Method and apparatus for controlled access to new capabilities of a health check-up skill package

Abstract

The application discloses a new ability controlled access method and device of a health examination skill package. The method obtains an artificial intelligence ability description object with a content hash, performs idempotent registration and same version conflict blocking according to an ability identifier and a version; positions a candidate component according to a processing dependency graph and a field blood relationship, performs five-dimensional compatible matching of a field contract, a data mode, an interface, a permission, a resource and an environment condition, determines an existing anchor component and a newly added adaptive component; generates an access patch and a candidate skill package, and after environment-oriented playback, hard constraint verification and gating, solidifies the ability version, the component workpiece, the effective configuration and the applicable environment into an immutable combined version and performs directional release. Thus, traceable access, local adaptation and compatible rollback of new abilities are realized. The method only processes collected examination data and outputs a candidate object for professional review.

Description

Technical Field

[0001] This invention relates to the fields of health checkup data processing, artificial intelligence agents, and computer software version control, and in particular to a controlled access method and apparatus for processing collected health checkup data and outputting candidate health checkup skill packages for professional review. Background Technology

[0002] A health checkup data processing skill package typically consists of data preprocessing components, model or tool invocation components, result normalization components, hard constraint validation nodes for health checkup data, and candidate object generation components. As local models, remote model interfaces, rule tools, and data processing services continue to iterate, the skill package needs to integrate new artificial intelligence capabilities while maintaining existing field relationships, running permissions, and processing paths.

[0003] Conventional capability registration mechanisms typically register calling objects using capability names and interface addresses, with developers modifying the workflow according to the interface documentation. This approach usually lacks unified machine verification of capability content identity, field lineage, permission boundaries, resource requirements, and applicable environments. Consequently, it is difficult to determine whether the same version has been modified, or which component a capability should replace or where it should be connected to in processing.

[0004] The closest existing technology is to directly replace the existing model calling component and then check the output usability through a small number of samples. This approach uses interface callability as the main admission criterion, but the new and old capabilities may differ in terms of field types, quantitative relationships, units, coordinate references, sequence references, data retention ranges, and device protocols. When these differences are temporarily handled in downstream components, the scope of the patch is unclear, non-target components are prone to change, and it is difficult to restore a consistent combination of artifacts and configurations during rollback.

[0005] Therefore, existing technologies have at least two interrelated problems: First, capability descriptions lack verifiable content identities and complete contracts, making it difficult to prevent duplicate registrations, content conflicts with the same version, and permission expansion before integration; second, candidate component positioning, difference adaptation, and environment release are separated from each other, making it difficult to prove that new capabilities only run through a mandatory verification path under the adapted organization, device family, and inspection protocol, thereby increasing the risk of field breakage, out-of-bounds environment calls, and unreproducible rollback. Summary of the Invention

[0006] This invention aims to solve the technical problems of difficulty in uniformly verifying the identity and contract of new artificial intelligence capabilities when they are integrated into health checkup skill packages, as well as the difficulty in forming a traceable closed loop in terms of the scope of adaptation, verification scope and release environment.

[0007] To address the aforementioned technical problems, this invention provides a novel controlled access method for a health checkup skills package. This health checkup skills package is applied to health checkup institutions to process collected test results, images, questionnaires, and past records. It does not perform any human body testing operations and outputs candidate data for professional review rather than diagnostic conclusions, including:

[0008] Obtain a capability description object containing capability identifier, version, content hash, field pattern, permission requirements, resource requirements, and environmental conditions, and perform source and content verification;

[0009] Using the capability identifier and the version as unique keys and the content hash as a consistency check value, perform idempotent registration and same-version conflict blocking on the capability description object;

[0010] Based on the processing dependency graph and field lineage of the baseline skill package, candidate components are located, and five-dimensional compatibility matching of field contracts, data modalities and interfaces, permissions, resources and environmental conditions is performed. Existing anchored components and newly added adaptable components are determined for deterministic transformation differences. The baseline skill package is a skill package version that has been registered and is reproducible before the new capability is accessed. The field lineage includes the source component of the field, the transformation process, the downstream consumer component, and the reference relationship with the original inspection sequence. The new capability is the artificial intelligence capability described by the capability description object.

[0011] Based on the existing anchoring component and the newly added adaptation component, an access patch is generated, and the access patch is applied to the baseline skill pack to form a candidate skill pack.

[0012] The candidate skill package undergoes environment-oriented replay and hard constraint verification. After gating passes, the capability version, component artifact, effective configuration, and applicable environment are bound to an immutable combination version and released in a targeted manner.

[0013] Furthermore, the capability description object is validated for required fields, field types, content hashes, digital signatures, and maximum permissions. If the unique key does not exist, it is written to the registry. If both the unique key and the content hash are the same, an idempotent return is returned. If the unique key is the same but the content hash is different, a version conflict record is generated and overwriting is blocked.

[0014] Furthermore, in the process of dependency graph, candidate components whose inputs can be provided by upstream and whose outputs affect the target task fields are located. Existing anchored components are determined based on field lineage. Item-by-item mandatory matching of field contracts, data modalities and interfaces, permissions, resources and environmental conditions is performed respectively. New adaptive components are generated based on deterministic transformation differences.

[0015] Furthermore, the patch generator version, configuration, and deterministic parameters are fixed to generate multiple trial patches and record the parent version, component artifact closure hash, capability description object identifier, applicable environment, and failure handling. After partial test replay, the patches are deterministically sorted according to the number of complete involved components, field lineage distance, actual number of operations, and component identifier to select the access patch with a smaller complete access range.

[0016] Furthermore, based on the downstream closures of the target components of the baseline skill package and candidate skill packages, the set of influencing components is determined. Paired replay is performed on the environment matching records, and zero-call verification is performed on the environment mismatch records and path mismatch records. An independent verifier recalculates the consistency of coordinates or sequence back-pointing, the consistency of project and equipment protocols, the retention of existing risk level markers, the traceability of sources, and data access constraints.

[0017] Furthermore, before the capability description object is obtained, a structured capability gap request can be generated from the task gap, and a capability description object with necessary fields can be formed through two levels of routing: the capability service layer and the specific capability layer; this discovery process does not obtain installation permissions or production call permissions.

[0018] Furthermore, revocable permission leases can be issued in isolated environments to perform functional tasks and unauthorized access inducement tasks, compare the declared permission set, the actual permission invocation set, and the permission denial attempt set, and incorporate permission differentials into access control.

[0019] Furthermore, when two or more qualified capabilities can replace the same target component or shared resources, causing the call latency to vary with the routing load, a joint constraint model of resources and routing can be established based on the specific performance profile configured, and the resource configuration and routing strategy through joint replay can be solidified into an immutable combination version.

[0020] Furthermore, by matching the organization identifier, device family identifier, inspection protocol identifier, data format identifier, and validity period through the environment router, shadow operation, restricted release, formal release, isolation, and compatibility rollback can be controlled.

[0021] The present invention also provides a controlled access device for a health checkup skill package with new artificial intelligence capabilities, including an object verification module, a registration control module, a compatibility positioning module, a patch building module, and a gating release module, each module being used to execute the corresponding processing steps in the above method.

[0022] By employing capability description objects with content hashes and source signatures, and using unique keys and content hashes for identity identification and content consistency verification respectively, duplicate registrations, different content within the same version, and excessive permission requests can be identified before generating access candidates, reducing the possibility of untraceable overwrites entering subsequent processes. Since candidate components are located based on the processing dependency graph and field lineage, and the five-dimensional mandatory matching, deterministic component adaptation, dual-graph influence range, zero-call environment mismatch, and independent hard constraint recalculation are connected into the same gating chain, access differences can be locally encapsulated, and non-target components and mandatory verification paths can remain stable. Furthermore, because capability versions, component artifacts, effective configurations, applicable environments, and effective times are all fixed, environment routing and compatibility fallback have clear version basis, thereby improving the reproducibility and environment orientation of access results. Attached Figure Description

[0023] Figure 1 This is a schematic diagram illustrating the activity of controlled access to embedded service chains for new capabilities provided in embodiments of the present invention.

[0024] Figure 2 A flowchart illustrating the new controlled access method for artificial intelligence capabilities provided in this embodiment of the invention;

[0025] Figure 3 This is a schematic diagram of the benchmark skill package business process and corresponding code provided in the embodiments of the present invention;

[0026] Figure 4 A schematic diagram illustrating the process of capability description object verification, idempotent registration, and version conflict blocking provided in embodiments of the present invention;

[0027] Figure 5 This is a schematic diagram of the access patch generation process provided in an embodiment of the present invention;

[0028] Figure 6 This is a flowchart illustrating the environmental orientation playback and hard constraint gating provided in an embodiment of the present invention.

[0029] Figure 7 A schematic diagram of the module structure of the new artificial intelligence capability controlled access device provided in an embodiment of the present invention. Detailed Implementation

[0030] The embodiments of the present invention will be further described below with reference to the accompanying drawings. These embodiments are used to illustrate the technical concept of the present invention and are not intended to limit the scope of protection of the present invention. Unless otherwise stated, the physical examination data in this specification refers to data that has been collected by external devices and entered into the computer system; the system does not include physical examination data collection operations, and the generated objects are all candidate objects for review by professional auditors.

[0031] like Figure 1As shown, the controlled access process of this invention can be summarized in five steps. S1 Object Verification: Check the required fields, field types, content hash, source signature, and permission limits of the capability description object. Only the reason for rejection is recorded upon failure. S2 Registration Control: Use the capability identifier and version as unique keys, and verify the actual content using the content hash. Same keys with the same hash are returned as idempotent, while different hashes with the same key are blocked and overwritten. S3 Compatibility Positioning: Find the access location based on the processing dependency graph and field lineage. Check field contracts, data modalities and interfaces, permissions, resources, and environmental conditions item by item. Only set adaptation components for differences that can be deterministically converted. S4 Patch Construction: Form differential patches within the local scope defined by existing anchor components. After local replay and deterministic sorting, candidate skill packages are generated. S5 Gated Release: Perform pairwise replay of the baseline skill package and candidate skill packages for the matching environment. Perform zero-call verification on environment mismatch records and path mismatch records. Then, an independent verifier recalculates hard constraints. Only after all verifications pass is the version solidified and released in a targeted manner.

[0032] The key boundaries of the above process are: this invention only processes health checkup data that has been collected and input into the system, and the output is still candidate data for professional review; all five compatibility dimensions and each gating objective are mandatory conditions, and a better result cannot offset a failure of another; new capabilities and newly added adaptation components must not modify, close, or bypass hard constraint verification nodes. Failure of any condition will result in blocking, isolation, or rollback at the corresponding stage.

[0033] For ease of understanding, the following are the necessary terms used in this specification and their explicit meanings; terms not listed are to be understood in the usual sense in the art.

[0034] Necessary terms Clear meaning Health checkup skills package This program processes collected physical examination data and generates a set of executable components and configurations for candidates awaiting professional review. Baseline Skills Package The skill pack versions that were registered and reproducible before the new capabilities were added serve as a reference for location, comparison, playback, and rollback. Ability description object A machine-readable structured object that describes the identity, content, inputs and outputs, permissions, resources, and applicable environment of a new capability. Content Hash Consistency verification values ​​are calculated based on the actual content of the capability or the standardized interface and the workpiece list. Idempotent registration / version conflict If the capability identifier and version are the same and the hash is the same, return the existing record; if the unique key is the same but the hash is different, record the conflict and prevent overwriting. Processing dependency graphs A directed relationship with skill package components as nodes and field dependencies or control dependencies as edges. Field lineage The fields trace the relationship from the source component, through transformation, to the downstream consumer component and the original inspection records. Five-dimensional compatibility matching Each of the five items—field contract, data modality and interface, permissions, resources, and environmental conditions—is subject to mandatory selection. Anchoring components already exist Existing components whose business functions are not changed by patches, and whose lineage is determined by fields, are used to fix the boundaries of patch insertion. Added compatible components It only encapsulates components with deterministic transformation differences, independent versions, and input / output contracts. Access patch It contains a differential object including the parent version, target component, anchor boundaries, adaptation or replacement operations, and failure handling; it is not a complete repackaging. Candidate skill pack The skill pack is created after the access patch is applied to the baseline skill pack, and is yet to be replayed and gated verified. Environment-oriented playback / zero-call verification Select records by organization, equipment family, inspection protocol, data format, and validity period; verify that the number of related calls for environment or path mismatch records is zero. Hard constraint verification The independent validator recalculates unavoidable conditions such as fields, units, quantities, coordinates or sequence references, risk markers, sources, and permissions. Invariable combinatorial version A reproducible release unit that integrates capability version, component artifact, effective configuration, applicable environment, and effective time. Candidates Structured results generated by the skills package and subject to review by professionals are not directly used as diagnostic conclusions for natural persons.

[0035] Example 1

[0036] like Figure 2 As shown, this embodiment provides a new controlled access method for a health checkup skill package. The health checkup skill package is applied to the processing of collected test, imaging, questionnaire and past record data by health checkup institutions. It does not perform any testing operations on the human body and outputs candidate data for professional review rather than diagnostic conclusions. The method includes the following steps.

[0037] In a health checkup scenario, the system receives the collected checkup data. Each data set includes the checkup order identifier, department, combined examination items, specific examination items, examination results, and findings. Depending on business needs, it may also include units of measurement, reference ranges, abnormality alerts, questionnaire answers, and past records. The system establishes a hierarchical relationship between department, combined examination items, and specific examination items, and retains the original record identifier for each result for subsequent verification and source attribution.

[0038] like Figure 3 As shown, the baseline skill package performs the following processes sequentially: verifying the input structure; splitting composite inspection results and unifying item names; generating preliminary results based on configured rules, models, or screening tools; verifying the correspondence between each result and the original inspection record; removing duplicate results and merging similar conclusions; calculating risks, ranking them, and generating explanations; and finally verifying the output structure and forming candidate objects. If any step fails, the system records the failure stage and reason, and stops forming candidate objects.

[0039] S1. Obtain a capability description object containing capability identifier, version, content hash, field pattern, permission requirements, resource requirements, and environmental conditions, and perform source and content verification;

[0040] Capability description objects employ a machine-readable data structure and can be normalized, serialized structured files or registration requests. In addition to the fields specified in the claims, capability description objects may also include input patterns, output patterns, interface definitions, applicable tasks, timeout handling, error handling, source identification, and digital signatures. Content hashing can be calculated based on the byte content of local model files or on the normalized concatenation result of the interface specifications, execution artifact identifiers, and version lists of remote tools. During normalized serialization, the field order, character encoding, and null value representation are fixed to avoid different hashes for the same content due to serialization differences.

[0041] Source and content verification is divided into schema verification, content identity verification, signature verification, and authorization limit verification. Schema verification confirms the existence of required fields and that the field types match the capability description object schema; content identity verification recalculates the content hash and compares it with the declared value; signature verification uses a pre-registered source public key or trusted signature chain to verify that the object has not been replaced; authorization limit verification compares the permission requirements with the read range, network destination, and retention period allowed by the intended connection component and target organization. If any required verification fails, the system only generates a structured rejection reason and does not generate an executable patch.

[0042] S2. Using the capability identifier and the version as unique keys and the content hash as a consistency check value, perform idempotent registration and same-version conflict blocking on the capability description object;

[0043] A unique key is used to locate the capability version record, and a content hash is used to prove the actual content corresponding to that version record. The registration process uses a capability registry, which is a storage structure that registers capability description objects, content hashes, and verification statuses using capability identifiers and versions as unique keys, and supports idempotent returns and version conflict records. When a unique key appears for the first time, the capability registry writes the capability description object and verification status and generates a registration event; when both the unique key and the content hash are the same, the system returns an existing record identifier and does not initiate candidate location again; when the unique key is the same but the content hash is different, the system establishes a version conflict record associated with the original record, suspends automatic access, and requires the capability provider to change the version or restore the original content. This process separates the verification of "same version" and "same content," avoiding the use of overwrite to mask artifact changes.

[0044] S3. Based on the processing dependency graph and field lineage of the baseline skill package, locate candidate components, perform five-dimensional compatibility matching of field contracts, data modalities and interfaces, permissions, resources and environmental conditions, and determine existing anchoring components and new adaptable components for deterministic transformation differences. The baseline skill package is a skill package version that has been registered and is reproducible before the new capability is accessed. The field lineage includes the source component of the field, the transformation process, the downstream consumer component, and the reference relationship with the original inspection sequence. The new capability is the artificial intelligence capability described by the capability description object.

[0045] The baseline skill set used in this step is a version of the skill set that was registered and reproducible before the new capability was added. The new capability to be added is the artificial intelligence capability described by the capability description object. Nodes in the dependency graph represent skill set components, and edges represent fields or control dependencies. Field lineage records the source component, transformation process, downstream consuming component, and reference relationship with the original inspection sequence. The system first selects components to call according to the applicable task screening model or tool, then checks whether the input required by the new capability can be directly provided or deterministically transformed from the upstream field of the candidate position, and whether the output of the new capability can affect the target task field, thereby excluding components that only have similar text descriptions but lack executable data paths.

[0046] Five-dimensional compatibility matching employs a mandatory dimension-by-dimensional evaluation, rather than using the overall average score to mask individual failures. In one implementation, the compatibility gating variable is calculated according to equation (1):

[0047] Equation (1):

[0048] in, Represents a five-dimensional compatible gated quantity; A binary quantity indicating whether the contract dimension of the field satisfies the mandatory conditions; A binary quantity representing whether the data modality and interface dimension satisfy the mandatory conditions; A binary quantity representing whether the permission dimension satisfies the mandatory conditions; A binary quantity indicating whether a resource dimension satisfies the mandatory conditions; A binary quantity representing whether the environmental condition dimension satisfies the mandatory conditions; each binary quantity is set to 1 if it satisfies the corresponding mandatory condition, and 0 otherwise. Only Only candidates that meet the criteria can directly enter patch generation; differences in the field contract dimension that can be eliminated through deterministic transformation first enter the adaptation requirement queue, and are recalculated after being processed by the newly added adaptation component. .

[0049] Deterministic transformation means that the same input and configuration will inevitably produce the same output, and the transformation relationship can be recorded, recalculated, and traced back. The transformation may include field renaming, unit conversion, enumeration value mapping, array dimension adjustment, coordinate baseline transformation, sequence index baseline transformation, result object normalization, and compatibility degradation after timeout. Existing anchoring components are used to define patch insertion boundaries and do not require changes to the business functionality of existing anchoring components; newly added adaptation components have independent versions, input / output contracts, test records, and artifact hashes. Locked components and hard constraint verification nodes for physical examination data are only allowed to be read and not added to the modification set.

[0050] S4. Generate an access patch based on the existing anchoring component and the newly added adaptation component, and apply the access patch to the baseline skill pack to form a candidate skill pack;

[0051] The access patch is not a repackaging of the entire skill set, but rather a differential object with a clearly defined parent version, target component set, anchored boundaries, and artifact closure. The patch generator, with its fixed version, configuration, candidate enumeration order, and parallel item sorting rules, constructs probe patches for replacing model call components, inserting input adapter components, inserting output adapter components, adding failure handling components, or combinations of these operations. Each probe patch records the hash of the component artifact closure before and after modification to verify that components outside the target set have not changed during the build process.

[0052] For multiple trial patches replayed through local trials, the ranking index can be determined according to equation (2):

[0053] Equation (2):

[0054] in, Indicates a test patch Ranking indicators; This indicates the sorting weight based on the total number of components involved. Indicates a test patch The total number of components involved; The sorting weight for the bloodline distance field; Indicates a test patch Field lineage distance from input boundary to output boundary; Indicates the sorting weight of the actual operands; Indicates a test patch Total number of replacement, insertion, and configuration modification operations included; The sorting weights represent stable parallel orders; This represents the non-negative order value obtained from the component identifier according to a fixed encoding rule. Each sorting weight is a pre-defined non-negative number, and priority is given to... The comparison result is not canceled out by other items; when multiple probe patches have the same integrity condition, the selected option is chosen. The smaller one. Alternatively, weighted summation can be avoided, and a method based on... , , and Both methods maintain determinism by comparing lexicographically sequentially. Field lineage distance is the number of components or transformation steps traversed from the candidate component's output field along the field lineage to the target task field. The field lineage distance of the trial patch is calculated based on the corresponding field pairs between its input and output boundaries.

[0055] After applying the access patch to the baseline skill pack, the system regenerates the candidate processing dependency graph and calculates the candidate skill pack identifier. During the build process, it checks that the artifact closure hashes of components outside the target set match the baseline values, fixes the new capability version and runtime parameters, and saves remote response snapshots or verifiable content identifiers. If patch application fails, the baseline skill pack is not modified; only the failure logs and replayable build inputs are retained.

[0056] S5. Perform environment-oriented replay and hard constraint verification on the candidate skill package. After gating passes, bind the capability version, component artifact, effective configuration and applicable environment into an immutable combination version and release it in a targeted manner.

[0057] Environment-oriented replay covers environment matching records, environment mismatch records, and path mismatch records. Environment matching records are used to compare the processing differences between the baseline skill pack and the candidate skill pack under the same input, runtime, non-target dependencies, and evaluator; environment mismatch records are used to demonstrate that when the agency, device family, inspection protocol, data format, or applicable time conditions are not met, the number of new capability calls and the number of candidate skill pack calls are both zero; path mismatch records are used to demonstrate that processing paths that do not reach the target component will not be incorrectly rerouted due to access patches.

[0058] The independent validator and candidate skill packages use independent execution artifacts and configuration sources. The independent validator recalculates field types, units, quantitative relationships, sequence references, and coordinate transformations, confirms that existing risk level markers have not been deleted or changed without basis, and verifies source identifiers, processing chain identifiers, and permission boundaries. All executable paths leading to candidate outputs must pass through the health check data hard constraint verification node; new capabilities and newly added adaptation components have no right to modify, close, or bypass this node.

[0059] Gated objectives are compared separately, and excess results from one objective are not used to compensate for failures of another. A candidate skill set can only be formed into an immutable combined version when the field contract compliance rate, confirmed sample quality condition, and coordinate or sequence retracement rate reach their respective thresholds, while the degradation of non-target fields, failure rate, latency, and resource consumption do not exceed their respective limits. The first release state of this combined version is shadow running, where shadow output does not replace baseline output; only after shadow running reaches a preset number of observations and no isolation events are triggered are restricted release and formal release allowed sequentially.

[0060] Example 2

[0061] like Figure 4 As shown, this embodiment refines the capability description object verification, idempotent registration, and version conflict blocking process based on embodiment 1.

[0062] S1.1. Obtain the capability description object carrying the source identifier and digital signature;

[0063] The source identifier is associated with the capability provider's signature certificate, registration status, and allowed capability scope. For local model files, the digital signature covers the capability description object and the model file content hash; for remote model interfaces or tool interfaces, the digital signature covers the capability description object, interface specification hash, execution artifact identifier, and testable endpoint identifier. The signature coverage is recorded in the object schema to avoid different interpretations of the signature content between the verifying and issuing ends.

[0064] S1.2. Verify the required fields, field types, content hash, and digital signature of the capability description object;

[0065] The verification order can be executed sequentially according to object schema, content hash, signature chain, and revocation status. If object schema verification fails, there is no need to continue building candidate component queries; if the content hash is inconsistent, content integrity is marked as failed; if digital signature or signature chain verification fails, source verification is marked as failed. The verification result consists of a structured record containing field path, expected conditions, actual value summary, and validator version.

[0066] S1.3. Compare the permission requirements with the target component's authorization limit. If the requirement exceeds the target component's authorization limit, terminate the candidate generation process. The target component's authorization limit is the registered permission boundary of the target component in the baseline skill pack that is intended to interact with the new capability.

[0067] The target component's authorization limit is determined by the intersection of the skill pack authorization policy and the target institution's environment policy. The skill pack authorization policy is fixed when the target component registers with the baseline skill pack, recording the permission boundaries that the target component is allowed to execute. The target institution's environment policy reflects the restrictions imposed by the medical examination institution on data domains, network exits, storage locations, and execution environments. The intersection is compared item by item, taking the strictest option. If no dimension has a common allowed range, the authorization limit for that dimension is recorded as empty, indicating a complete prohibition. The comparison objects include the set of readable fields, the set of writable objects, allowed network destinations, data retention periods, callable sub-tools, and the maximum concurrency range. The comparison is performed item by item according to the above dimensions. For each dimension, it is determined whether the request range of the permission request falls within the allowed range of the authorization limit. If any part of the request range exceeds the limit, it is determined to exceed the authorization limit of the target component. If multiple dimensions exceed the limit simultaneously, they are recorded separately to facilitate the capability provider in determining the direction of permission contraction. If there are permission items in the permission request that cannot be mapped to the authorization policy, the permission item is considered unauthorized, rather than being allowed by default. When an authorization limit is exceeded, the entire process of candidate generation, including halting subsequent compatibility positioning, patch building, and gating release, is terminated. The dimensions exceeded and the differences between the requested and allowed ranges are recorded as the termination reason, which is then fed back to the capability provider. When the capability provider resubmits the capability description object after reducing permission requirements or adjusting the authorization registration of the target component, the resubmitted object is reprocessed according to the source and content verification process. Permission items that do not exceed the authorization limit continue to enter the idempotent registration process.

[0068] S2.1. Write to the capability registry and generate a registration event when the unique key does not exist;

[0069] The registration event only carries the capability record identifier, verification status, and the minimum fields required for the candidate process, and does not carry production call credentials. The registration event's permissions are limited to initiating candidate component location and isolation tests, and cannot directly change the production skill package, environment routing, or combined version status.

[0070] S2.2. If the unique key exists and the content hash is the same, return the existing registration result without generating the registration event again;

[0071] Idempotency refers to the characteristic that repeated processing of the same input produces the same result as a single processing without any additional side effects. In this step, "same input" means the unique key and content hash are the same. Returning an existing registration result demonstrates idempotency; repeated submissions and the first submission yield the same processing result without additional side effects. The registration result is the capability record content written to the capability registry during initial registration. The idempotent return includes the existing capability record identifier, the first registration time, the most recent verification time, and the current access status. The existing capability record identifier allows the caller to associate this submission with an existing capability record. The first registration time and the most recent verification time represent the creation time and the most recent verification time of the capability record, respectively. The current access status indicates the current access stage of the capability version. Repeated submissions do not repeatedly write to the capability registry, generate registration events repeatedly, or trigger candidate positioning repeatedly. The caller continues the original process based on the returned registration result, without needing to distinguish whether this submission is the first or a repeated submission. If the caller requests re-verification, a verification task that does not change the capability identity can be created, but the capability record or candidate version will not be recreated.

[0072] S2.3. When the unique key exists and the content hash is different, generate a version conflict record and prevent overwriting of existing registered content.

[0073] Version conflict records reference both the original content hash and the new content hash, and store the source verification result of the newly submitted object. The system does not determine the overwrite relationship based on the order of time, nor does it automatically treat new content as a patch version; a new unique key record is only allowed to be formed after the capability provider submits a new version identifier, or after authorized personnel confirm that the original record registration is incorrect and complete the audit process.

[0074] Example 3

[0075] This embodiment refines the candidate component localization and five-dimensional compatibility matching process based on Embodiment 1.

[0076] S3.1. In the processing dependency graph, find model calling components or tool calling components whose input fields can be provided by upstream fields and whose output fields can affect the target task fields to obtain the candidate components;

[0077] During the search, the process first traverses backwards along the field lineage from the target task field to the model invocation component or tool invocation component. Then, it verifies the field availability upstream from the input contract of each component. Field availability includes three states: direct availability with the same name, availability through a registered deterministic mapping, and availability through the adaptation requirements to be generated. Components with only semantically similar task descriptions but whose upstream cannot generate the required input fields will not enter the candidate component set.

[0078] S3.2. Determine the field mapping component, result normalization component, or workflow node adjacent to the candidate insertion position based on the field lineage, and determine the existing anchoring component from among them;

[0079] Existing anchor components are used to fix the upstream input boundaries and downstream output boundaries of the patch. If the output contract differs after replacing the candidate component, the result normalization component that is immediately downstream of the candidate component and consumes the target field is usually selected as the existing anchor component on the output side; if the upstream fields need to be combined or split, the field mapping component can be selected as the existing anchor component on the input side. Existing anchor components can be included in the closure hash without changing the artifact content.

[0080] S3.3. Perform mandatory matching on each of the following: type, quantity relationship, unit, coordinate, sequence reference and semantics of input and output fields; modality of input data, calling protocol, authentication method, timeout and error code; data reading range, network destination and retention period; computing resources, video memory, latency and concurrency requirements; and organization, equipment family, inspection protocol, data format and applicable time.

[0081] Each dimension can contain multiple atomic conditions. Only when all mandatory conditions of that dimension pass will the binary value in the corresponding equation (1) be 1. The field contract dimension also checks null semantics, enumeration range, and whether fields allow defaults. The environment condition dimension uses the whitelist range declared by the capability description object and does not infer applicability based on undeclared environments. The resource dimension can set the single call limit, concurrent window limit, and batch processing completion time limit to adapt to the different operating modes of real-time components and offline batch processing components.

[0082] S3.4. Mark the differences that cannot be resolved through deterministic transformation as blocking causes, and convert the differences that can be resolved through deterministic transformation into adaptation requirements;

[0083] The reasons for blocking are represented by dimension identifier, field path, baseline contract value, capability contract value, and non-convertible basis. Adaptation requirements record the source field, target field, transformation function type, required metadata, failure conditions, and reference requirements. Semantic changes that cannot be verified, data transfers exceeding authorized limits, or calls outside the environment whitelist are not treated as adaptation requirements.

[0084] S3.5. Generate the new adaptation components for input field transformation, output field transformation, coordinate or sequence backreference, result normalization or failure degradation according to the adaptation requirements;

[0085] The newly added adaptation component uses an executable artifact separate from the model calling component. The coordinate transformation component at least stores the source coordinate values, source coordinate system identifier, target coordinate values, target coordinate system identifier, transformation parameters, and target sequence reference; the failure degradation component selects to retry, return a structured unavailable flag, or switch to a compatible reference component based on the error code type, timeout status, and output integrity. All transformations can be recalculated by an independent validator using the same input and fixed parameters.

[0086] S3.6. Exclude the locking component and the physical examination data hard constraint verification node from the modifiable objects, wherein the locking component is a component that is pre-registered as locked and prohibited from modification.

[0087] The processing dependency graph sets immutable attributes for locked components and hard constraint verification nodes for physical examination data. The patch generator can read the input-output contracts of the components or nodes to verify paths, but cannot generate replacement, deletion, bypass, or configuration shutdown operations. If a candidate processing dependency graph contains a path that bypasses the hard constraint verification node for physical examination data to reach a candidate output, the graph verification immediately fails.

[0088] Locked components are pre-registered by the skill package provider or assessment agency during the baseline skill package registration. Registration records include the component identifier, reason for locking, and scope of locking. Typical locked components include components bound to source identifiers, components maintaining risk level markers, permission policy enforcement components, and audit log components. The scope of locking covers the component artifacts, runtime configurations, insertion points in the dependency graph, and upstream and downstream interface contracts. This means that patch generators cannot rewrite the artifacts and configurations of locked components, nor can they insert components before or after the locked component that alter its inputs or outputs.

[0089] The hard constraint validation node for physical examination data is set up before candidate output generation on the skill package processing path to validate the mandatory data processing conditions of the physical examination data. Validation includes field integrity, consistency of coordinate or sequence backreferences, units and value ranges, retention of existing risk level markers, source traceability, and data access constraints. Data is allowed to pass validation; if validation fails, a blocking status is generated. The blocking status cannot be swallowed, overwritten, or eliminated by retrying in subsequent components.

[0090] Excluded from modifiable objects during the candidate component location phase. Locked components and hard constraint verification nodes for physical examination data are directly removed from the component set during enumeration. Each subsequent patch operation is reviewed against unmodifiable attributes before being written as a trial patch. Any replacement, deletion, bypass, configuration shutdown, or modification of runtime parameters such as timeout, retries, and sampling rates applied to the components or nodes is rejected and recorded as a blocking reason.

[0091] Example 4

[0092] like Figure 5 As shown, this embodiment refines the construction, local replay, and deterministic selection process of the trial patch based on embodiment 3.

[0093] S4.1. Fixed patch generator version, generator configuration, and deterministic parameters;

[0094] The fixed content includes the candidate enumeration order, allowed adapter component templates, artifact parser version, hash algorithm identifier, random number seed, and parallel sorting rules. The patch generation task writes the aforementioned content into the generation task fingerprint. The same baseline skill pack, capability description object, and generation task fingerprint should produce the same set of probe patches.

[0095] S4.2. Generate trial patches corresponding to replacing model call components, adding input adaptation, adding output adaptation, adding failure handling, or operation combinations, respectively. The operation combination is a combination of at least two of the following operations: replacing model call components, adding input adaptation, adding output adaptation, and adding failure handling.

[0096] The trial patch is represented by a declarative operation list, with each operation including operation type, target component, preconditions, and result component identifier. Replacement operations require that the current hash of the target component is consistent with the parent version record; insertion operations require that the upstream output contract is consistent with the input contract of the newly adapted component, and that the output contract of the newly adapted component is consistent with the downstream input contract; failure handling operations cannot swallow the blocking state generated by the hard constraint verification node of the health check data.

[0097] S4.3. In each of the trial patches, record the component to be modified, the existing anchored component, the newly added adaptable component, the insertion position, the parent version, and the parent version hash, wherein the parent version is the version of the base skill pack;

[0098] The insertion position is determined by the upstream component identifier, downstream component identifier, and field set of a directed dependency edge, avoiding drift after graph updates caused by describing it solely by the flow program number. The parent version hash is used to ensure that patches are only applied to the baseline skill pack version verified when the patch was generated.

[0099] S4.4. In each of the trial patches, record the component artifact closure hash, capability description object identifier, applicable environment, and failure handling method before and after modification;

[0100] Component artifact closures include component code or model artifacts, direct configurations, referenced transformation tables, runtime dependency lists, and strategy fragments. Before-and-after closure hashes allow the build system to prove that artifacts outside the target set have not changed. Capability description object identifiers bind patches to the signature descriptions of the capabilities to be integrated, while the applicable environment and failure handling methods become inputs for subsequent environment routing and compatibility fallback.

[0101] S4.5. Perform a local test replay on the probe patch on the trigger evidence record and the deterministic boundary record, wherein the trigger evidence record is the input record of the new ability actually being executed in the baseline skill pack, and the deterministic boundary record is a record covering null values, boundary values ​​and error codes;

[0102] Trigger evidence records include input records that cause the target component to actually execute and the expected target task fields; deterministic boundary records cover null values, minimum length, maximum allowed length, coordinate boundaries, sequence start and end, and error code boundaries. Local trial replay only runs the artifact closure from the patch input boundary to the patch output boundary. If the output field contract is not satisfied or the mandatory verification state changes in a prohibited manner, the trial patch is discarded.

[0103] S4.6. From the probe patches that satisfy the target conditions, field contract conditions, locking conditions, and security environment conditions, perform deterministic sorting according to the number of complete involved components, field lineage distance, actual number of operands, and component identifier;

[0104] In one implementation, the target condition, field contract condition, locking condition, and security environment condition are elaborated as follows: The target condition is used to confirm that the trial patch does indeed enable the new capability to act on the predetermined target task field, that is, the output of the new capability can affect the predetermined target task field along the processing dependency graph without affecting unexpected fields; the field contract condition is used to confirm that the patch boundary and the upstream and downstream contracts are closed, that is, the input and output field types, quantity relationships, units, and sequence references between the component to be modified, the existing anchored component, and the newly added adaptation component are consistent item by item; the locking condition is used to confirm that the locked component or the hard constraint verification node of the physical examination data has not been modified, that is, there are no replacement, deletion, bypass, or configuration shutdown operations pointing to the locked component or the hard constraint verification node of the physical examination data in the operation list of the trial patch; the security environment condition is used to confirm that the patch has not expanded the network destination or data reading range, that is, the set of network access destinations and the data reading range after the patch implementation are both subsets of the set corresponding to the baseline skill pack.

[0105] The sorting can be done using Equation (2) or lexicographical comparison, and the computational input and sorting results are written into the selection evidence.

[0106] S4.7. The probe patch that is ranked first and whose complete number of involved components meets the preset component number priority condition is determined as the access patch.

[0107] The complete number of components involved includes the components to be modified, existing anchored components that must be included to maintain boundary integrity, and all newly added adaptive components, rather than only counting components whose code has been actually rewritten. The default component number priority condition requires that there are no probe patches that can meet the same goal and all mandatory conditions and have a smaller number of components involved; this definition makes the "smaller access range" a recalculated basis for comparison.

[0108] Example 5

[0109] like Figure 6 As shown, this embodiment refines the environment-oriented replay, hard constraint verification, deployment, and rollback processes based on embodiment 1.

[0110] S5.1. Determine the set of affected components based on the downstream closures of the existing target components in the baseline skill pack and the downstream closures of all target components in the candidate skill pack. The target component is a component in the skill pack that has an interaction relationship with the new capability or is affected by the access patch. The downstream closure is a set of all downstream components and fields that the target component can reach in its skill pack, including the target component.

[0111] The target components are determined based on the candidate component location and patch construction results, and include two types of sources. Components that interact with the new capability include components that call the new capability, components that are called by the new capability, and components that exchange inputs or outputs with the newly added adaptation component; components affected by the access patch include replaced components, adjacent components at the insertion position, and components whose input sources or output destinations change due to the patch insertion.

[0112] The downstream closure includes all components reachable from the target component and the fields they consume. The closure is calculated by traversing the field dependency edges and control dependency edges in the processing dependency graph, starting at the target component itself and ending at the component with no more downstream outgoing edges. The closure also records the fields consumed by each downstream component for subsequent field-based comparisons of impact scope. Taking the union of the downstream closures of the baseline skill set and candidate skill sets covers the original impact paths of the replaced component and the new impact paths generated by newly added adaptable components. The impact component set also includes components that share fields that must not change with the target field to avoid missing bypass data dependencies. The impact component set is used to select replay records and define the scope of verification. Environment matching records cover the actual paths to the impact component set in the applicable environment. Paired replays and independent validator recalculations focus on the components and fields within the impact component set for verification.

[0113] S5.2. Based on the set of influencing components, the baseline actual path, the candidate static reachability range, and the applicable environment, select environment matching records, environment mismatch records, and path mismatch records;

[0114] Before selecting records, the replay range is limited based on the candidate static reachability range and the applicable environment. Only records that run within the applicable environment and whose processing path is within the reachability range of the candidate skill package are retained. Candidate skill packages that cannot be reached based on the processing dependency graph and frozen input are excluded from the replay, so as to avoid including records that are not related to access in the replay.

[0115] Environment matching records cover the actual paths to the affected component set within the applicable environment. Selected from processed records of the baseline actual path, these records verify whether the processing of candidate skill packages and baseline skill packages is consistent when the environment is perfectly correct. Paired playback is used to compare the differences between the baseline and candidates component by component. Environment mismatch records set at least one mismatch condition for each of the following: organization, device family, inspection protocol, data format, and applicable time. For example, the record might carry a device family identifier not within the applicable environment, or its processing time might exceed the applicable time range. This verifies whether the system strictly avoids calling candidate capabilities when the environment is incorrect. Subsequent verification ensures that the number of calls to candidate skill packages and the AI ​​capabilities to be integrated on these records is zero. Path mismatch records select processing paths within the same environment that do not reach the target component but share a preceding entry point with it. This verifies whether traffic not passing through the target component is unaffected by the integration, i.e., checking whether the integration patch unexpectedly changed calls not passing through the target component. Record selection rules and sample identifiers are fixed to a single version and are not added or deleted afterward to avoid biased comparison results due to different samples, ensuring that the baseline and candidates use the same set of records.

[0116] It should be noted that the baseline actual path is the sequence of components and fields that the baseline skill package actually traverses when processing a record; the candidate static reachability is the set of components inferred from the candidate processing dependency graph, input / output field contract, and frozen input without running the candidate skill package. This set contains all components that the candidate skill package may traverse when processing the frozen input, constituting the upper bound of the candidate skill package's actual processing path, and the components actually traversed belong to a subset of this set.

[0117] S5.3. Based on the fixed input data, runtime environment, non-target dependencies, and evaluator of the environment matching record, perform pairwise replay of the baseline skill set and the candidate skill set;

[0118] Paired replay establishes a baseline execution instance and a candidate execution instance for each record, both sharing de-identified input snapshots, runtime images, non-target external dependency response snapshots, and evaluator versions. For model calls with randomness, the model version and allowed fixed runtime parameters are fixed, and the output content hash is saved; remote capabilities that cannot be completely fixed are calculated based on multiple repeated results, while the veto nature of hard constraints on fields must not be changed.

[0119] S5.4. Compare the pre-release route with the to-be-released route for the environment mismatch record and the path mismatch record, and verify that the number of calls to the AI ​​capability to be accessed and the candidate skill package are both zero;

[0120] Pre-release routes are the route configurations currently in effect, i.e., those prior to the release of the immutable combined version. Post-release routes are the route configurations that will take effect after the immutable combined version passes gating. Route comparison is performed rule by rule, comparing three elements for each rule: the target version, the environment identifier hit condition, and the default destination. The corresponding items on the baseline side are taken from the pre-release routes, and the corresponding items on the candidate side are taken from the post-release routes. The comparison results are listed item by item for differences. Under mismatch conditions, the target version of the post-release route must be compatible with the baseline version; that is, the route decision is determined before the call occurs, and it cannot rely on a fallback to the baseline version after a candidate capability call fails.

[0121] Zero-call verification checks routing decision logs, component entry counts, and external endpoint call logs simultaneously to prevent misjudgment based on only one layer of logs. The acquisition and comparison of these three layers of evidence are as follows: At the routing decision log level, when replaying environment mismatch and path mismatch records, each routing decision records the hit rule and target version identifier. During verification, each target version identifier is checked to see if it points to a candidate skill pack or an immutable combination version. At the component entry count level, both the entry components of the candidate skill pack and the newly added adaptation components have call counters set. The count is read once before replay and again after replay. A difference of zero between the two readings indicates no call has entered. At the external endpoint call log level, the request logs for remote model interfaces and tool endpoints involved in the new capability should be empty during the replay period. These three layers of evidence complement each other. Looking only at routing logs may miss direct calls that bypass routing, and looking only at component counts may be interfered with by background asynchronous tasks. Therefore, zero-call verification is only considered successful when all three readings are zero. If any layer has a non-zero record, the call source, occurrence time, and request characteristics are extracted to form evidence of environment mismatch calls. The route to be published must be explicitly selected as the compatibility baseline version under mismatch conditions, and a fallback after the failure of the candidate capability call cannot replace the zero-call beforehand.

[0122] S5.5. Use an independent verifier to recalculate coordinate or sequence back-pointing consistency, project and equipment agreement consistency, retention of existing risk level markings, source traceability, and data access constraints;

[0123] The independence of the independent verifier is reflected in the fact that the execution artifact, configuration source and operating environment are all separated from the candidate skill package. The recalculation process only reads the frozen input snapshot, the adaptation component transformation record and the candidate output object, and does not execute the candidate skill package's own code, thereby avoiding the verified object from proving its own correctness.

[0124] The independent verifier recalculates the target sequence reference and coordinate values ​​from the original inspection sequence metadata, adapter component transformation records, and candidate output objects. The recalculation is performed in reverse along the transformation chain. The original inspection sequence metadata stores the original layer number, coordinate reference, pixel spacing, and sequence length. The adapter component transformation records sequentially record the transformation type and transformation parameters traversed by each coordinate or index. Starting from the coordinates or sequence references in the candidate output, the original value corresponding to each coordinate or index is calculated in reverse along the transformation records and then compared with the original inspection sequence metadata. If the deviation between the calculated value and the original value is within a preset tolerance, they are considered consistent; otherwise, they are considered inconsistent if they exceed the tolerance or cannot be traced back along the transformation chain. The verification data is obtained from the project identifier, device family identifier, and inspection protocol identifier. For combination relationships, verify whether the item name, unit, and reference range of each result item in the candidate output belong to the allowed combinations of that item under the equipment family and the inspection protocol. Values ​​outside the combination are judged as protocol inconsistencies. Compare the existing risk level markers between the baseline output and the candidate output to see if they have been deleted or changed without basis. The comparison is performed item by item by marker. If a marker is missing or its value has changed in the candidate and there is no corresponding modification basis in the processing chain record, it is judged that the marker has been changed. Check the source identifier and permission policy hit results from the processing chain record. Verify that each data item in the candidate output can be traced back to the source identifier of the input inspection record along the processing chain record, and verify that the output field set does not exceed the field range allowed by the permission policy. If any hard constraint is not met, the quality score will not participate in compensation.

[0125] S5.6. Compare the field contract compliance rate, confirmed sample quality condition, coordinate or sequence retracement rate, non-target field degradation, failure rate, latency and resource consumption with the corresponding gating conditions respectively;

[0126] In this embodiment, the field contract compliance rate is the proportion of the number of output fields that satisfy the field contract in the candidate skill package output to the total number of output fields that should be output. The coordinate or sequence retracement rate is the proportion of the number of outputs in the candidate skill package output that can retrace back to the original inspection sequence or coordinates to the total number of outputs that should be retraced. Each gating condition is jointly specified by the target skill package version and the applicable environment and is saved with the candidate version. The sample quality confirmation condition compares the data processing results of existing audit results or authorized and confirmed benchmark labels, and does not output status conclusions for natural persons. The degradation amount of non-target fields is only compared with fields outside the patch target set. Fields that are prohibited from changing are required to be consistent value by value, while other fields can use a preset difference range.

[0127] S5.7. Enter shadow operation when all departmental control conditions are met, and enter restricted release or formal release according to the organization, equipment family and inspection protocol after the shadow operation is passed;

[0128] Shadow runs receive copied input from applicable environments, but candidate results only enter isolated storage and the evaluation chain, and do not replace baseline results. Restricted releases select calls according to a fixed traffic ratio or a fixed subset of environments, while official releases are allowed to take effect in all applicable environments of the immutable combined version declaration. Each state transition records the previous state, the next state, the gating evidence identifier, and the execution operation identifier.

[0129] S5.8. When an environment mismatch call, hard constraint violation, or difference in prohibited change fields occurs, the immutable combination version is isolated and switched to a compatible baseline version, wherein the compatible baseline version is the version in the baseline skill pack used to take over the call when the immutable combination version is isolated.

[0130] The isolation operation first prevents new calls from entering the target composite version, then saves the logs, input snapshot identifiers, and intermediate object identifiers of calls that have already started. Intermediate objects are data objects generated by components within the skill set during processing and potentially readable by subsequent components. The compatibility conversion component is verified and solidified along with the immutable composite version, used to convert intermediate objects generated by that composite version into a compatible base version readable mode. Compatibility rollback selects the return target based on the base version reference in the target composite version; if the intermediate object mode has changed, the compatibility conversion component solidified with the composite version is called first, and then the compatible base version continues processing to avoid secondary field breaks caused by simple switching.

[0131] Example 6

[0132] This embodiment provides a proactive capability discovery approach based on embodiment 1, characterized by demand-driven approach, hierarchical retrieval, and minimal disclosure. Proactive capability discovery only generates descriptions of capabilities awaiting review and does not alter the registration, compatibility matching, patch generation, and version gating chain processes of embodiment 1.

[0133] Step A1. Extract missing input types, expected output fields, allowed data fields, prohibited permissions, latency limits, and quality thresholds from unfinished tasks, component failure evidence, and manual requirements to form a structured capability gap request that does not include the capability provider's name;

[0134] Evidence of incomplete tasks and component failures is first de-identified, retaining only field patterns, error categories, and operational constraints related to the capability gap. Capability gap requests describe requirements as target functions rather than specific suppliers, thereby reducing the bias of candidate searches towards existing capability names. Permissions are prohibited from being written to requests as hard filter fields and cannot be overridden in subsequent semantic ranking.

[0135] Unfinished tasks, component failure evidence, and manual requests are collected from skill pack processing chain records, verification and gating process records, and reviewer records, respectively. Unfinished tasks include processing requests transferred to manual review in the baseline skill pack processing chain but not closed, as well as inspection data processing requests that remain in the pending queue due to a lack of adaptation capabilities. Component failure evidence includes verification and gating process records such as version conflict records, reasons for blocking compatibility matching tags and adaptation requirements, gate condition failures, and hard constraint violations. This type of evidence reflects input categories or output requirements that existing components cannot handle. Manual requests are records of processing requests submitted by medical examination agency reviewers in the form of work orders or review comments.

[0136] The extracted missing input type records the types of data that the gap capability should receive, the expected output field records the fields that the gap capability should produce, the allowed data field records the range of data that can be provided to the gap capability for processing, the prohibited permissions record the permission categories that cannot be granted to the gap capability, and the latency cap and quality threshold respectively record the maximum processing latency and minimum output quality requirements that the gap capability is allowed in the business chain.

[0137] Step A2. Based on the structured capability gap request, first perform capability service layer routing in the audited capability catalog according to task domain, data boundary and deployment environment, and eliminate capability services that are incompatible with permission conditions or regional conditions;

[0138] Capability service layer entries describe a set of capabilities provided by the same service endpoint or the same locally running container. Routing first compares the task domain and data modality, then compares whether the data can leave the current runtime domain, the service deployment location, and network access conditions. Services that fail to meet these hard criteria are not included in semantic similarity ranking, avoiding substituting descriptive similarity for authorization decisions.

[0139] Furthermore, the approved capability directory is a structured directory that registers approved capability service items and their subordinate capability items. Both service and capability items in the directory are registered after application by the capability provider and review by the administrator. The registration content varies depending on the service level and includes task domain, data boundaries, deployment environment, permission conditions, geographic conditions, input / output field contracts, functional descriptions, resource requirements, testable endpoints, and review status. The task domain is the category of data processing tasks served by the capability. In the context of physical examination data processing, it is divided according to processing stages, such as data format organization and structuring, preprocessing of examination items and standardization of item names, screening and auxiliary judgment, result merging and evidence retrieval, risk ranking and output organization. The task domain corresponds to the processing stages in the benchmark skill package business chain, enabling structured capability gap requests to locate candidate capabilities according to the processing stages to be supplemented. Data boundaries are the range of data that the capability is allowed to access and process, including whether the data can leave the current operating domain, data retention period, and data usage restrictions. The deployment environment is the location and method of capability operation, including local operating containers within the organization and approved remote service endpoints. Geographic conditions are the geographical range of the data that the capability service provider is allowed to process. The capability service layer routing compares the structured capability gap requests with the registered content of each capability service entry according to the above dimensions. Service entries that do not match the task domain, conflict with data boundaries, are unreachable in the deployment environment, or are incompatible with permission conditions or regional conditions are eliminated and will no longer participate in subsequent routing.

[0140] Step A3. Within the capability service routed through the capability service layer, execute the specific capability layer route based on the input / output field contract, function description, resource requirements, and testable endpoints;

[0141] The specific capability layer routing maps each required input and expected output of a structured capability gap request to a capability entry field, checks whether testable endpoints support isolated calls, and excludes capabilities that cannot be deployed based on resource limits. Semantic matching of feature descriptions is only used to rank candidates that pass the aforementioned hard criteria.

[0142] Testable endpoints are interfaces registered in the capability entries for initiating test calls. Isolated calls are verification calls initiated to capabilities without accessing production data, generating production side effects, or consuming production call quotas. The specific capability layer routing compares each required input in the structured capability gap request with the input fields of the capability entry in terms of type, quantity, and unit; and compares each expected output with the output fields of the capability entry in terms of field name and value format. Candidate capabilities that cannot be mapped are excluded or marked as candidates to be adapted. Capability entries whose resource requirements exceed the resource limit are not included in the candidate list.

[0143] Step A4. For each round of specific capability layer routing, only disclose the fields required to complete the routing within the current capability service to obtain a specific capability candidate list;

[0144] Minimal disclosure fields typically include a capability summary, input / output field summaries, resource level, permission summary, and test endpoint status. Credentials, internal addresses, or irrelevant capability patterns from the capability catalog are not disclosed to the discovery model. If the current candidates cannot cover all gap fields, the unmet fields in the structured capability gap request can form the next round of routing inputs, but the output of each round remains a candidate list rather than an execution instruction.

[0145] The discovery model is a model component that performs semantic retrieval and candidate ranking. It only obtains the minimum disclosed fields and does not access the complete registration content of the capability catalog. The specific capability candidate list is a collection of candidate capability identifiers and their matching criteria output from this round of specific capability layer routing. The matching criteria record the correspondence between candidate capabilities and each gap field in the structured capability gap request, allowing for tracing the source when extracting necessary fields later.

[0146] Step A5. Extract the interfaces, permissions, resources, versions, and test information required to complete the structured capability gap request from the specific capability candidate list, and form a necessary field capability description object with marked source, confidence information, and unconfirmed fields;

[0147] The required field capability description object retains the identity and signature fields necessary for source and content verification in Example 1. Fields missing or unverifiable in the directory are explicitly marked as unconfirmed and not filled in based on inferences from similar capabilities. Confidence information is used to prioritize manual completion and is not used as a basis for skipping field contracts or permission verifications.

[0148] Step A6. Submit the necessary field capability description object as the capability description object to be reviewed, and do not grant installation permissions or production call permissions to the capability discovery process.

[0149] Submission operations only write to the candidate receiving area. Only after the source signature, content hash, permission limit, and required fields have been verified according to Example 1 can the necessary field capability description object enter idempotent registration. The candidate receiving area is a storage area isolated from the capability registry. The candidate receiving area only accepts writes to capability description objects to be reviewed. Write operations do not generate registration events or trigger candidate location. The retrieval component, sorting component, and submission component in the capability discovery process run with minimal permissions, only having the permission to read the capability catalog disclosure fields and write to the candidate receiving area. They do not have the permission to read the complete capability catalog, write to the capability registry, or call production endpoints. Installation permissions and production call permissions may only be granted after the source and content verification passes and are confirmed by review. Proactive discovery expands the demand identification and candidate retrieval entry points, but does not expand the production authorization chain.

[0150] Example 7

[0151] This embodiment provides a capability assessment method based on a least privilege differential sandbox, building upon Embodiment 1. The permission effects of real tools are verified in isolated tenant or supplier test endpoints; real identity data, production credentials, and irreversible production write operations are not included in the testing process.

[0152] Step B1. Based on the permission requirements, the field mapping of the newly added adaptation component, and the target task, expand the read operation, write operation, network access operation, external storage operation, and execution operation to form a declared permission set and usage constraints;

[0153] The permission set is represented by a combination of resource type, allowed actions, object scope, network destination, validity period, and purpose tags. Field mapping may introduce the need to read image metadata or sequence references, so permission expansion checks both new capabilities and newly added adaptation components to avoid only verifying new capability interfaces while overlooking adaptation artifacts.

[0154] Step B2. Based on the stated permission set, issue a revocable permission lease in the isolated environment that only allows synthetic medical examination data, test accounts, limited network destinations, and limited validity periods, wherein the isolated environment is an environment isolated from the production environment and can only access synthetic data;

[0155] Revocable permission leases are enforced by the policy execution layer; the text returned by the tool or the model output cannot change the lease scope. Test accounts belong to isolated tenants; synthetic physical examination data uses constructed fields, image metadata, and sequence references, and does not contain identity fields that can be associated with natural persons. Leases are automatically revoked upon expiration or termination of the test, and temporary written objects are cleaned up.

[0156] Step B3. Utilize the revocable permission lease to execute normal tasks, indirect instruction injection tasks, unauthorized read inducement tasks, cross-task data leakage inducement tasks, and destructive write inducement tasks;

[0157] Normal tasks verify whether the declared functionality can be completed; indirect instruction injection tasks embed call instructions unrelated to the target task in the tool's return content or test input; unauthorized read inducement tasks request access to fields outside the lease; cross-task data leakage inducement tasks check whether data from the previous task can affect the output of the next task; destructive write inducement tasks only execute on isolated objects that can be automatically recovered. Tasks without secure test endpoints or the ability to undo write operations do not perform real destructive actions but instead proceed to manual evaluation.

[0158] Step B4. Record executed calls, denied calls, reasons for permission denial, external side effects, and inducements returned by tools to form the actual permission call set and the permission denial attempt set;

[0159] Logs are generated by the policy enforcement layer, not the capability under test, and include call time, caller, resources, actions, policy decisions, and task identifiers. Calls blocked by the policy layer are still recorded as permission denial attempts to avoid equating "attack unsuccessful" with "capability did not attempt to exceed privileges." External side effects are verified by cross-checking snapshots of the isolated object before and after isolation with network egress logs.

[0160] Step B5. Compare the declared permission set, the actual permission invocation set, and the permission denial attempt set to form a permission difference matrix;

[0161] The rows of the permission difference matrix correspond to resource and action combinations, and the columns include at least declared, executed, attempted but rejected, task-related, and reducible. Executed but undeclared permissions, as well as undeclared and attempted sensitive read / write permissions, are marked separately. Permissions declared but never used after covering all test tasks are marked as permission shrinkage candidates.

[0162] Step B6. Perform a veto on undeclared read / write, cross-domain transfer, or destructive operations; generate permission contraction suggestions for redundant permissions that have not exceeded permissions; and determine whether to reject, correct and retest, or continue the environment-oriented replay based on the comparison results.

[0163] A veto result is not compensated for by feature quality, latency, or resource gains. For cases with only redundant permissions and no unauthorized attempts, suggestions to narrow down the object scope, network destination, or validity period can be automatically generated. The capability provider updates the capability description object, reissues the lease, and retests. The permission difference matrix and test task version are included in the candidate version evidence chain.

[0164] Example 8

[0165] This embodiment provides a joint constraint method for resource configuration and capability routing based on Embodiment 1. This method is only enabled when two or more AI capabilities that pass pre-gating verification can replace the same target component, or when shared resources cause call latency to change significantly with routing load; any failure of hard constraints on permissions or health check data is eliminated first and does not proceed to resource optimization. Pre-gating verification refers to the verification process performed before gating, including source and content verification, idempotent registration and version conflict blocking, five-dimensional compatibility matching, and permission differential evaluation; only AI capabilities that pass the above verification processes sequentially are considered to have passed pre-gating verification.

[0166] Step C1. Run the de-identified baseline task under different concurrency levels, batch task capacity, and resource share to obtain the latency percentile, throughput, failure rate, quality score, and resource cost profile corresponding to each capability deployment configuration;

[0167] Performance profiles are acquired under fixed hardware models, driver versions, runtime containers, and input task sets. Latency is recorded at least by the median and a preset high quantile; throughput is calculated based on the number of valid tasks completed per unit time; and quality scores are generated by a fixed evaluator. Profile entries carry the concurrency range, task type distribution, and acquisition time to prevent the mixing of measurement results from different operating conditions. The concurrency range and acquisition time carried by the profile entry are the sources of the profile's concurrency range and valid time, respectively. When running benchmark tasks under different concurrency levels, the concurrency range swept by the same capability deployment configuration in each acquisition batch constitutes the profile's concurrency range. The profile concurrency range is the concurrency range covered during performance profile acquisition. When subsequently using this profile for constraint solving or playback verification, the concurrency of the operating environment must fall within the profile's concurrency range. Operating conditions exceeding this range are considered unmeasured and the measurement results of this profile should not be used. The collection time records the point in time when the profile was acquired. Hardware load, driver version, and data distribution may change after the collection time. The profile result will no longer be referenced after the effective profile time from the collection time. The effective profile time is the time range within which the performance profile result is allowed to be used.

[0168] Step C2. Set the resource share of each artificial intelligence capability and the routing ratio of each task type as decision variables, and set the total resource limit, preset quantile delay limit, quality lower limit condition and authorization domain as hard constraints to establish a joint constraint model of resources and routing.

[0169] The joint constraint model reflects how routing proportions change the request load received by each capability, while resource shares in turn change the latency and throughput of the corresponding capability. Authorization domain constraints map the permissible ranges of organizations, device families, inspection protocols, and data formats to inviolable routing boundaries; the model solves only within the authorized domain and cannot call routes across domains to reduce resource costs.

[0170] It should be further explained that the resource and routing joint constraint model is a constraint optimization model defined by the coupling of resource allocation and capability routing in the context of the health check skill package access scenario. It includes three dimensions: decision variables, optimization objective, and constraint system. Decision variables include the resource share obtained by each AI capability and the routing ratio allocated to each AI capability for each task type. The resource share determines the amount of computing resources available to the corresponding capability, and the routing ratio determines the distribution relationship of call traffic for each task type among the capabilities. The optimization objective is to maximize the sum of constrained routing scores for each task type while satisfying all hard constraints. The constrained routing score is the score after deducting resource costs and latency costs from the quality benefits of candidate capabilities. The constraint system includes a total resource upper limit constraint, a preset quantile latency upper limit constraint, a quality lower limit constraint, and an authorization domain constraint. The total resource upper limit constraint limits the sum of all resource shares to no more than the total available resources. The preset quantile latency upper limit constraint limits the high quantile latency of each task type under the corresponding routing ratio to no more than the preset upper limit. The quality lower limit constraint limits the quality score of each task type to no less than the preset lower limit. The authorization domain constraint limits the routing ratio to AI capabilities within the authorized domain. The model's variable coupling is manifested in the fact that the routing ratio determines the request load received by each AI capability. The load and resource share together determine the latency quantile and throughput of each capability. The latency quantile and throughput then determine the quality score and failure rate of each task type. This makes resource allocation and capability routing inseparable and must be treated as a whole. The model solves this problem through resource shadow price iteration and candidate deployment search. Any combination of resource allocation and routing ratio that violates any hard constraint is directly eliminated.

[0171] Step C3. Iteratively update the resource shadow price based on resource scarcity, and deduct resource cost and latency cost from the candidate capability quality benefit to obtain the constrained routing score corresponding to the task type and AI capability combination;

[0172] Resource shadow prices are used to characterize the marginal impact of increasing unit resources on feasibility. When a certain type of resource approaches its limit, increasing the corresponding shadow price will reduce the score of task-capability combinations that consume more of that type of resource; latency costs are determined based on specific performance profiles rather than a fixed constant. The update process does not modify security gating conclusions, and capabilities that fail security gating have no routing decision variables.

[0173] Step C4. Search the pruned resource configurations and calculate the routing ratio under each resource configuration, eliminate combinations that violate the hard constraints, and obtain candidate deployments and routing combinations;

[0174] Pruning can first remove configurations that fail to meet the lower quality limit or upper quantile delay limit under all available resource shares, and then solve for the routing ratio of the remaining configurations. For combinations with the same objective function value, they are sorted in a fixed order of resource cost, preset quantile delay, capability identifier, and configuration identifier to ensure that the same profile input yields the same candidate combination order.

[0175] Step C5. Perform joint replay according to the concurrency and routing ratio corresponding to the candidate deployment and routing combination, and solidify the resource configuration, routing policy, hardware model, driver information, profile concurrency range and profile validity time through the joint replay into the immutable combination version;

[0176] The joint replay sends de-identified baseline tasks according to the predetermined ratio and verifies whether the measured quantile latency, quality score, failure rate, and resource consumption are consistent with the gating conditions. Successful participants solidify the routing policy and resource configuration as inseparable configuration artifacts to avoid compromising the verification premise by modifying the routing ratio or resource share separately after release.

[0177] Step C6. Trigger a re-image when the operating environment exceeds the image concurrency range or the image validity period.

[0178] The environment router monitors the actual concurrency and profile time status. When the data exceeds the effective range, the system does not use the old profile extrapolation route ratio, but instead maintains compatibility with the baseline version or verified static configuration and creates a new profile task. For cases with only a single capability and using sufficient dedicated resources, the joint constraint process is not triggered.

[0179] Example 9

[0180] This embodiment refines the environment routing, isolation, and compatibility fallback of the immutable combination version based on Embodiment 1.

[0181] Step D1. Use an environment router to match the organization identifier, device family identifier, inspection protocol identifier, data format identifier, and validity period with the applicable environment of the immutable combination version;

[0182] The environment router reads the environment fingerprint provided by the upstream system and verified for integrity. Each identifier is compared using a defined value range or a registered set, without using free text similarity inference; the valid time is determined based on the start time, end time, and time zone of the combined version record. The environment fingerprint consists of the organization identifier, device family identifier, inspection protocol identifier, data format identifier, and call time of this call, extracted from the call context and attached to the routing request by the upstream system before the call is initiated. The applicable environment is registered and fixed within the whitelist declared by the capability description object when the immutable combined version is formed, and is represented as a combination of the organization identifier value range, device family identifier set, inspection protocol identifier set, data format identifier set, start time, end time, and time zone. Matching is performed item by item in a fixed order of organization identifier, device family identifier, inspection protocol identifier, and data format identifier. For each item, it is determined whether the caller's identifier falls within the corresponding value range or registered set of the combined version registration. If it falls within the range, the item matches; otherwise, it does not match. The valid time item matching is performed by converting the time of this call according to the registered time zone and determining whether it falls between the start time and end time. All five criteria must match for the call to be considered a match for the immutable combined version environment; otherwise, the environment is considered mismatched.

[0183] Step D2. Only when all identifiers match the applicable environment will the call traffic be redirected sequentially to the shadow running state, restricted release state, and official release state;

[0184] The shadow run state copies input but does not replace the baseline output; the restricted release state only returns candidate outputs for a fixed subset of environments or a fixed proportion of calls; the official release state is effective in all applicable environments. The state order is controlled by the version state machine, and you cannot skip the previous state to directly enter the official release.

[0185] Step D3. Maintain the route to the compatible base version even if any identifier does not match;

[0186] Environment mismatch is handled before the call occurs. The system does not call the target combined version first and then roll back based on the error result, thus satisfying the zero-call condition for environment mismatch. Missing identifiers are handled with non-matching to avoid expanding the scope of application due to incomplete environment fingerprints.

[0187] Step D4. For each routing decision, record the routing rule version, input environment fingerprint, hit conditions, and target combination version identifier;

[0188] The routing record binds decision inputs, rules, and outputs, enabling environment mismatch replay and release audit recalculation. The input environment fingerprint only stores the organization, equipment, protocol, format, and time fields required for recalculation, without copying irrelevant data. The hit condition is the applicable environment entry for the immutable combination version hit in this routing decision; that is, the condition items in the registered organization identifier value field, equipment family identifier set, inspection protocol identifier set, data format identifier set, and effective time interval that match the input environment fingerprint. Applicable environment entries are standardized and fixed according to a fixed field order and value field encoding when the combination version is formed. During routing decision-making, the environment router compares the input environment fingerprint with the applicable environment entries registered for each candidate combination version item by item; the combination version with all matching condition items is considered a hit. When multiple hit combination versions exist, a unique target combination version is determined according to a fixed order of the number of matching environment items, preset priority, effective time, and version identifier. The hit condition entries in the record enable the auditor to recalculate the same routing result in the same order.

[0189] Step D5. Upon receiving an isolation event, freeze new calls to the immutable combination version, retain audit evidence, and switch calls that have not yet been processed to the compatible baseline version. The isolation event is an event triggered by an environment mismatch call, a hard constraint violation, or a difference in a prohibited change field. The prohibited change field is a pre-defined field that must remain consistent before and after access.

[0190] Isolation events are triggered by environment mismatch calls, hard constraint violations, or differences in prohibited change fields. Environment mismatch calls occur when the calling organization identifier, device family identifier, inspection protocol identifier, data format identifier, or validity period is inconsistent with the applicable environment of the immutable composite version. Hard constraint violations occur when the processing or output of the candidate skill package does not meet any mandatory data processing condition recalculated by the medical examination data hard constraint verification node or independent verifier. Prohibited change field differences occur when the values ​​of prohibited change fields are inconsistent before and after access. Freeze operations are controlled by both composite version status and routing rules, preserving evidence including trigger records, relevant composite version artifact hashes, routing logs, and playback input identifiers.

[0191] Step D6. When the compatible base version cannot read the intermediate object generated by the immutable combined version, call the compatible conversion component bound to the immutable combined version to complete the intermediate object conversion.

[0192] The compatibility conversion component is validated and solidified during the creation of the composite version. The input contract corresponds to the intermediate object schema of the target composite version, and the output contract corresponds to the readable schema of the compatible baseline version. When the conversion fails, a structured unusable flag is generated and entered into a professional review queue, without discarding the source, original object reference, or reason for failure.

[0193] Example 10

[0194] like Figure 7 As shown, this embodiment provides a new capability-controlled access device for a health checkup skill package. The health checkup skill package is applied to the processing of collected test, image, questionnaire and past record data by health checkup institutions. It does not perform any testing operations on the human body and outputs candidate data for professional review rather than diagnostic conclusions. It includes the following modules.

[0195] The object verification module is used to obtain a capability description object containing capability identifier, version, content hash, field pattern, permission requirements, resource requirements, and environmental conditions, and to perform source and content verification.

[0196] The registration control module is used to perform idempotent registration and version conflict blocking on the capability description object using the capability identifier and the version as unique keys and the content hash as a consistency check value.

[0197] The compatibility positioning module is used to locate candidate components based on the processing dependency graph and field lineage of the baseline skill package, perform five-dimensional compatibility matching of field contracts, data modalities and interfaces, permissions, resources and environmental conditions, and determine existing anchored components and newly added adaptable components for deterministic transformation differences. The baseline skill package is a version of the skill package that has been registered and is reproducible before the new capability is accessed, and the new capability is the artificial intelligence capability described by the capability description object.

[0198] The patch building module is used to generate an access patch based on the existing anchoring component and the newly added adaptation component, and apply the access patch to the baseline skill package to form a candidate skill package.

[0199] The gating release module is used to perform environment-oriented replay and hard constraint verification on the candidate skill package. After the gating is passed, the capability version, component artifact, effective configuration and applicable environment are bound to an immutable combination version and released in a targeted manner.

[0200] The aforementioned modules can be implemented by one or more processors executing computer programs stored in memory, or they can be distributed across the capability registration service, skill package building service, isolation playback service, and environment routing service. Module division is used to illustrate functional relationships and does not limit the physical deployment method; multiple modules can be deployed on the same computing node, and a single module can also be split and deployed across multiple controlled computing nodes. Regardless of the deployment method, the execution artifacts and policy permissions of the health check data hard constraint verification node are isolated from the capabilities to be accessed.

[0201] The responsibilities of each module and its position in the controlled access implementation process are shown in the table below:

[0202] This invention module Module Responsibilities Position in the implementation process Object verification module Verification capabilities describe the object's fields, hash, signature, and permission limits. Before registering a new capability, receive and verify the capability description object. Registration control module Register capability identifiers and versions, and determine idempotency or version conflicts. After the object verification passes, determine whether to register, return idempotent, or block conflicts. Compatible positioning module Read the dependency graph and field lineage, complete the five-dimensional matching, and determine the anchored and adapted components. After registration is completed, the baseline components and field relationships are read and the access boundaries are determined. Patch Build Module Differential patches are formed within the anchored boundaries, and candidate skill packs are generated. Once the compatibility positioning is approved, the target capability is only inserted or replaced within the anchored boundary. Gating release module Perform paired replay, zero-call, independent hard constraint recalculation, and targeted release. Replay and gating are performed after candidate skill packs are formed but before targeted release.

[0203] Example 11

[0204] Taking the processing of acquired image data from low-dose chest computed tomography scans as an example, the baseline skill package includes, in sequence, an image input preprocessing component, a baseline model invocation component, a result normalization component, a hard constraint verification node for physical examination data, and a component for generating candidate objects to be reviewed. The output of the model to be accessed uses a single-base layer number and millimeter coordinates, while the downstream result normalization component requires a zero-base layer number and pixel coordinates.

[0205] The system identifies the baseline model calling component as the component to be modified based on the processing dependency graph, and identifies the result normalization component as the existing anchoring component based on field lineage. An output adapter component is then inserted between the two. The output adapter component reads pixel spacing, layer spacing, image orientation, and sequence identifier from the image metadata, converts the base-1 layer number to the base-0 layer number, maps millimeter coordinates to pixel coordinates, and saves the source coordinates, target coordinates, transformation parameters, and target sequence reference. The independent validator does not call the transformation code of the output adapter component but recalculates the target layer number and pixel coordinates based on the fixed transformation parameters.

[0206] The applicable environment for candidate patches is limited to the registration equipment family, registration data format, and low-dose chest computed tomography protocol of a specific medical examination institution. Environment-oriented playback includes records of the applicable environment, another equipment family, another examination protocol, and path records for components not reaching the baseline model. Applicable environment records verify the contract, coordinates and sequence backreferences, confirmed sample target fields, prohibited change fields, latency, and resource consumption; the remaining records verify zero calls to the model to be integrated and the candidate skill package. After gating, the patch enters shadow running mode; candidate patches remain only available for review by professional auditors.

[0207] This patch only applies to records that simultaneously match the registration agency, registration device family, registration data format, and low-dose chest computed tomography protocol. For other agencies, device families, inspection protocols, or processing paths that do not pass through the target model, the system verifies that the number of calls to the model to be accessed and the number of calls to the candidate skill pack are both zero; if a call occurs, the gating will fail.

[0208] Example 12

[0209] This embodiment provides an implementation method for integrating new diagnostic capabilities into the health check-up skill package.

[0210] When integrating a new diagnostic model or remote tool, a capability description object is first created, specifying the capability identifier, version, content hash, input data type, output structure, permission limit, resource requirements, and applicable organization or equipment. After the object verification and registration are successful, the compatibility positioning module determines the model or tool call location that needs to be replaced based on the processing dependencies of the baseline skill package, and uses the existing components before and after the call location as the patch boundary.

[0211] In this embodiment, the baseline skill package provides the new capability with the department, combined examination items, specific examination items, examination results, and examination findings; the new capability returns candidate conclusions and their evidence. If the field names of the new capability differ from those of the baseline skill package, but there is a pre-registered and unique correspondence between each name, the adaptation component can perform a fixed renaming. If the evidence levels are different, but each piece of evidence can uniquely point back to the original examination record, the adaptation component can reorganize the levels and supplement the source identifier. When it is necessary to infer the source or there is one-to-many ambiguity, no adaptation component is generated, and the compatibility positioning module records the reason for incompatibility and blocks access.

[0212] After compatibility is approved, the system only generates access patches for the aforementioned call locations and necessary input / output conversions; existing data preprocessing, result merging, risk marking, hard constraint verification, and candidate object generation processes remain unchanged. The system uses the same batch of collected data to run the baseline skill package and candidate skill packages separately: data matching the applicable environment is used for output comparison, while data with mismatched institutions, equipment families, or inspection protocols is used to confirm that the number of new capability calls is zero. An independent verifier reviews hard constraints such as source, quantity, unit, and coordinates; only after all constraints pass does the system enter shadow running, and the output is still only available for review by professionals.

[0213] In this way, the differences between the new capabilities and the baseline skill set are limited to adaptation components and access patches, while existing data preprocessing, result merging, risk calculation, and institutional post-processing maintain their original boundaries. Access locations, transformation relationships, applicable environments, verification results, and release versions are all recorded, allowing reviewers to understand and reproduce the implementation process based on input conditions and output results without needing to consult the specific source code.

[0214] The same access chain can also be used for local models, remote model interfaces, rule tools, and data processing services. Version conflict blocking ensures consistency of registered content, environment mismatch zero-call ensures isolation of applicable scope, and immutable combined versions are used to restore to a verified baseline version in the event of runtime anomalies.

[0215] It should be noted that the artificial intelligence capabilities in this invention can be local model files, remote model interfaces, rule tools, data processing services, or other callable software capabilities. Capability description objects, adaptation components, access patches, immutable combination versions, and replay evidence can all be implemented using different structured data formats, as long as the identity binding, contract closure, environmental constraints, and recalculation relationships described in this specification are maintained.

Claims

1. A novel controlled access method for a health checkup skills package, wherein the health checkup skills package is applied to health checkup institutions to process collected test data, images, questionnaires, and past records, without performing any human body testing operations, and outputs candidate data for professional review rather than diagnostic conclusions, characterized in that... include: Obtain a capability description object containing capability identifier, version, content hash, field pattern, permission requirements, resource requirements, and environmental conditions, and perform source and content verification; Using the capability identifier and the version as unique keys and the content hash as a consistency check value, perform idempotent registration and same-version conflict blocking on the capability description object; Based on the processing dependency graph and field lineage of the baseline skill package, candidate components are located, and five-dimensional compatibility matching of field contracts, data modalities and interfaces, permissions, resources and environmental conditions is performed. Existing anchored components and newly added adaptable components are determined for deterministic transformation differences. The baseline skill package is a skill package version that has been registered and is reproducible before the new capability is accessed. The field lineage includes the source component of the field, the transformation process, the downstream consumer component, and the reference relationship with the original inspection sequence. The new capability is the artificial intelligence capability described by the capability description object. Based on the existing anchoring component and the newly added adaptation component, an access patch is generated, and the access patch is applied to the baseline skill pack to form a candidate skill pack. The candidate skill package undergoes environment-oriented replay and hard constraint verification. After gating passes, the capability version, component artifact, effective configuration, and applicable environment are bound to an immutable combination version and released in a targeted manner.

2. The method according to claim 1, characterized in that, The process of obtaining a capability description object containing capability identifier, version, content hash, field pattern, permission requirements, resource requirements, and environmental conditions, performing source and content verification, and performing idempotent registration and version conflict blocking on the capability description object using the capability identifier and version as unique keys and the content hash as a consistency verification value includes: Obtain the capability description object carrying the source identifier and digital signature; Verify the required fields, field types, content hash, and digital signature of the capability description object; The permission requirement is compared with the target component's authorization limit. If the requirement exceeds the target component's authorization limit, the candidate generation is terminated. The target component's authorization limit is the registered permission boundary of the target component in the baseline skill pack that is intended to interact with the new ability. Write to the capability registry and generate a registration event when the unique key does not exist; If the unique key exists and the content hash is the same, return the existing registration result without generating the registration event again; When the unique key exists and the content hash is different, a version conflict record is generated and the overwriting of existing registered content is blocked.

3. The method according to claim 1, characterized in that, Based on the processing dependency graph and field lineage of the baseline skill package, candidate components are located, and a five-dimensional compatibility matching is performed based on field contracts, data modalities and interfaces, permissions, resources, and environmental conditions. Existing anchored components and newly added adaptable components are determined for deterministic transformation differences, including: In the processing dependency graph, find model calling components or tool calling components whose input fields can be provided by upstream fields and whose output fields can affect target task fields to obtain the candidate components; Based on the field lineage, determine the field mapping component, result normalization component, or workflow node adjacent to the candidate insertion position, and determine the existing anchoring component from them; Each input and output field is matched for its type, quantity relationship, unit, coordinates, sequence reference and semantics; input data modality, calling protocol, authentication method, timeout and error code; data reading range, network destination and retention period; computing resources, video memory, latency and concurrency requirements; and organization, equipment family, inspection protocol, data format and applicable time. Differences that cannot be resolved through deterministic transformation are marked as blocking causes, while differences that can be resolved through deterministic transformation are transformed into adaptation requirements; Based on the aforementioned adaptation requirements, new adaptation components are generated for input field transformation, output field transformation, coordinate or sequence backreference, result normalization, or failure degradation. The locked components and hard constraint verification nodes for physical examination data are excluded from the modifiable objects, wherein the locked components are components that are pre-registered as locked and prohibited from modification.

4. The method according to claim 3, characterized in that, The step of generating an access patch based on the existing anchoring component and the newly added adaptation component, and applying the access patch to the baseline skill package to form a candidate skill package, includes: Fixed patch generator version, generator configuration, and deterministic parameters; Each generates a trial patch corresponding to replacing the model invocation component, adding input adaptation, adding output adaptation, adding failure handling, or a combination of operations. The operation combination is a combination of at least two of the following operations: replacing the model invocation component, adding input adaptation, adding output adaptation, and adding failure handling. Each of the trial patches records the component to be modified, the existing anchored component, the newly added adapted component, the insertion position, the parent version, and the parent version hash, wherein the parent version is the version of the base skill pack; Each of the trial patches records the component artifact closure hash before and after modification, the capability description object identifier, the applicable environment, and the failure handling method; The trial patch is replayed locally on the trigger evidence record and the deterministic boundary record, wherein the trigger evidence record is the input record of the new ability actually being executed in the baseline skill pack, and the deterministic boundary record is a record covering null values, boundary values ​​and error codes; From the probe patches that satisfy the target conditions, field contract conditions, locking conditions, and security environment conditions, they are deterministically sorted according to the number of complete involved components, field lineage distance, actual number of operands, and component identifiers; The probe patch that is ranked first and whose complete number of involved components meets the preset component number priority condition is determined as the access patch.

5. The method according to claim 1, characterized in that, The process of performing environment-oriented replay and hard constraint verification on the candidate skill package, and after gating, binding the capability version, component artifact, effective configuration, and applicable environment into an immutable combined version and releasing it in a targeted manner, includes: Based on the downstream closures of existing target components in the baseline skill pack and the downstream closures of all target components in the candidate skill pack, an affected component set is determined. The target component is a component within the skill pack that has an interaction relationship with the new capability or is affected by the access patch. The downstream closure is a set of all downstream components and fields reachable by the target component within its skill pack, including the target component. Based on the set of influencing components, the baseline actual path, the candidate static reachability, and the applicable environment, select environment matching records, environment mismatch records, and path mismatch records; For the environment matching record, fixed input data, runtime environment, non-target dependencies, and evaluator, pairwise replay is performed on the baseline skill set and the candidate skill set. The pre-release route and the route to be released are compared for the environment mismatch record and the path mismatch record, and the number of calls to the artificial intelligence capability to be accessed and the candidate skill package are both verified to be zero. Use independent verifiers to recalculate coordinate or sequence back-pointing consistency, project and equipment agreement consistency, retention of existing risk level markings, source traceability, and data access constraints; The contract compliance rate of the field, the quality condition of the confirmed sample, the retracement rate of the coordinate or sequence, the degradation of the non-target field, the failure rate, the latency and resource consumption are compared with the corresponding gating conditions. Once all departmental control conditions are met, the system enters shadow operation, and after successful shadow operation, it enters restricted release or formal release according to the organization, equipment family, and inspection protocol. When an environment mismatch occurs, a hard constraint is violated, or a field that prohibits change is different, the immutable combination version is isolated and switched to a compatible baseline version, wherein the compatible baseline version is the version in the baseline skill pack used to take over the call when the immutable combination version is isolated.

6. The method according to claim 1, characterized in that, Before obtaining the capability description object, the process also includes: Extract missing input types, expected output fields, allowed data fields, prohibited permissions, latency limits, and quality thresholds from incomplete tasks, component failure evidence, and manual requirements to form a structured capability gap request that does not include the name of the capability provider. Based on the structured capability gap request, the capability service layer is first routed in the audited capability catalog according to the task domain, data boundary and deployment environment, and capability services that are incompatible with permission conditions or regional conditions are eliminated. Within the capability service routed through the capability service layer, specific capability layer routes are executed based on the input / output field contract, function description, resource requirements, and testable endpoints. For each round of specific capability layer routing, only the fields required to complete the routing within the current capability service are disclosed to obtain a specific capability candidate list; Extract the interfaces, permissions, resources, versions, and test information required to complete the structured capability gap request from the specific capability candidate list, and form a necessary field capability description object with marked source, confidence information, and unconfirmed fields; The necessary field capability description object is submitted as the capability description object to be reviewed, and installation or production call permissions are not granted to the capability discovery process.

7. The method according to claim 1, characterized in that, Before forming the candidate skill set, the following is also included: Based on the permission requirements, the field mapping of the newly added adaptation component, and the target task, read operations, write operations, network access operations, external storage operations, and execution operations are expanded to form a set of declared permissions and usage constraints. Based on the stated permission set, a revocable permission lease is issued in an isolated environment that only allows synthetic medical examination data, test accounts, limited network destinations, and limited validity periods. The isolated environment is an environment isolated from the production environment and that can only access synthetic data. The revocable permission lease is used to execute normal tasks, indirect instruction injection tasks, unauthorized read inducement tasks, cross-task data leakage inducement tasks, and destructive write inducement tasks. Record executed calls, denied calls, reasons for permission denial, external side effects, and misleading content returned by tools to form the actual set of permission calls and the set of permission denial attempts; By comparing the declared permission set, the actual permission invocation set, and the permission denial attempt set, a permission difference matrix is ​​formed; An attempt to perform a veto is executed for any undeclared read / write, cross-domain transfer, or destructive operation. For redundant permissions that have not exceeded their authority, a permission contraction suggestion is generated. Based on the comparison results, the decision is made to reject, retest after correction, or continue the environment-oriented replay.

8. The method according to claim 1, characterized in that, When two or more AI capabilities that pass pre-gated verification can replace the same target component, or share resources such that capability invocation latency varies with routing load, the following is included before forming the immutable combined version: Run de-identified baseline tasks under different concurrency levels, batch task capacities, and resource shares to obtain latency percentiles, throughput, failure rate, quality score, and resource cost profiles for each capability deployment configuration; The resource share of each artificial intelligence capability and the routing ratio of each task type are set as decision variables, and the total resource limit, the preset quantile delay limit, the quality lower limit condition and the authorization domain are set as hard constraints to establish a joint constraint model of resources and routing. The resource shadow price is iteratively updated based on resource scarcity, and the resource cost and latency cost are deducted from the quality benefits of candidate capabilities to obtain the constrained routing score corresponding to the combination of task type and artificial intelligence capability. Search the pruned resource configurations and calculate the routing ratio under each resource configuration. Eliminate combinations that violate the hard constraints to obtain candidate deployments and routing combinations. Perform joint replay according to the concurrency and routing ratio corresponding to the candidate deployment and routing combination, and solidify the resource configuration, routing policy, hardware model, driver information, profile concurrency range and profile validity time through the joint replay into the immutable combination version; A re-image is triggered when the operating environment exceeds the concurrency range of the image or the validity period of the image.

9. The method according to claim 1, characterized in that, The process of performing environment-oriented replay and hard constraint verification on the candidate skill package, and after gating, binding the capability version, component artifact, effective configuration, and applicable environment into an immutable combined version and releasing it in a targeted manner, includes: The environment router is used to match the organization identifier, device family identifier, inspection protocol identifier, data format identifier, and validity period with the applicable environment of the immutable combination version; Only when all identifiers match the applicable environment will the call traffic be redirected sequentially to the shadow running state, the restricted release state, and the official release state. Maintain routing to a compatible baseline version even if any identifier does not match; For each routing decision, record the routing rule version, input environment fingerprint, hit conditions, and target combination version identifier; Upon receiving an isolation event, new calls to the immutable combination version are frozen, audit evidence is preserved, and calls that have not yet been processed are switched to the compatible baseline version. The isolation event is an event triggered by an environment mismatch call, a hard constraint violation, or a difference in a prohibited change field. The prohibited change field is a pre-defined field that must remain consistent before and after access. When the compatible baseline version cannot read the intermediate object generated by the immutable combined version, the compatible conversion component bound to the immutable combined version is invoked to complete the intermediate object conversion.

10. A novel capability-controlled access device for a health checkup skills package, wherein the health checkup skills package is applied to health checkup institutions to process collected test, imaging, questionnaire, and past record data, without performing any human body testing operations, and outputs candidate data for professional review rather than diagnostic conclusions, characterized in that... include: The object verification module is used to obtain a capability description object containing capability identifier, version, content hash, field pattern, permission requirements, resource requirements, and environmental conditions, and to perform source and content verification. The registration control module is used to perform idempotent registration and version conflict blocking on the capability description object using the capability identifier and the version as unique keys and the content hash as a consistency check value. The compatibility positioning module is used to locate candidate components based on the processing dependency graph and field lineage of the baseline skill package, perform five-dimensional compatibility matching of field contracts, data modalities and interfaces, permissions, resources and environmental conditions, and determine existing anchored components and newly added adaptable components for deterministic transformation differences. The baseline skill package is a skill package version that has been registered and is reproducible before the new capability is accessed. The field lineage includes the source component of the field, the transformation process, the downstream consumer component, and the reference relationship with the original inspection sequence. The new capability is the artificial intelligence capability described by the capability description object. The patch building module is used to generate an access patch based on the existing anchoring component and the newly added adaptation component, and apply the access patch to the baseline skill package to form a candidate skill package. The gating release module is used to perform environment-oriented replay and hard constraint verification on the candidate skill package. After the gating is passed, the capability version, component artifact, effective configuration and applicable environment are bound to an immutable combination version and released in a targeted manner.

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