System configuration abnormity positioning method and device, equipment, storage medium and product

Abstract

The invention discloses a system configuration exception positioning method and device, equipment, a storage medium and a product, and relates to the technical field of big data processing. Receiving configuration input of a user on the first evaluation object information and evaluation process variable information of the first evaluation object information in a configuration interface of the system, and packaging the configuration input into an executable calculation task descriptor; according to the descriptor, index original data corresponding to the first evaluation object information is extracted from the data source in real time, a calculation function or script corresponding to the evaluation process variable information is called, an evaluation result is generated, and an evaluation instance snapshot is generated; displaying an evaluation result and a multi-dimensional feedback channel on a result display interface; when feedback information input by a user through the multi-dimensional feedback channel is received, searching for historical evaluation instance snapshots similar to contents recorded in the evaluation instance snapshots; according to the feedback information, the historical evaluation instance snapshots and the evaluation instance snapshots are compared, the abnormal point position of the configuration is determined, and the configuration efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of big data processing technology, and in particular to a method, apparatus, device, computer-readable storage medium, and computer program product for locating system configuration anomalies. Background Technology

[0002] In various business scenarios such as enterprise operation, service supervision, and product evaluation, it is often necessary to build an evaluation system to objectively evaluate target objects, such as institutions, employees, and products, in order to support relevant decision-making.

[0003] Traditional evaluation systems typically rely on pre-defined evaluation processes to evaluate specific objects. While these systems can adapt to multiple scenarios to a certain extent, they often require redevelopment or deep modification when business scenarios change or evaluation dimensions expand. This process necessitates redeveloping the system, configuring information, and repeatedly debugging to pinpoint configuration anomalies, leading to low configuration efficiency. Summary of the Invention

[0004] This application provides a method, apparatus, device, computer-readable storage medium, and computer program product for locating system configuration anomalies, which can improve configuration efficiency.

[0005] In a first aspect, embodiments of this application provide a method for locating system configuration anomalies, including: Receive user configuration inputs for the first evaluation object information and the evaluation process variable information of the first evaluation object information in the system configuration interface; The information of the first evaluation object and the evaluation process variables of the first evaluation object are encapsulated into an executable computation task descriptor; Using the rule execution engine, the original data of the indicators corresponding to the information of the first evaluation object are extracted from the data source in real time according to the descriptor. The calculation function or script corresponding to the variable information of the evaluation process is called to generate the evaluation result and generate an evaluation instance snapshot. The evaluation instance snapshot records the input information, process information, intermediate evaluation results and evaluation results. The results display interface shows the evaluation results and a multi-dimensional feedback channel, which includes a natural language feedback box and a structured feedback component. Upon receiving feedback information from a user through a multi-dimensional feedback channel, search for historical evaluation instance snapshots whose content has a similarity greater than a preset value to the content recorded in the evaluation instance snapshot. Based on the feedback information, compare the historical evaluation instance snapshots with the evaluation instance snapshots to determine the location of the anomaly in this configuration.

[0006] In one possible implementation, receiving user configuration input from the system's configuration interface regarding the first evaluation object information and the evaluation process variable information of the first evaluation object information includes: The system receives the user's first input (selecting the target indicator template), second input (selecting the first evaluation object information and the first evaluation subject information) and third input (selecting the first target business type) from the system's configuration interface. If the evaluation object corresponding to the first evaluation object information meets the evaluation admission rules, the first evaluation indicator of the target indicator template is selected from the indicator library according to the template-indicator mapping relationship. The first evaluation indicator includes the second evaluation indicators corresponding to multiple business types, and the multiple business types include the first target business type. Based on the information of the first evaluation object and the information of the first evaluation subject, select multiple optional evaluation indicators from the second evaluation indicators corresponding to the first target business type, and display the multiple optional evaluation indicators in the configuration interface; The system receives a fourth input from the user selecting target optional evaluation indicators, a fifth input from the user's weight information for the target optional evaluation indicators, and a sixth input from the first target evaluation rule. The multiple optional evaluation indicators include the target optional evaluation indicators.

[0007] In one possible implementation, the first evaluation subject information includes descriptive information about the evaluation subject to which the first evaluation object information belongs; based on the first evaluation object information and the first evaluation subject information, multiple optional evaluation indicators are selected from the second evaluation indicators corresponding to the first target business type, including: Using a large model, based on the semantic similarity between the first evaluation object information and multiple preset evaluation object information in the knowledge base, the first evaluation object information is matched with multiple preset evaluation object information in the knowledge base to obtain the second evaluation object information corresponding to the first evaluation object information. Using a large model, the descriptive information is matched with multiple preset evaluation subject information in the knowledge base to obtain at least one second evaluation subject information corresponding to the first evaluation subject information; Based on the mapping relationship between objects and indicators, select the third evaluation indicator corresponding to the information of the second evaluation object from the second evaluation indicators corresponding to the first target business type; Based on the mapping relationship between the subject and the indicator, select multiple optional evaluation indicators from the third evaluation indicators that correspond to at least one second evaluation subject information.

[0008] In one possible implementation, based on the object-indicator mapping relationship, a third evaluation indicator corresponding to the second evaluation object information is selected from the second evaluation indicators corresponding to the first target business type, including: Obtain the attribute information corresponding to the information of the second evaluation object; Based on the mapping relationship between object attributes and indicators, a third evaluation indicator corresponding to the attribute information is selected from the second evaluation indicators corresponding to the first target business type.

[0009] In one possible implementation, receiving a sixth input to the first target evaluation rule includes: Based on the mapping relationship between business type and rule, select the first evaluation rule corresponding to the first target business type from the preset evaluation rules; Based on the mapping relationship between object attributes and rules, select the second evaluation rule corresponding to the information of the second evaluation object from the first evaluation rule; Based on the mapping relationship between the subject and the rule, select multiple optional evaluation rules from the second evaluation rules that correspond to at least one second evaluation subject information; Receive the sixth input from the user, indicating their selection of the first objective evaluation rule.

[0010] In one possible implementation, after displaying multiple optional evaluation metrics in the configuration interface, the method further includes: Receive new evaluation metrics input by users; Save the newly added evaluation indicators to the indicator requirement library.

[0011] In one possible implementation, the method further includes: Warning information is generated if the weight information of the target optional evaluation indicators does not meet the weight setting rules, or the object corresponding to the first evaluation object information does not meet the evaluation admission rules, or the first target evaluation rules do not match the first evaluation object information or the first evaluation subject information, or the first evaluation object information and the first evaluation subject information do not match.

[0012] In one possible implementation, after determining the location of the anomaly in the current configuration by comparing historical evaluation instance snapshots and evaluation instance snapshots based on feedback information, the method further includes: Identify the modified target historical evaluation instance snapshot from the historical evaluation instance snapshots; Obtain information on the modification process of historical evaluation instance snapshots of the target; Displays information about the modification process.

[0013] Secondly, embodiments of this application provide a system configuration anomaly locating device, comprising: The receiving module is used to receive the configuration input from the user in the system's configuration interface for the first evaluation object information and the evaluation process variable information of the first evaluation object information; The encapsulation module is used to encapsulate the first evaluation object information and the evaluation process variable information of the first evaluation object information into an executable computation task descriptor. The generation module is used to utilize the rule execution engine to extract the original indicator data corresponding to the first evaluation object information from the data source in real time according to the descriptor, call the calculation function or script corresponding to the evaluation process variable information, generate the evaluation result, and generate an evaluation instance snapshot. The evaluation instance snapshot records the input information, process information, intermediate evaluation results and the evaluation result. The display module is used to display the evaluation results and multi-dimensional feedback channels on the results display interface. The multi-dimensional feedback channels include natural language feedback boxes and structured feedback components. The search module is used to search for historical evaluation instance snapshots whose similarity to the content recorded in the evaluation instance snapshot is greater than a preset value when the user inputs feedback information through the multi-dimensional feedback channel. The determination module is used to determine the location of the anomaly point in the current configuration by comparing the historical evaluation instance snapshot with the evaluation instance snapshot based on the feedback information.

[0014] Thirdly, embodiments of this application provide an electronic device, the device comprising: A processor and a memory storing computer program instructions; a method for locating the aforementioned system configuration anomalies when the processor executes the computer program instructions.

[0015] Fourthly, embodiments of this application provide a computer storage medium on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the above-mentioned method for locating system configuration anomalies is implemented.

[0016] Fifthly, embodiments of this application provide a computer program product in which instructions, when executed by the processor of an electronic device, enable the electronic device to perform the aforementioned system configuration anomaly location method.

[0017] The system configuration anomaly localization method, apparatus, device, computer-readable storage medium, and computer program product of this application embodiment receive configuration input from a user in the system configuration interface for first evaluation object information and evaluation process variable information of the first evaluation object information; encapsulate the first evaluation object information and the evaluation process variable information of the first evaluation object information into an executable calculation task descriptor; utilize a rule execution engine, based on the descriptor, extract the original indicator data corresponding to the first evaluation object information from the data source in real time, call the calculation function or script corresponding to the evaluation process variable information, generate evaluation results, and generate an evaluation instance snapshot, the evaluation instance snapshot recording input information, process information, intermediate evaluation results, and evaluation results; display the evaluation results and a multi-dimensional feedback channel in the result display interface, the multi-dimensional feedback channel including a natural language feedback box and a structured feedback component; upon receiving feedback information input by the user through the multi-dimensional feedback channel, search for historical evaluation instance snapshots whose similarity to the content recorded in the evaluation instance snapshot is greater than a preset value; based on the feedback information, compare the historical evaluation instance snapshots and the current evaluation instance snapshot to determine the location of the anomaly point in the current configuration.

[0018] This application embodiment generates an executable computing task descriptor, i.e., all the necessary information in the system execution process, through the user's configuration input in the configuration interface. This enables the system to be fully configurable through the evaluation process of the configuration interface, reducing the coupling of configuration information within the system. This eliminates the need for redevelopment or deep modification of the system, thus improving configuration efficiency. Furthermore, if the user feels there is a problem with the configuration, feedback information can be received through the result display interface to quickly determine the location of the anomaly in the configuration, further improving configuration efficiency. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the structure of an evaluation system provided in one embodiment of this application; Figure 2 This is a flowchart illustrating a method for locating system configuration anomalies provided in another embodiment of this application; Figure 3 This is a flowchart illustrating a method for locating system configuration anomalies provided in another embodiment of this application; Figure 4 This is a flowchart illustrating a method for locating system configuration anomalies provided in another embodiment of this application; Figure 5This is a flowchart illustrating a method for locating system configuration anomalies provided in another embodiment of this application; Figure 6 This is a flowchart illustrating a method for locating system configuration anomalies provided in another embodiment of this application; Figure 7 This is a schematic diagram of the structure of a system configuration anomaly locating device provided in another embodiment of this application; Figure 8 This is a schematic diagram of the structure of an electronic device provided in another embodiment of this application. Detailed Implementation

[0021] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0022] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0023] It should be noted that the acquisition, storage, use, and processing of data in this application embodiment all comply with the relevant provisions of national laws and regulations.

[0024] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0025] In various business scenarios such as enterprise operation, service supervision, and product evaluation, it is often necessary to build an evaluation system to objectively evaluate target objects, such as institutions, employees, and products, in order to support relevant decision-making.

[0026] Traditional evaluation systems typically rely on pre-defined evaluation processes to evaluate specific objects. While these systems can adapt to multiple scenarios to a certain extent, they often require redevelopment or deep modification when business scenarios change or evaluation dimensions expand. This process necessitates redeveloping the system, configuring information, and repeatedly debugging to pinpoint configuration anomalies, leading to low configuration efficiency.

[0027] To address the problems of existing technologies, embodiments of this application provide a method, apparatus, device, computer-readable storage medium, and computer program product for locating system configuration anomalies. The system configuration anomaly location method provided in this application generates an executable computation task descriptor—that is, all necessary information during system execution—based on user configuration input in the configuration interface. This enables the system to be fully configurable throughout the evaluation process via the configuration interface, reducing the coupling of configuration information within the system and eliminating the need for redevelopment or deep modification of the system, thus improving configuration efficiency. Furthermore, if the user suspects a configuration problem, feedback can be received through the results display interface, quickly pinpointing the location of the configuration anomaly, further improving configuration efficiency.

[0028] In some embodiments, the system configuration anomaly localization method provided in this application can be applied to an evaluation system. Figure 1 A schematic diagram of the evaluation system is shown. For example... Figure 1As shown, the evaluation system 100 has a built-in knowledge base 110, which includes multiple preset evaluation object information and multiple preset evaluation subject information. The evaluation objects corresponding to the preset evaluation object information can include, but are not limited to, organizations, employees, customers, products, etc., and can be set according to user needs. The evaluation subjects corresponding to the preset evaluation subject information can include, but are not limited to, organizations, positions, customer groups, etc., and can be set according to user needs. The evaluation subject refers to the affiliation of the evaluation object. It should be noted that setting according to user needs includes setting according to the needs of users using the evaluation system. The evaluation system 100 also has a built-in indicator library 120, which includes a large number of evaluation indicators for users to choose from. The weights corresponding to the evaluation indicators can be preset, and the preset weights are saved corresponding to the evaluation indicators. In addition, the evaluation period corresponding to the evaluation indicator is saved corresponding to the evaluation indicator, and the evaluation period refers to the data range of the evaluation indicator. For example, if the evaluation indicator is click volume, and the evaluation period for click volume is one month, then when obtaining the value corresponding to the click volume, the number of clicks in the previous month is obtained. The evaluation system 100 also has a built-in evaluation rule library 130, which includes evaluation formulas and evaluation parameters. Evaluation formulas may include, but are not limited to, the median method, the M-value method, addition and subtraction of points, completion rate, four arithmetic operations, four arithmetic operations in combination, ranking, evaluation proportion, mean, etc. Evaluation parameters may include, but are not limited to, the median standard score, the upper limit of the score, the lower limit of the score, the benchmark score, the benchmark standard, the upper limit of the threshold value, the lower limit of the threshold value, the target value, etc.

[0029] The method for locating system configuration anomalies provided in the embodiments of this application will be described in detail below.

[0030] like Figure 2 As shown, the system configuration anomaly location method provided in this application embodiment includes S201~S206.

[0031] S201. Receive the user's configuration input for the first evaluation object information and the evaluation process variable information of the first evaluation object information in the system's configuration interface.

[0032] In some embodiments, after a user logs into the evaluation system, the system displays a configuration interface. The configuration interface includes multiple input fields for the user to input information about the first evaluation object and evaluation process variables related to that object.

[0033] Understandably, the configuration interface serves as the front-end interface for the evaluation system.

[0034] In some embodiments, the first evaluation object information refers to the entity or target being evaluated. The first evaluation object information may differ depending on the application scenario of the evaluation system. For example, the objects corresponding to the first evaluation object information may include organizations, employees, and products.

[0035] In some embodiments, the evaluation process variable information of the first evaluation object information may include all necessary information of the evaluation process. User input for the evaluation process variable information of the first evaluation object information is received through a graphical configuration interface of a web or desktop application. The configuration interface may include interactive components such as a drag-and-drop indicator selector, a weight slider, and a rule template drop-down menu, used to implement the configuration input of the evaluation process variable information.

[0036] S202. Encapsulate the information of the first evaluation object and the evaluation process variable information of the first evaluation object into an executable computation task descriptor.

[0037] In some embodiments, after receiving the user's configuration input regarding the first evaluation object information and the evaluation process variable information of the first evaluation object information in the system's configuration interface, the loosely configured user information at the front end is encapsulated into an executable computation task descriptor, that is, transformed into a standardized, structured task instruction that can be executed by the backend rule execution engine. Here, the descriptor is a structured data object or configuration file. Its core function is to completely and standardizedly describe all the necessary information of a computation task to be executed, enabling an execution engine to independently understand and run the task without relying on other contexts.

[0038] In some embodiments, the front end serializes the configured data into a descriptor and passes it to the backend server via an Application Programming Interface (API) call to perform computational tasks using the rule execution engine.

[0039] Understandably, this decouples the information between different evaluation objects.

[0040] S203. Using the rule execution engine, based on the descriptor, extract the original data of the indicators corresponding to the information of the first evaluation object from the data source in real time, call the calculation function or script corresponding to the variable information of the evaluation process, generate the evaluation result, and generate an evaluation instance snapshot. The evaluation instance snapshot records the input information, process information, intermediate evaluation results and evaluation results.

[0041] In some embodiments, a rule execution engine is used to parse descriptors, understand task requirements, and retrieve raw indicator data corresponding to the current first evaluation object from the relevant database, data warehouse, or via API. The calculation functions or scripts corresponding to the evaluation process variable information are then invoked, and combined with the retrieved raw indicator data, evaluation results are generated.

[0042] In some embodiments, the evaluation instance snapshot is an immutable data packet that records in detail each step of generating the evaluation result, including input information, process information, intermediate evaluation results, and the final evaluation result.

[0043] Understandably, evaluating instance snapshots ensures data traceability.

[0044] S204. Display the evaluation results and multi-dimensional feedback channels on the results display interface. The multi-dimensional feedback channels include natural language feedback boxes and structured feedback components.

[0045] In some embodiments, after receiving configuration input from the user in the system's configuration interface regarding the first evaluation object information and the evaluation process variable information of the first evaluation object information, the system jumps to the results display interface in response to the configuration input. The results display interface displays the evaluation results and the multi-dimensional feedback channel.

[0046] In some embodiments, the natural language feedback box allows users to freely input feedback information. For example, the feedback information could be "The evaluation result exceeds a preset threshold".

[0047] In some embodiments, to facilitate user configuration, the results display interface shows a structured feedback component. Users can input feedback information by directly clicking on the structured feedback component.

[0048] In some embodiments, the results display interface includes a satisfaction slider corresponding to the structured feedback component. Users can input their satisfaction level for the structured feedback component by sliding the satisfaction slider. If the satisfaction level is lower than a preset satisfaction threshold, outlier locations are generated.

[0049] S205. Upon receiving feedback information input by the user through the multi-dimensional feedback channel, find historical evaluation instance snapshots whose similarity to the content recorded in the evaluation instance snapshot is greater than a preset value.

[0050] In some embodiments, a natural language feedback box and / or a structured feedback component are used to receive user-inputted feedback information. Upon receiving feedback information input by the user through a multidimensional feedback channel, a historical evaluation instance snapshot with a similarity greater than a preset value to the content recorded in the current evaluation instance snapshot of the first evaluation object information is searched from the evaluation instance snapshots corresponding to evaluation object information of the same type as the first evaluation object information.

[0051] In one example, the content recorded in the evaluation instance snapshot is converted into a feature vector, and a vector similarity algorithm, such as cosine similarity, is used to find historical evaluation instance snapshots whose similarity to the content recorded in the evaluation instance snapshot is greater than a preset value.

[0052] S206. Based on the feedback information, compare the historical evaluation instance snapshot with the evaluation instance snapshot to determine the location of the anomaly point in this configuration.

[0053] In some embodiments, based on the feedback information, the input information, process information, intermediate evaluation results, and evaluation results recorded in the historical evaluation instance snapshot are compared with the input information, process information, intermediate evaluation results, and evaluation results recorded in the evaluation instance snapshot to determine the location of the anomaly point in the current configuration.

[0054] In one example, the evaluation process variable information includes evaluation metrics. By comparing the input information, process information, intermediate evaluation results, and evaluation results recorded in the historical evaluation instance snapshots with the input information, process information, intermediate evaluation results, and evaluation results recorded in the current evaluation instance snapshots, the location of the anomaly point configured this time is determined as the target evaluation metric.

[0055] This application embodiment generates an executable computing task descriptor, i.e., all the necessary information in the system execution process, through the user's configuration input in the configuration interface. This enables the system to be fully configurable through the evaluation process of the configuration interface, reducing the coupling of configuration information within the system. This eliminates the need for redevelopment or deep modification of the system, thus improving configuration efficiency. Furthermore, if the user feels there is a problem with the configuration, feedback information can be received through the result display interface to quickly determine the location of the anomaly in the configuration, further improving configuration efficiency.

[0056] In some embodiments, such as Figure 3 As shown, S201 can specifically include S210 to S240.

[0057] S210. Receive the user's first input for selecting the target indicator template, the second input for selecting the first evaluation object information and the first evaluation subject information, and the third input for selecting the first target business type in the system's configuration interface.

[0058] In some embodiments, after a user logs into the evaluation system, the system displays a configuration interface. The configuration interface includes an indicator template input box, an evaluation object input box, an evaluation subject input box, and a business type input box. The indicator template input box displays identifiers (such as names or numbers) of multiple indicator templates for the user to select. The evaluation object input box displays preset evaluation object information for the user to select. The evaluation subject input box displays preset evaluation subject information for the user to select. The business type input box displays multiple business types for the user to select. The system receives the user's first input (selecting a target indicator template), second input (first evaluation object information and first evaluation subject information), and third input (selecting a first target business type) from the configuration interface.

[0059] In some embodiments, the indicator templates can be managed on a project-by-project basis, and different projects may have different indicator templates. That is, each project may optionally use different evaluation indicators. Because data security is crucial in some business scenarios, data from different projects is isolated to avoid data coupling. The indicator templates may be publicly released or licensed for use.

[0060] In some embodiments, users can also directly input the first evaluation object information and the first evaluation subject information in natural language in the evaluation object input box and the evaluation subject input box. The evaluation system can automatically parse out the evaluation object and the evaluation subject. The evaluation subject can be a user-defined evaluation subject.

[0061] In some embodiments, users can also directly input pseudocodes for the first evaluation object information and the first evaluation subject information in the evaluation object input box and the evaluation subject input box. The evaluation system can automatically parse out the evaluation object and the evaluation subject, and the evaluation subject can be a user-defined evaluation subject.

[0062] In some embodiments, users can also directly input Structured Query Language (SQL) statements for the first evaluation object information and the first evaluation subject information in the evaluation object input box and the evaluation subject input box. The evaluation system can automatically parse out the evaluation object and the evaluation subject, and the evaluation subject can be a user-defined evaluation subject.

[0063] S220. If the evaluation object corresponding to the first evaluation object information meets the evaluation admission rules, select the first evaluation indicator of the target indicator template from the indicator library according to the template-indicator mapping relationship. The first evaluation indicator includes the second evaluation indicators corresponding to multiple business types, and the multiple business types include the first target business type.

[0064] In some embodiments, the evaluation admission rules are pre-set according to actual needs. Only when the evaluation object corresponding to the first evaluation object information meets the evaluation admission rules can the evaluation object corresponding to the first evaluation object information be evaluated, and the evaluation result determined.

[0065] In one example, the evaluation admission rule includes the first evaluation object information being an entity. If the first evaluation object information is action information, it means that the user-inputted first evaluation object information does not meet the evaluation admission rule, and the evaluation system automatically generates a warning, notifying the user that the first evaluation object information entered is non-compliant.

[0066] In another example, when the evaluation object corresponding to the first evaluation object information is a device (product), the evaluation admission rules include that the device assembly progress is greater than 90% and the employee task completion rate is less than 50% and they are not allowed to participate in the evaluation.

[0067] In some embodiments, if the evaluation object corresponding to the first evaluation object information meets the evaluation admission rules, a template-indicator mapping relationship is obtained. This template-indicator mapping relationship includes the mapping relationship between indicator templates and evaluation indicators. Based on the template-indicator mapping relationship, a first evaluation indicator corresponding to the target indicator template input by the user is selected from the indicator library. The first evaluation indicator includes second evaluation indicators corresponding to multiple business types, including a first target business type. These business types include employee rating, product evaluation, deposit business, transfer business, storage performance, etc. Different business types may have different optional evaluation indicators.

[0068] It should be noted that the evaluation indicators are marked with indicator template identifiers.

[0069] S230. Based on the first evaluation object information and the first evaluation subject information, select multiple optional evaluation indicators from the second evaluation indicators corresponding to the first target business type, and display the multiple optional evaluation indicators in the configuration interface.

[0070] In some embodiments, evaluation indicators can only be used by specific evaluation objects and specific evaluation subjects. For example, when the evaluation object information corresponds to a product, only product evaluation indicators can be used. When the evaluation subject information corresponds to organization A, only organization A's evaluation indicators can be used. When the first evaluation subject information corresponds to multiple evaluation subjects, the selectable evaluation indicators can only be those that can be used by all these multiple evaluation subjects. Here, the fact that the first evaluation subject information corresponds to multiple evaluation subjects means that the user has defined the evaluation subject to which the evaluation object belongs.

[0071] It should be noted that the evaluation indicators are marked with object identifiers and subject identifiers.

[0072] In some embodiments, based on the first evaluation object information and the first evaluation subject information input by the user, multiple optional evaluation indicators are selected from the second evaluation indicators corresponding to the first target business type, and multiple optional evaluation indicators are displayed in the configuration interface so that the user can select the required evaluation indicators in the configuration interface.

[0073] S240, receiving a fourth input from the user selecting target optional evaluation indicators, a fifth input from the weight information of the target optional evaluation indicators, and a sixth input from the first target evaluation rule, wherein the multiple optional evaluation indicators include the target optional evaluation indicators.

[0074] In some embodiments, a user can select a target optional evaluation indicator from a plurality of optional evaluation indicators and configure weights for the target optional evaluation indicator. The system receives a fourth input indicating the user's selection of the target optional evaluation indicator, a fifth input indicating the weights of the target optional evaluation indicator, and a sixth input indicating a first target evaluation rule. The plurality of optional evaluation indicators includes the target optional evaluation indicator.

[0075] The target optional evaluation indicators include at least one evaluation indicator. When the target optional evaluation indicators include only one indicator, weights can be configured for each indicator to generate the evaluation results the user wants to see. For example, the weight of the risk indicator can be set to 2 to strengthen the evaluation results.

[0076] In some embodiments, the weight information of the target optional evaluation index can be in natural language. For example, the weight information could be "the weight of target optional evaluation index a is set to 2".

[0077] In some embodiments, the weight information of the target optional evaluation index can be a value entered in the weight input box of the target optional evaluation index.

[0078] In some embodiments, the first target evaluation rule can be an evaluation rule selected from an evaluation rule library, or it can be an evaluation rule directly entered by the user in the configuration interface according to their needs. When the first target evaluation rule is selected from the evaluation rule library, multiple preset evaluation rules will be displayed in the corresponding position of the evaluation rule box in the configuration interface for the user to choose from. When the first target evaluation rule is directly entered by the user in the configuration interface, the evaluation rule box in the configuration interface can directly receive the evaluation rule entered by the user.

[0079] In some embodiments, the first target evaluation rule is a rule for performing various calculations on the evaluation index, and its specific calculation formula is not specifically limited here.

[0080] In some embodiments, the weight information of the target optional evaluation indicators needs to meet the weight setting rules. Only when the weight information of the target optional evaluation indicators meets the weight setting rules can the evaluation result of the evaluation object corresponding to the first evaluation object information within the evaluation subject corresponding to the first evaluation subject information be determined using the first target evaluation rules, based on the target optional evaluation indicators and the weight information. For example, if the weight of a risk indicator is set to 0.1, but the weight setting rules for risk indicators stipulate that the weight of a risk indicator can only be greater than 1, then the weight information of the target optional evaluation indicators does not meet the weight setting rules.

[0081] Understandably, if the weights of risk indicators are set too low during risk assessment, the assessed object will be classified as low-risk, leading to a series of safety issues. For example, setting the weight of device damage risk to 0.1 will fail to detect impending device damage.

[0082] In some embodiments, the first target evaluation rule needs to satisfy the evaluation admission rule, which is set in advance according to the requirements.

[0083] In one example, the evaluation admission rules include the following: when the evaluation object is a product, the first objective evaluation rule can only be an evaluation rule for product-related evaluation indicators. If, when the evaluation object is a product, the first objective evaluation rule is an institutional evaluation rule, then the first objective evaluation rule does not meet the evaluation admission rules, and the evaluation result cannot be determined.

[0084] In some embodiments, after obtaining the evaluation results, the evaluation results can also be written into the results dataset for user use.

[0085] In some embodiments, the configuration interface of the evaluation system also provides personalized input boxes.

[0086] The embodiments of this application allow for flexible adjustments to the evaluation objects, evaluation indicators, the evaluation subject to which the evaluation objects belong, and evaluation rules. Furthermore, rule checks are implemented to prevent system abuse. This approach balances flexibility and standardization while adapting to multiple business scenarios, significantly improving the flexibility of using the evaluation system for object evaluation.

[0087] In some embodiments, the first evaluation subject information includes descriptive information about the evaluation subject to which the first evaluation object information belongs; such as... Figure 4 As shown, the above S230 may specifically include S231 to S234.

[0088] S231. Using a large model, based on the semantic similarity between the first evaluation object information and multiple preset evaluation object information in the knowledge base, the first evaluation object information is matched with multiple preset evaluation object information in the knowledge base to obtain the second evaluation object information corresponding to the first evaluation object information.

[0089] In some embodiments, since the first evaluation object information input by the user may not be a standard representation of the evaluation object, a large model is used to match the first evaluation object information with multiple preset evaluation object information in the knowledge base to obtain the second evaluation object information corresponding to the first evaluation object information, which is then transformed into a standard representation of the evaluation object. It is understood that the large model can be a pre-trained model capable of automatically recognizing the core intent of natural language instructions.

[0090] In this embodiment, the first evaluation object information is matched with multiple preset evaluation object information in the knowledge base to obtain the second evaluation object information corresponding to the first evaluation object information, including: Calculate the similarity between the first evaluation object information and multiple preset evaluation object information in the knowledge base, and select the second evaluation object information with the highest similarity to the first evaluation object information from the multiple preset evaluation object information.

[0091] In some embodiments, the evaluation system can evaluate preset evaluation objects. Using a large model, the information of a first evaluation object is matched with multiple preset evaluation object information in a knowledge base to obtain second evaluation object information corresponding to the first evaluation object information. In this case, the first evaluation object information input by the user can be evaluated.

[0092] S232. Using a large model, the descriptive information is matched with multiple preset evaluation subject information in the knowledge base to obtain at least one second evaluation subject information corresponding to the first evaluation subject information.

[0093] In some embodiments, since the first evaluation subject information input by the user may not be a standard description of the evaluation subject, a large model is used to match the first evaluation subject information with multiple preset evaluation subject information in the knowledge base to obtain the second evaluation subject information corresponding to the first evaluation subject information, which is then transformed into a standard description of the evaluation subject. The first evaluation subject information includes descriptive information about the evaluation subject to which the evaluation object belongs, corresponding to the first evaluation object information.

[0094] In this embodiment, the first evaluation subject information is matched with multiple preset evaluation subject information in the knowledge base to obtain the second evaluation subject information corresponding to the first evaluation subject information, including: Calculate the similarity between the first evaluation subject information and multiple preset evaluation subject information in the knowledge base, and select the second evaluation subject information with the highest similarity to the first evaluation subject information from the multiple preset evaluation subject information.

[0095] In some embodiments, the description information of the evaluation subject to which the first evaluation object information belongs can be the description information of multiple evaluation subjects. Using a large model, the description information is matched with multiple preset evaluation subject information in the knowledge base to obtain at least one second evaluation subject information corresponding to the first evaluation subject information.

[0096] S233. Based on the mapping relationship between objects and indicators, select the third evaluation indicator corresponding to the information of the second evaluation object from the second evaluation indicators corresponding to the first target business type.

[0097] In some embodiments, after determining the evaluation object using a large model, a third evaluation indicator corresponding to the second evaluation object information can be selected from the second evaluation indicators corresponding to the first target business type, based on the object-indicator mapping relationship. The object-indicator mapping relationship is a preset mapping relationship between the evaluation object information and the evaluation indicators in the indicator library.

[0098] S234. Based on the mapping relationship between the subject and the indicator, select multiple optional evaluation indicators from the third evaluation indicators that correspond to at least one second evaluation subject information.

[0099] In some embodiments, after determining the evaluation subject using a large model, multiple optional evaluation indicators corresponding to the second evaluation subject information can be selected from the third evaluation indicators based on the subject-indicator mapping relationship. The subject-indicator mapping relationship is a preset mapping relationship between the evaluation subject information and the evaluation indicators in the indicator library.

[0100] This application's embodiments support configuring evaluation objects and the evaluation subjects to which the evaluation objects belong using natural language, and utilize a large model to filter out suitable evaluation indicators for users to choose from. The evaluation scope of the evaluation object is configurable, not within a pre-defined fixed evaluation subject, thus improving evaluation flexibility.

[0101] In some embodiments, a knowledge base is constructed prior to S230 above. The knowledge base includes preset evaluation object information and preset evaluation subject information. The knowledge base may also include professional terminology related to the evaluation scenario, such as weighted efficiency score, benchmark score, and defect rate threshold. The knowledge base may also include preset evaluation indicators, such as completion rate, achievement rate, and fulfillment rate. The knowledge base may also include context association rules, such as quarterly numerical cycles and quarterly related parameters. By constructing the knowledge base, the problem of semantic ambiguity in general natural language parsing in specific scenarios is solved, effectively addressing key technical issues such as ambiguity resolution and accurate mapping between semantics and computational logic in natural language. For example, these specific scenarios include financial scenarios and device manufacturing scenarios.

[0102] In some embodiments, S233 may specifically include: Obtain the attribute information corresponding to the information of the second evaluation object; Based on the mapping relationship between object attributes and indicators, a third evaluation indicator corresponding to the attribute information is selected from the second evaluation indicators corresponding to the first target business type.

[0103] In this embodiment, the attribute information is a unique attribute of the evaluation object, such as an attribute specific to a certain type of product.

[0104] In one example, if Product 1, Product 2, and Product 3 are hardware products manufactured by the same machine, then the attribute information of Product 1, Product 2, and Product 3 is the same.

[0105] This application embodiment filters evaluation indicators based on the attributes of the evaluation object, distinguishing the evaluation indicators by the attributes of the evaluation object, avoiding the fragmentation caused by the distinction of the evaluation object, and providing users with accurate evaluation indicators while reducing the maintenance cost of the evaluation indicators.

[0106] In some embodiments, receiving a sixth input to the first target evaluation rule in S240 above includes: Based on the mapping relationship between business type and rule, select the first evaluation rule corresponding to the first target business type from the preset evaluation rules; Based on the mapping relationship between object attributes and rules, select the second evaluation rule corresponding to the information of the second evaluation object from the first evaluation rule; Based on the mapping relationship between the subject and the rule, select multiple optional evaluation rules from the second evaluation rules that correspond to at least one second evaluation subject information; Receive the sixth input from the user, indicating their selection of the first objective evaluation rule.

[0107] It should be noted that the evaluation system includes an evaluation indicator library, from which multiple optional evaluation rules can be selected for users to choose from.

[0108] In one example, the first objective evaluation rule includes a formula for calculating the resource import / export completion rate.

[0109] In another example, the first objective evaluation rule includes a formula for calculating the defect rate.

[0110] The first objective evaluation rule in this application embodiment can be selected by the user from multiple evaluation rules recommended by the evaluation system. The evaluation system can filter multiple suitable evaluation rules based on user input, allowing users to choose flexibly and improving user experience.

[0111] In some embodiments, the evaluation system further includes a knowledge base; the first objective evaluation rule includes a first basic evaluation indicator; such as Figure 5 As shown, the process of generating evaluation results includes S251~S253.

[0112] S251. Using the large model, the basic evaluation indicators are matched with the preset evaluation indicators in the knowledge base to obtain the second basic evaluation indicator corresponding to the first basic evaluation indicator.

[0113] In some embodiments, the first target evaluation rule can also be a user-defined evaluation rule, which can be input via natural language. The first basic evaluation indicator can be the calculation result of evaluation rules in the evaluation rule base, such as the defect rate. The first target evaluation rule can be a calculation formula that determines the evaluation result through the first basic evaluation indicator. For example, the first target evaluation rule is "Weighted efficiency score = (resource import completion rate × 60% + resource export completion rate × 40%) × (1 - defect rate × 0.5)". The weighted efficiency score is the final evaluation result.

[0114] It should be noted that the first objective evaluation rule can be input via natural language, without the need to write SQL or code, thus improving the user experience.

[0115] In some embodiments, user-defined evaluation rules are stored in an evaluation rule library.

[0116] In some embodiments, the evaluation system automatically parses the formula logic of the first objective evaluation rule and associates it with preset evaluation indicators in the knowledge base. That is, using a large model, the basic evaluation indicators are matched with the preset evaluation indicators in the knowledge base to obtain a second basic evaluation indicator corresponding to the first basic evaluation indicator. The second basic evaluation indicator is the evaluation indicator with the highest similarity to the first basic evaluation indicator.

[0117] S252. Obtain the evaluation rules corresponding to the second basic evaluation index.

[0118] In some embodiments, the evaluation rule base includes evaluation rules corresponding to the second basic evaluation index. Based on the second basic evaluation index, the evaluation rules corresponding to the second basic evaluation index can be directly obtained from the evaluation rule base.

[0119] In one example, evaluation rules are used to obtain resource import completion rate, resource export completion rate, and defect rate.

[0120] S253. Based on the target optional evaluation indicators and weight information, and using the evaluation rules corresponding to the first target evaluation rules and the second basic evaluation indicators, determine the evaluation result of the evaluation object corresponding to the first evaluation object information within the evaluation subject corresponding to the first evaluation subject information.

[0121] Understandably, it is necessary to first calculate the value of the second basic evaluation indicator based on the target optional evaluation indicators and weight information before determining the evaluation result of the evaluation object corresponding to the first evaluation object information within the evaluation subject corresponding to the first evaluation subject information according to the first target evaluation rules.

[0122] In some embodiments, the evaluation indicators corresponding to the evaluation results of the first objective evaluation rule are used as derived evaluation indicators and stored in the indicator library. It should be noted that the derived evaluation indicators are the parent indicators of the second basic evaluation indicators, and each evaluation indicator can have its own unique number.

[0123] This application embodiment can receive user configuration of evaluation rules, match basic evaluation indicators through a knowledge base, and store evaluation rules for derived evaluation indicators, i.e., the first target evaluation rule, so that the coverage of evaluation indicator processing and calculation is broader.

[0124] In some embodiments, the evaluation system also includes an indicator requirement library; such as Figure 6 As shown, after S230 above, the method may also include S310 to S320.

[0125] S310: Receive new evaluation indicators input by the user.

[0126] In some embodiments, if the user's desired evaluation indicator is not among the optional evaluation indicators, the user can enter a new evaluation indicator through the evaluation indicator input box. The evaluation indicator input box receives the newly added evaluation indicator entered by the user.

[0127] S320. Save the newly added evaluation indicators to the indicator requirement library.

[0128] In some embodiments, newly added evaluation indicators are saved to an indicator requirement library for technical personnel to view. Upon receiving input requesting to view the indicator requirement library, the newly added evaluation indicators in the library are displayed.

[0129] In some embodiments, if a new evaluation indicator is detected in the indicator library, the new evaluation indicator is deleted from the indicator requirement library.

[0130] The evaluation indicators in this application embodiment can be dynamically updated based on user input. Based on the indicator requirement library, technicians can store new evaluation indicators to further improve the flexibility of object evaluation.

[0131] In some embodiments, the method may further include: Warning information is generated if the weight information of the target optional evaluation indicators does not meet the weight setting rules, or the object corresponding to the first evaluation object information does not meet the evaluation admission rules, or the first target evaluation rules do not match the first evaluation object information or the first evaluation subject information, or the first evaluation object information and the first evaluation subject information do not match.

[0132] It should be noted that a mismatch between the information of the first evaluation object and the information of the first evaluation subject means that the evaluation object corresponding to the information of the first evaluation object cannot be attributed to the evaluation subject corresponding to the information of the first evaluation subject. For example, personnel cannot be attributed to products.

[0133] In some embodiments, weight setting rules are stored in a compliance rule base. Evaluation admission rules are stored in a business rule base. The compliance rule base and the business rule base can be built-in rule bases of the evaluation system.

[0134] In some embodiments, a warning message is generated when the weight information of the target optional evaluation indicators does not meet the weight setting rules, or the object corresponding to the first evaluation object information does not meet the evaluation admission rules, or the first target evaluation rules do not match the first evaluation object information or the first evaluation subject information, or the first evaluation object information and the first evaluation subject information do not match. The warning message includes the warning reason.

[0135] This application embodiment generates a warning message when a user performs non-compliant operations, so as to prompt the user to operate in a standardized manner.

[0136] In some embodiments, after S206 above, the method further includes: Identify the modified target historical evaluation instance snapshot from the historical evaluation instance snapshots; Obtain information on the modification process of historical evaluation instance snapshots of the target; Displays information about the modification process.

[0137] This application embodiment provides users with modification strategies by displaying modification process information, thereby improving the efficiency of system configuration.

[0138] In some embodiments, the method further includes: Receive user-inputted evaluation description information; Identify the second target business type in the evaluation description information; Based on the mapping relationship between business type and rule, a recommended evaluation rule corresponding to the second target business type is selected from the preset evaluation rules, and a recommendation reason is generated.

[0139] This application's embodiments can handle heterogeneous evaluation objects such as organizations, employees, and customers through a single framework, avoiding redundant development. Business personnel do not need to rely on technical personnel to modify code and adjust indicator rules, improving system configuration speed. Through a visual configuration engine, it supports three parameter definition modes: natural language, pseudocode, and SQL, and generates executable calculation instruction sets, improving flexibility.

[0140] Based on the system configuration anomaly location method provided in the above embodiments, this application also provides a specific implementation of a system configuration anomaly location device. Please refer to the following embodiments.

[0141] See Figure 7 The system configuration anomaly locating device 400 provided in this application embodiment includes: The receiving module 410 is used to receive the configuration input from the user in the system's configuration interface for the first evaluation object information and the evaluation process variable information of the first evaluation object information; The encapsulation module 420 is used to encapsulate the first evaluation object information and the evaluation process variable information of the first evaluation object information into an executable computation task descriptor. The generation module 430 is used to use the rule execution engine to extract the original indicator data corresponding to the first evaluation object information from the data source in real time according to the descriptor, call the calculation function or script corresponding to the evaluation process variable information, generate the evaluation result, and generate an evaluation instance snapshot. The evaluation instance snapshot records the input information, process information, intermediate evaluation results and the evaluation result. Display module 440 is used to display the evaluation results and multi-dimensional feedback channels on the result display interface. The multi-dimensional feedback channels include natural language feedback boxes and structured feedback components. The search module 450 is used to search for historical evaluation instance snapshots whose similarity to the content recorded in the evaluation instance snapshot is greater than a preset value when receiving feedback information input by the user through the multi-dimensional feedback channel. The determination module 460 is used to determine the location of the anomaly point in the current configuration by comparing the historical evaluation instance snapshot with the evaluation instance snapshot based on the feedback information.

[0142] In some embodiments, the receiving module 410 may specifically include: The receiving unit is used to receive the user's first input (selecting the target indicator template), second input (selecting the first evaluation object information and the first evaluation subject information) and third input (selecting the first target business type) in the system's configuration interface. The selection unit is used to select the first evaluation indicator of the target indicator template from the indicator library according to the template-indicator mapping relationship when the evaluation object corresponding to the first evaluation object information meets the evaluation admission rules. The first evaluation indicator includes the second evaluation indicators corresponding to multiple business types, and the multiple business types include the first target business type. The selection unit is also used to select multiple optional evaluation indicators from the second evaluation indicators corresponding to the first target business type based on the first evaluation object information and the first evaluation subject information, and to display the multiple optional evaluation indicators in the configuration interface. The receiving unit is also used to receive a fourth input from the user selecting a target optional evaluation indicator, a fifth input of the weight information of the target optional evaluation indicator, and a sixth input of the first target evaluation rule, wherein the multiple optional evaluation indicators include the target optional evaluation indicator.

[0143] In some embodiments, the first evaluation subject information includes descriptive information about the evaluation subject to which the first evaluation object information belongs; the selection unit may specifically be used for: Using a large model, based on the semantic similarity between the first evaluation object information and multiple preset evaluation object information in the knowledge base, the first evaluation object information is matched with multiple preset evaluation object information in the knowledge base to obtain the second evaluation object information corresponding to the first evaluation object information. Using a large model, the descriptive information is matched with multiple preset evaluation subject information in the knowledge base to obtain at least one second evaluation subject information corresponding to the first evaluation subject information; Based on the mapping relationship between objects and indicators, select the third evaluation indicator corresponding to the information of the second evaluation object from the second evaluation indicators corresponding to the first target business type; Based on the mapping relationship between the subject and the indicator, select multiple optional evaluation indicators from the third evaluation indicators that correspond to at least one second evaluation subject information.

[0144] In some embodiments, the selection unit may specifically be used for: Obtain the attribute information corresponding to the information of the second evaluation object; Based on the mapping relationship between object attributes and indicators, a third evaluation indicator corresponding to the attribute information is selected from the second evaluation indicators corresponding to the first target business type.

[0145] In some embodiments, the receiving unit may specifically be used for: Based on the mapping relationship between business type and rule, select the first evaluation rule corresponding to the first target business type from the preset evaluation rules; Based on the mapping relationship between object attributes and rules, select the second evaluation rule corresponding to the information of the second evaluation object from the first evaluation rule; Based on the mapping relationship between the subject and the rule, select multiple optional evaluation rules from the second evaluation rules that correspond to at least one second evaluation subject information; Receive the sixth input from the user, indicating their selection of the first objective evaluation rule.

[0146] In some embodiments, the device 400 may further include: The receiving module 410 is used to receive new evaluation indicators input by the user after multiple optional evaluation indicators are displayed in the configuration interface. The save module is used to save newly added evaluation indicators to the indicator requirement library.

[0147] In some embodiments, the device 400 may further include: The generation module is used to generate warning information when the weight information of the target optional evaluation indicators does not meet the weight setting rules, or the object corresponding to the first evaluation object information does not meet the evaluation admission rules, or the first target evaluation rules do not match the first evaluation object information or the first evaluation subject information, or the first evaluation object information and the first evaluation subject information do not match.

[0148] In some embodiments, the device 400 may further include: The determination module 460 is also used to determine the modified target historical evaluation instance snapshot in the historical evaluation instance snapshot after comparing the historical evaluation instance snapshot and the evaluation instance snapshot based on the feedback information and determining the location of the anomaly point in the current configuration. The acquisition module is used to acquire information about the modification process of historical evaluation instance snapshots of the target. Display module 440 is also used to display modification process information.

[0149] Each module of the system configuration anomaly locating device provided in this application embodiment can realize the functions of each step of the system configuration anomaly locating method provided above, and can achieve its corresponding technical effects. For the sake of brevity, it will not be described in detail here.

[0150] Based on the same inventive concept, embodiments of this application also provide an electronic device.

[0151] Figure 8 A schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application is shown.

[0152] An electronic device may include a processor 501 and a memory 502 storing computer program instructions.

[0153] Specifically, the processor 501 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0154] Memory 502 may include mass storage for data or instructions. For example, and not limitingly, memory 502 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 502 may include removable or non-removable (or fixed) media. Where appropriate, memory 502 may be internal or external to an electronic device. In a particular embodiment, memory 502 is a non-volatile solid-state memory.

[0155] Memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the methods according to one aspect of this disclosure.

[0156] The processor 501 reads and executes computer program instructions stored in the memory 502 to implement any of the system configuration anomaly location methods in the above embodiments.

[0157] In one example, the electronic device may also include a communication interface 503 and a bus 510. Wherein, as... Figure 8 As shown, the processor 501, memory 502, and communication interface 503 are connected through bus 510 and complete communication with each other.

[0158] The communication interface 503 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0159] Bus 510 includes hardware, software, or both, that couples components of an electronic device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Linear Predictive Coding (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (Peripheral Component Interconnect-X, PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VESA Local Bus, VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 510 may include one or more buses. Although specific buses are described and illustrated in the embodiments of this application, this application contemplates any suitable bus or interconnection. The electronic device can execute the system configuration anomaly localization method described in the embodiments of the present invention, thereby implementing the system configuration anomaly localization method described above.

[0160] Furthermore, in conjunction with the system configuration anomaly localization method described in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the system configuration anomaly localization methods described in the above embodiments.

[0161] This application also provides a computer program product in which the instructions, when executed by the processor of an electronic device, cause the electronic device to perform various processes implementing any of the above-described system configuration anomaly localization method embodiments.

[0162] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0163] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, read-only memory (ROM), flash memory, erasable read-only memory (EROM), floppy disks, compact disc read-only memory (CD-ROM), optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0164] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0165] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0166] The above are merely specific embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for locating system configuration anomalies, characterized in that, include: Receive user configuration inputs in the system's configuration interface for the first evaluation object information and the evaluation process variable information of the first evaluation object information; The information of the first evaluation object and the evaluation process variable information of the first evaluation object are encapsulated into an executable computation task descriptor; Using the rule execution engine, based on the descriptor, the original indicator data corresponding to the first evaluation object information is extracted from the data source in real time, the calculation function or script corresponding to the evaluation process variable information is called, the evaluation result is generated, and an evaluation instance snapshot is generated. The evaluation instance snapshot records the input information, process information, intermediate evaluation results and the evaluation result. The evaluation results and multidimensional feedback channels are displayed on the results display interface. The multidimensional feedback channels include natural language feedback boxes and structured feedback components. Upon receiving feedback information input by the user through the multi-dimensional feedback channel, search for historical evaluation instance snapshots whose similarity to the content recorded in the evaluation instance snapshot is greater than a preset value; Based on the feedback information, the location of the anomaly point in this configuration is determined by comparing the historical evaluation instance snapshot with the evaluation instance snapshot.

2. The method according to claim 1, characterized in that, The step of receiving user configuration input in the system's configuration interface for the first evaluation object information and the evaluation process variable information of the first evaluation object information includes: The system receives the user's first input (selecting the target indicator template), second input (selecting the first evaluation object information and the first evaluation subject information) and third input (selecting the first target business type) from the system's configuration interface. If the evaluation object corresponding to the first evaluation object information meets the evaluation admission rules, the first evaluation indicator of the target indicator template is selected from the indicator library according to the template and indicator mapping relationship. The first evaluation indicator includes second evaluation indicators corresponding to multiple business types, and the multiple business types include the first target business type. Based on the first evaluation object information and the first evaluation subject information, select multiple optional evaluation indicators from the second evaluation indicators corresponding to the first target business type, and display the multiple optional evaluation indicators in the configuration interface; The system receives a fourth input from the user selecting a target optional evaluation indicator, a fifth input from the user regarding the weight information of the target optional evaluation indicator, and a sixth input from the user regarding a first target evaluation rule. The plurality of optional evaluation indicators include the target optional evaluation indicator.

3. The method according to claim 2, characterized in that, The first evaluation subject information includes descriptive information about the evaluation subject to which the first evaluation object information belongs; the step of selecting multiple optional evaluation indicators from the second evaluation indicators corresponding to the first target business type based on the first evaluation object information and the first evaluation subject information includes: Using a large model, based on the semantic similarity between the first evaluation object information and multiple preset evaluation object information in the knowledge base, the first evaluation object information is matched with multiple preset evaluation object information in the knowledge base to obtain the second evaluation object information corresponding to the first evaluation object information. Using the large model, the description information is matched with multiple preset evaluation subject information in the knowledge base to obtain at least one second evaluation subject information corresponding to the first evaluation subject information; Based on the mapping relationship between objects and indicators, a third evaluation indicator corresponding to the second evaluation object information is selected from the second evaluation indicators corresponding to the first target business type. Based on the mapping relationship between the subject and the indicator, multiple optional evaluation indicators corresponding to the at least one second evaluation subject information are selected from the third evaluation indicators.

4. The method according to claim 3, characterized in that, The step of selecting a third evaluation indicator corresponding to the second evaluation object information from the second evaluation indicators corresponding to the first target business type based on the object-indicator mapping relationship includes: Obtain the attribute information corresponding to the second evaluation object information; Based on the mapping relationship between object attributes and indicators, a third evaluation indicator corresponding to the attribute information is selected from the second evaluation indicators corresponding to the first target business type.

5. The method according to claim 4, characterized in that, The sixth input to the first target evaluation rule is received, including: Based on the mapping relationship between business type and rule, select the first evaluation rule corresponding to the first target business type from the preset evaluation rules; Based on the mapping relationship between object attributes and rules, select the second evaluation rule corresponding to the second evaluation object information from the first evaluation rule; Based on the mapping relationship between the subject and the rule, select multiple optional evaluation rules from the second evaluation rules that correspond to the at least one second evaluation subject information; Receive the sixth input from the user's selection of the first target evaluation rule.

6. The method according to claim 2, characterized in that, After displaying the multiple optional evaluation metrics in the configuration interface, the method further includes: Receive the new evaluation metrics input by the user; Save the newly added evaluation indicators to the indicator requirement library.

7. The method according to any one of claims 2-6, characterized in that, The method further includes: If the weight information of the target optional evaluation indicators does not meet the weight setting rules, or the object corresponding to the first evaluation object information does not meet the evaluation admission rules, or the first target evaluation rules do not match the first evaluation object information or the first evaluation subject information, or the first evaluation object information and the first evaluation subject information do not match, an early warning message is generated.

8. The method according to claim 1, characterized in that, After determining the location of the anomaly point in the current configuration by comparing the historical evaluation instance snapshot with the current evaluation instance snapshot based on the feedback information, the method further includes: Identify the modified target historical evaluation instance snapshot in the historical evaluation instance snapshot; Obtain information on the modification process of the target historical evaluation instance snapshot; Display the modification process information.

9. A positioning device for system configuration anomalies, characterized in that, include: The receiving module is used to receive the configuration input from the user in the system's configuration interface for the first evaluation object information and the evaluation process variable information of the first evaluation object information; The encapsulation module is used to encapsulate the first evaluation object information and the evaluation process variable information of the first evaluation object information into an executable computation task descriptor. The generation module is used to utilize the rule execution engine to extract the original indicator data corresponding to the first evaluation object information from the data source in real time according to the descriptor, call the calculation function or script corresponding to the evaluation process variable information, generate the evaluation result, and generate an evaluation instance snapshot. The evaluation instance snapshot records the input information, process information, intermediate evaluation results and the evaluation result. The display module is used to display the evaluation results and multi-dimensional feedback channels on the results display interface. The multi-dimensional feedback channels include natural language feedback boxes and structured feedback components. The search module is used to search for historical evaluation instance snapshots whose similarity to the content recorded in the evaluation instance snapshot is greater than a preset value when the user inputs feedback information through the multi-dimensional feedback channel. The determination module is used to determine the location of the anomaly point in the current configuration by comparing the historical evaluation instance snapshot with the evaluation instance snapshot based on the feedback information.

10. An electronic device, characterized in that, The device includes: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, it implements the system configuration anomaly location method as described in any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the system configuration anomaly localization method as described in any one of claims 1-8.

12. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device is able to perform the system configuration anomaly localization method as described in any one of claims 1-8.

Images