A heterogeneous link observability determination method, apparatus, device, medium and product

Abstract

The application discloses a heterogeneous link measurability determination method, device, equipment, medium and product, and the method comprises the following steps: obtaining measurement basic information in a link, the measurement basic information comprising a target measurement quantity, a logical node, an adjacent communication relationship and an existing observation capability; constructing a first observable constraint graph according to the measurement basic information, and extracting a first solving subgraph according to a preset measurement constraint condition; when it is judged according to the first solving subgraph that the corresponding target measurement quantity satisfies the measurement constraint condition and the target measurement quantity can be uniquely determined, the target measurement quantity is determined to be measurable; when only a maximum non-separable quantity set containing the target measurement quantity and a corresponding minimum outer measurable interval can be uniquely determined, the target measurement quantity is determined to be limitedly measurable; if neither can be uniquely determined, the target measurement quantity is determined to be unmeasurable; and for the limitedly measurable and unmeasurable cases, an unmeasurable proof is output. By using the method, the accuracy of automatic determination of heterogeneous link measurability is ensured.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of networked information service technology, and in particular to a method, apparatus, device, medium and product for determining the testability of heterogeneous links. Background Technology

[0002] In cross-domain information services, business processes typically involve multiple independent service nodes and require data collection based on observed events to record various business performance indicators.

[0003] Existing link observation methods mostly involve pre-deploying embedded points to collect events, relying on the raw event data collected to perform index calculations, and adopting a holistic and rough evaluation method for link observation capabilities.

[0004] Different service nodes generate their own business observation events, which can easily lead to problems such as event semantic mismatch, incomparable time bases, and lack of message identity. This results in the inability to correctly pair the starting business observation event with the ending business observation event, and thus, the inability to obtain an effective target quantity. Summary of the Invention

[0005] This invention provides a method, apparatus, device, medium, and product for determining the measurability of heterogeneous links, so as to achieve accurate determination of the measurability of target quantities.

[0006] According to one aspect of the present invention, embodiments of the present invention provide a method for determining the testability of heterogeneous links, including: Acquire basic measurement information in the link, including target measurement quantities, logical nodes, adjacent communication relationships, and existing observation capabilities; A first observable constraint graph is constructed based on the measurement basic information, and a first solution subgraph is extracted based on the preset measurement constraints and the first observable constraint graph. The measurement constraints include: boundary semantic constraints, time domain constraints, message attribution constraints, and original evidence constraints. When the target measurement quantity is determined to satisfy the measurement constraint condition based on the first solution subgraph, and the target measurement quantity can be uniquely determined, the target measurement quantity is determined to be measurable. When the target measurement quantity satisfies the measurement constraint condition, and the target measurement quantity cannot be uniquely determined at the required output granularity, but can uniquely determine the largest incompensable set containing the target measurement quantity and the corresponding smallest bounded measurable interval, the target measurement quantity is determined to be restricted measurable. If the target measurable quantity, the maximum unmeasurable component set, and the corresponding minimum outer measurable interval cannot be uniquely determined, then the target measurable quantity is determined to be unmeasurable. For the cases of limited measurability and unmeasurability, an unmeasurability proof is output.

[0007] According to another aspect of the present invention, embodiments of the present invention provide a heterogeneous link testability determination device, comprising: The information acquisition module is used to acquire basic measurement information in the link, including target measurement quantities, logical nodes, adjacent communication relationships, and existing observation capabilities. The subgraph extraction module is used to construct a first observable constraint graph based on the measurement basic information, and extract a first solution subgraph based on the first observable constraint graph according to the preset measurement constraint conditions. The measurement constraint conditions include: boundary semantic constraints, time domain constraints, message attribution constraints, and original evidence constraints. The measurability determination module is used to determine that the target measurement quantity is measurable when the corresponding target measurement quantity satisfies the measurement constraint condition based on the first solution subgraph and the target measurement quantity can be uniquely determined. The restricted measurability determination module is used to determine that the target measurement quantity is restricted measurable when the target measurement quantity satisfies the measurement constraint condition, the target measurement quantity cannot be uniquely determined at the required output granularity, but can uniquely determine the largest incompensable set containing the target measurement quantity and the corresponding smallest bounded measurable interval. The proof output module is used to determine that the target measurable quantity is unmeasurable if the target measurable quantity, the maximum unmeasurable component set, and the corresponding minimum outer measurable interval cannot be uniquely determined. For the cases of limited measurability and unmeasurability, the module outputs proof of unmeasurability.

[0008] According to another aspect of the present invention, an embodiment of the present invention provides an electronic device, the electronic device comprising: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the heterogeneous link testability determination method of any embodiment of the present invention.

[0009] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the heterogeneous link testability determination method according to any embodiment of the present invention.

[0010] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the heterogeneous link testability determination method provided in the first aspect embodiment of the present invention.

[0011] The technical solution of this invention involves acquiring basic measurement information in a link, including target measurement quantities, logical nodes, adjacent communication relationships, and existing observation capabilities. A first observable constraint graph is constructed based on this information, and a first solution subgraph is extracted based on preset measurement constraints and the first observable constraint graph. These constraints include boundary semantic constraints, time domain constraints, message attribution constraints, and original evidence constraints. When the first solution subgraph determines that the corresponding target measurement quantity satisfies the measurement constraints and the target measurement quantity can be uniquely determined, the target measurement quantity is determined to be measurable. If the target measurement quantity satisfies the measurement constraints but cannot be uniquely determined at the required output granularity, yet can uniquely determine the largest indeterminate set containing the target measurement quantity and the corresponding smallest outgoing measurable interval, the target measurement quantity is determined to be restricted measurable. If the target measurement quantity, the largest indeterminate set, and the corresponding smallest outgoing measurable interval cannot be uniquely determined, the target measurement quantity is determined to be unmeasurable. For both restricted measurable and unmeasurable cases, an unmeasurable proof is output. The above technical solution, by determining the measurability of target quantities based on observable constraint diagrams before data acquisition, solves the problems of difficulty in identifying measurement capabilities in advance and the inability to distinguish between measurable, limited measurable, and unmeasurable states in cross-entity heterogeneous links. It effectively locates measurement constraint defects and improves the reliability of link service observation.

[0012] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a flowchart of a heterogeneous link testability determination method provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart of a heterogeneous link testability determination method provided in Embodiment 2 of the present invention; Figure 3 A flowchart illustrating a service link observability model is provided. Figure 4 This is a schematic diagram of a heterogeneous link testability determination device provided in Embodiment 3 of the present invention; Figure 5 This is a schematic diagram of the structure of an electronic device that implements the heterogeneous link testability determination method of Embodiment 4 of the present invention. Detailed Implementation

[0015] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0016] Example 1 Figure 1 This is a flowchart of a heterogeneous link testability determination method provided in Embodiment 1 of the present invention. This embodiment is applicable to determining whether a target quantity is measurable in cross-subject service links. This method can be executed by a heterogeneous link testability determination device, which can be implemented in software and configured in electronic devices participating in distributed link monitoring. Figure 1 As shown, the method includes: S101. Obtain basic measurement information in the link, including target measurement quantity, logical node, adjacent communication relationship and existing observation capability.

[0017] In this embodiment, the heterogeneous link testability determination method provided can be implemented by a server deployed in the cloud for business monitoring. The server can implement this method internally using a physical processor, memory, hard disk, and network communication interface. Specifically, the network communication interface can be used to acquire basic measurement information in the link, the hard disk can be used to store pre-set testability determination conditions, the memory can be used to hold intermediate data generated during method execution, and the processor can be used to execute all logical operations for testability determination.

[0018] Among them, heterogeneous links are directed business paths or directed subgraphs that carry the flow of certain types of business messages or business objects. They can include structures such as serialization, branching, aggregation, splitting, retrying, and bypassing. For a specific target measurement, the corresponding business path or sub-path should be selected from the overall topology for solution.

[0019] The measurement foundation information can be considered as the core data set before conducting heterogeneous service link testability assessment. It summarizes all the basic elements required for link measurement and provides complete data support for subsequent testability assessment. The target measurement quantity can be considered as the specific link measurement indicators that users or business scenarios need to obtain. It is the core object of this testability assessment and is used to clarify the core measurement objectives and avoid invalid assessment calculations. Common examples include business end-to-end latency and single-node processing latency.

[0020] Specifically, each of the target measurements corresponds to at least one business path, a business path instance or a business path candidate, a start event, a termination event, and the output granularity; the start event, the termination event, and the output granularity define the boundary of the target measurement.

[0021] Here, the business path can be considered as the link formed by the logical nodes and inter-node communication segments through which the business message flows in the cross-subject heterogeneous service link; the start event can be considered as the business observation event used by the business message on the business path to mark the start of the target measurement interval; and the termination event is the business observation event used by the business message on the business path to mark the end of the target measurement interval.

[0022] Output granularity can be considered as a pre-defined statistical level and measurement range limit for the target measurement quantity. It is used to specify the fineness of the measurement results and can distinguish different ranges of measurement indicators such as node processing volume, adjacent node communication volume, multi-segment merging volume, and end-to-end volume, thus defining the statistical caliber corresponding to the final output measurement value. Start events and termination events, together with output granularity, define the calculation boundary of the target measurement quantity.

[0023] Logical nodes can be considered as business units or interface boundaries in heterogeneous links that possess independent business processing or data observation capabilities. Distinguished from physical hardware devices, they can be used to abstract and organize the business topology of the link, accurately locate measurement start and end boundaries and observation points, and form the foundation for building the link topology and matching boundary semantic constraints. These include data sources, processing platforms, boundary devices, message middleware, communication gateways, terminal applications, or virtualization functions.

[0024] Adjacent communication relationships can be considered as directed communication relationships between two adjacent logical nodes on a selected business path in a link, related to the flow of target business messages. They clearly define the upstream and downstream connection logic of each node, can reconstruct the complete business transmission path, and can carry attributes such as sending interface, receiving interface, transmission bearer, direction, and valid conditions. Examples include the upstream and downstream communication flow relationships between a gateway and an order service, or between an order service and a payment service.

[0025] Existing observation capabilities are structured capability records registered according to observation points, business events, or link boundaries. They characterize the actual observation evidence that can be obtained from the link within the current configuration and validity period. They are used to evaluate whether existing tracking points can meet measurement constraints such as semantics, time base, and message identity, serving as a realistic basis for determining the measurability of the target measurement quantity. For example, if each node in the link has deployed tracking points that can output business events carrying message identifiers and timestamps, this ability to reliably acquire events belongs to existing observation capabilities.

[0026] In this context, observation points can be considered as data collection locations deployed within the business chain to capture business events. These points record observational events generated as messages flow through the chain, providing raw observational evidence for measurability assessment. For example, the data collection point configured at the order service entry point is an observation point. Link boundaries, on the other hand, define the start and end ranges for a single measurement, allowing for the identification of start and end events. For instance, to measure the latency from the gateway to the payment service, the gateway entry point and the payment service exit point constitute the link boundaries for that measurement.

[0027] Measurement constraints can be considered as a set of judgment rules for determining whether a target measurement quantity can be effectively measured. They include boundary semantic constraints, time domain constraints, message attribution constraints, and original evidence constraints, and are used to verify whether the existing observation capabilities of the link meet the prerequisites for measurement.

[0028] Among them, the boundary semantic constraint can be considered as requiring the existence of valid observation events with matching meanings on the boundary of the measurement link; the time domain constraint can be considered as requiring the time at both ends of the business path to be comparable, or the existence of a valid cross-clock conversion path; the message attribution constraint can be considered as ensuring that messages can be uniquely matched and form an event association relationship with consistent order; the original evidence constraint can be considered as requiring that the observation evidence can be actually obtained and that there are no unknown quantities that cannot be collected. This set of constraints together serves as the core basis for the measurability determination.

[0029] S102. Construct a first observable constraint graph based on the measurement basic information, and extract a first solution subgraph based on the preset measurement constraint conditions combined with the first observable constraint graph. The measurement constraint conditions include: boundary semantic constraints, time domain constraints, message attribution constraints, and original evidence constraints.

[0030] The first observable constraint graph can be considered as a topology graph constructed based on the link measurement information. It can serve as a carrier for subsequent extraction of solution subgraphs and for determining the measurability of target measurements. Specifically, the first observable constraint graph includes: event vertices, clock vertices, message entity vertices, target quantity vertices, and measurement relationships; the measurement relationship edges include: available time record relationships, differential relationships within the same clock domain, calibrated cross-clock domain transformation relationships, message identity relationships, and service path sequence relationships.

[0031] The available time record relationship can be considered as connecting the event vertex to the clock vertex that generated the time record, or connecting two event vertices based on the time record attribute of the event vertex and then pointing to the clock vertex; the same clock domain differential relationship connects two event vertices located in the same clock domain, or the two are jointly connected to the same clock vertex.

[0032] The calibrated cross-clock domain transformation relationship connects two clock vertices and includes attributes such as calibration method, effective range, and uncertainty. The calibration method can be considered the time calibration technique used between the two clock domains to establish conversion rules between different clocks; the effective range can be considered the time range within which the calibration relationship can function normally; outside this range, the calibration result is no longer reliable; and the uncertainty can be considered the inherent error range of the time result after cross-clock conversion, used to characterize the reliability of the time conversion result.

[0033] The message identity relationship connects event vertices and message entity vertices, or connects message entity vertices before and after transformation, indicating that the messages have a one-to-one correspondence, or that a derived association can be established under preset constraints; the business path sequence relationship connects event vertices before and after according to the message flow direction. Target quantity vertices are then connected to the start and end events that define their boundaries, and carry the output granularity requirements.

[0034] In this embodiment, the first solution subgraph can be considered as a local subgraph formed by selecting and retaining vertices and measurement relationship edges related to the current target measurement quantity from the first observable constraint graph, with the target quantity vertex as the core, and used to verify whether the target measurement quantity meets the measurement constraint conditions.

[0035] Specifically, this step can be considered as constructing a first observable constraint graph based on the collected measurement basic information, generating event vertices, clock vertices, message entity vertices, and target quantity vertices in the graph, and establishing various measurement relationship edges to fully carry the link topology, existing observation capabilities, and associated constraint information.

[0036] Using the target quantity vertex corresponding to the target measurement quantity to be determined as the core, and combined with the preset measurement constraints, the first observable constraint graph can be filtered, retaining the vertices related to the target measurement quantity and the corresponding measurement relationship edges, and removing irrelevant vertices and edges. This process yields a local first solution subgraph, which is used to subsequently verify whether the target measurement quantity satisfies the various measurement constraints.

[0037] In one implementation, this step can be considered as reading the basic measurement information, instantiating event vertices, clock vertices, message entity vertices, and target quantity vertices, and creating various measurement relationship edges between the vertices to construct the first observable constraint graph; taking the target quantity vertex as the traversal starting point, performing graph traversal within the first observable constraint graph according to the preset measurement constraints, filtering out vertices and corresponding measurement relationship edges related to the current target measurement determination, eliminating irrelevant vertices and edges, and extracting the first solution subgraph.

[0038] S103. When the target measurement quantity is determined to satisfy the measurement constraint condition according to the first solution subgraph, and the target measurement quantity can be uniquely determined, the target measurement quantity is determined to be measurable.

[0039] In this embodiment, this step can be considered as traversing the first solution subgraph and verifying whether all measurement constraints corresponding to all measurement relationship edges in the first solution subgraph are met. If all constraints are met, and the target measurement quantity obtained by solving the first solution subgraph has only one result and there is no ambiguity of multiple solutions, then the target measurement quantity is determined to be measurable.

[0040] Specifically, this step can be considered as determining whether the first solution subgraph corresponding to the current target measurement satisfies all measurement constraints, taking each target measurement as a unit. This can be achieved by obtaining time record relationships to confirm that the business event vertices have valid time records; checking differential relationships within the same clock domain or calibrated cross-clock domain transformation relationships to confirm that event times can be compared; checking message identity relationships to confirm that start and end events can be associated with the same message entity; and checking business path sequence relationships to confirm that the timing of business events conforms to the message flow order.

[0041] Once all the above constraints are satisfied, within the measurement boundary defined by the output granularity corresponding to the current target measurement, the target measurement is bound to the target message instance and its determined business path. If the target measurement has a unique solution under the business path and corresponding measurement constraints, it is considered that the target measurement can be uniquely determined, and the target measurement is deemed measurable. Subsequently, the target measurement can be directly calculated based on the corresponding event data without the need for additional data entry points or supplementary point configurations.

[0042] S104. When the target measurement quantity satisfies the measurement constraint condition, the target measurement quantity cannot be uniquely determined at the required output granularity, but can uniquely determine the largest incompensable set containing the target measurement quantity and the corresponding smallest bounded measurable interval, the target measurement quantity is determined to be restricted measurable.

[0043] The largest indivisible set can be considered as a measurement quantity formed by the combination of multiple internal subquantities with a coarser boundary granularity. It is used to describe the set of target quantities that cannot be further divided under the current observation conditions. Under the current observation capabilities, the largest indivisible combined quantity can be uniquely solved as a whole, but it cannot be divided into individual internal subquantities.

[0044] The minimum outgoing measurable interval can be considered as the smallest continuous measurement interval that satisfies all measurement constraints and has a unique solution, obtained by expanding outward along the business path, using the start and end events of the target measurement as the basic boundary, when the target measurement cannot be solved independently due to missing constraints. It describes the smallest boundary range that can cover the set and can be actually measured. The corresponding merged measurement quantity is the maximum indivisible merged quantity. If a continuous interval that satisfies the constraints cannot be found after expanding outward, then there is no minimum outgoing measurable interval, and the target measurement quantity is determined to be unmeasurable.

[0045] In this embodiment, this step can be considered as follows: when the first solution subgraph satisfies all measurement constraints, but a unique target measurement quantity cannot be solved independently, only an incompatible component can be solved. This incompatible component includes the target measurement quantity, and this incompatible component is already the maximum range that can be solved under the current observation capability, and cannot be further divided into smaller independent measurement units, then the target measurement quantity is determined to be limited and measurable.

[0046] S105. If the target measurable quantity, the maximum unmeasurable component set, and the corresponding minimum outer measurable interval cannot be uniquely determined, then the target measurable quantity is determined to be unmeasurable. For the cases of limited measurability and unmeasurability, an unmeasurable proof is output.

[0047] The proof of unmeasurability can be considered to include at least two aspects: first, the measurement constraints that are currently not satisfied; and second, the correspondence between the unsatisfied constraints and the affected target measurements, which can be determined based on the constraint dependencies recorded in the first solution subgraph corresponding to each target measurement. For the restricted measurable state, the proof of unmeasurability may also include the largest set of unmeasurable components to which the target measurement belongs under the current observation conditions and its corresponding smallest outer measurable interval; for the unmeasurable state, the proof of unmeasurability may also include the reasons why effective merged measurements cannot be formed.

[0048] Unmet measurement constraints can be considered to include at least one of the following: missing business observation events, event semantic mismatch, missing time records, missing or invalid clock calibration relationships, non-unique message associations, unavailable original evidence, and unmet deployment conditions.

[0049] The proof of unmeasurability can be considered as explaining the root cause, the affected object, and the currently available measurement boundary that the target measurement cannot be solved independently. Specifically, for the restricted measurable state, the proof of unmeasurability includes: a missing constraint identifier, the affected target measurement, and the set of the largest unmeasurable components to which the target measurement belongs, as well as the corresponding smallest bounding measurable interval; for the unmeasurable state, the proof of unmeasurability includes: the missing constraint identifier, the affected target measurement, and the reason why a valid merged measurement cannot be formed.

[0050] Among them, the missing constraint identifier can be considered as a unique identifier of the failed constraint item that causes the target measurement quantity to be unable to be solved independently. Specifically, it can be various unsatisfied measurement constraints such as missing clock calibration constraints, invalid event timing constraints, invalid message identity constraints, and missing key observation events. It is the root cause label for determining measurement anomalies.

[0051] The affected target measurements can be considered as the constraint dependencies based on the first solution subgraph. All measurements that can only be solved independently with the help of the missing constraints are located. Due to the lack of corresponding constraints, these target measurements cannot be solved separately and cannot obtain a unique and refined result.

[0052] The inability to form an effective merged measurement can be considered as the fact that, under the existing observation conditions, even if the measurement boundary is expanded, the relevant vertices and constraint edges still do not satisfy the measurement constraint conditions, and a maximum incompatibility set containing the target measurement cannot be uniquely determined.

[0053] The reason why an effective merged measurement cannot be formed may be that the solution subgraph lacks necessary business event vertices or business path sequence relationships, the business message flow link is incomplete, and it is impossible to delineate a continuous measurement interval; or there may be problems such as cross-clock calibration failure, messages cannot be uniquely matched, and original evidence is unavailable. No matter how much the merging range is expanded, a unique and definite merged measurement result cannot be obtained. Therefore, the target measurement is judged as unmeasurable.

[0054] In this embodiment, this step can be considered as follows: if the first solution subgraph does not satisfy all measurement constraints, the target measurement quantity cannot be uniquely determined, and the above-mentioned maximum uncomponent set and the corresponding minimum outer measurable interval cannot be uniquely obtained, then the target measurement quantity is determined to be unmeasurable.

[0055] In the case of limited measurability, the output proof of unmeasurability includes: missing constraint identifier, affected target measurement quantity, and the maximum indivisible merge quantity set corresponding to the target measurement quantity and its minimum outer measurable interval; in the case of unmeasurability, the output proof of unmeasurability includes: missing constraint identifier, affected target measurement quantity, and the reason why a valid merge measurement quantity cannot be formed.

[0056] The technical solution of this embodiment obtains basic measurement information, constructs a first observable constraint graph carrying joint measurement constraints, and extracts a solution subgraph to complete the measurability judgment of the target measurement quantity. It also generates proof of unmeasurability for restricted measurable or unmeasurable scenarios, effectively realizing the automated hierarchical judgment of measurement measurability under cross-subject heterogeneous service links, and improving the accuracy and traceability of measurability judgment.

[0057] Based on the above, the method provided in this embodiment further includes: for the target measurement quantities that are limited to measurable and unmeasurable, obtaining the corresponding candidate supplementary action, supplementary observation capability, and supplementary action set; deploying the supplementary action set on the link to obtain the updated first observable constraint graph and recording it as the second observable constraint graph; Extract the second solution subgraph from the second observable constraint graph, and verify whether the target measurement quantity corresponding to the second solution subgraph satisfies the measurement constraint condition; when the measurement constraint condition is satisfied and the target measurement quantity is unique, determine that the target measurement quantity is measurable, and output an executable acquisition configuration.

[0058] The candidate point-filling action can be considered as the operation of adding or adjusting observation points based on the missing constraints marked in the aforementioned proof of unmeasurability. Executing this point-filling action can fill in the missing constraints, thereby enabling the observation capability required to independently solve for the target measurement quantity.

[0059] Each supplementary action may include at least one of the following: adding new business event points, adding paired collection, establishing relative clock calibration, adding message identity pass-through or generation, and opening the original evidence interface; each supplementary action also has at least one of the following attributes: deployment location, resource overhead, operational impact, permission conditions, and evidence reliability.

[0060] Supplementary observation capability can be considered as the new observation capability that the system can add after executing candidate supplementary point actions. It is used to explain which missing constraints can be filled after the supplementary point is completed or whether the target measurement can be solved independently. The supplementary point action set can be considered as a combination of one or more supplementary point actions selected from the candidate supplementary point actions for the same restricted measurable or unmeasurable target measurement, which can fill the corresponding missing measurement constraints and meet the deployment restrictions. The selection process can comprehensively consider coverage effect, deployment cost, security restrictions, verification conditions and implementation risks.

[0061] The second observable constraint graph can be considered as the observable constraint graph reconstructed after deploying the set of supplementary actions to the business link and updating the observation capabilities. It is used to carry the observation capabilities and constraint relationships brought about by the new observation points. The second solution subgraph can be considered as the solution subgraph extracted from the second observable constraint graph based on the same target measurement quantity. It is used to verify whether the target measurement quantity meets the measurement constraint conditions after the supplementary points are completed.

[0062] An executable data acquisition configuration can be considered a standardized data acquisition scheme that is machine-readable and automatically executed after the target measurement quantity has been verified to be compliant and has a unique solution. It can be directly parsed and run by data acquisition programs, deployment tools, or test orchestration systems. It can include core parameters such as measurement task identifier, observation deployment location, business event semantics, data acquisition source, clock and timing calibration rules, message association rules, data acquisition filtering conditions, data storage and anomaly handling, configuration effective range and version, etc.

[0063] In this embodiment, this step can be considered as obtaining corresponding candidate supplementary actions and their supplementary observation capabilities for a target measurement quantity that is limited to measurability or unmeasurability, based on the aforementioned proof of unmeasurability; selecting a set of supplementary actions from the candidate supplementary actions; deploying the set of supplementary actions to the business link; updating the first observable constraint graph after verification to obtain the second observable constraint graph; extracting the second solution subgraph from the second observable constraint graph; verifying whether the target measurement quantity satisfies the measurement constraint conditions; if the measurement constraint conditions are satisfied and the target measurement quantity is unique, then the target measurement quantity is determined to be measurable, and an executable acquisition configuration is output.

[0064] The technical solution of this embodiment generates a set of supplementary actions for limited measurable and unmeasurable target measurements to redeploy the observable constraint graph, re-determine measurability, and output an executable acquisition configuration when it is measurable. This effectively realizes iterative verification of observation capabilities, improves the feasibility of measurement schemes across heterogeneous service links, and ensures that the acquisition configuration can be directly deployed and executed.

[0065] Example 2 Figure 2 This is a flowchart of a heterogeneous link testability determination method according to Embodiment 2 of the present invention. The embodiments of the present invention further refine the above-mentioned optional technical solutions, such as... Figure 2 As shown, the method includes: S201. Obtain basic measurement information in the link, including target measurement quantity, logical node, adjacent communication relationship and existing observation capability.

[0066] S202. Using the target quantity vertex as the root, obtain the starting event vertex, ending event vertex, output granularity, and business path corresponding to the target quantity vertex from the first observable constraint graph.

[0067] In this context, the starting event vertex can be considered as the graph node corresponding to the business observation event representing the starting point of the target measurement in the first observable constraint graph; the ending event vertex is the graph node corresponding to the business observation event representing the ending point of the target measurement in the same constraint graph.

[0068] In this embodiment, this step can be considered as taking the target quantity vertex as the starting point of traversal, reading the starting event vertex, ending event vertex, output granularity and business path information bound to the target quantity vertex from the first observable constraint graph, so as to determine the measurement interval and granularity requirements corresponding to the target measurement quantity to be solved this time.

[0069] S203. Based on the starting event vertex, the ending event vertex, the output granularity, and the business path, determine the vertex set and topological edges associated with the target quantity vertex.

[0070] In this embodiment, this step can be considered as filtering all vertices within the first observable constraint graph that have a dependency relationship with the target vertex within the scope of the business path, as well as the topological edges between vertices that represent various constraint relationships, based on the starting event vertex, the ending event vertex, the output granularity, and the business path, thereby obtaining the vertex set and topological edges associated with the target vertex.

[0071] S204. Based on the measurement basic information and the measurement constraints, obtain the measurable constraint dependencies between vertices in the vertex set.

[0072] Among them, measurable constraint dependencies can be considered as the association constraints that must be satisfied between different vertices within a vertex set to ensure that the target measurement can be solved. These include various dependencies such as event semantic matching, time base comparability, message identity, and output granularity. They are used to characterize whether the observation elements corresponding to a vertex are valid, which will affect the ability of another vertex to participate in the calculation of the target quantity.

[0073] In this embodiment, this step may involve combining measurement basic information and measurement constraints to determine the measurable constraint dependencies between vertices within the vertex set. These dependencies characterize the constraint dependencies that need to be satisfied between elements such as observation events, clocks, and message associations corresponding to each vertex, serving as the basis for constructing the topological relationships of the observable constraint graph.

[0074] S205. Based on the measurable constraint dependency relationship, collect each vertex and topological edge in reverse, and construct the first solution subgraph corresponding to the target quantity vertex according to the collected vertices and topological edges.

[0075] In this embodiment, this step can be considered as starting from the target vertex, traversing backward along the measurable constraint dependencies, collecting all vertices with constraint dependencies and the corresponding topological edges between vertices, and removing irrelevant vertices and topological edges. The collected vertices and topological edges together form the first solution subgraph corresponding to the target vertex.

[0076] S206. When the target measurement quantity is determined to satisfy the measurement constraint condition based on the first solution subgraph, and the target measurement quantity can be uniquely determined, the target measurement quantity is determined to be measurable.

[0077] S207. When the target measurement quantity satisfies the measurement constraint condition, and the target measurement quantity cannot be uniquely determined at the required output granularity, but can uniquely determine the largest incompensable set containing the target measurement quantity and the corresponding smallest bounded measurable interval, the target measurement quantity is determined to be restricted measurable.

[0078] S208. If the target measurable quantity, the maximum unmeasurable component set, and the corresponding minimum outer measurable interval cannot be uniquely determined, then the target measurable quantity is determined to be unmeasurable. For the cases of limited measurability and unmeasurability, an unmeasurable proof is output.

[0079] S209. Based on the existing observation capabilities and the missing constraint identifier, obtain the candidate point-filling actions and the supplementary observation capabilities, and filter the candidate point-filling actions to obtain the point-filling action set.

[0080] In this embodiment, this step can be considered as combining existing observation capabilities and missing constraint identifiers to determine the corresponding candidate supplementary actions and the supplementary observation capabilities that each supplementary action can bring, then screening the candidate supplementary actions, eliminating redundant or invalid supplementary actions, and finally obtaining a set of supplementary actions.

[0081] In one implementation, this step can be considered as minimizing at least one of the following when selecting the set of point-filling actions, under the constraint that all specified target measurements are measurable: the number of point-filling actions, resource overhead, operational impact, or overall cost. This can be achieved by one or more of Boolean satisfiability solutions, integer programming, set covering, graph search, or heuristic search.

[0082] S210. Based on the verification message instance, verify the event semantics, time record, clock relationship, message attribution and original evidence of the supplementary observation capability to obtain the verified supplementary observation capability.

[0083] Among them, the verification message instance can be a message sample with a known identity or verifiable message lineage. The source can be a controlled injected test message, a de-identified replay of historical business messages, or a natural business message with a verifiable source selected within a controlled time window. It is used to complete the verification of the newly added observation point by flowing through the link after the set of supplementary action points is deployed.

[0084] Verified supplementary observation capabilities can be obtained by testing and verifying the observation capabilities in the link using verification message instances. During the verification process, the semantics of business events, time records, clock relationships, unique correspondence of messages, and original evidence exits are checked. The capability can only take effect after the verification is passed, thereby instantiating and generating various valid observation relationships. If the verification fails, a usable observation relationship cannot be generated and cannot participate in the measurability solution of the target measurement.

[0085] In this embodiment, this step can be considered as deploying the set of supplementary actions to the business link, obtaining verification message instances with known identities or verifiable message lineages, and allowing them to flow through the new observation points; based on the measured data generated by the verification message instances at each observation point, verifying the semantics of business events, time records, clock relationships, unique correspondence of messages, and original evidence exits; after verification, generating verified supplementary observation capabilities as a valid basis for subsequent measurability re-judgment.

[0086] In one implementation, this step can be considered as sending or replaying probe messages to supplement the observation capabilities after deploying the set of supplementary actions in the business link. These probe messages are verification message instances with known identities or verifiable message lineages. Furthermore, the semantic accuracy of business events, the authenticity of time records, cross-node clock matching relationships, message unique association capabilities, and the validity of original evidence exits are verified item by item through the probe messages.

[0087] If any verification fails, the current supplementary point optimization is deemed invalid, and the original measurability state of the corresponding target measurement remains unchanged; the observability constraint graph is not updated. Simultaneously, candidate supplementary point actions can be backtracked, and a new set of suitable supplementary point actions can be selected for deployment and verification again, avoiding errors in measurability determination due to a single supplementary point failure. Only after all verification items pass can a valid, verified supplementary observation capability be generated.

[0088] S211. Based on the verified supplementary observation capability, the updated first observable constraint graph is obtained and denoted as the second observable constraint graph.

[0089] In this embodiment, this step can be considered as adding corresponding observation event vertices to the first observable constraint graph based on the verified supplementary observation capabilities, supplementing corresponding constraint topology edges, and updating the observation validity, clock domain, message association, and other attributes of the relevant vertices; synchronously modifying the state of the original invalid or changed vertices and topology edges, completing the graph structure and attribute update, and obtaining the second observable constraint graph.

[0090] In one implementation, this solution continuously collects the real-time health status of each observation capability throughout the system testing and operation, enabling dynamic iterative management of measurability. During operation, if abnormal conditions such as failure of business observation events, failure of cross-clock domain calibration relationships, or decay of message association capabilities are detected, the update logic of the measurement observability constraint graph is triggered in real time, synchronously correcting the failed vertex attributes and topological constraint relationships in the graph.

[0091] S212. Extract the second solution subgraph from the second observable constraint graph, and verify whether the target measurement quantity corresponding to the second solution subgraph satisfies the measurement constraint condition.

[0092] In this embodiment, this step can be considered as taking the target vertex as the root, collecting vertices and topological edges with constraint dependencies from the second observable constraint graph in reverse, and extracting the second solution subgraph; then, based on the second solution subgraph, verifying whether the target measurement quantity satisfies all measurement constraint conditions corresponding to event semantic matching, time base comparability, message identity, and output granularity.

[0093] S213. When the measurement constraints are met and the target measurement quantity can be uniquely determined, the target measurement quantity is determined to be measurable, and an executable acquisition configuration is output.

[0094] In this embodiment, this step can be considered as follows: if the second solution subgraph verification result satisfies all measurement constraints and the target measurement quantity can be uniquely determined, then the target measurement quantity is determined to be measurable, and an executable acquisition configuration that can be read by a machine and directly deployed and executed is output.

[0095] In one implementation, while determining that the target measurement quantity is measurable and outputting an executable acquisition configuration, the system simultaneously outputs a complete set of traceability results, which specifically includes: a list of all current target measurement quantities, a snapshot of the existing observation capabilities after iterative updates, unique version information of this measurability solution, proof of non-measurability, a set of supplementary actions that have taken effect in this deployment, a review and verification after the supplementary actions are completed and their corresponding original configuration evidence, so that independent programs can repeatedly execute the measurability determination.

[0096] The technical solution of this embodiment, by taking the target measurement quantity as the core anchor point and combining standardized measurement basic information and measurement constraints, accurately constructs the measurable constraint dependency relationship between vertices. It can quantify the mutual constraint logic of each observation node from multiple dimensions such as event semantics, time base consistency, message association, and output granularity, realize the structuring and standardization of the measurable constraint dependency relationship, provide a rigorous and unified theoretical constraint basis for subsequent subgraph solving and measurability determination, and greatly improve the standardization and accuracy of measurement determination.

[0097] By adopting a reverse traversal collection method, only vertices and topological edges that have real constraint dependencies on the target measurement are selected, and observation nodes and topological relationships that are irrelevant in the business link are automatically eliminated. This effectively compresses the solution calculation range, avoids the computational redundancy and interference problems caused by full graph traversal, and can accurately focus on the effective observation constraint topology of the current target measurement.

[0098] By conducting real-world tests and verifications of the newly added observation points using real business scenarios or controllable probe message instances, the system verifies the authenticity of business event semantics, time records, clock relationships, message associations, and original evidence outputs from multiple dimensions. This ensures that the newly added observation points can effectively capture target business events, avoiding issues such as configuration taking effect but actual point recording failing, data distortion, or message mismatch. This fundamentally avoids measurement misjudgments caused by false observation capabilities.

[0099] By dynamically and iteratively updating the observable constraint graph with the effective observation capabilities verified by actual measurements, the vertex attributes, topological constraints, and dependencies are synchronously refreshed. This ensures that the constraint graph always reflects the latest and most realistic business observation topology and constraint dependency state, providing a reliable model foundation for secondary measurability verification and guaranteeing the effectiveness of iterative judgments. By re-extracting the solution subgraph based on the updated constraint graph and verifying the measurement constraints, a closed-loop verification mechanism after point-filling optimization is implemented, greatly improving the rigor and completeness of measurement measurability judgments.

[0100] By standardizing the output of directly deployable acquisition configurations under the premise that all constraints are satisfied and the measurement quantities are uniquely solvable, the uniqueness and accuracy of the target measurement quantity solution results are guaranteed, measurement deviations caused by ambiguous solutions are avoided, and a feasible acquisition deployment scheme can be directly output, realizing intelligent, precise and automated observation acquisition configuration output.

[0101] To better understand the implementation logic of the above scheme, this embodiment further provides an exemplary description. Figure 3 A flowchart illustrating a service link observability model is provided, such as... Figure 3 As shown, it includes: S1. Construct an observable model of the service link and define nodes, events, and target measurements.

[0102] In this embodiment, this step can be considered as completing the structured modeling of the business link. By registering the entry and exit events inside the node and the transmission and reception events of adjacent nodes, all key observation points that can be used for latency measurement are anchored. At the same time, the full-dimensional target measurement system is defined, providing a complete and standardized topological foundation and measurement object support for subsequent observation capability registration, constraint configuration and measurability determination.

[0103] Specifically, this step can be considered as representing a service link as a source node, a first processing node, a second processing node, and a terminal node connected sequentially. For each processing node, an entry event indicating that a complete service message has entered a processable state and an exit event indicating that processing is complete and accepted by the output interface are recorded; for each adjacent node, sending-side events and receiving-side events are recorded. The target measurement set includes the processing delay of two nodes, the communication delay of three adjacent nodes, and an end-to-end delay.

[0104] S2. Establish observation capabilities, clock relationships, and message identity data records.

[0105] In this embodiment, this step can be considered as establishing an independent and complete standardized capability record for each registered business observation event within the link. The capability record fully represents the observation attributes of a single event. Two types of core association records are established simultaneously: one is a clock relationship record, which records in detail the two paired clock domains, clock calibration method, effective calibration time interval, and clock relationship availability status; the other is a message identity record, which clarifies whether the original unique identifier of the business message can remain unchanged throughout the entire process across nodes. If the identifier changes, the system-approved and compliant message association conversion method is recorded.

[0106] In practice, it can be implemented using any of the following carriers: relational tables, graph databases, configuration files, or in-memory objects, to adapt to different deployment scenarios.

[0107] S3. Configure standardized solution conditions for various target measurement quantities.

[0108] In this embodiment, this step can be considered as configuring dedicated solution constraints for the internal processing latency of a node, the communication latency between adjacent nodes, and the end-to-end latency, and clarifying mandatory constraints such as event observability, semantic matching, clock differential, message correspondence, and path integrity.

[0109] Specifically, this step can be considered as processing latency within the node. The solution conditions include at least the following: both the entry and exit events are observable; their semantics are consistent with the boundaries of the complete business message processing; both can be differentially processed using the same local clock; and the messages can be uniquely matched.

[0110] For communication delay between adjacent nodes, the solution conditions must include at least the following: events on both the sending and receiving sides are observable; the two events belong to the same service message; and the two clock domains are identical or have a relative calibration relationship valid at the sample time. For end-to-end delay, in addition to start and end events and time base relationships, it is also required that the required intermediate path does not contain any unexplainable message branches, aggregations, or splits.

[0111] S4. Based on the missing items in the link observation, determine the measurability of measurement and generate proof of non-measurability.

[0112] In this embodiment, this step can be considered as forming a traceable conclusion of unmeasurability in scenarios where the lack of precise positioning and observation capabilities leads to unmeasurable or limited measurable scenarios, thereby avoiding erroneous measurements and data substitution.

[0113] In one implementation, this step can be considered as addressing the link defect where the second processing node only has an exit event and lacks an entry event. Based on the solution conditions, it is determined that: if the internal processing delay of the second processing node and the adjacent communication delay preceding the node cannot be separated to obtain a unique and accurate measurement result, then it is in a restricted measurable state; if only the combined delay from the exit of the first processing node to the exit of the second processing node can be measured under the premise that the cross-clock calibration relationship is valid, then an unmeasurable proof is generated.

[0114] Record the missing items and the root cause of the unmeasurability in the unmeasurability proof, such as: lack of second processing node entry business observation event, and clearly indicate that it is forbidden to use measurable merged delay to replace two unmeasurable subdivided target measurements to prevent data misuse.

[0115] S5. Generate and filter the optimal set of point-filling actions to construct the minimum point-filling configuration.

[0116] In this embodiment, this step can be considered as generating multiple candidate completion actions for missing items with unmeasurable constraints, and eliminating invalid or limited completion schemes based on event semantic compliance, permission constraints, deployment overhead, maintenance risks, and evidence reliability, and obtaining the optimal and minimized set of completion actions through algorithms such as set coverage and heuristic search.

[0117] In one implementation, this step can be considered as addressing the constraint defects and unpredictability issues of the missing entry event of the second processing node. The system automatically generates multiple candidate actions to fill the gaps, specifically including four solutions: deploying lightweight markers at the business entry point of the second processing node, deploying bypass receiving probes at the link network boundary, outputting consumption confirmation events carrying the original message identifier through message middleware, opening up existing asynchronous log information of the system, and verifying the compliance and validity of each candidate action.

[0118] Asynchronous logs cannot prove that a complete message has entered a processable state, failing to meet the semantic constraints of entry events and thus being directly deemed invalid. Network boundary bypass probes can prove that a packet has arrived at the link, but cannot prove that the business program has read the message; they only achieve limited measurability and cannot meet the requirements for detailed measurement and solution. Lightweight entry point marking and message middleware consumption confirmation events can fully match the entry event constraints, making them an effective supplementary solution. Furthermore, effective candidate actions are labeled with three attributes: deployment feasibility, runtime resource overhead, and subsequent maintenance risks.

[0119] Without modifying the definition of the target measurement quantity, the minimum optimal set of replacement actions covering all unmeasurable target quantities can be obtained through single or combined algorithms such as Boolean satisfiability, integer programming, set coverage, shortest path, and heuristic search. Candidate solutions that violate security and permission constraints are eliminated first, and then sorted and filtered according to the number of replacement points, running costs, and evidence reliability to finally determine the minimum replacement point configuration.

[0120] S6, deployment verification, dynamic review and automated configuration output.

[0121] Specifically, this step can be considered as deploying the selected optimal set of supplementary actions to the corresponding business link, generating probe message instances with verifiable lineages, and verifying the semantic accuracy of business events, the authenticity of time records, the effectiveness of cross-node clock calibration, the uniqueness of message association, and the availability of original evidence exits through each probe message instance.

[0122] The standardized output of the supplementary point results includes complete configuration information, such as the supplementary point identifier, deployment node, target business event, acquisition method, clock domain, calibration partner, message identifier source, original evidence output, and verification steps before effectiveness. Upon successful verification, the system permanently stores the supplementary point configuration and a complete set of verification records, generating a machine-readable acquisition configuration that can be directly deployed and executed. If verification fails, the original unmeasurable state of the target measurement remains, and the system automatically switches to the next candidate supplementary point configuration for re-verification.

[0123] During the test run, the health status of each observation capability is periodically collected. If the clock calibration relationship of the paired collection is found to be outside the effective range, the business observation event fails, or the message association capability is reduced, the clock topology edge and observation constraint are marked as failed and stop participating in the solution. At the same time, the historical records are retained and all affected target measurements are resolved.

[0124] For measurements that become restricted measurable or unmeasurable after failure, the corresponding time period measurement samples are isolated, and deterministic measurement results are prohibited from being output. Only the effective processing delay measurement capability recorded by the local monotonic clock is retained. The state change time, affected measurements, and the supplementary actions required for recovery are output synchronously. Data from the failure period are not included in the formal statistical results.

[0125] The technical solution in this embodiment achieves the quantification and standardized determination of the measurability of service latency-related measurements by using structured modeling of service link topology, standardized definition of observation events, capability attributes, and measurement solution constraints. This eliminates the subjectivity and randomness of traditional manual experience-based judgments. By accurately identifying missing observation capabilities and generating and filtering the minimum set of supplementary actions, the overhead of observation deployment and system performance loss are effectively reduced.

[0126] Simultaneously, by verifying the supplementary measurement capabilities through actual detection messages, theoretical observation capabilities are transformed into verifiable real observation capabilities, avoiding measurement misjudgments caused by configuration taking effect but actual measurement failure. Through a dynamic health review mechanism during runtime, observable constraints are updated in real time and measurement measurability is reassessed, ensuring the accuracy and reliability of measurement data throughout the entire time period. This solution is not only applicable to various link latency measurements but can also be extended to multiple scenarios such as service loss, continuity issues, or state transitions, enabling intelligent determination of full-link measurement measurability, accurate supplementary measurement, and automated configuration output.

[0127] Example 3 Figure 4 This is a schematic diagram of a heterogeneous link testability determination device provided in Embodiment 3 of the present invention. Figure 4 As shown, the device includes: an information acquisition module 41, a subgraph extraction module 42, a testability determination module 43, a restricted testability determination module 44, and a proof output module 45.

[0128] Among them, the information acquisition module 41 is used to acquire the basic measurement information in the link, which includes the target measurement quantity, logical nodes, adjacent communication relationships and existing observation capabilities; The subgraph extraction module 42 is used to construct a first observable constraint graph based on the measurement basic information, and extract a first solution subgraph based on the preset measurement constraint conditions combined with the first observable constraint graph. The measurement constraint conditions include: boundary semantic constraints, time domain constraints, message attribution constraints, and original evidence constraints. The measurability determination module 43 is used to determine that the target measurement quantity is measurable when the corresponding target measurement quantity satisfies the measurement constraint condition according to the first solution subgraph and the target measurement quantity can be uniquely determined. The restricted measurability determination module 44 is used to determine that the target measurement quantity is restricted measurable when the target measurement quantity satisfies the measurement constraint condition, the target measurement quantity cannot be uniquely determined at the required output granularity, but can uniquely determine the largest uncomponent set containing the target measurement quantity and the corresponding smallest outer measurable interval. The proof output module 45 is used to determine that the target measurement quantity is unmeasurable if the target measurement quantity, the maximum unmeasurable component set, and the corresponding minimum outer measurable interval cannot be uniquely determined. For the cases of limited measurability and unmeasurability, the module outputs proof of unmeasurability.

[0129] The technical solution of this embodiment obtains basic measurement information, constructs a first observable constraint graph carrying joint measurement constraints, and extracts a solution subgraph to complete the measurability judgment of the target measurement quantity. It also generates proof of unmeasurability for restricted measurable or unmeasurable scenarios, effectively realizing the automated hierarchical judgment of measurement measurability under cross-subject heterogeneous service links, and improving the accuracy and traceability of measurability judgment.

[0130] The heterogeneous link testability determination device provided in this embodiment of the invention can execute a heterogeneous link testability determination method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0131] Optionally, each of the target measurements corresponds to at least one service path, service path instance or service path candidate, a start event, a termination event, and the output granularity.

[0132] Optionally, the start event, the termination event, and the output granularity define the boundary of the target measurement.

[0133] Optionally, the first observable constraint graph includes: event vertices, clock vertices, message entity vertices, target quantity vertices, and measurement relationship edges.

[0134] Optionally, the measurement relationship edges include: available time record relationships, differential relationships within the same clock domain, calibrated cross-clock domain transformation relationships, message identity relationships, and service path sequence relationships. Optionally, the subgraph extraction module 42 can be specifically used for: Using the target quantity vertex as the root, obtain the starting event vertex, ending event vertex, output granularity, and business path corresponding to the target quantity vertex from the first observable constraint graph; Based on the starting event vertex, the ending event vertex, the output granularity, and the business path, determine the vertex set and topological edges associated with the target quantity vertex; Based on the measurement basic information and the measurement constraints, the measurable constraint dependencies between vertices in the vertex set are obtained; Based on the measurable constraint dependencies, each vertex and topological edge are collected in reverse, and the first solution subgraph corresponding to the target vertex is constructed according to the collected vertices and topological edges.

[0135] Optionally, for the restricted measurable state, the proof of unmeasurability includes: a missing constraint identifier, the affected target measurement quantity, and the set of the largest unmeasurable components to which the target measurement quantity belongs, and the corresponding smallest bounded measurable interval.

[0136] Optionally, for the unmeasurable state, the proof of unmeasurability includes: the missing constraint identifier, the affected target measurement, and the reason why a valid merged measurement cannot be formed.

[0137] Optionally, the heterogeneous link testability determination device provided in this embodiment further includes: The supplementary point action acquisition module is used to acquire corresponding candidate supplementary point actions, supplementary observation capabilities, and supplementary point action sets for the target measurement quantities that are limited to measurable and unmeasurable. The capability verification module is used to deploy the set of supplementary actions after the link, acquire, inject or replay verification message instances with known identities or verifiable lineages, and obtain the verified supplementary observation capabilities based on the verification message instances. The constraint graph update module is used to obtain an updated first observable constraint graph based on the verified supplementary observation capability and record it as the second observable constraint graph. The constraint verification module is used to extract a second solution subgraph from the second observable constraint graph and verify whether the target measurement quantity corresponding to the second solution subgraph satisfies the measurement constraint condition. The configuration output module is used to determine that the target measurement quantity is measurable when the measurement constraints are met and the target measurement quantity can be uniquely determined, and to output an executable acquisition configuration.

[0138] Optionally, the point-filling action acquisition module can be used for: Based on the existing observation capabilities and the missing constraint identifier, the candidate point-filling actions and the supplementary observation capabilities are obtained, and the candidate point-filling actions are filtered to obtain the point-filling action set.

[0139] Optionally, the capability verification module can be used specifically for: Based on the verification message instance, the event semantics, time records, clock relationships, message attribution, and original evidence of the supplementary observation capability are verified to obtain the verified supplementary observation capability.

[0140] According to embodiments of the present invention, the present invention also provides an electronic device, a readable storage medium, and a computer program product.

[0141] Example 4 Figure 5This is a schematic diagram of the structure of an electronic device 50 implementing a heterogeneous link testability determination method according to Embodiment 4 of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0142] like Figure 5 As shown, the electronic device 50 includes at least one processor 51 and a memory, such as a read-only memory (ROM) 52 and a random access memory (RAM) 53, communicatively connected to the at least one processor 51. The memory stores computer programs executable by the at least one processor. The processor 51 can perform various appropriate actions and processes based on the computer program stored in the ROM 52 or loaded from storage unit 58 into the RAM 53. The RAM 53 can also store various programs and data required for the operation of the electronic device 50. The processor 51, ROM 52, and RAM 53 are interconnected via a bus 54. An input / output (I / O) interface 55 is also connected to the bus 54.

[0143] Multiple components in electronic device 50 are connected to I / O interface 55, including: input unit 56, such as keyboard, mouse, etc.; output unit 57, such as various types of monitors, speakers, etc.; storage unit 58, such as disk, optical disk, etc.; and communication unit 59, such as network card, modem, wireless transceiver, etc. Communication unit 59 allows electronic device 50 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0144] Processor 51 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 51 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 51 performs the various methods and processes described above, such as heterogeneous link testability determination methods.

[0145] In some embodiments, the heterogeneous link testability determination method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 58. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 50 via ROM 52 and / or communication unit 59. When the computer program is loaded into RAM 53 and executed by processor 51, one or more steps of the heterogeneous link testability determination method described above may be performed. Alternatively, in other embodiments, processor 51 may be configured to perform the heterogeneous link testability determination method by any other suitable means (e.g., by means of firmware).

[0146] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0147] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0148] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0149] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0150] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0151] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0152] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0153] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining the testability of heterogeneous links, characterized in that, include: Acquire basic measurement information in the link, including target measurement quantities, logical nodes, adjacent communication relationships, and existing observation capabilities; A first observable constraint graph is constructed based on the measurement basic information, and a first solution subgraph is extracted based on the preset measurement constraints and the first observable constraint graph. The measurement constraints include: boundary semantic constraints, time domain constraints, message attribution constraints, and original evidence constraints. When the target measurement quantity is determined to satisfy the measurement constraint condition based on the first solution subgraph, and the target measurement quantity can be uniquely determined, the target measurement quantity is determined to be measurable. When the target measurement quantity satisfies the measurement constraint condition, and the target measurement quantity cannot be uniquely determined at the required output granularity, but can uniquely determine the largest incompensable set containing the target measurement quantity and the corresponding smallest bounded measurable interval, the target measurement quantity is determined to be restricted measurable. If the target measurable quantity, the maximum unmeasurable component set, and the corresponding minimum outer measurable interval cannot be uniquely determined, then the target measurable quantity is determined to be unmeasurable. For the cases of limited measurability and unmeasurability, an unmeasurability proof is output.

2. The method according to claim 1, characterized in that, include: Each of the target measurements corresponds to at least one service path, a service path instance or a service path candidate, a start event, a termination event, and the output granularity. The start event, the termination event, and the output granularity define the boundaries of the target measurement.

3. The method according to claim 1, characterized in that, The first observable constraint graph includes: event vertices, clock vertices, message entity vertices, target quantity vertices, and measurement relationship edges; The measurement relationship edges include: available time record relationship, differential relationship within the same clock domain, calibrated cross-clock domain transformation relationship, message identity relationship, and service path sequence relationship; The step of extracting the first solution subgraph based on preset measurement constraints and the first observable constraint graph includes: Using the target quantity vertex as the root, obtain the starting event vertex, ending event vertex, output granularity, and business path corresponding to the target quantity vertex from the first observable constraint graph; Based on the starting event vertex, the ending event vertex, the output granularity, and the business path, determine the vertex set and topological edges associated with the target quantity vertex; Based on the measurement basic information and the measurement constraints, the measurable constraint dependencies between vertices in the vertex set are obtained; Based on the measurable constraint dependencies, each vertex and topological edge are collected in reverse, and the first solution subgraph corresponding to the target vertex is constructed according to the collected vertices and topological edges.

4. The method according to claim 1, characterized in that, include: For the restricted measurable state, the proof of unmeasurability includes: missing constraint identifier, affected target measurement quantity, and the set of the largest unmeasurable components to which the target measurement quantity belongs, and the corresponding smallest bounded measurable interval; For the unmeasurable state, the proof of unmeasurability includes: the missing constraint identifier, the affected target measurement, and the reason why a valid merged measurement cannot be formed.

5. The method according to claim 1, characterized in that, Also includes: For the target measurement quantities that are limited to measurable and unmeasurable, obtain the corresponding candidate supplementary point actions, supplementary observation capabilities, and supplementary point action sets; After the set of supplementary actions is deployed in the link, a verification message instance with a known identity or verifiable lineage is acquired, injected, or replayed, and the verified supplementary observation capability is obtained based on the verification message instance. Based on the verified supplementary observation capability, the updated first observable constraint graph is obtained and denoted as the second observable constraint graph; Extract the second solution subgraph from the second observable constraint graph, and verify whether the target measurement quantity corresponding to the second solution subgraph satisfies the measurement constraint condition; When the measurement constraints are met and the target measurement quantity can be uniquely determined, the target measurement quantity is determined to be measurable, and an executable acquisition configuration is output.

6. The method according to claim 5, characterized in that, The acquisition of the corresponding candidate point-filling actions, supplementary observation capabilities, and point-filling action set includes: Based on the existing observation capabilities and the missing constraint identifier, the candidate point-filling actions and the supplementary observation capabilities are obtained, and the candidate point-filling actions are filtered to obtain the set of point-filling actions. The process of obtaining verified supplementary observation capabilities based on the verification message instance includes: Based on the verification message instance, the event semantics, time records, clock relationships, message attribution, and original evidence of the supplementary observation capability are verified to obtain the verified supplementary observation capability.

7. A heterogeneous link testability determination device, characterized in that, include: The information acquisition module is used to acquire basic measurement information in the link, including target measurement quantities, logical nodes, adjacent communication relationships, and existing observation capabilities. The subgraph extraction module is used to construct a first observable constraint graph based on the measurement basic information, and extract a first solution subgraph based on the first observable constraint graph according to the preset measurement constraint conditions. The measurement constraint conditions include: boundary semantic constraints, time domain constraints, message attribution constraints, and original evidence constraints. The measurability determination module is used to determine that the target measurement quantity is measurable when the corresponding target measurement quantity satisfies the measurement constraint condition based on the first solution subgraph and the target measurement quantity can be uniquely determined. The restricted measurability determination module is used to determine that the target measurement quantity is restricted measurable when the target measurement quantity satisfies the measurement constraint condition, the target measurement quantity cannot be uniquely determined at the required output granularity, but can uniquely determine the largest incompensable set containing the target measurement quantity and the corresponding smallest bounded measurable interval. The proof output module is used to determine that the target measurable quantity is unmeasurable if the target measurable quantity, the maximum unmeasurable component set, and the corresponding minimum outer measurable interval cannot be uniquely determined. For the cases of limited measurability and unmeasurability, the module outputs proof of unmeasurability.

8. An electronic device, characterized in that, The electronic device includes: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the heterogeneous link testability determination method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the heterogeneous link testability determination method according to any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the heterogeneous link testability determination method according to any one of claims 1-6.

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