019] 4. Receipt Trajectory and Natural Number Folding Integrity Verification: This invention records all process operations as plain text receipts (e.g., generating record lines such as "STEP|seam=…|adapter=…|level=Geometry|mu_before=M|mu_after=M-1|Δμ=1" according to a predefined syntax format), and concatenates them to form a receipt trajectory. Each receipt contains key operation information (such as operation type, selected adapter and level, μ values ​​before and after repair, etc.), equivalent to step-by-step operation vouchers or audit logs in the medical process. To ensure the non-repudiation and integrity of these receipt records, the system introduces a deterministic natural number folding function (NAT FENCE) to generate a signature verification value for the receipt trajectory. Specifically, natural number folding can be achieved as follows: calculate the sum of the American Standard Code for Information Interchange (ASCII) encoding of each line in the receipt sequence, then take the modulo of a large prime number (e.g., 1009), and then weight and accumulate them in row order (e.g., multiply the i-th row by the weight of *(i+1)) to obtain the final folded value F. The system updates and outputs the current verification information in real time when each new receipt is generated (e.g., recorded as "FENCE|nat=F|items=k|order=canonical", where items* is the number of receipts and order=canonical indicates canonical order processing). This natural number fence serves as a deterministic signature, independent of any key or encryption algorithm, and can be recalculated and verified by any third party using the same rules. Medical regulators or auditors can use this to independently recalculate and verify all receipts, confirming that the diagnosis / modeling process has not been tampered with. Through this receipt tracking mechanism, a human-readable and verifiable chain of credentials is established for the medical process, significantly improving the system's transparency and credibility, achieving true non-repudiation: no participating party can deny their actions in the process afterward, because the receipt track is public and the calculation results of all parties are consistent.

[0020] 5. Boolean-Guided Gated Decision Making (Optional): At the critical decision execution level (e.g., confirmation of the final diagnosis or implementation of the treatment plan), this invention can employ a gated Boolean decision mechanism, where multiple independent structural conditions jointly control the authorization of critical actions. Preferably, six Boolean conditions are introduced to externally guide the process, including checks on six aspects: Pairity, Phase, Twist, Label, Gauge, and Glue. Specifically:

[0021] Odd-even orbitals are used to ensure paired balance in the diagnostic and treatment process. For example, in medical trials, case and control samples are required to be paired, and results analysis must be performed in pairs to avoid unmatched, one-sided results.

[0022] The phase orbit corresponds to the consistency of the examination operation stage, ensuring that each diagnostic and treatment step is performed in the correct sequence. For example, the next diagnostic step is only allowed after the previous examination is completed and the results are entered, avoiding stage confusion.

[0023] The reverse trajectory is used to detect any contradictory reversals in the process, i.e., the absence of unreasonable loops or backtracking. For example, it avoids overturning a previous diagnosis without providing new evidence, ensuring that the diagnostic process is free of logical "dead ends".

[0024] The label track is used to verify the correctness of the labels on data and operations, ensuring that all samples and results are correctly labeled (such as emergency, blinded sample, high risk, etc.) so that decision control can be made based on the Boolean rules of the labels (e.g., whether a sample is marked as high risk affects subsequent processes).

[0025] Metrological tracks are used for metric specification checks, verifying in Boolean terms that all units of measurement, consistency checks, or compliance conditions meet the specifications. Instead of directly comparing specific numerical values, they confirm non-quantitative conditions such as "whether cross-instrument calibration has been performed" or "whether quality control has been passed."

[0026] Adhesion tracks correspond to consistency checks in multi-team or multi-system collaborations, ensuring seamless integration of process interfaces across departments or institutions. For example, whether reporting data is consistent between the laboratory and clinical departments, and whether multi-source diagnostic records for the same patient are aligned.

[0027] By employing the aforementioned six-track Boolean gating, any decision conditions involving specific numerical thresholds are excluded, replaced by structured Boolean rules (i.e., any condition involving numerical comparisons such as ">" or "<" is replaced with logical judgments based on labels or states). This design avoids the subjective adjustment space and ambiguity inherent in traditional numerical thresholds, preventing participants from circumventing established principles by fine-tuning values. Simultaneously, it ensures that the decision-making basis is transparent and consistent to all relevant parties. This is significant in medical risk control and compliance review: it solidifies decision-making strategies into discrete logical rules, eliminating the influence of subjective human judgment and parameter adjustments, thereby improving the fairness and predictability of the diagnostic process. For regulatory authorities, since all decision rules are clear and auditable, it also facilitates rapid inspection and response.

[0028] 6. Additional features (optional): In some embodiments, the present invention may further include the following improved functions to enhance system performance and adapt to more complex medical scenarios:

[0029] Two-step pre-planning: Before performing single-step repair, a two-step pre-assessment based on the deviation lower bound Γ is added. The system simulates and evaluates the impact of at least two different repair operation sequences on equivalent deviation elimination, and calculates the corresponding deviation reduction lower bound index Γ. When the Γ value of a candidate sequence is higher than other schemes, the first repair operation of that sequence is selected as the current step to improve the overall convergence efficiency. Through this pre-planning mechanism, the system obtains global optimization guidance while strictly performing single-step repair, reducing the number of steps required for convergence.

[0030] Parallel process retiming: When multiple periodic parallel processes (such as the patient's physiological cycle and drug administration cycle) may be periodically coupled, retiming repair is performed. If two or more periodic processes are detected to have a common factor causing mutual interference, a new time step based on the Chinese Remainder Theorem (CRT) is calculated to make the cycles of each process re-prime to eliminate coupling. This time step is then used at the field level to synchronize the relevant processes, restoring the independence of each parallel process. This technique can avoid resonance conflicts between different periodic processes, such as avoiding adverse superposition between the treatment rhythm and the patient's physiological rhythm.

[0031] Lineage and Coverage Tracking: The system records the origin and inheritance relationships of each operation within the process, as well as a globally unified view of the equivalent state. When an equivalence tear occurs, the system marks the source node or data tag that caused the inconsistency and updates the coverage information after repair. This allows regulators or researchers to trace the source and scope of inconsistencies and clarify responsibility. The unified state view ensures all participants have a shared understanding of the current system's consistency, preventing fragmented actions.

[0032] Multi-party scenario coordination: In complex scenarios involving multiple teams (e.g., multi-center clinical trials where each center operates its own analysis workflow), this invention provides a cross-participant consistency verification and coordination mechanism. Once each participant's internal workflow achieves equivalent closure, the shared boundary between any two participants with data interaction is... The conservation check verifies whether the inputs and outputs or results of the interaction between the two parties are balanced and consistent. If inconsistencies are found across participants, they are recorded as obstacles and trigger the council coordination mechanism, whereby all relevant parties jointly address the conflict until it is resolved. If a global conservation loop is not achieved after a preset maximum number of repair steps, the system will generate a fault certificate (CULPRIT), recording the unresolved problem type and relevant participant information, and suspend the process pending manual intervention. This mechanism ensures consistent closed-loop control on a larger scale (across institutions or teams). If the automatic mechanism cannot resolve the issue, it is promptly reported for manual intervention, thus providing a safety net to prevent the accumulation and spread of errors.

[0033] The beneficial effects of the novel equivalence device structure closure and single-step repair method and system in medical diagnosis, drug discovery, and physiological state modeling of this invention are as follows:

[0034] (1) This invention establishes a closed-loop control method encompassing structural equivalence, tear detection, single-step repair, and receipt verification. This ensures structural consistency and result reliability in complex medical diagnostic and physiological simulation processes, enabling the gradual elimination of abnormal deviations in a refined and interpretable manner. Simultaneously, it eliminates the need for traditional threshold voting or re-encrypting logs, reducing implementation and deployment costs. While guaranteeing real-time performance, it achieves full-process auditability and traceability, providing higher commercial value and regulatory compliance possibilities for medical AI systems. Attached Figure Description

[0035] The present invention will now be described in further detail with reference to the accompanying drawings and specific implementation methods.

[0036] Figure 1 Witnessing the equivalence: ABBA order matrix + Budget Triangle (Medical Production Line - Manufacturer Level);

[0037] Figure 2 For multi-source fusion closed loop: μ decreasing (Δμ=1) path diagram;

[0038] Figure 3 Site-level pharmacological response: rectangular tear + adapter repair diagram;

[0039] Figure 4 For CRT consensus + retiming graph;

[0040] Figure 5 A flowchart for drug discovery (components → structure → site → response prediction);

[0041] Figure 6 Diagram of the autohealer architecture (geometry > frame > field);

[0042] Figure 7 A diagram illustrating the natural number folding verification process (US Standard Code for Information Interchange weighted folding);

[0043] Figure 8 Diagram of Boolean gating receiver path + violation exclusion (no number near the gating);

[0044] Figure 9 For multi-center collaboration: stratified bonding + council coordination diagram;

[0045] Figure 10 ABBA+ provides a closed loop for visual synchronous localization and mapping. +CRT (Manufacturer-grade Pipeline) diagram. Detailed Implementation

[0046] The present invention will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the present application can be combined with each other.

[0047] To make the technical solution of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] Example 1: Monitoring and Repairing the Structural Consistency of the Diagnostic Process

[0049] A hospital developed a digital diagnostic assistance system, including multiple tests (imaging, laboratory, physiological signals) and AI model analysis steps. During the diagnosis of a complex case, the system performed a structural equivalence check on the workflow: First, it checked the commutativity of the order of two parallel analysis branches (e.g., reading images first and then synthesizing medical history vs. synthesizing medical history first and then reading images). The results showed that the preliminary diagnostic conclusions given by the AI ​​model under the two orders were inconsistent, indicating a sequence tear. Simultaneously, the system performed a conservation equivalence check on the closed loop analysis involving the patient's energy metabolism, calculating the balance of nutrient intake, metabolic output, and energy storage. It found a total difference ρ≈+5% (non-zero), suggesting a conservation tear in the patient's energy budget model.

[0050] Once the aforementioned structural anomaly is detected, the system automatically selects a repair adapter based on priority. Due to the lack of a crucial cross-validation step in the diagnostic process (incomplete topology), the system first introduces a new feedback node at the geometry layer—arranging a multidisciplinary consultation to discuss the results, serving as a bridge between image analysis and medical history analysis. This is equivalent to adding an expert feedback path to bridge the two analytical branches, ensuring that regardless of the order, there is a convergence step to interpret and unify the differences in results. This geometry layer repair operation is recorded as a receipt, with content such as: "STEP|seam=sequence|adapter=feedback_consult|level=Geometry|mu_before=2|mu_after=1|Δμ=1". Before repair, the system bias metric μ=2 (containing one term each for sequence tear and conservation tear); after repair, μ decreases to 1, satisfying the convergence requirement of Δμ=1.

[0051] Next, the system re-evaluates the equivalence state. The sequence tear has been eliminated, but the energy conservation tear still exists (μ=1). The system then selects the next repair adapter according to priority: there are no more topological defects to repair in the geometry layer, so it turns to the frame layer. The system finds that the imbalance in energy calculation may come from unit conversion errors—patient nutritional intake is measured in kilocalories, while metabolic consumption is measured in kilojoules, and the different units cause bias in statistics. Therefore, the "unit affine conversion" adapter is applied to uniformly convert all historical data to kilojoules. This frame layer repair operation is also written to the receipt log: "STEP|seam=conservation|adapter=unit_convert|level=Frame|mu_before=1|mu_after=0|Δμ=1". At this time, μ_before=1 and μ_after=0, successfully eliminating the energy conservation bias, and the system reaches an equivalent closed state.

[0052] Throughout the process, the operating system appends and generates receipts at each step, and calculates and updates the natural number folded checksum in real time. For example, when the first receipt is written (feedback_consult step), since it is the first item in the log chain, the FENCE|nat value is the weighted result F1 of the line's US Information Interchange Standard code and modulo prime. When the second receipt is written (unit_convert step), the system recalculates the cumulative folded value F2 of the two receipts and appends it to the second receipt. Anyone attempting to modify the content of the first receipt will cause the FENCE|nat value in the second receipt to differ from the recalculated value according to the original rules, making it easily detectable. In this way, when hospital administrators audit the diagnostic process later, they only need to check the receipt text and its NAT signature to confirm that the process has not been tampered with, ensuring the credibility of the diagnostic conclusions.

[0053] Furthermore, in this embodiment, the system utilizes a Boolean gating decision unit to control the issuance of the final diagnostic conclusion. Only when all six Boolean conditions are met (e.g., the case has been labeled and all necessary examinations have been completed = label track passed, data alignment in all stages is consistent = adhesion track passed, etc.), will the system automatically generate the final comprehensive diagnostic report; otherwise, the process will pause and prompt for manual review. This ensures that the final conclusion is based on a structurally sound and consistent foundation, avoiding premature conclusions or omissions of crucial steps.

[0054] Example 2: Model Equivalence Closed Application in Drug Development

[0055] A pharmaceutical company is using computer simulations to optimize the metabolic pathways of new drugs. The simulation model incorporates multiple interacting biochemical reaction steps and is designed to predict the distribution of metabolites and energy changes of the drug in vivo. Researchers have applied the method of this invention to this simulation process to improve the model's reliability:

[0056] First, the system performs ABBA analysis on the sequence of key steps in the simulation. A new drug's metabolic pathway has two parallel reactions, A and B, which the model simulates by default in the order of A followed by B. ABBA equivalence analysis reveals that exchanging the reaction order (B followed by A) leads to a slight difference in the final metabolite concentration, indicating a sequence tear. This means the model results are overly sensitive to reaction order; in actual physiology, these two reactions may be considered parallel or mutually influential, but the model fails to capture this dependency. Meanwhile, The conservation check calculates the mass conservation of the entire metabolic network: the initial drug dose is compared with each metabolite and the total amount eliminated. A mass deficit of approximately 5%, i.e., ρ = -5% (a negative value indicates that the total output and storage are less than the input), triggers a conservation tear alarm. This indicates that the model may have missed a part of the metabolic fate (e.g., ignoring a certain excretion pathway or sample measurement error).

[0057] To address the sequence tear, the system prioritizes geometric layer repair: adding a cross-inhibition connection to the model structure allows reactions A and B to proceed in parallel and influence each other (e.g., the product of B slightly inhibits the progress of A, consistent with physiological feedback). This topological adjustment essentially introduces a feedback mechanism that might exist in actual physiology, reducing the impact of reaction order on the outcome. This operation reduces the model's dependence on sequence, decreasing μ from 2 to 1. Subsequently, the system addresses the conservation tear: analysis revealed a missing excretion pathway in the model, resulting in some mass destinations not being recorded. Therefore, geometric layer repair is applied again, adding an "excretion" node at the end of the metabolic network and connecting it to the corresponding reaction, attributing the missing 5% of mass to this pathway. After this operation, the conservation bias ρ returns to zero, μ decreases from 1 to 0, and the model achieves closed-loop conservation. All repair steps are recorded in the receipt chain and verified, ensuring the transparency and reliability of the model modification process.

[0058] After the correction was completed, the pharmaceutical company compared the simulation results before and after the modification. The modified model was no longer sensitive to the order of reactions A and B, and the difference in metabolite concentrations between the two orders disappeared; at the same time, the mass budget was completely closed, and no more unexplained losses occurred. The entire model optimization process employed a single-step convergence strategy, involving only two explicit adjustments, each ensuring the correctness of the improvement direction, eliminating repeated trial and error, and significantly saving development time. This demonstrates the value of this invention in improving the reliability and interpretability of models in the field of drug discovery.

[0059] Example 3: Multicenter Collaborative Physiological State Monitoring

[0060] In a remote chronic disease monitoring system involving multiple hospitals, each hospital independently runs a patient status assessment model and exchanges key indicators via the cloud. The method of this invention is used to ensure the consistency of multi-center data: each hospital's internal assessment process implements equivalence closure, and each hospital's own log records local operations and verification values. When data is aggregated in the cloud, the system executes the shared indicators reported by different hospitals. Conservation balance verification. For example, Hospital X records the difference ΔX between a patient's daily intake and expenditure, while Hospital Y records the net change ΔY measured by the activity sensor worn by the same patient. A cloud-based comparison reveals that ΔX + ΔY ≠ 0, indicating a conservation imbalance across institutions—the patient's reported intake / expenditure does not match the objective measurement. The system generates a blocking entry record and triggers council coordination: notifying Hospitals X and Y to jointly investigate the source of the discrepancy (which could be inaccurate patient diaries or sensor calibration errors). After consultation, the two parties calibrate and correct the patient data, and resubmit it to eliminate the discrepancy. Throughout this process, whenever a cross-institutional inconsistency occurs and is resolved through coordination, the system updates the overlay information, recording the source of the inconsistency (sensor calibration issue) and the resolution process for future audit traceability. If a problem cannot be resolved after multiple coordination attempts, such as a patient's data being persistently unbalanced and exceeding a certain number of repair attempts, the system generates a fault certificate (CULPRIT), indicating that the patient's data is abnormal and requires manual intervention, and suspends related automatic analysis. Through this mechanism, the multi-center collaborative system achieves global consistency control: it ensures the trustworthiness within each system and establishes a verification and correction chain between systems, so that any data anomaly will not be ignored and will affect subsequent decisions.

[0061] The above embodiments illustrate the specific application of the present invention in different scenarios. Through structural equivalence testing, single-step repair, and receipt verification, the present invention can significantly improve the stability, interpretability, and security of medical diagnostic and physiological model systems. Without losing generality, those skilled in the art can make modifications or adjustments to the implementation details according to specific applications, but these all fall within the protection scope of the claims of the present invention. Any equivalent substitutions and improvements that do not depart from the spirit of the present invention should be included within the scope of the present invention.

[0062] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. An equivalent structure closure and single step repair method in medical diagnostics, drug discovery and physiological state modeling, comprising: monitoring the sequential and conservation equivalences during the run to detect structural anomalies; when detecting equivalence tears, performing a single structural or parametric adjustment in the geometric, frame or field layer according to a predetermined priority selection of a single repair adapter; and recalculating the system variant metric μ after each adjustment and verifying that it is reduced by 1 (Δμ = 1); characterized in that the method further comprises recording each operational step in the above process as an appended plain text receipt and calculating a deterministic natural number folding checksum to verify the integrity of the receipt chain, making the structural consistency restoration and verification process of the flow fully traceable and non-repudiable.

2. The medical diagnostic, drug discovery and physiological state modeling equivalent structure closure and single step repair method according to claim 1, characterized in that: The sequential equivalence is used to determine the destruction of the interchangeability of any two operation steps, specifically to compare the results of operations performed in different sequences under mirror conditions, and if the results are inconsistent, it is marked as a sequence tear; the conservation equivalence is used to monitor the conservation balance of the multi-step closed loop process, specifically by calculating whether the difference ρ of input, output and storage / loss is zero, if ρ≠0, it is determined that a conservation tear has occurred and an alarm is issued.

3. The medical diagnostic, drug discovery and physiological state modeling equivalent structure closure and single step repair method of claim 1, wherein: The repair adapter selection follows the rule of geometric layer priority, when detecting equivalence tears, it preferentially attempts to repair the topology in the geometric layer, then attempts to repair the reference standard in the frame layer, and finally attempts to repair the environmental variable in the field layer; And only one adapter is applied at a time to perform the minimum granularity correction operation, and after completing the repair, the equivalence state is monitored again to ensure the pertinence and effectiveness of each repair action.

4. The medical diagnostic, drug discovery and physiological state modeling equivalent structure closure with single step repair method as claimed in claim 1 wherein: After each repair operation is completed, the variant metric μ value before and after the repair is calculated, and only when μ_after = μ_before - 1 is met, the repair is accepted, otherwise the operation is revoked; through the Δμ = 1 convergence criterion, it is guaranteed that the system deviation is monotonically reduced within a limited number of steps until equivalence closure, avoiding infinite loops or oscillations.

5. The medical diagnostic, drug discovery and physiological state modeling equivalent structure closure and single step repair method of claim 1, wherein: The receipt record uses a predefined fixed syntax, including fields such as step identification, tear type or joint marker, selected repair adapter, repair level, and μ value change before and after repair; And a deterministic natural number folding function is used to calculate the checksum of the current receipt chain when generating each receipt, which is recorded in the receipt, which can be recalculated by any third party according to the same rules for comparison to detect whether the receipt chain has been tampered with.

6. The medical diagnostic, drug discovery and physiological state modeling equivalent structure closure with single step repair method according to claim 1, characterized in that: It also includes the step of introducing an external Boolean gate unit to control key decisions, which jointly decides whether to execute diagnostic conclusion output or intervention measures based on multiple Boolean condition signals, where Boolean conditions include parity pairing check, stage completion check, logical kink detection, label identification check, metrology reference consistency, and cross-system gluing interface check; and any condition containing numerical threshold comparison is excluded from the decision logic, and a predefined structural rule signal is used as the basis for decision.

7. A structural consistency repair system for medical diagnostics, drug discovery and physiological state modeling for implementing the method of claim 1, comprising one or more processors and memory, and coupled thereto a number of functional modules, characterized in that, The system comprises: a detection module for real-time monitoring of the sequential and conservation equivalence states in the diagnostic or simulation flow, outputting equivalence tear alarm information; a repair module for selecting a single repair adapter from the geometry layer, the frame layer or the field layer according to a preset priority, and performing a corresponding structure or parameter correction on the flow to correct the detected structural anomaly; a verification module for calculating the deviation metric μ value before and after the correction and determining whether it is reduced by one unit to verify whether the repair step is effective, and only in the case of Δμ = 1 is confirmed to apply the repair; a record module for generating a receipt record of each repair operation information to the receipt chain and calculating and updating the deterministic natural number folding checksum attached to the receipt for integrity verification; a gate decision module for collecting a plurality of external Boolean condition signals to logically determine and control the key actions in the flow (including diagnostic conclusion output or treatment execution), and only when all preset structural conditions are met, the action execution is authorized, otherwise it is intercepted or warned. Through the cooperative operation of the above-mentioned modules, the system can automatically monitor and repair structural inconsistencies in the medical diagnosis and modeling process, record verifiable operation traces, and structurally check the final decision to ensure the reliability and explainability of the whole process results.

8. The structural consistency repair system for medical diagnosis, drug discovery, and physiological state modeling of claim 7, wherein: The record module is configured to record the receipt log in plain text and use a natural number folding function to protect the signature of the log, wherein the folding function obtains a checksum by summing the American Standard Code for Information Interchange (ASCII) code of each receipt content modulo and weighted accumulation. When any historical record in the log is tampered with, the folding checksum in the subsequent receipt will not match, so it can be detected by the verification module or external auditors.

9. The structural consistency repair system for medical diagnosis, drug discovery, and physiological state modeling of claim 7, wherein: The detection module further includes a prediction unit and a conservation unit for identifying sequential dependency patterns in the diagnostic reasoning chain and conservation bias sources in the physiological model closed loop, respectively; when an equivocator tear occurs, the detection module will mark the source operation node or data label of the inconsistency and send it to the repair module to assist it in determining the target of repair, and after the repair is completed, the record module updates the unified state coverage view to trace the source of the inconsistency problem and its impact range.

10. A computer readable storage medium having stored thereon a computer program, which, when run on a computing device, is able to perform all the steps of the equiv-alizer structure closure and single step repair method according to claim 1 ; characterized in that, The program includes code instructions for implementing the following steps: monitoring the sequential equivocator and conservation equivocator states in the medical diagnosis or physiological simulation flow; selecting a unique repair solution according to the priority order of the geometry layer, the frame layer and the field layer and performing a single-step repair when structural tearing is detected; verifying that the repair reduces the deviation metric μ by one unit; recording the steps as a text receipt and calculating and updating the natural number folding checksum to ensure the integrity and reliability of the log; and externally controlling the key decisions of the flow based on predefined Boolean structural conditions, thereby ensuring the structural consistency, explainability and traceability of the entire flow.

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