A quality evaluation method, device and equipment based on call chain data and a medium

Abstract

This invention discloses a quality assessment method, apparatus, device, and medium based on call chain data. The method includes: acquiring a target call chain data set corresponding to a target distributed system; performing data verification on the target call chain data set based on preset multi-dimensional data verification rules to obtain a target verification result set corresponding to the target call chain data set; wherein the target verification result set contains arrays of various target verification results, and one target call chain data in the target call chain data set corresponds to one array of target verification results; and performing quality assessment on the target verification result set based on a preset score threshold to determine the quality assessment result corresponding to the target call chain data set. Through the technical solution of this invention, the data quality of call chain data can be systematically and intelligently assessed, improving the accuracy and generation efficiency of the quality assessment results.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a quality assessment method, apparatus, device, and medium based on call chain data. Background Technology

[0002] In a distributed microservice architecture, a business request often needs to traverse multiple independently deployed service nodes, forming a complex call chain. Therefore, tracing, collecting, and analyzing distributed call chain data has become fundamental for achieving system observability, rapid fault location, and performance bottleneck analysis.

[0003] However, in real-world production environments, the collected call chain data often suffers from various quality issues due to factors such as unstable network transmission, limited probe resources, asynchronous service node clocks, abnormal data serialization, or configuration errors. These problems can lead to biased root cause analyses based on such data, potentially resulting in completely erroneous conclusions and severely interfering with operational decisions.

[0004] In existing technologies, static matching methods based on fixed rules are typically used to perform quality checks on call chain data. However, static matching methods based on fixed rules can only verify the existence of basic data structures or fields, lacking multi-dimensional in-depth verification, resulting in incomplete detection dimensions. Secondly, they cannot adapt to new service types, new protocols, or changed data models that emerge during the dynamic evolution of distributed systems. Furthermore, the evaluation results lack quantification, typically only outputting Boolean "pass / fail" statuses, failing to provide system-level, trend-based quality scores, and making it difficult to effectively measure the overall improvement effect.

[0005] Therefore, how to systematically and intelligently evaluate the data quality of the call chain data and improve the accuracy and efficiency of the quality evaluation results is an urgent problem to be solved. Summary of the Invention

[0006] This invention provides a method, apparatus, device, and medium for quality assessment based on call chain data, which can solve the problems of low accuracy and low generation efficiency of quality assessment results for call chain data.

[0007] According to one aspect of the present invention, a quality assessment method based on call chain data is provided, comprising: Obtain the target call chain data set corresponding to the target distributed system; The target call chain data set is validated based on preset multi-dimensional data validation rules to obtain a target validation result set corresponding to the target call chain data set; wherein, the target validation result set contains various target validation result arrays, and one target call chain data in the target call chain data set corresponds to one target validation result array; The quality of the target verification result set is evaluated based on a preset score threshold to determine the quality evaluation result corresponding to the target call chain data set.

[0008] According to another aspect of the present invention, a quality assessment apparatus based on call chain data is provided, comprising: The data acquisition module is used to acquire the target call chain data set corresponding to the target distributed system; The data verification module is used to perform data verification on the target call chain data set based on preset multi-dimensional data verification rules, and obtain the target verification result set corresponding to the target call chain data set; wherein, the target verification result set contains various target verification result arrays, and one target call chain data in the target call chain data set corresponds to one target verification result array; The quality assessment module is used to perform quality assessment on the target verification result set based on a preset score threshold, and determine the quality assessment result corresponding to the target call chain data set.

[0009] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the quality assessment method based on call chain data as described in any embodiment of the present invention.

[0010] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the quality assessment method based on call chain data as described in any embodiment of the present invention.

[0011] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the quality assessment method based on call chain data as described in any embodiment of the present invention.

[0012] The technical solution of this invention involves obtaining a target call chain data set corresponding to a target distributed system. Then, based on preset multi-dimensional data verification rules, the target call chain data set is verified to obtain a target verification result set corresponding to the target call chain data set. The target verification result set contains arrays of various target verification results, with one target call chain data point corresponding to one array of target verification results. Finally, a quality assessment is performed on the target verification result set based on a preset score threshold to determine the quality assessment result corresponding to the target call chain data set. By utilizing multi-dimensional data verification rules to collaboratively verify the call chain data set and aggregating the verification results of all call chain data to obtain the overall quality status of the call chain data set, the problem of low accuracy and low generation efficiency of call chain data quality assessment results is solved. This allows for a systematic and intelligent assessment of call chain data quality, improving the accuracy and generation efficiency of quality assessment results.

[0013] 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

[0014] 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.

[0015] Figure 1 This is a flowchart of a quality assessment method based on call chain data provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart of a quality assessment method based on call chain data provided in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of a quality assessment device based on call chain data according to Embodiment 3 of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device that implements the quality assessment method based on call chain data according to an embodiment of the present invention. Detailed Implementation

[0016] 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.

[0017] It should be noted that the terms "first," "second," "target," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0018] Example 1 Figure 1 This is a flowchart of a quality assessment method based on call chain data provided in Embodiment 1 of the present invention. This embodiment is applicable to the automatic quality assessment of distributed call chain data. The method can be executed by a quality assessment device based on call chain data, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes: S110. Obtain the target call chain data set corresponding to the target distributed system.

[0019] In this context, a distributed system refers to a large software system composed of components distributed across different computers. Components may include services or databases. Typically, the components in a distributed system can communicate and coordinate through a network to jointly complete a common task. For example, taking a logistics system as an example, each service in the distributed system, such as user service, order service, and payment service, is responsible for a specific duty. Services call each other through Application Programming Interfaces (APIs) to complete complex business processes. The target distributed system can refer to the distributed system selected for quality assessment. Typically, the target distributed system can be determined based on actual application requirements; this embodiment of the invention does not impose specific limitations on this. Call chain data refers to data used to record the complete flow path and details between all relevant services and components in a distributed system for an end-to-end request, such as a user clicking a purchase button. Typically, call chain data uses traces as the basic unit, with each trace containing multiple spans. A trace can represent a complete distributed request and has a globally unique trace identifier (TraceID). A span can represent a single operation within a service or component. Typically, a Span can contain a unique identifier (SpanID), the parent Span ID (ParentSpanId), the associated Trace ID (TraceID), the operation name (Name), such as the interface name or method name, the start timestamp (StartTime), the end timestamp (EndTime), the status code (Status), and critical event logs (Events). Target call chain data can refer to the call chain data corresponding to the target distributed system. A call chain data set can refer to a collection of call chain data corresponding to the same distributed system over a period of time. The target call chain data set can refer to the call chain data set corresponding to the target distributed system.

[0020] S120. Perform data verification on the target call chain data set based on preset multi-dimensional data verification rules to obtain a target verification result set corresponding to the target call chain data set; wherein, the target verification result set contains various target verification result arrays, and one target call chain data in the target call chain data set corresponds to one target verification result array.

[0021] The preset multi-dimensional data validation rules refer to pre-defined rules used to validate call chain data from multiple dimensions. Typically, preset multi-dimensional data validation rules may include data validation objects and specific validation processes. The validation result refers to the validation result obtained after validating each call chain data in the target call chain data set using the preset multi-dimensional data validation rules. Typically, one validation dimension corresponds to one validation result for the same call chain data. For example, the validation result can be 0 indicating successful validation or 1 indicating failed validation. The validation result array refers to a combination used to store the validation results of the same call chain data across different validation dimensions. Typically, one call chain data corresponds to one validation result array, and the number of data in the validation result array is the same as the number of validation dimensions. The target validation result array refers to the validation result array corresponding to the selected target call chain data in the target call chain data set. The validation result set refers to a set composed of the validation result arrays corresponding to each call chain data in the same call chain data set. The target validation result set refers to the validation result set corresponding to the target call chain data set.

[0022] S130. Perform a quality assessment on the target verification result set based on a preset score threshold, and determine the quality assessment result corresponding to the target call chain data set.

[0023] The preset score threshold can refer to a pre-defined value used to assess the quality of the target verification result set. For example, the preset score threshold can be determined based on historical experience. The quality assessment result can refer to the overall assessment result obtained after assessing the quality of the target verification result set using the preset score threshold. For example, the quality assessment result can be either meeting the preset score threshold or not meeting the preset score threshold.

[0024] In an optional implementation, after performing a quality assessment on the target verification result set based on a preset score threshold and determining the quality assessment result corresponding to the target call chain data set, the method further includes: determining candidate call chain data corresponding to the target call chain data set based on the quality assessment result; performing data analysis on the candidate call chain data based on the target large language model and preset knowledge base rules, and determining the repair scheme corresponding to the candidate call chain data.

[0025] Here, "candidate call chain data" refers to the data results obtained after filtering the target call chain data set based on quality assessment results. For example, candidate call chain data could be call chain data containing errors in the target call chain data set. "Large language model" can refer to an artificial intelligence model trained on massive amounts of text and data. Typically, a large language model can deeply understand the relationship between human language, code logic, and world knowledge, and can reason, generate, and solve problems based on the input context. "Target large language model" can refer to a pre-trained large language model that can be used directly. "Pre-defined knowledge base rules" can refer to a set of explicit, structured judgment logic and standards predefined by human experts. Typically, in computers, pre-defined knowledge base rules can be represented as a rule base, configuration table, or set of conditional statements. "Remediation plan" can refer to executable remediation suggestions for data quality issues in the candidate call chain data.

[0026] Specifically, after determining the quality assessment results corresponding to the target call chain data set, if the quality assessment results do not meet the preset score threshold, call chain data with verification failures can be selected from the target call chain data set as candidate call chain data. Then, the candidate call chain data and preset knowledge base rules are input into the target large language model, which generates a corresponding remediation plan for the candidate call chain data. Thus, by analyzing the hidden patterns, correlations, and root causes in problematic call chain data through the large language model and generating targeted, actionable remediation suggestions using natural language, the efficiency of the distributed system can be improved.

[0027] The technical solution of this invention involves obtaining a target call chain data set corresponding to a target distributed system. Then, based on preset multi-dimensional data verification rules, the target call chain data set is verified to obtain a target verification result set corresponding to the target call chain data set. The target verification result set contains arrays of various target verification results, with one target call chain data point corresponding to one array of target verification results. Finally, a quality assessment is performed on the target verification result set based on a preset score threshold to determine the quality assessment result corresponding to the target call chain data set. By utilizing multi-dimensional data verification rules to collaboratively verify the call chain data set and aggregating the verification results of all call chain data to obtain the overall quality status of the call chain data set, the problem of low accuracy and low generation efficiency of call chain data quality assessment results is solved. This allows for a systematic and intelligent assessment of call chain data quality, improving the accuracy and generation efficiency of quality assessment results.

[0028] Example 2 Figure 2This is a flowchart of a quality assessment method based on call chain data provided in Embodiment 2 of the present invention. This embodiment is a refinement based on the above embodiment. Specifically, this embodiment refines the step of "performing a quality assessment of the target verification result set based on a preset score threshold, and determining the quality assessment result corresponding to the target call chain data set". Specifically, it may include: statistically processing the target verification result set based on the data dimensions corresponding to each target verification result in the target verification result array, and determining the target quality score corresponding to the target verification result set; performing a threshold judgment on the target quality score based on the preset score threshold, and determining the quality assessment result corresponding to the target call chain data set based on the threshold judgment result. Figure 2 As shown, the method includes: S210. Obtain the target call chain data set corresponding to the target distributed system.

[0029] S220. If the preset multidimensional data verification rule is the preset integrity dimension verification rule, then the target call chain data set is subjected to field verification, format verification and data integrity verification to obtain the first verification result corresponding to the target verification result array in the target verification result set.

[0030] In this context, integrity dimension refers to data validation to ensure that the basic components of data are complete and without missing elements. Preset integrity dimension validation rules refer to pre-defined rules that define the integrity dimension validation process. Field validation refers to operations used to verify the absence of key fields. For example, field validation can verify the existence of fields such as TraceId, SpanId, and StartTime in a Span. Typically, this can be done by iterating through each Span and checking for the existence of these fields. Format validation refers to validation operations used to ensure the correct format of field content. For example, format validation can check whether the field format conforms to a set format, such as whether the timestamp is a 13-digit millisecond value, whether the status code is an enumerated value, whether the time elapsed is a non-negative integer, and whether the ID format conforms to a Universally Unique Identifier (UUID). Format validation can typically be performed using regular expressions, type conversion functions, or enumerated value lists. Data integrity validation can refer to validation operations used for more macro-level integrity checks. For example, data integrity verification can include: verifying whether the number of spans is within a reasonable range, verifying whether the root span (parentId) is included, and verifying whether the value of a specific field is within a valid range. Typically, this can be achieved through statistical analysis or by matching and checking against a pre-defined business process diagram. The first verification result can refer to the identifier used to represent the integrity status of data in each call chain.

[0031] Specifically, if the preset multidimensional data verification rule is the preset integrity dimension verification rule, then field verification, format verification, and data integrity verification can be performed on each target call chain data in the target call chain data set, thereby obtaining the verification result of the target call chain data under the integrity dimension.

[0032] S230. If the preset multidimensional data verification rule is the preset time-series dimension verification rule, then the target call chain data set is subjected to time sequence verification and time rationality verification to obtain the second verification result corresponding to the target verification result array in the target verification result set.

[0033] The temporal dimension refers to a data verification dimension used to ensure that the time records of all operations conform to the causal logic of the real world. Preset temporal dimension verification rules refer to pre-defined rules used to limit the temporal dimension verification process. Time sequence verification refers to verification operations used to check whether the order in which events occur is reasonable. For example, time sequence verification can include: ensuring that the start time of a child span is not earlier than the start time of its parent span, ensuring that the end time of a child span is not later than the end time of its parent span, and ensuring that the times of spans at the same level do not overlap unless called concurrently. Typically, spans can be associated using TraceId and ParentSpanId, and then their timestamps can be compared. Time reasonableness verification refers to verification operations used to check whether the timestamp itself is within a reliable physical time range. For example, time reasonableness verification can include: ensuring that the start time must be less than the end time, ensuring that the time consumed is not negative, and ensuring that the timestamp is not a future time. The second verification result refers to the identification result used to represent the temporal state of each call chain data.

[0034] Specifically, if the preset multidimensional data verification rule is the preset time-series dimension verification rule, then time sequence verification and time rationality verification can be performed on each target call chain data in the target call chain data set, thereby obtaining the verification result of the target call chain data in the time-series dimension.

[0035] S240. If the preset multidimensional data verification rule is the preset structural dimension verification rule, then the target call chain data set is subjected to structural integrity verification and reference integrity verification to obtain the third verification result corresponding to the target verification result array in the target verification result set.

[0036] The structural dimension refers to the data verification dimension used to ensure that the topology of the call chain is a logically correct tree diagram. Preset structural dimension verification rules refer to pre-defined rules used to limit the structural dimension verification process. Structural integrity verification refers to the verification operation that verifies whether the skeleton of the entire call chain is complete. For example, structural integrity verification can be: verifying the existence of multiple root spans, verifying the existence of circular references, and verifying the existence of isolated spans. Typically, for each Trace, the number of spans with null ParentSpanId can be counted, and a depth-first traversal can be used to check whether all spans can be traversed from the root span, thus achieving structural integrity verification. Referential integrity verification refers to the verification operation that verifies whether the references between components are accurate. For example, referential integrity verification can be: verifying whether all parent span IDs point to existing spans and whether there are dangling references. Typically, a hash mapping of SpanId->Span can be established for each Trace, and each span can be traversed to check whether its ParentSpanId exists in this mapping. The third verification result refers to the identification result used to represent the structural state of each call chain data.

[0037] Specifically, if the preset multidimensional data verification rule is the preset structural dimension verification rule, then structural integrity verification and referential integrity verification can be performed on each target call chain data in the target call chain data set, thereby obtaining the verification result of the target call chain data in the structural dimension.

[0038] S250. If the preset multidimensional data verification rule is the preset consistency dimension verification rule, then the target call chain data set is subjected to consistency verification based on the preset consistency knowledge base to obtain the fourth verification result corresponding to the target verification result array in the target verification result set.

[0039] In this context, consistency dimension refers to a data validation dimension that ensures all data adheres to a unified naming and value standard, facilitating global management and understanding. Preset consistency dimension validation rules refer to pre-defined rules that define the consistency dimension validation process. A pre-defined consistency knowledge base refers to a predefined, authoritative data dictionary or specification manual. For example, a consistency validation knowledge base can be built using a general, industry-recognized dictionary of data naming and value standards, such as the semantic convention from the OpenTelemetry community. Consistency validation refers to validation operations used to detect whether data maintains consistency in naming and value. For example, consistency validation can include attribute naming validation and value validation. Attribute naming validation verifies whether the name of the key in the data matches the name specified in the pre-defined consistency knowledge base. Value validation checks the type, format, and whether the value is within a predefined enumeration range. The fourth validation result refers to an identifier used to represent the consistency status of data in each call chain.

[0040] Specifically, if the preset multidimensional data verification rule is the preset consistency dimension verification rule, then the consistency verification of each target call chain data in the target call chain data set can be performed based on the preset consistency knowledge base, thereby obtaining the verification result of the target call chain data under the consistency dimension.

[0041] S260. Based on the data dimensions corresponding to each target verification result in the target verification result array, statistically process the target verification result set to determine the target quality score corresponding to the target verification result set.

[0042] The quality score can refer to the overall score calculated using the pass rate across various data dimensions. For example, if a pass result is 0 and a fail result is 1, then the number of passes with a result of 0 for each data dimension can be counted. The ratio between the number of passes and the number of data points in the call chain is then calculated as the pass rate for that data dimension. Finally, the pass rates are weighted and summed according to the weights of different data dimensions to obtain the quality score. The target quality score can refer to the quality score corresponding to the target set of pass results.

[0043] S270. Based on a preset scoring threshold, a threshold judgment is made on the target quality score, and the quality assessment result corresponding to the target call chain data set is determined according to the threshold judgment result.

[0044] The threshold judgment result can refer to the judgment result obtained after judging the target quality score using a preset score threshold. For example, the threshold judgment result can be either that the preset score threshold is met or that the preset score threshold is not met.

[0045] Specifically, after obtaining the target validation result set by performing data validation on the integrity, temporal, structural, and consistency dimensions respectively, the target validation result set can be statistically processed using the data dimensions corresponding to each target validation result in the target validation result array within the target validation result set. This yields the pass rate for each data dimension. Then, based on the weights between different data dimensions, the pass rates are weighted and summed to obtain the quality score. Finally, a preset score threshold is used to determine the target quality score, and the quality assessment result corresponding to the target call chain data set is determined based on the threshold determination result. Therefore, by quantifying the validation results, staff can more intuitively understand the final overall quality assessment result, providing a solid foundation for subsequent operations.

[0046] The technical solution of this invention involves obtaining a target call chain data set corresponding to a target distributed system. Then, if the preset multi-dimensional data verification rule is a preset integrity dimension verification rule, the target call chain data set is subjected to field verification, format verification, and data integrity verification to obtain a first verification result corresponding to the target verification result array in the target verification result set. If the preset multi-dimensional data verification rule is a preset time-series dimension verification rule, the target call chain data set is subjected to time sequence verification and time rationality verification to obtain a second verification result corresponding to the target verification result array in the target verification result set. If the preset multi-dimensional data verification rule is a preset structure-dimensional verification rule, the target call chain data set is subjected to structure integrity verification and reference integrity verification to obtain a third verification result corresponding to the target verification result array in the target verification result set. If the preset multi-dimensional data verification rule is a preset consistency dimension verification rule, the target call chain data set is subjected to consistency verification based on a preset consistency knowledge base to obtain a fourth verification result corresponding to the target verification result array in the target verification result set. Finally, based on the data dimensions corresponding to each target verification result in the target verification result array, the target verification result set is statistically processed to determine the target quality score corresponding to the target verification result set. The system uses a preset scoring threshold to determine the target quality score and then determines the quality assessment result corresponding to the target call chain data set based on the threshold determination result. By utilizing multi-dimensional data verification rules to collaboratively verify the call chain data set and aggregating the verification results of all call chain data to obtain the overall quality status of the call chain data set, the system solves the problems of low accuracy and low generation efficiency of call chain data quality assessment results. It can systematically and intelligently evaluate the data quality of call chain data, thereby improving the accuracy and generation efficiency of quality assessment results.

[0047] It is worth noting that, in the embodiments of the present invention, the execution order of the preset integrity dimension verification rules, preset timing dimension verification rules, preset structure dimension verification rules and preset consistency dimension verification rules can be executed serially or in parallel. The embodiments of the present invention are only explained by the above examples and are not specifically limited in this regard.

[0048] Example 3 Figure 3 This is a schematic diagram of a quality assessment device based on call chain data provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes: a data acquisition module 310, a data verification module 320, and a quality assessment module 330; Among them, the data acquisition module 310 is used to acquire the target call chain data set corresponding to the target distributed system; The data verification module 320 is used to perform data verification on the target call chain data set based on preset multi-dimensional data verification rules, and obtain a target verification result set corresponding to the target call chain data set; wherein, the target verification result set contains various target verification result arrays, and one target call chain data in the target call chain data set corresponds to one target verification result array; The quality assessment module 330 is used to perform quality assessment on the target verification result set based on a preset score threshold, and determine the quality assessment result corresponding to the target call chain data set.

[0049] The technical solution of this invention involves obtaining a target call chain data set corresponding to a target distributed system. Then, based on preset multi-dimensional data verification rules, the target call chain data set is verified to obtain a target verification result set corresponding to the target call chain data set. The target verification result set contains arrays of various target verification results, with one target call chain data point corresponding to one array of target verification results. Finally, a quality assessment is performed on the target verification result set based on a preset score threshold to determine the quality assessment result corresponding to the target call chain data set. By utilizing multi-dimensional data verification rules to collaboratively verify the call chain data set and aggregating the verification results of all call chain data to obtain the overall quality status of the call chain data set, the problem of low accuracy and low generation efficiency of call chain data quality assessment results is solved. This allows for a systematic and intelligent assessment of call chain data quality, improving the accuracy and generation efficiency of quality assessment results.

[0050] Optionally, the data verification module 320 can be used to: if the preset multidimensional data verification rule is a preset integrity dimension verification rule, then perform field verification, format verification and data integrity verification on the target call chain data set to obtain the first verification result corresponding to the target verification result array in the target verification result set.

[0051] Optionally, the data verification module 320 can be used to: if the preset multi-dimensional data verification rule is a preset time-series dimension verification rule, then perform time sequence verification and time rationality verification on the target call chain data set to obtain the second verification result corresponding to the target verification result array in the target verification result set.

[0052] Optionally, the data verification module 320 can be used to: if the preset multi-dimensional data verification rule is a preset structural dimension verification rule, then perform structural integrity verification and reference integrity verification on the target call chain data set to obtain the third verification result corresponding to the target verification result array in the target verification result set.

[0053] Optionally, the data verification module 320 can be used to: if the preset multidimensional data verification rule is a preset consistency dimension verification rule, then perform consistency verification on the target call chain data set based on the preset consistency knowledge base, and obtain the fourth verification result corresponding to the target verification result array in the target verification result set.

[0054] Optional, the quality assessment module 330 can be used for: Based on the data dimensions corresponding to each target verification result in the target verification result array, the target verification result set is statistically processed to determine the target quality score corresponding to the target verification result set. The target quality score is determined based on a preset scoring threshold, and the quality assessment result corresponding to the target call chain data set is determined based on the threshold determination result.

[0055] Optionally, the quality assessment device based on call chain data may further include: a scheme repair module, used to perform quality assessment on the target verification result set based on a preset score threshold, determine the quality assessment result corresponding to the target call chain data set, determine the candidate call chain data corresponding to the target call chain data set based on the quality assessment result; and perform data analysis on the candidate call chain data based on the target large language model and preset knowledge base rules to determine the repair scheme corresponding to the candidate call chain data.

[0056] The quality assessment device based on call chain data provided in the embodiments of the present invention can execute the quality assessment method based on call chain data provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0057] Example 4 Figure 4 A schematic diagram of an electronic device 410 that can be used to implement embodiments of the present invention is shown. 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 (e.g., 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.

[0058] like Figure 4 As shown, the electronic device 410 includes at least one processor 420 and a memory, such as a read-only memory (ROM) 430 or a random access memory (RAM) 440, communicatively connected to the at least one processor 420. The memory stores computer programs executable by the at least one processor. The processor 420 can perform various appropriate actions and processes based on the computer program stored in the ROM 430 or loaded into the RAM 440 from storage unit 490. The RAM 440 may also store various programs and data required for the operation of the electronic device 410. The processor 420, ROM 430, and RAM 440 are interconnected via a bus 450. An input / output (I / O) interface 460 is also connected to the bus 450.

[0059] Multiple components in electronic device 410 are connected to I / O interface 460, including: input unit 470, such as keyboard, mouse, etc.; output unit 480, such as various types of monitors, speakers, etc.; storage unit 490, such as disk, optical disk, etc.; and communication unit 4100, such as network card, modem, wireless transceiver, etc. Communication unit 4100 allows electronic device 410 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0060] Processor 420 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 420 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 420 performs the various methods and processes described above, such as quality assessment methods based on call chain data.

[0061] The method includes: Obtain the target call chain data set corresponding to the target distributed system; The target call chain data set is validated based on preset multi-dimensional data validation rules to obtain a target validation result set corresponding to the target call chain data set; wherein, the target validation result set contains various target validation result arrays, and one target call chain data in the target call chain data set corresponds to one target validation result array; The quality of the target verification result set is evaluated based on a preset score threshold to determine the quality evaluation result corresponding to the target call chain data set.

[0062] In some embodiments, the call chain data-based quality assessment method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 490. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 410 via ROM 430 and / or communication unit 4100. When the computer program is loaded into RAM 440 and executed by processor 420, one or more steps of the call chain data-based quality assessment method described above may be performed. Alternatively, in other embodiments, processor 420 may be configured to perform the call chain data-based quality assessment method by any other suitable means (e.g., by means of firmware).

[0063] 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), system-on-a-chip (SoCs), complex 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.

[0064] 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.

[0065] 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.

[0066] 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).

[0067] 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.

[0068] 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.

[0069] This application also discloses a computer program product, which includes a computer program that, when executed by a processor, implements the call chain data-based quality assessment method provided in any embodiment of this application. This program product shares the same inventive concept as the call chain data-based quality assessment methods disclosed in the embodiments of this application, and therefore will not be described further here.

[0070] 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.

[0071] 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 quality assessment method based on call chain data, characterized in that, include: Obtain the target call chain data set corresponding to the target distributed system; The target call chain data set is validated based on preset multi-dimensional data validation rules to obtain a target validation result set corresponding to the target call chain data set; wherein, the target validation result set contains various target validation result arrays, and one target call chain data in the target call chain data set corresponds to one target validation result array; The quality of the target verification result set is evaluated based on a preset score threshold to determine the quality evaluation result corresponding to the target call chain data set.

2. The method according to claim 1, characterized in that, The step of performing data verification on the target call chain data set based on preset multi-dimensional data verification rules to obtain a target verification result set corresponding to the target call chain data set includes: If the preset multidimensional data verification rule is the preset integrity dimension verification rule, then the target call chain data set is subjected to field verification, format verification and data integrity verification to obtain the first verification result corresponding to the target verification result array in the target verification result set.

3. The method according to claim 1, characterized in that, The step of performing data verification on the target call chain data set based on preset multi-dimensional data verification rules to obtain a target verification result set corresponding to the target call chain data set includes: If the preset multidimensional data verification rule is the preset time-series dimension verification rule, then the target call chain data set is subjected to time sequence verification and time rationality verification to obtain the second verification result corresponding to the target verification result array in the target verification result set.

4. The method according to claim 1, characterized in that, The step of performing data verification on the target call chain data set based on preset multi-dimensional data verification rules to obtain a target verification result set corresponding to the target call chain data set includes: If the preset multidimensional data verification rule is the preset structural dimension verification rule, then the target call chain data set is subjected to structural integrity verification and reference integrity verification to obtain the third verification result corresponding to the target verification result array in the target verification result set.

5. The method according to claim 1, characterized in that, The step of performing data verification on the target call chain data set based on preset multi-dimensional data verification rules to obtain a target verification result set corresponding to the target call chain data set includes: If the preset multidimensional data verification rule is the preset consistency dimension verification rule, then the target call chain data set is subjected to consistency verification based on the preset consistency knowledge base to obtain the fourth verification result corresponding to the target verification result array in the target verification result set.

6. The method according to claim 1, characterized in that, The step of performing a quality assessment on the target verification result set based on a preset score threshold, and determining the quality assessment result corresponding to the target call chain data set, includes: Based on the data dimensions corresponding to each target verification result in the target verification result array, the target verification result set is statistically processed to determine the target quality score corresponding to the target verification result set. The target quality score is determined based on a preset scoring threshold, and the quality assessment result corresponding to the target call chain data set is determined based on the threshold determination result.

7. The method according to claim 1, characterized in that, After performing a quality assessment on the target verification result set based on a preset score threshold and determining the quality assessment result corresponding to the target call chain data set, the method further includes: Based on the quality assessment results, candidate call chain data corresponding to the target call chain data set is determined; Based on the target large language model and preset knowledge base rules, data analysis is performed on the candidate call chain data to determine the corresponding repair scheme.

8. A quality assessment device based on call chain data, characterized in that, include: The data acquisition module is used to acquire the target call chain data set corresponding to the target distributed system; The data verification module is used to perform data verification on the target call chain data set based on preset multi-dimensional data verification rules, and obtain the target verification result set corresponding to the target call chain data set; wherein, the target verification result set contains various target verification result arrays, and one target call chain data in the target call chain data set corresponds to one target verification result array; The quality assessment module is used to perform quality assessment on the target verification result set based on a preset score threshold, and determine the quality assessment result corresponding to the target call chain data set.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the quality assessment method based on call chain data as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the quality assessment method based on call chain data as described in any one of claims 1-7.

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