Data index diagnosis method and device, equipment, medium and program product

Abstract

The invention relates to the technical field of data diagnosis, and discloses a data index diagnosis method and device, equipment, a medium and a program product.The data index diagnosis method and device respond to a diagnosis instruction for a target index, an attribution diagnosis model of the target index is obtained, and the attribution diagnosis model is obtained based on mapping of an index attribution model and an index diagnosis model; the index attribution model is used for representing a hierarchical relationship and a logical relationship among the indexes, and the index diagnosis model is used for representing a diagnosis rule of each index; determining a target attribution model corresponding to a target index based on positioning of the target index in the attribution diagnosis model; performing attribution analysis on the target attribution model to obtain an attribution result of the target index; and performing diagnosis rule matching based on the attribution result to obtain a diagnosis result of the target index. According to the method and the device, the diagnosis result of the index transaction can be obtained through rule matching, so that the problem root of the index transaction can be quickly positioned, and the accuracy and the efficiency of analysis and diagnosis of the index transaction are improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of data diagnosis, and in particular to a diagnosis method, device, equipment, medium and program product for data indicators. Background Art

[0002] In the big data indicator system, it is necessary to promptly discover, identify and attribute indicator changes. Since an indicator is usually composed of multiple levels of sub-indicators, when attributing and diagnosing indicator changes, it is often necessary to drill down to the bottom-level sub-indicators for analysis and diagnosis, which makes it difficult to locate the indicator that causes the indicator change at the root. Summary of the invention

[0003] In view of this, the present disclosure provides a data indicator diagnosis method, device, equipment, medium and program product to solve the problem of difficulty in locating the root cause of the problem in the analysis and diagnosis of indicator changes.

[0004] In a first aspect, the present disclosure provides a method for diagnosing a data indicator, the method comprising:

[0005] In response to a diagnostic instruction for a target indicator, an attribution diagnostic model of the target indicator is obtained, the attribution diagnostic model is obtained by mapping the indicator attribution model and the indicator diagnostic model, the indicator attribution model is used to characterize the hierarchical relationship and logical relationship between various indicators, the indicator diagnostic model is used to characterize the diagnostic rules of various indicators, and the indicator attribution model and the indicator diagnostic model have the same node structure;

[0006] Based on the positioning of the target indicator in the attribution diagnosis model, determine the target attribution model corresponding to the target indicator;

[0007] Perform attribution analysis on the target attribution model to obtain the attribution results of the target indicators;

[0008] Based on the attribution results, the diagnostic rules are matched to obtain the diagnostic results of the target indicators.

[0009] In a second aspect, the present disclosure provides a diagnostic device for data indicators, the device comprising:

[0010] A model acquisition module, for obtaining an attribution diagnosis model of a target indicator in response to a diagnosis instruction for the target indicator, wherein the attribution diagnosis model is obtained by mapping the indicator attribution model and the indicator diagnosis model, wherein the indicator attribution model is used to characterize the hierarchical relationship and logical relationship between various indicators, and the indicator diagnosis model is used to characterize the diagnosis rules of various indicators, and the indicator attribution model and the indicator diagnosis model have the same node structure;

[0011] An indicator positioning module, used to determine a target attribution model corresponding to the target indicator based on the positioning of the target indicator in the attribution diagnosis model;

[0012] The attribution analysis module is used to perform attribution analysis on the target attribution model to obtain the attribution results of the target indicators;

[0013] The rule matching module is used to match the diagnostic rules based on the attribution results to obtain the diagnostic results of the target indicators.

[0014] In a third aspect, the present disclosure provides an electronic device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to execute the diagnostic method of data indicators of the first aspect or any corresponding embodiment thereof.

[0015] In a fourth aspect, the present disclosure provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for diagnosing data indicators of the first aspect or any corresponding embodiment thereof.

[0016] In a fifth aspect, the present disclosure provides a computer program product, including computer instructions, which are used to enable a computer to execute the method for diagnosing data indicators of the first aspect or any corresponding embodiment thereof.

[0017] The diagnostic method for data indicators provided by the embodiment of the present disclosure obtains an attribution diagnostic model of the target indicator in response to a diagnostic instruction for the target indicator, wherein the attribution diagnostic model is obtained by mapping the indicator attribution model and the indicator diagnostic model, the indicator attribution model is used to characterize the hierarchical relationship and logical relationship between each indicator, the indicator diagnostic model is used to characterize the diagnostic rules of each indicator, and the indicator attribution model and the indicator diagnostic model have the same node structure, so that the hierarchical structure between the indicators and the diagnostic rules of the indicators can be flexibly configured to achieve flexible configuration of indicator change judgment; at the same time, based on the positioning of the target indicator in the attribution diagnostic model, the target attribution model corresponding to the target indicator is determined, and the target attribution model is attribution parsed to obtain the attribution result of the target indicator, so that the multi-level indicators can be positioned and disassembled to achieve rapid attribution of indicator changes; and based on the attribution result, the diagnostic rule is matched to obtain the diagnostic result of the target indicator, so that the diagnostic result of the indicator change can be obtained through rule matching, thereby quickly locating the root cause of the indicator change problem and improving the accuracy and efficiency of indicator change analysis and diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific embodiments of the present disclosure or the technical solutions in the related technologies, the drawings required for use in the specific embodiments or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 is a schematic diagram of an application of a method for diagnosing data indicators according to an embodiment of the present disclosure;

[0020] Figure 2 is a flow chart of a method for diagnosing data indicators according to an embodiment of the present disclosure;

[0021] Figure 3 is a schematic diagram of generating an attribution diagnosis model in a method for diagnosing data indicators according to an embodiment of the present disclosure;

[0022] Figure 4 is a schematic diagram of a data storage layer in a method for diagnosing data indicators according to an embodiment of the present disclosure;

[0023] Figure 5 is a flow chart of another method for diagnosing data indicators according to an embodiment of the present disclosure;

[0024] Figure 6 is a schematic diagram of attribution analysis in a method for diagnosing data indicators according to an embodiment of the present disclosure;

[0025] Figure 7 is a schematic diagram of an attribution result obtained by parsing in a method for diagnosing data indicators according to an embodiment of the present disclosure;

[0026] Figure 8 It is a flowchart of querying and obtaining a diagnosis result in the diagnosis method of the data indicator of the embodiment of the present disclosure;

[0027] Fig. 9 is a flow chart of a diagnostic method for another data indicator according to an embodiment of the present disclosure;

[0028] Fig.10 is an interactive schematic diagram of a specific application of the data indicator diagnosis method according to an embodiment of the present disclosure;

[0029] Fig.11 is a structural block diagram of a diagnostic device for data indicators according to an embodiment of the present disclosure;

[0030] Fig.12 It is a structural schematic diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present disclosure.

[0032] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0033] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0034] As an optional but non-limiting implementation, in response to receiving an active request from the user, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0035] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet the relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0036] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and relevant provisions.

[0037] In the related art, in order to timely discover problems in the business in the big data indicator system, it is necessary to timely identify indicator changes, and quickly and accurately attribute and diagnose indicator changes. However, since an indicator is generally composed of multiple levels of sub-indicators, when attributing and diagnosing indicator changes, it is often necessary to drill down to the bottom-level sub-indicators for analysis and diagnosis. However, on the one hand, due to the complex structure of multi-level indicators, it is difficult to drill down along the indicator level layer by layer and analyze the impact of sub-indicators on indicator changes after judging indicator changes. When attributing changes to some indicators involving complex calculations, it is difficult to locate the sub-indicators that are the root cause of the changes; on the other hand, most of the current indicator change judgments are based on algorithm models that are biased towards black boxes. For users, since they are not clear about the analysis and processing content of the algorithm model on indicator changes, the confidence or trustworthiness of the diagnosis results is relatively low. At the same time, there are also abnormality diagnoses that use biased fixed thresholds, but such methods have the problems of low flexibility and limited scope of application.

[0038] Based on this, the present disclosure provides a method for diagnosing data indicators. In response to a diagnostic instruction for a target indicator, an attribution diagnostic model of the target indicator is obtained, wherein the attribution diagnostic model is obtained by mapping an indicator attribution model and an indicator diagnostic model, the indicator attribution model is used to characterize the hierarchical relationship and logical relationship between each indicator, the indicator diagnostic model is used to characterize the diagnostic rules of each indicator, and the indicator attribution model and the indicator diagnostic model have the same node structure, so that the hierarchical structure between indicators and the diagnostic rules of indicators can be flexibly configured to achieve flexible configuration of indicator change judgment; at the same time, based on the positioning of the target indicator in the attribution diagnostic model, the target attribution model corresponding to the target indicator is determined, and the target attribution model is attribution parsed to obtain the attribution result of the target indicator, so that the multi-level indicators can be positioned and disassembled to achieve rapid attribution of indicator changes; and based on the attribution result, the diagnostic rule is matched to obtain the diagnostic result of the target indicator, so that the diagnostic result of the indicator change can be obtained through one rule matching, thereby quickly locating the root cause of the indicator change problem and improving the accuracy and efficiency of indicator change analysis and diagnosis.

[0039] Figure 1 is an application diagram of the diagnostic method of data indicators according to an embodiment of the present disclosure, please refer to Figure 1 The diagnostic method of data indicators in the embodiment of the present disclosure can be deployed in the server. The server, as a terminal device, can be a mobile terminal such as a mobile phone, a computer, and a tablet, or a server, and provides a configuration page and a diagnostic page to the user through the client; the user can configure or modify the hierarchical structure between indicators and the diagnostic rules of each indicator in the configuration page, perform attribution diagnosis on the target indicator in the diagnostic page, and send a diagnostic instruction for the target indicator to the server through the diagnostic page, thereby obtaining a diagnostic result of the target indicator.

[0040] According to an embodiment of the present disclosure, an embodiment of a method for diagnosing data indicators is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0041] In this embodiment, a method for diagnosing data indicators is provided, which can be used in a mobile terminal or a server. Figure 2 is a flow chart of a method for diagnosing data indicators according to an embodiment of the present disclosure, such as Figure 2 As shown, the process includes the following steps:

[0042] Step S201, in response to a diagnosis instruction for a target indicator, obtaining an attribution diagnosis model for the target indicator.

[0043] In the disclosed embodiment, the attribution diagnosis model is obtained by mapping based on the indicator attribution model and the indicator diagnosis model. The attribution diagnosis model aggregates the indicator architecture and the diagnosis rules. Therefore, the attribution diagnosis model can realize the drilling down and analysis diagnosis of the sub-indicators. Among them, the indicator attribution model is used to characterize the hierarchical relationship and logical relationship between the various indicators, so as to construct the indicator architecture. The indicator attribution model can be used to determine the subordinate indicators corresponding to any one of the indicators, that is, the sub-indicators, and the related dimensions involved; the indicator diagnosis model is used to characterize the diagnosis rules of each indicator. The indicator diagnosis model can be used to determine the diagnosis rules corresponding to any one of the indicators. At the same time, the indicator attribution model and the indicator diagnosis model have the same node structure, and the nodes in the indicator attribution model and the indicator diagnosis model correspond one to one, and the same node position corresponds to the same indicator or dimension.

[0044] Among them, in the attribution diagnosis model, or the indicator attribution model and the indicator diagnosis model, the indicator is used to measure and describe specific business goals or technical goals, which corresponds to the nodes in the model except the last-level node. In particular, the root node, that is, the indicator corresponding to the first-level node is the parent indicator; the dimension is a specific parameter variable. The dimension splits and refines the indicator, so as to clarify the impact of different factors on the indicator and analyze the indicator from different angles. It corresponds to the last-level node in the model.

[0045] In an optional implementation, the indicator attribution model and the indicator diagnosis model can adopt a tree structure, and the indicator or dimension corresponding to the subnode of the node where an indicator is located is the sub-indicator or dimension corresponding to the indicator. At the same time, in order to achieve flexible configuration of the indicator architecture and diagnostic rules, a physical tree and a logical tree are designed for the indicator attribution model respectively; the physical tree is used to define the association between each node in the model and the underlying data, and the logical tree is used to define the specific attributes and parameters of each node in the model. In actual applications, the indicator attribution model is configured through the logical tree, and the indicator attribution model and the indicator diagnosis model are parsed through the physical tree. The physical tree and the logical tree are interrelated, so as to ensure the accuracy and reliability of the analysis of the indicator attribution model while ensuring the configuration flexibility.

[0046] Specifically, the indicator attribution model includes a logical attribution tree and a physical attribution tree. In the logical attribution tree and the physical attribution tree, each node may include but is not limited to the following information: a unique identifier of the attribution tree, a node type, a node name, an attribution type, a node calculation method, and a list of child nodes; wherein the unique identifier of the attribution tree is used to uniquely identify an attribution tree to distinguish different attribution trees, the node type is used to characterize whether the node is a root node or a child node, the node name may be an indicator name or a dimension name, the attribution type is used to characterize whether the next level of the node is decomposed into an indicator or a dimension, the node calculation method is used to characterize any valid calculation logic for calculating the node value, and the child node list is used to characterize the child nodes corresponding to the node. At the same time, the physical attribution tree may also include the association information between the node and the underlying data, such as the list of filtering parameters applied by the node when querying, and the field name corresponding to the node in the query interface; the physical attribution tree may also include the algorithm model and query code used by the node in attribution diagnosis.

[0047] In an optional embodiment, the indicator diagnosis model includes an indicator diagnosis tree. In the indicator diagnosis tree, each node may include but is not limited to the following information: the algorithm model used in the attribution diagnosis, the indicator name, the diagnosis rule list, the diagnosis result template, the diagnosis date and period, the corresponding query filter parameter list, etc.

[0048] In an optional implementation, in the indicator diagnosis model, the nodes corresponding to the indicators that need to be analyzed can be selectively configured according to the diagnosis requirements, while the nodes corresponding to other indicators are not configured, so that the attribution diagnosis of indicator movement can be flexibly adjusted.

[0049] In an optional implementation, the attribution diagnosis model can be constructed in the following manner:

[0050] Step a1, obtaining model configuration information, and generating an indicator attribution model and an indicator diagnosis model based on the model configuration information.

[0051] In step a1, the model configuration information is input by the user, which includes the configuration information of the node attributes and parameters of the indicator attribution model, and the configuration information of the diagnosis rules of the indicator diagnosis model. According to the corresponding model configuration information, each node is configured to construct the indicator attribution model and the indicator diagnosis model.

[0052] Step a2, establishing a mapping relationship between nodes with the same node position in the indicator attribution model and the indicator diagnosis model to obtain an attribution diagnosis model.

[0053] In step a2, after the indicator attribution model and the indicator diagnosis model are generated, a mapping relationship is established between the nodes corresponding to the same node position of the two, so as to associate the relevant information attributed to the diagnosis of the same indicator or dimension and obtain the attribution diagnosis model.

[0054] In an alternative embodiment, Figure 3 is a schematic diagram of generating an attribution diagnosis model in a method for diagnosing data indicators according to an embodiment of the present disclosure, such as Figure 3 As shown, users construct data according to the production logic of the business scope, including the construction of indicator layers and logical relationships and the construction of diagnostic rules to obtain model configuration information. Then the user inputs the model configuration information, and the platform constructs an attribution tree to obtain an indicator attribution model and a diagnostic tree to obtain an indicator diagnostic model based on the model configuration information input by the user. A mapping relationship is established between the indicator attribution model and the indicator diagnostic model to construct an attribution diagnostic model.

[0055] In an optional implementation, the generated attribution diagnosis model may be stored in an indicator data storage layer. Figure 4 is a schematic diagram of a data storage layer in a method for diagnosing data indicators according to an embodiment of the present disclosure, such as Figure 4 As shown, the indicator data storage layer may include a data management system, a storage computing engine, and a data query service. The data management system is used to store and manage the underlying indicator data, which may be composed of a Hive system and a Nuwa system; the storage computing engine is used to store the indicators of each level in the generated attribution diagnosis model in the corresponding storage format, and the storage computing engine may also provide an interface service for indicator query. The storage computing engine may be determined based on the user-defined QPS and the engine selection model defined by the query method, and it may be built based on database management systems such as Clickhouse, ES, Doris, and Abase; the data query service provides external data query services, which are used to query and call data in the indicator data storage layer during the attribution diagnosis process of the indicator, and may include an MFS (Multiple File System) system and a OneService system.

[0056] Step S202, based on the positioning of the target indicator in the attribution diagnosis model, determine the target attribution model corresponding to the target indicator.

[0057] In the disclosed embodiment, the node position of the target indicator is located in the attribution diagnosis model, and based on the node position of the target indicator, the attribution diagnosis model is trimmed, and the nodes related to the target indicator in the attribution diagnosis model are trimmed to obtain the target attribution model. The indicators and dimensions involved in the target attribution model are all indicators and dimensions related to the target indicator and may cause the target indicator to change.

[0058] Step S203, performing attribution analysis on the target attribution model to obtain the attribution result of the target indicator.

[0059] In the disclosed embodiment, based on the node structure of the target attribution model, the target attribution model is drilled down and analyzed layer by layer, and the calculation method of the node in the target attribution model is configured to perform calculations, determine the logical operation relationship between each indicator or dimension and the target indicator, and obtain the attribution result of the target indicator.

[0060] Step S204, matching the diagnosis rules based on the attribution results to obtain the diagnosis results of the target indicators.

[0061] In the disclosed embodiment, the attribution result obtained by attribution is matched with the corresponding diagnostic rule to obtain the diagnostic result of the target indicator. The diagnostic rule corresponding to the attribution result is determined based on the target attribution model. Since the target attribution model is clipped from the attribution diagnostic model, each node therein is associated with the diagnostic rule of the indicator. Therefore, after obtaining the attribution result, the diagnostic rule directly corresponding to the target attribution model can be used.

[0062] In the embodiment of the present disclosure, the diagnosis rules define a method for determining whether the corresponding indicators or dimensions are abnormal. The attribution results are matched with the corresponding diagnosis rules to determine whether the attribution results meet the requirements of the diagnosis rules, thereby determining whether the relevant indicators and dimensions corresponding to the attribution results are abnormal, and determining the diagnosis results of the target indicators accordingly.

[0063] In an optional implementation, after obtaining the diagnostic result of the target indicator, the diagnostic template can be filled based on the diagnostic result to obtain the diagnostic report of the target indicator and output the diagnostic report. The diagnostic template is configured in the attribution diagnostic model, or the indicator diagnostic model, and the corresponding diagnostic template can be directly called and the diagnostic result can be filled into the diagnostic template; if the diagnostic result involves multiple indicators or dimensions, the diagnostic templates of the indicators or dimensions involved are called and filled accordingly.

[0064] In an optional implementation, the diagnosis report may also include suggested measures for the diagnosis results. The suggested measures may also be configured in the indicator diagnosis model and directly called and filled into the diagnosis template when used.

[0065] In an optional implementation, the user may configure the output method of the diagnostic report so that the diagnostic report is output to the user in a manner as required by the user. For example, the output method of the diagnostic report may be configured as text message, email, etc.

[0066] The diagnostic method for data indicators provided by the embodiment of the present disclosure obtains an attribution diagnostic model of the target indicator in response to a diagnostic instruction for the target indicator, wherein the attribution diagnostic model is obtained by mapping the indicator attribution model and the indicator diagnostic model, the indicator attribution model is used to characterize the hierarchical relationship and logical relationship between each indicator, the indicator diagnostic model is used to characterize the diagnostic rules of each indicator, and the indicator attribution model and the indicator diagnostic model have the same node structure, so that the hierarchical structure between the indicators and the diagnostic rules of the indicators can be flexibly configured to achieve flexible configuration of indicator change judgment; at the same time, based on the positioning of the target indicator in the attribution diagnostic model, the target attribution model corresponding to the target indicator is determined, and the target attribution model is attribution parsed to obtain the attribution result of the target indicator, so that the multi-level indicators can be positioned and disassembled to achieve rapid attribution of indicator changes; and based on the attribution result, the diagnostic rule is matched to obtain the diagnostic result of the target indicator, so that the diagnostic result of the indicator change can be obtained through one rule matching, thereby quickly locating the root cause of the indicator change problem and improving the accuracy and efficiency of indicator change analysis and diagnosis.

[0067] In this embodiment, a method for diagnosing data indicators is provided, which can be used in a mobile terminal or a server. Figure 5 is a flow chart of another method for diagnosing data indicators according to an embodiment of the present disclosure, such as Figure 2 As shown, the process includes the following steps:

[0068] Step S501, in response to a diagnostic instruction for a target indicator, obtain an attribution diagnostic model for the target indicator. Figure 2 Step S201 of the illustrated embodiment will not be described in detail here.

[0069] Step S502, based on the positioning of the target indicator in the attribution diagnosis model, determine the target attribution model corresponding to the target indicator.

[0070] Specifically, step S502 includes:

[0071] Step S5021, locate the target indicator in the attribution diagnosis model to obtain the target node position of the target indicator.

[0072] In the disclosed embodiment, query and positioning are performed in the attribution diagnosis model to determine the position of the target indicator in the node architecture corresponding to the attribution diagnosis model, and determine it as the target node position.

[0073] Step S5022, cutting the attribution diagnosis model from the target node position to obtain the target attribution model.

[0074] In the disclosed embodiment, in the attribution diagnosis model, the target node position is cut out from all the child nodes and subordinate structures associated with the target node position to obtain the target attribution model. The node where the target indicator is located is the parent node of the target attribution model and is the parent indicator of the target attribution model.

[0075] Step S503, performing attribution analysis on the target attribution model to obtain the attribution result of the target indicator.

[0076] Specifically, step S503 includes:

[0077] Step S5031 , generating concurrent diagnosis requests for the first nodes in the target attribution model respectively.

[0078] In the disclosed embodiment, the first node is a child node corresponding to the target indicator in the target attribution model, that is, a subordinate node corresponding to the parent node in the target attribution model. Since the target indicator may correspond to multiple first nodes, a concurrent request is adopted to generate concurrent diagnosis requests for the multiple first nodes respectively, so that attribution analysis is performed in parallel from the indicators corresponding to the multiple first nodes according to the concurrent diagnosis requests.

[0079] In an optional implementation, the number of concurrent diagnostic requests, that is, the number of first nodes, is determined as the request concurrency; taking into account the request concurrency, system resources and load conditions, it is determined whether to process the concurrent diagnostic requests in batches to avoid excessive data processing pressure.

[0080] Step S5032: In response to the concurrent diagnosis request corresponding to the first node, perform attribution analysis on the first node to obtain an attribution result of the first node.

[0081] In the disclosed embodiment, in response to the concurrent diagnosis request corresponding to the first node, based on the target attribution model, attribution analysis is performed from the first node to the lower level to obtain the attribution result of the first node. By summarizing all the first node attribution results corresponding to the target indicator, the attribution result of the target indicator can be obtained.

[0082] In an alternative embodiment, Figure 6 is a schematic diagram of attribution analysis in the diagnostic method of data indicators according to an embodiment of the present disclosure, such as Figure 6As shown, after obtaining the diagnostic instructions for the target indicator, the attribution diagnostic model is parsed to determine the indicators and dimensions in the attribution diagnostic model; a search is performed based on the analysis of the attribution diagnostic model, and the nodes that hit the search are parsed, so as to locate the target node position of the target indicator in the attribution diagnostic model; then the attribution diagnostic model is trimmed according to the target node position to obtain a mapping of nodes related to the diagnosis of the target indicator and diagnostic rules, that is, the target attribution model; finally, the request concurrency and concurrent diagnostic attribution are determined based on the target attribution model.

[0083] In an optional implementation, performing attribution analysis on the first node to obtain an attribution result of the first node includes:

[0084] Step b1, based on the node structure of the target attribution model, determining the sub-nodes associated with the first node and the logical relationship between the first node and the corresponding sub-nodes.

[0085] In step b1, based on the node structure of the target attribution model, the sub-nodes associated with the first node and the logical relationship between the first node and the corresponding sub-nodes are determined. The sub-nodes of the first node can be determined by parsing and querying the physical attribution tree corresponding to the target attribution model, and the logical relationship between the first node and the sub-nodes can be determined by parsing the configuration of the nodes in the physical attribution tree.

[0086] Step b2: determining the attribution result of the first node based on the logical relationship between the first node and the corresponding child nodes.

[0087] In step b2, based on the logical relationship between the first node and the corresponding sub-node, combined with the configuration of the algorithm in the target attribution model, calculation is performed to obtain the attribution result of the first node; the attribution result of the first node includes the attribution of the sub-indicators and dimensions associated with the first node.

[0088] In an alternative embodiment, Figure 7 is a schematic diagram of the attribution result obtained by parsing in the diagnosis method of the data indicator according to the embodiment of the present disclosure, such as Figure 7 As shown, before performing attribution analysis on the first node, first call the attribution atomic capability interface, call the relevant data of the target indicator, and perform abnormal diagnosis on the target indicator. Only when the target indicator changes, perform attribution analysis on the first node to avoid invalid attribution diagnosis. The abnormal diagnosis of the target indicator can be based on the algorithm model or based on the user-defined abnormal diagnosis rules.

[0089] In an optional embodiment, if Figure 7As shown, after determining that the target indicator has changed, the first node corresponding to the target indicator is attributed and analyzed based on the above steps b1 and b2 to obtain the attribution result of the first node. Specifically, by calling the attribution atomic capability interface, calling the target attribution model and the relevant data of the indicator, the indicator query is performed based on the parent-child indicator decomposition in the target attribution model, and the subordinate indicators and dimensions are further decomposed based on the query results, and at the same time, the corresponding algorithm and node settlement method are called to perform attribution calculation to obtain the attribution result of the first node.

[0090] Step S504, matching the diagnosis rules based on the attribution result to obtain the diagnosis result of the target indicator.

[0091] In the disclosed embodiment, the diagnostic rules are matched for the attribution results of the multiple first nodes respectively to obtain the diagnostic results of the respective first nodes, and the diagnostic results of the respective first nodes are summarized to obtain the diagnostic results of the target indicators.

[0092] In an optional implementation, after obtaining the diagnosis result of the first node, the diagnosis result can be stored in a storage space; at the same time, before performing attribution analysis on the first node, it is possible to first query in the storage space whether there is a usable diagnosis result, thereby reducing the amount of data processing and improving the efficiency of indicator change analysis and diagnosis. Specifically, the following steps may be included:

[0093] Step c1, in response to the concurrent diagnosis request corresponding to the first node, query the historical diagnosis results of the first node in the storage space, and verify the validity of the historical diagnosis results.

[0094] In step c1, in response to the concurrent diagnosis request corresponding to the first node, the historical diagnosis results of the first node are first queried in the storage space, and the relevant storage information of the historical diagnosis results is obtained to verify the validity of the historical diagnosis results. If the historical diagnosis results are not queried, or the validity verification of the historical diagnosis results fails, the attribution analysis is performed on the first node to obtain the attribution result of the first node.

[0095] In step c1, the storage space can be divided into internal memory and external storage. Historical diagnostic results are stored in the internal memory first, and the data in the internal memory will be cleaned up regularly. When the data in the internal memory is cleaned up, the historical diagnostic results stored in the internal memory are transferred to the external storage. When querying the historical diagnostic results, the internal memory is queried first. If the historical diagnostic results cannot be queried in the internal memory, or the validity verification of the historical diagnostic results in the internal memory fails, the external storage is queried.

[0096] In step c1, the validity of the historical diagnostic results is verified by the data update time of the historical diagnostic results. Specifically, the data update time of the historical diagnostic results is obtained; if the data update time indicates that the historical diagnostic results are valid, it is determined that the validity verification of the historical diagnostic results has passed. Among them, it can be calculated based on the data update time and the data query period corresponding to the historical diagnostic results. If the current time is within the data query period corresponding to the data update time, that is, the current time is less than the sum of the data update time and the data query period, then the historical diagnostic results are valid.

[0097] Step c2: if the validity verification of the historical diagnosis result is passed, the historical diagnosis result is determined as the diagnosis result of the first node.

[0098] In step c2, if the validity verification of the historical diagnostic result is passed, it means that the data has not been updated during the period from the acquisition of the historical diagnostic result to the present, and the historical diagnostic result can characterize the abnormal diagnosis status of the current first node, and the historical diagnostic result can be determined as the diagnostic result of the first node.

[0099] In an alternative embodiment, Figure 8 This is a flow chart of querying and obtaining diagnostic results in the diagnostic method of the data indicator of the embodiment of the present disclosure. Figure 8 Further explanation is given for the diagnostic results obtained from the above query. Figure 8 As shown, in response to a concurrent diagnosis request, an attribution diagnosis query is triggered, and historical diagnosis results are queried in the storage.

[0100] First, query the memory cache. If the memory cache is hit, verify the validity of the historical diagnostic results that hit the memory cache. Specifically, parse the hit memory cache, determine the data query cycle, query the data update time of the historical diagnostic results in the hit memory cache, and verify the validity of the historical diagnostic results based on the data query cycle and data update time. If the validity verification of the historical diagnostic results passes, obtain the historical diagnostic results, use them as the diagnostic results of the first node and return them.

[0101] If the memory cache is not hit, or the validity verification of the historical diagnostic results in the memory cache fails, the cache of the external storage is queried. In the present embodiment, the external storage used is the Abase storage system. If the cache of Abase is hit, the validity of the historical diagnostic results that hit the memory cache is verified. Specifically, the cache that hits the external storage is parsed, the data query cycle is determined, the data update time of the historical diagnostic results in the cache that hits the external storage is queried, and the validity of the historical diagnostic results is verified based on the data query cycle and the data update time. If the validity verification of the historical diagnostic results passes, the historical diagnostic results are obtained, and they are used as the diagnostic results of the first node and returned.

[0102] If the cache of the external storage is not hit, or the validity verification of the historical diagnostic results in the cache of the external storage fails, the attribution diagnostic query is triggered again, the attribution analysis and rule matching are performed on the first node, the diagnostic result of the first node is obtained, and the diagnostic result is stored in the storage space, and the diagnostic result is returned at the same time.

[0103] In an optional implementation, after determining the diagnostic result of the target indicator, the diagnostic result of the target indicator can be directly stored in the storage space. When performing diagnostic analysis on the target indicator, the same method as in the above embodiment can be directly used to obtain the historical diagnostic result of the target indicator as the diagnostic result. When the historical diagnostic result that has passed the validity verification is not obtained, the target attribution model corresponding to the target indicator is determined to perform attribution analysis and rule matching on the target indicator to obtain the diagnostic result, thereby further improving the efficiency of indicator anomaly analysis and diagnosis.

[0104] The data indicator diagnosis method provided by the embodiment of the present disclosure trims the attribution diagnosis model based on the target node position of the target indicator in the attribution diagnosis model to obtain the target attribution model, so that the attribution diagnosis model can be flexibly adjusted according to the analysis and diagnosis requirements of the indicator change, thereby improving the efficiency of the indicator change analysis and diagnosis; at the same time, concurrent diagnosis requests are adopted to perform attribution analysis on multiple first nodes concurrently. Before performing attribution analysis on the first node, the storage space is first queried to determine whether there are valid historical diagnosis results as the diagnosis results, thereby further improving the efficiency of the indicator change analysis and diagnosis.

[0105] In this embodiment, a method for diagnosing data indicators is provided, which can be used in a mobile terminal or a server. Fig. 9 is a flow chart of another method for diagnosing data indicators according to an embodiment of the present disclosure, such as Fig. 9 As shown, the process includes the following steps:

[0106] Step S901, in response to a diagnostic instruction for a target indicator, obtain an attribution diagnostic model for the target indicator. Figure 2 Step S201 of the illustrated embodiment will not be described in detail here.

[0107] Step S902: Based on the positioning of the target indicator in the attribution diagnosis model, determine the target attribution model corresponding to the target indicator. Figure 2 Step S202 of the illustrated embodiment will not be described in detail here.

[0108] Step S903: Perform attribution analysis on the target attribution model to obtain the attribution result of the target indicator. Figure 2 Step S203 of the illustrated embodiment will not be described in detail here.

[0109] Step S904, matching the diagnosis rules based on the attribution result to obtain the diagnosis result of the target indicator.

[0110] Specifically, step S904 includes:

[0111] Step S9041, obtaining indicator data of the target indicator, and matching the indicator data of the target indicator with the corresponding diagnosis rule to determine the abnormal situation of the target indicator.

[0112] In the disclosed embodiment, the indicator data in the indicator data storage layer is called to obtain the indicator data of the target indicator, and the indicator data of the target indicator is matched with the diagnostic rules corresponding to the target indicator to determine whether there is any abnormality in the target indicator, thereby determining the abnormal situation of the target indicator.

[0113] In an optional implementation, if it has been pre-determined whether there is any abnormality in the target indicator when performing attribution analysis on the target attribution model, and the attribution analysis of the target attribution model is triggered only when there is an abnormality in the target indicator, then it is not necessary to judge the abnormality of the target indicator at this time.

[0114] Step S9042: If the abnormal situation of the target indicator is that there is an abnormal change, the indicator data of the attribution result is obtained.

[0115] In the disclosed embodiment, if the abnormal situation of the target indicator is that there is an abnormal change, the indicator data of the indicators and dimensions involved in the attribution result are obtained by calling the indicator data in the indicator data storage layer.

[0116] Step S9043, matching the indicator data of the attribution result with the corresponding diagnosis rule, determining the abnormality of the attribution result, and obtaining the diagnosis result.

[0117] In the disclosed embodiment, the indicator data of the attribution result is matched with the corresponding diagnostic rules, the algorithm or model corresponding to the attribution result is called to perform calculation matching, the abnormal situation of the attribution result is determined, the abnormal situation of the attribution result is summarized, and the diagnostic result of the target indicator is obtained.

[0118] The data indicator diagnosis method provided by the embodiment of the present disclosure matches the indicator data of the attribution result with the corresponding diagnosis rule when there is an abnormality in the target indicator to obtain a diagnosis result, thereby accurately analyzing and diagnosing the indicator abnormality.

[0119] As a specific application example of the embodiment of the present disclosure, Fig.10 is an interactive schematic diagram of a specific application of the diagnostic method of data indicators according to an embodiment of the present disclosure, such as Fig.10 As shown, the diagnosis platform, attribution platform, MFS (Multiple File System) system, that is, the data query service and storage system adopted in this embodiment, and the Auto-insight algorithm, that is, the diagnosis algorithm model adopted in this embodiment, can be deployed on the same platform or on multiple platforms respectively.

[0120] Specifically, the user can input the name id of the target indicator into the diagnosis platform to obtain the indicator attribution model corresponding to the target indicator. After receiving the user's instruction to obtain the indicator attribution model, the diagnosis platform sends a request to the attribution platform; the attribution platform obtains the corresponding indicator attribution model based on the name id of the target indicator, and returns it to the diagnosis platform; the diagnosis platform performs analysis based on the indicator attribution model, generates a UI-recognizable model and displays it to the user, so that the user can adjust the indicator attribution model or clarify the target indicator that needs to be analyzed for indicator changes based on the indicator attribution model.

[0121] Then, when analyzing and diagnosing the target indicator's change, in order to improve the efficiency of the indicator change analysis and diagnosis, this embodiment adopts a solution of first querying the historical diagnosis results, and then performing attribution diagnosis on the target indicator when the historical diagnosis results that have passed the validity verification are not obtained; accordingly, in actual applications, the following can also be adopted Figure 5 The scheme corresponding to the embodiment shown first performs attribution analysis on the target indicator, then obtains the historical diagnosis results of the corresponding first node, and then performs rule matching on the first node if the historical diagnosis results that have passed the validity verification are not obtained, so as to improve the accuracy of indicator anomaly analysis and diagnosis.

[0122] Among them, the user instructs the diagnosis platform to analyze and diagnose the changes of the target indicator. First, the diagnosis platform queries whether the target indicator hits the cache. If the memory cache or external cache hits the historical diagnosis result that has passed the validity verification, the diagnosis platform returns the historical diagnosis result to the user as the diagnosis result of the target indicator, and generates a diagnosis report based on the diagnosis result and returns it to the user.

[0123] If the target indicator hit cache is not queried through the diagnostic platform, the nodes are disassembled based on the indicator attribution model, and the attribution diagnostic model is trimmed to build diagnostic attribution rules for the target indicator, obtain the target attribution model, and initiate a diagnostic attribution request to the diagnostic platform based on the first node in the target attribution model.

[0124] In response to the diagnostic attribution request, the diagnostic platform parses the target attribution model, determines the first node to be attributed based on the node structure of the target attribution model, and concurrently attributes the first node. The diagnostic platform initiates a concurrent indicator change attribution request to the attribution platform, and at the same time sends the information related to the first node corresponding to the indicator change attribution request to the attribution platform, such as attribution type, indicator name, execution algorithm, etc., so that the attribution platform can concurrently attribute multiple first nodes.

[0125] The attribution platform parses the indicator change attribution request, determines the corresponding node, obtains the node-related configuration, and constructs the MFS request based on it to request the node-related data from the MFS system. The MFS system queries the storage system based on the MFS request and returns the query results to the attribution platform. The attribution platform constructs the input parameters required by the Auto-insight algorithm based on the data returned by the MFS system, calls the Auto-insight algorithm, obtains the attribution results, and returns the attribution results to the diagnosis platform.

[0126] The diagnostic platform collects the attribution results, matches the attribution results with diagnostic rules, obtains the diagnostic results, and returns the diagnostic results to the user. At the same time, a diagnostic report is generated based on the diagnostic results and sent to the diagnostic report; and the diagnostic results of the target indicators are updated in the storage space.

[0127] In the present embodiment, a diagnostic device for data indicators is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware is also possible and conceived.

[0128] This embodiment provides a diagnostic device for data indicators, such as Fig.11 As shown, including:

[0129] The model acquisition module 1101 is used to obtain the attribution diagnosis model of the target indicator in response to the diagnosis instruction of the target indicator. The attribution diagnosis model is obtained by mapping the indicator attribution model and the indicator diagnosis model. The indicator attribution model is used to characterize the hierarchical relationship and logical relationship between various indicators. The indicator diagnosis model is used to characterize the diagnosis rules of various indicators. The indicator attribution model and the indicator diagnosis model have the same node structure.

[0130] The indicator positioning module 1102 is used to determine the target attribution model corresponding to the target indicator based on the positioning of the target indicator in the attribution diagnosis model;

[0131] The attribution analysis module 1103 is used to perform attribution analysis on the target attribution model to obtain the attribution result of the target indicator;

[0132] The rule matching module 1104 is used to match the diagnosis rules based on the attribution results to obtain the diagnosis results of the target indicators.

[0133] In an optional implementation, the indicator positioning module 1102 includes:

[0134] A position positioning unit is used to locate the target indicator in the attribution diagnosis model and obtain the target node position of the target indicator;

[0135] The model clipping unit is used to clip the attribution diagnosis model from the target node position to obtain the target attribution model.

[0136] In an optional implementation, the attribution analysis module 1103 includes:

[0137] A request generation unit, used to generate concurrent diagnosis requests for first nodes in the target attribution model respectively, where the first nodes are subnodes corresponding to the target indicators in the target attribution model;

[0138] The node analysis unit is used to respond to the concurrent diagnosis request corresponding to the first node, perform attribution analysis on the first node, and obtain the attribution result of the first node.

[0139] In an optional implementation, the node parsing unit includes:

[0140] A relationship determination subunit, configured to determine, based on the node construction of the target attribution model, a subnode associated with the first node and a logical relationship between the first node and the corresponding subnode;

[0141] The node attribution unit is used to determine the attribution result of the first node based on the logical relationship between the first node and the corresponding child node.

[0142] In an optional embodiment, the device further comprises:

[0143] A result query module, configured to query the historical diagnosis results of the first node in the storage space in response to the concurrent diagnosis request corresponding to the first node, and verify the validity of the historical diagnosis results;

[0144] The result determination module is used to determine the historical diagnosis result as the diagnosis result of the first node if the validity verification of the historical diagnosis result is passed.

[0145] In an optional implementation, the result query module includes:

[0146] A time acquisition unit, used to obtain the data update time of historical diagnosis results;

[0147] The validity verification unit is used to determine that the validity verification of the historical diagnosis result has passed if the data update time indicates that the historical diagnosis result is valid.

[0148] In an optional implementation, the rule matching module 1104 includes:

[0149] An abnormality determination unit, used to obtain indicator data of a target indicator, and match the indicator data of the target indicator with a corresponding diagnosis rule to determine an abnormality of the target indicator;

[0150] A data acquisition unit, used for acquiring the indicator data of the attribution result if the abnormal situation of the target indicator is that there is an abnormal change;

[0151] The matching diagnosis unit is used to match the indicator data of the attribution result with the corresponding diagnosis rules, determine the abnormal situation of the attribution result, and obtain the diagnosis result.

[0152] In an optional embodiment, the device further comprises:

[0153] A model generation module is used to obtain model configuration information and generate an indicator attribution model and an indicator diagnosis model based on the model configuration information;

[0154] The mapping establishment module is used to establish a mapping relationship between nodes with the same node position in the indicator attribution model and the indicator diagnosis model to obtain an attribution diagnosis model.

[0155] In an optional embodiment, the device further comprises:

[0156] The report generation module is used to fill in the diagnosis template based on the diagnosis results, obtain the diagnosis report of the target indicator, and output the diagnosis report.

[0157] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0158] The diagnostic device for data indicators in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0159] The present disclosure also provides an electronic device having the above Fig.11 The data indicators shown are diagnostic devices.

[0160] See also Fig.12 , Fig.12 is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure, such as Fig.12 As shown, the electronic device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to an interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig.12 A processor 10 is taken as an example.

[0161] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0162] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0163] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0164] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0165] The electronic device further comprises a communication interface 30 for the electronic device to communicate with other devices or a communication network.

[0166] The embodiments of the present disclosure also provide a computer-readable storage medium. The above-mentioned method according to the embodiments of the present disclosure can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium and downloaded through a network, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0167] A part of the present disclosure may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present disclosure through the operation of the computer. Those skilled in the art should understand that the existence of computer program instructions in computer-readable media includes, but is not limited to, source files, executable files, installation package files, etc., and accordingly, the way in which computer program instructions are executed by a computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to the computer.

[0168] Although the embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations are all within the scope defined by the present disclosure.

Claims

1. A method for diagnosing data indicators, characterized in that: The method comprises: In response to a diagnostic instruction for a target indicator, an attribution diagnostic model of the target indicator is obtained, the attribution diagnostic model is obtained by mapping an indicator attribution model and an indicator diagnostic model, the indicator attribution model is used to characterize the hierarchical relationship and logical relationship between various indicators, the indicator diagnostic model is used to characterize the diagnostic rules of various indicators, and the indicator attribution model and the indicator diagnostic model have the same node structure; Based on the positioning of the target indicator in the attribution diagnosis model, determining the target attribution model corresponding to the target indicator; Performing attribution analysis on the target attribution model to obtain the attribution result of the target indicator; The diagnostic rules are matched based on the attribution results to obtain the diagnostic results of the target indicators.

2. The method according to claim 1, characterized in that The determining of the target attribution model corresponding to the target indicator based on the positioning of the target indicator in the attribution diagnosis model includes: Locating the target indicator in the attribution diagnosis model to obtain a target node position of the target indicator; The attribution diagnosis model is clipped from the target node position to obtain the target attribution model.

3. The method according to claim 1, characterized in that The attribution analysis of the target attribution model to obtain the attribution result of the target indicator includes: respectively generating concurrent diagnosis requests for first nodes in the target attribution model, the first nodes being child nodes corresponding to the target indicators in the target attribution model; In response to the concurrent diagnosis request corresponding to the first node, attribution analysis is performed on the first node to obtain an attribution result of the first node.

4. The method according to claim 3, characterized in that The performing attribution analysis on the first node to obtain an attribution result of the first node includes: Determining, based on the node structure of the target attribution model, a sub-node associated with the first node, and a logical relationship between the first node and the corresponding sub-node; Based on the logical relationship between the first node and the corresponding child node, an attribution result of the first node is determined.

5. The method according to claim 3, characterized in that: The method further comprises: In response to a concurrent diagnosis request corresponding to the first node, querying a storage space for a historical diagnosis result of the first node, and verifying the validity of the historical diagnosis result; If the validity verification of the historical diagnosis result passes, the historical diagnosis result is determined as the diagnosis result of the first node.

6. The method according to claim 5, characterized in that The verifying the validity of the historical diagnosis result includes: Obtaining the data update time of the historical diagnosis results; If the data update time indicates that the historical diagnosis result is valid, it is determined that the validity verification of the historical diagnosis result has passed.

7. The method according to claim 1, characterized in that The matching of the diagnostic rules based on the attribution result to obtain the diagnostic result of the target indicator includes: Acquiring indicator data of the target indicator, and matching the indicator data of the target indicator with corresponding diagnostic rules to determine abnormal conditions of the target indicator; If the abnormal situation of the target indicator is that there is an abnormal change, then obtaining the indicator data of the attribution result; The indicator data of the attribution result is matched with the corresponding diagnosis rule to determine the abnormality of the attribution result to obtain the diagnosis result.

8. The method according to claim 1, characterized in that The method further comprises: Acquire model configuration information, and generate the indicator attribution model and the indicator diagnosis model based on the model configuration information; A mapping relationship is established between nodes with the same node position in the indicator attribution model and the indicator diagnosis model to obtain the attribution diagnosis model.

9. The method according to claim 1, characterized in that: The method further comprises: The diagnosis template is filled based on the diagnosis result to obtain a diagnosis report of the target indicator, and the diagnosis report is output.

10. A diagnostic device for data indicators, characterized in that: The device comprises: A model acquisition module, for acquiring an attribution diagnosis model of the target indicator in response to a diagnosis instruction for the target indicator, wherein the attribution diagnosis model is obtained by mapping the indicator attribution model and the indicator diagnosis model, wherein the indicator attribution model is used to characterize the hierarchical relationship and logical relationship between the indicators, and the indicator diagnosis model is used to characterize the diagnosis rules of the indicators, and the indicator attribution model and the indicator diagnosis model have the same node structure; An indicator positioning module, used to determine a target attribution model corresponding to the target indicator based on the positioning of the target indicator in the attribution diagnosis model; An attribution analysis module, used to perform attribution analysis on the target attribution model to obtain the attribution result of the target indicator; A rule matching module is used to match the diagnosis rules based on the attribution result to obtain the diagnosis result of the target indicator.

11. An electronic device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the diagnostic method for the data indicator according to any one of claims 1 to 9 by executing the computer instructions.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for diagnosing data indicators according to any one of claims 1 to 9.

13. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the method for diagnosing a data indicator according to any one of claims 1 to 9.

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