Index root cause analysis method and device, equipment and storage medium

Abstract

The invention provides an index root cause analysis method and device, equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: constructing an index tree according to the operation logic of an index to be analyzed; wherein a root node in the index tree corresponds to an index to be analyzed, and a non-root node corresponds to a subordinate index contained in the arithmetic logic; determining adjacent superior indexes of the subordinate indexes in the index tree, and determining sub contribution degrees of the subordinate indexes to the adjacent superior indexes according to an operation composition relation of the subordinate indexes to the adjacent superior indexes; according to the step-by-step sub-contribution degree corresponding to the structural relationship between the lower-level index and the to-be-analyzed index in the index tree, determining the accumulated contribution degree of the lower-level index to the to-be-analyzed index; and determining a root cause index influencing the to-be-analyzed index according to the accumulated contribution degree of the lower-level index to the to-be-analyzed index. According to the method, applicable algorithms corresponding to different operation composition relationships can be flexibly adopted, and the specific influence of the lower-level indexes on the to-be-analyzed indexes can be accurately quantified.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to a method, apparatus, device and storage medium for root cause analysis of indicators. Background Technology

[0002] With the deep integration of information technology and manufacturing, the level of intelligent manufacturing is getting higher and higher. The large amount of business data generated in the process of intelligent manufacturing can be used for indicator analysis, thereby promoting business development.

[0003] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] The purpose of this disclosure is to provide an indicator root cause analysis method, apparatus, device and storage medium.

[0005] According to a first aspect of the present disclosure, a root cause analysis method for an indicator is provided, comprising: constructing an indicator tree based on the operational logic of an indicator to be analyzed; wherein the root node in the indicator tree corresponds to the indicator to be analyzed, and the non-root nodes correspond to the lower-level indicators included in the operational logic; determining the adjacent upper-level indicators of the lower-level indicators in the indicator tree, and determining the sub-contribution degree of the lower-level indicators to the adjacent upper-level indicators based on the operational relationship between the lower-level indicators and the adjacent upper-level indicators; determining the cumulative contribution degree of the lower-level indicators to the indicator to be analyzed based on the step-by-step sub-contribution degree corresponding to the structural relationship between the lower-level indicators and the indicator to be analyzed in the indicator tree; and determining the root cause indicators affecting the indicator to be analyzed based on the cumulative contribution degree of the lower-level indicators to the indicator to be analyzed.

[0006] In some implementations, determining the sub-contribution of a lower-level indicator to an adjacent higher-level indicator based on the operational relationship between the lower-level indicator and the adjacent higher-level indicator includes: determining the operational relationship between the lower-level indicator and the adjacent higher-level indicator according to the operational logic; determining the relationship type to which the operational relationship belongs; and using a contribution algorithm corresponding to the relationship type to determine the sub-contribution of the lower-level indicator to the adjacent higher-level indicator.

[0007] In some implementations, the root cause analysis method further includes: in response to the relationship type being a product relationship, using the log-mean Dijkstra index method to determine the sub-contribution of the lower-level indicator to the adjacent higher-level indicator.

[0008] In some implementations, the root cause analysis method further includes: in response to the relationship type being a summary relationship of relative indicators, using a two-factor analysis algorithm to determine the sub-contribution of the lower-level indicator to the adjacent higher-level indicator.

[0009] In some implementations, the root cause analysis method further includes: in response to the relationship type being an additive relationship, using a sensitivity analysis algorithm to determine the sub-contribution of the lower-level indicator to the adjacent higher-level indicator.

[0010] In some implementations, an indicator tree is constructed based on the computational logic of the indicator to be analyzed, including: creating a root node corresponding to the indicator to be analyzed; determining each lower-level indicator and its computational hierarchy relationship according to the computational logic; creating non-root nodes corresponding to each lower-level indicator, and determining the node connection relationship in the indicator tree according to the computational hierarchy relationship.

[0011] In some implementations, determining the sub-contribution of a lower-level indicator to an adjacent higher-level indicator based on the operational relationship between the lower-level indicator and the adjacent higher-level indicator includes: traversing the indicator tree and sequentially determining the sub-contribution of each child node to its corresponding parent node based on the operational relationship between each child node and its corresponding parent node, so as to serve as the sub-contribution of the corresponding lower-level indicator to the adjacent higher-level indicator.

[0012] In some implementations, determining the cumulative contribution of the lower-level indicator to the indicator to be analyzed based on the hierarchical sub-contribution degree corresponding to the structural relationship between the lower-level indicator and the indicator to be analyzed in the indicator tree includes: determining the contribution path from the non-root node corresponding to the lower-level indicator to the root node according to the indicator tree; determining the sub-contribution degree generated on the contribution path as the hierarchical sub-contribution degree; and determining the cumulative contribution of the lower-level indicator to the indicator to be analyzed based on the hierarchical sub-contribution degree.

[0013] In some implementations, determining the cumulative contribution of the lower-level indicator to the indicator to be analyzed based on the progressive sub-contribution degree includes: multiplying the progressive sub-contribution degrees to obtain the cumulative contribution of the lower-level indicator to the indicator to be analyzed.

[0014] In some implementations, determining the root cause indicator affecting the indicator to be analyzed based on the cumulative contribution of the lower-level indicators to the indicator to be analyzed includes: determining the bottom-level lower-level indicators corresponding to the leaf nodes in the indicator tree; selecting the target cumulative contribution of the bottom-level lower-level indicators to the indicator to be analyzed in descending order, and calculating the sum of the contribution of the selected target cumulative contributions; in response to the sum of the contribution being greater than or equal to a contribution threshold, determining the bottom-level lower-level indicator corresponding to the selected target cumulative contribution as the root cause indicator.

[0015] According to a second aspect of the present disclosure, an indicator root cause analysis apparatus is provided, comprising: a construction unit, configured to construct an indicator tree according to the operational logic of an indicator to be analyzed; wherein the root node in the indicator tree corresponds to the indicator to be analyzed, and the non-root nodes correspond to the lower-level indicators included in the operational logic; a sub-contribution determination unit, configured to determine the adjacent upper-level indicators of the lower-level indicators in the indicator tree, and determine the sub-contribution of the lower-level indicators to the adjacent upper-level indicators according to the operational relationship between the lower-level indicators and the adjacent upper-level indicators; a cumulative contribution determination unit, configured to determine the cumulative contribution of the lower-level indicators to the indicator to be analyzed according to the step-by-step sub-contribution corresponding to the structural relationship between the lower-level indicators and the indicator to be analyzed in the indicator tree; and a root cause determination unit, configured to determine the root cause indicators affecting the indicator to be analyzed according to the cumulative contribution of the lower-level indicators to the indicator to be analyzed.

[0016] In some implementations, the sub-contribution determination unit determines the sub-contribution of the lower-level indicator to the adjacent upper-level indicator based on the operational relationship between the lower-level indicator and the adjacent upper-level indicator, including: determining the operational relationship between the lower-level indicator and the adjacent upper-level indicator according to the operational logic; determining the relationship type to which the operational relationship belongs; and using a contribution algorithm corresponding to the relationship type to determine the sub-contribution of the lower-level indicator to the adjacent upper-level indicator.

[0017] In some implementations, the sub-contribution determination unit is further configured to: in response to the relationship type being a product relationship, use the log-mean Dijkstra index method to determine the sub-contribution of the lower-level indicator to the adjacent upper-level indicator.

[0018] In some implementations, the sub-contribution determination unit is further configured to: in response to the relationship type being a summary relationship of relative numerical indicators, use a two-factor analysis algorithm to determine the sub-contribution of the lower-level indicator to the adjacent upper-level indicator.

[0019] In some implementations, the sub-contribution determination unit is further configured to: in response to the relationship type being an additive relationship, use a sensitivity analysis algorithm to determine the sub-contribution of the lower-level indicator to the adjacent upper-level indicator.

[0020] In some implementations, the construction unit constructs an indicator tree based on the computational logic of the indicator to be analyzed, including: creating a root node corresponding to the indicator to be analyzed; determining each lower-level indicator and its computational hierarchy relationship according to the computational logic; creating non-root nodes corresponding to each lower-level indicator, and determining the node connection relationship in the indicator tree according to the computational hierarchy relationship.

[0021] In some implementations, the sub-contribution determination unit determines the sub-contribution of the lower-level indicator to the adjacent upper-level indicator based on the operational relationship between the lower-level indicator and the adjacent upper-level indicator. This includes: traversing the indicator tree and determining the sub-contribution of each child node to its corresponding parent node in turn based on the operational relationship between each child node and its corresponding parent node, so as to serve as the sub-contribution of the corresponding lower-level indicator to the adjacent upper-level indicator.

[0022] In some implementations, the cumulative contribution determination unit determines the cumulative contribution of the lower-level indicator to the indicator to be analyzed based on the hierarchical sub-contribution corresponding to the structural relationship between the lower-level indicator and the indicator to be analyzed in the indicator tree. This includes: determining the contribution path from the non-root node corresponding to the lower-level indicator to the root node according to the indicator tree; determining the sub-contribution generated on the contribution path as the hierarchical sub-contribution; and determining the cumulative contribution of the lower-level indicator to the indicator to be analyzed based on the hierarchical sub-contribution.

[0023] In some implementations, the cumulative contribution determination unit determines the cumulative contribution of the lower-level indicator to the indicator to be analyzed based on the progressive sub-contribution, including: multiplying the progressive sub-contribution to obtain the cumulative contribution of the lower-level indicator to the indicator to be analyzed.

[0024] In some implementations, the root cause determination unit determines the root cause indicator affecting the indicator to be analyzed based on the cumulative contribution of the lower-level indicators to the indicator to be analyzed, including: determining the bottom-level lower-level indicators corresponding to the leaf nodes in the indicator tree; selecting the target cumulative contribution of the bottom-level lower-level indicators to the indicator to be analyzed in descending order, and calculating the sum of the contribution of the selected target cumulative contributions; in response to the sum of the contribution being greater than or equal to a contribution threshold, determining the bottom-level lower-level indicator corresponding to the selected target cumulative contribution as the root cause indicator.

[0025] According to a third aspect of the present disclosure, an electronic device is provided, characterized in that it includes: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to implement the above-described root cause analysis method.

[0026] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium is provided. When instructions in the storage medium are executed by a processor of a mobile terminal, the mobile terminal is enabled to execute a root cause analysis method for an indicator. The method includes: constructing an indicator tree based on the computational logic of an indicator to be analyzed; wherein the root node in the indicator tree corresponds to the indicator to be analyzed, and non-root nodes correspond to lower-level indicators included in the computational logic; determining the adjacent upper-level indicators of the lower-level indicators in the indicator tree, and determining the sub-contribution degree of the lower-level indicators to the adjacent upper-level indicators based on the computational relationship between the lower-level indicators and the adjacent upper-level indicators; determining the cumulative contribution degree of the lower-level indicators to the indicator to be analyzed based on the step-by-step sub-contribution degree corresponding to the structural relationship between the lower-level indicators and the indicator to be analyzed in the indicator tree; and determining the root cause indicators affecting the indicator to be analyzed based on the cumulative contribution degree of the lower-level indicators to the indicator to be analyzed.

[0027] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the above-described root cause analysis method.

[0028] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0029] This disclosure decomposes a complex indicator system into a tree structure, making the entire analysis process more systematic and organized. The position of each lower-level indicator in the indicator tree and its relationship with other indicators are clearly understood, facilitating comprehension and analysis. Furthermore, this disclosure fully considers the computational composition methods of lower-level indicators when forming adjacent higher-level indicators, flexibly employing applicable algorithms corresponding to different computational relationships to accurately quantify the specific impact of lower-level indicators on adjacent higher-level indicators. This allows the cumulative contribution of each lower-level indicator, determined based on its hierarchical sub-contribution, to more accurately and objectively reflect the impact of lower-level indicators on the analyzed indicators.

[0030] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0031] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0032] Figure 1 This is a flowchart illustrating an indicator root cause analysis method according to some embodiments of the present disclosure.

[0033] Figure 2This is a flowchart illustrating the determination of sub-contribution in an indicator root cause analysis method according to some embodiments of the present disclosure.

[0034] Figure 3 This is a flowchart illustrating the determination of sub-contribution in an indicator root cause analysis method according to some embodiments of the present disclosure.

[0035] Figure 4 This is a flowchart illustrating the construction of an indicator tree in an indicator root cause analysis method according to some embodiments of the present disclosure.

[0036] Figure 5 This is a flowchart illustrating the determination of cumulative contribution in an indicator root cause analysis method according to some embodiments of the present disclosure.

[0037] Figure 6 This is a schematic diagram of a tree structure in an index root cause analysis method according to some embodiments of the present disclosure.

[0038] Figure 7 This is a block diagram of an indicator root cause analysis device according to some embodiments of the present disclosure.

[0039] Figure 8 This is a block diagram illustrating an apparatus for indicator root cause analysis according to some embodiments of the present disclosure. Detailed Implementation

[0040] Exemplary embodiments of this disclosure will be described in detail herein, examples of which are illustrated in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings denote the same or similar elements unless otherwise indicated. Various changes, modifications, and equivalents of the methods, apparatus, and / or systems described herein will become apparent upon understanding this disclosure. For example, the order of operations described herein is merely illustrative and is not limited to those orders set forth herein, but can be changed as will become apparent upon understanding this disclosure, except for operations that must be performed in a particular order. Furthermore, for clarity and brevity, descriptions of features known in the art may be omitted.

[0041] The embodiments described below, which are examples of some of the embodiments of this disclosure, do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0042] In related technologies, root cause analysis refers to the systematic use of methods and tools to identify the fundamental indicators affecting a problem and conduct in-depth analysis to find its root cause. With the massive generation of business data, indicator analysis has become increasingly complex and sophisticated. Traditional indicator analysis methods rely on manual reasoning to determine the root causes of indicator changes, which cannot accurately quantify the influence relationships between lower-level and higher-level indicators. As more and more business data is collected, and the number of indicator levels increases, traditional indicator analysis methods, for multi-level composite indicators, cannot accurately quantify the influence relationships between higher and lower-level indicators, and cannot accurately locate the root causes affecting the indicator being analyzed. This leads to inaccurate decision-making, delayed problem improvement, and unnecessary increased manufacturing costs.

[0043] The specific implementation methods of the embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0044] Figure 1 This is a flowchart illustrating an indicator root cause analysis method according to some embodiments of this disclosure, such as... Figure 1 As shown, the root cause analysis method for indicators may include the following steps.

[0045] In step S110, an indicator tree is constructed according to the calculation logic of the indicator to be analyzed; wherein, the root node in the indicator tree corresponds to the indicator to be analyzed, and the non-root nodes correspond to the lower-level indicators included in the calculation logic.

[0046] In this embodiment of the disclosure, the computational logic of the indicator to be analyzed can indicate how the indicator to be analyzed is calculated from other indicators (i.e., lower-level indicators) through one or more levels of specific computational relationships (such as addition, subtraction, multiplication, division, ratio, etc.). The computational logic can reflect the various lower-level indicators and the hierarchical relationships between them.

[0047] A tree structure (i.e., an indicator tree) can be constructed based on the computational logic. In this tree structure, the root node represents the indicator to be analyzed, and each non-root node represents a lower-level indicator. The edges in the tree structure can be determined based on the computational relationships contained in the computational logic, and these edges connect the lower-level indicators to their directly adjacent higher-level indicators.

[0048] This embodiment demonstrates how a complex indicator system can be broken down into a tree structure, making the subsequent analysis process more systematic and organized. Furthermore, the position of each sub-indicator within the indicator tree and its relationship with other indicators are clearly defined, facilitating understanding and analysis.

[0049] In an exemplary embodiment, the lower-level indicators corresponding to the leaf nodes in the indicator tree are indicators that are not further subdivided; the lower-level indicators corresponding to the internal nodes in the indicator tree are indicators that participate in the calculation of other indicators (i.e., adjacent upper-level indicators), and are also indicators that can be further subdivided.

[0050] In an exemplary embodiment, the indicator to be analyzed can be first determined as the root node. Then, the computational logic of the indicator can be analyzed to identify all first-level lower-level indicators involved in the computation. These lower-level indicators can be connected to the root node as child nodes to form the first level. The same analysis can be performed recursively on each lower-level indicator to construct a complete indicator tree.

[0051] In step S120, the adjacent upper-level indicators of the lower-level indicator in the indicator tree are determined, and the sub-contribution degree of the lower-level indicator to the adjacent upper-level indicator is determined according to the operation relationship between the lower-level indicator and the adjacent upper-level indicator.

[0052] In this embodiment of the disclosure, for each lower-level indicator, its adjacent higher-level indicator in the indicator tree can be determined. Here, the adjacent higher-level indicator can be a lower-level indicator of the indicator to be analyzed, or it can be the indicator to be analyzed itself.

[0053] First, analyze the operational relationships (such as addition, multiplication, etc.) between lower-level indicators and adjacent higher-level indicators. Then, based on the type of operational relationship, use the corresponding algorithm to calculate the sub-contribution of the lower-level indicator to the adjacent higher-level indicator. This sub-contribution reflects the importance or influence of the lower-level indicator in constituting the higher-level indicator; that is, it reflects the degree to which changes in the lower-level indicator affect changes in the indicator to be analyzed.

[0054] In this embodiment, the calculation method of lower-level indicators when forming adjacent higher-level indicators is fully considered. Applicable algorithms corresponding to different calculation relationships can be used to accurately quantify the impact of lower-level indicators on adjacent higher-level indicators, providing more accurate and powerful support for business decisions, and thus providing a scientific basis for optimizing resource allocation and formulating improvement strategies.

[0055] In some embodiments of this disclosure, determining the sub-contribution of a lower-level indicator to an adjacent higher-level indicator based on the operational relationship between the lower-level indicator and the adjacent higher-level indicator may include: traversing the indicator tree and sequentially determining the sub-contribution of each child node to its corresponding parent node based on the operational relationship between each child node and its corresponding parent node, so as to serve as the sub-contribution of the corresponding lower-level indicator to the adjacent higher-level indicator.

[0056] In this embodiment, the tree can be traversed layer by layer downwards from the root node using a tree traversal algorithm (such as depth-first search or breadth-first search). For each child node, the operational relationship between it and its parent node is analyzed, such as addition, multiplication, division, or other complex operations. Based on this operational relationship, an appropriate algorithm is used to calculate the child node's contribution to its parent node. This process can continue until the leaf nodes of the tree, ultimately obtaining the child contribution of each lower-level indicator to its adjacent higher-level indicator.

[0057] This embodiment allows for the calculation of sub-contributions by traversing the indicator tree, quantifying the influence of each lower-level indicator on its adjacent higher-level indicator in an ordered manner, thereby providing accurate data support for subsequent root cause analysis.

[0058] In step S130, the cumulative contribution of the lower-level indicator to the indicator to be analyzed is determined according to the step-by-step sub-contribution degree corresponding to the structural relationship between the lower-level indicator and the indicator to be analyzed in the indicator tree.

[0059] In this embodiment of the disclosure, the cumulative contribution of each lower-level indicator to the indicator to be analyzed can be calculated from the bottom of the indicator tree, based on the progressively generated sub-contribution values. The cumulative contribution can be seen as a reflection of the comprehensive influence of the lower-level indicator on the indicator to be analyzed.

[0060] This step quantifies the total impact of each lower-level indicator on the indicator to be analyzed by considering all paths from the lower-level indicator to the indicator to be analyzed. Here, the lower-level indicator can be the lowest-level indicator (i.e., the lower-level indicator corresponding to the leaf node in the indicator tree) or an intermediate-level indicator; this disclosure does not limit it in this way.

[0061] In step S140, the root cause indicators affecting the indicator to be analyzed are determined based on the cumulative contribution of the lower-level indicators to the indicator to be analyzed.

[0062] In this embodiment of the disclosure, root cause indicators affecting the indicator to be analyzed can be identified based on the cumulative contribution of lower-level indicators to the indicator to be analyzed. These root cause indicators are those lower-level indicators with significant cumulative contributions, which can serve as key factors or root causes affecting data changes (such as performance values ​​or result values) of the indicator to be analyzed.

[0063] In an exemplary embodiment, the cumulative contribution of all subordinate indicators can be sorted, and the top-ranked subordinate indicators can be identified as root cause indicators. Alternatively, subordinate indicators whose contribution exceeds a preset threshold can be identified as root cause indicators. Since each cumulative contribution is specific to the indicator to be analyzed, this embodiment calculates each subordinate indicator within the dimension of the indicator to be analyzed. This allows the influence of each subordinate indicator to be compared on the same dimension (the dimension of the indicator to be analyzed), resulting in more reasonable root cause indicators.

[0064] As can be seen from the above steps, the root cause analysis method provided in this disclosure can construct a tree structure based on the operational logic of the indicators to be analyzed. Then, based on the tree structure, the adjacent parent indicators of the lower-level indicators are determined. The sub-contribution of the lower-level indicator to its adjacent parent indicator is determined based on the operational relationship formed by the operations of the lower-level indicator on the adjacent parent indicator. Furthermore, the cumulative contribution of the lower-level indicator to the indicator to be analyzed can be calculated based on the sub-contributions generated level by level from the lower-level indicator to the indicator to be analyzed. Finally, root cause indicators are selected for the indicator to be analyzed based on the cumulative contribution. It is evident that, on the one hand, this scheme decomposes the complex indicator system into a tree structure, making the entire analysis process more systematic and organized. The position of each lower-level indicator in the indicator tree and its relationship with other indicators are clear and easy to understand and analyze. On the other hand, by fully considering the computational composition of lower-level indicators when forming adjacent higher-level indicators, applicable algorithms corresponding to different computational composition relationships can be flexibly adopted to accurately quantify the specific impact of lower-level indicators on adjacent higher-level indicators. This allows the cumulative contribution of each lower-level indicator determined based on the hierarchical sub-contribution degree to more accurately and objectively reflect the impact of lower-level indicators on the indicator to be analyzed, realizing a quantitative assessment of the influence of each lower-level indicator. In turn, it can accurately locate the root cause indicators affecting the indicator to be analyzed, providing accurate and powerful data support for business analysis and decision-making.

[0065] Figure 2 This is a flowchart illustrating the determination of sub-contribution in an indicator root cause analysis method according to some embodiments of this disclosure. In some embodiments of this disclosure, determining the sub-contribution of a lower-level indicator to an adjacent higher-level indicator based on the operational relationship between the lower-level indicator and the adjacent higher-level indicator may include the following steps.

[0066] In step S210, the operational relationship between the lower-level indicator and the adjacent upper-level indicator is determined according to the operational logic.

[0067] In this embodiment of the disclosure, the calculation methods involved in the calculation of adjacent higher-level indicators, i.e., the calculation relationship, can be determined by analyzing the calculation logic. The calculation relationship may include, for example, addition, subtraction, multiplication, division, or other complex calculation relationships.

[0068] For example, assuming that indicator A is the adjacent superior indicator of indicators a1 and a2, and the operation logic indicates that A = a1 + a2, then it can be determined that the operation relationship between the subordinate indicator a1 and the adjacent superior indicator A is addition.

[0069] In step S220, the relation type to which the operation constitutes the relation is determined.

[0070] In this embodiment of the disclosure, the operational relationships can be pre-classified. For example, operational relationships that include addition and / or subtraction can be classified as addition relationships. Specifically, for the lower-level index of the subtrahend (the number after the minus sign) participating in subtraction, it can be converted into its corresponding opposite index so that it can participate in the addition operation.

[0071] In step S230, a contribution algorithm corresponding to the relationship type is used to determine the sub-contribution of the lower-level indicator to the adjacent upper-level indicator.

[0072] In this embodiment of the disclosure, once the relationship type is determined, the contribution algorithm corresponding to that relationship type can be used to determine the sub-contribution of the lower-level indicator to the adjacent upper-level indicator. Different types of contribution algorithms can be applied to different operational relationships to ensure accurate quantification of the impact of each lower-level indicator on the adjacent upper-level indicator.

[0073] Through the embodiments of this disclosure, the contribution of each lower-level indicator to its adjacent higher-level indicator can be accurately determined. Then, the contribution can be calculated level by level in the entire indicator tree, and finally the cumulative contribution of each lower-level indicator to the indicator to be analyzed can be accurately determined. This improves the accuracy and reliability of root cause analysis and helps to more effectively identify the root cause indicators that affect the indicator to be analyzed.

[0074] Figure 3 This is a flowchart illustrating the determination of sub-contribution in an indicator root cause analysis method according to some embodiments of the present disclosure.

[0075] In this embodiment of the disclosure, Figure 2 In the method for determining sub-contribution shown, steps S310 and S320 are respectively related to... Figure 2 Steps S210 and S220 in the method for determining sub-contribution shown correspond to each other and will not be repeated here.

[0076] In this embodiment of the disclosure, Figure 2 Based on the method for determining sub-contribution shown, Figure 3 The method for determining sub-contribution may also include the following steps.

[0077] In step S330, in response to the relationship type being a product relationship, the log-mean Dijkstra index method is used to determine the sub-contribution of the lower-level indicator to the adjacent upper-level indicator.

[0078] In this embodiment of the disclosure, for operational relationships involving multiplication and / or division, such operational relationships can be classified as product relationships. Specifically, for a lower-level index in the denominator involved in division, it can be converted into its corresponding reciprocal index, thus enabling it to participate in the multiplication operation.

[0079] When there is a product relationship between a lower-level indicator and its adjacent higher-level indicator, the Logarithmic Mean Divisia Index (LMDI) can be used to determine the sub-contribution. LMDI is a method for decomposing and analyzing the contribution of each factor to the overall change in a multiplication operation. It allocates the contribution of each factor through logarithmic average weighting and is suitable for handling complex product relationships.

[0080] In an exemplary embodiment, assume the lower-level indicators are N, A, and S, and their adjacent higher-level indicator is Y = N × A × S. The sub-contribution of the lower-level indicator to the adjacent higher-level indicator under the product relationship (including the sub-contribution EP of N to Y) can be determined using the following formula. N EP of A with respect to Y A S for Y's EP S ):

[0081]

[0082] Among them, L(Y 1 ,Y 0 This represents the Logarithmic Weight Average.

[0083]

[0084] In this context, the superscript 1 for Y, N, A, and S represents the actual value (current period), and the superscript 0 represents the target value (previous period).

[0085] The accuracy of the sub-contribution determined using the logarithmic average Dijkstra index method under the product relationship in the embodiments of this disclosure is explained below.

[0086] In the exemplary embodiment, the overall equipment effectiveness (OEE) of the production line is set as Y, the production line time utilization rate is set as N, the production line performance utilization rate is set as A, and the production line yield is set as S. Then the following relationship exists: Y = N × A × S.

[0087] By obtaining the values ​​of line time utilization rate, line performance utilization rate, and line yield in the base period and the current period, the line OEE results in the base period and the current period can be calculated.

[0088] The results of calculating the sub-contributions of line time utilization rate, line performance utilization rate, and line yield to line OEE using sensitivity analysis algorithms are shown in Table 1. The results of calculating the sub-contributions of line time utilization rate, line performance utilization rate, and line yield to line OEE using the logarithmic mean Dijkstra's index (LMDI) method are shown in Table 2.

[0089] Table 1

[0090]

[0091] As shown in Table 1, the sum of the effects of each factor on the objective function Y obtained through sensitivity analysis does not equal the overall effect of the three factors on the objective function Y, thus leading to unclear interpretation issues.

[0092] Table 2

[0093]

[0094]

[0095] As shown in Table 2, after calculating the impact of changes in each factor using LMDI, the sum of the contributions of each factor will equal the rate of change of the overall index Y, and there will be no unclear parts.

[0096] The above comparison verifies that the contribution calculated by the LMDI method is more reasonable, and the calculated sub-contribution can more reasonably and accurately reflect the influence of the lower-level indicator on the adjacent upper-level indicator.

[0097] In step S340, in response to the relationship type being a summary relationship of relative indicators, a two-factor analysis algorithm is used to determine the sub-contribution of the lower-level indicator to the adjacent upper-level indicator.

[0098] In this embodiment of the disclosure, when the subordinate indicator is a relative indicator (such as a ratio, percentage, etc.), and these indicators need to be aggregated to form adjacent superior indicators, a two-factor analysis algorithm is used to determine the sub-contribution. The two-factor analysis algorithm allocates contribution by considering changes in two factors (such as base and ratio), and is suitable for handling the aggregation relationship of relative indicators.

[0099] For example, suppose indicator B is the adjacent superior indicator of indicators b1 and b2, and indicators B, b1, and b2 are all relative indicators, where b1 = x1 / y1, b2 = x2 / y2, and B = (x1+x2) / (y1+y2). Then, the relative indicator B can be called the summary indicator of relative indicators b1 and b2, and the operational relationship between the subordinate indicators b1 and b2 and their adjacent superior indicator B can be called the "summary relationship of relative indicators".

[0100] In an exemplary embodiment, it is assumed that the lower-level indicators are the time utilization rates Yi (i∈{1,…,n}) of multiple lines, and their adjacent upper-level indicators are the project time utilization rates Y.

[0101] The project time utilization rate is defined as follows:

[0102] The business definition of line time utilization rate is:

[0103] The business definition of project time utilization rate is:

[0104]

[0105] Where, x i To represent different production lines, P represents the percentage of each production line's load time in the total project load time. i The percentage of each line's operation time in the total project operation time is represented by S. i Therefore, we have:

[0106]

[0107] Based on this, the following formula can be used to determine the sub-contribution of the lower-level indicator to the adjacent higher-level indicator under the summary relationship of relative number indicators (including each line body x). i For the sub-contribution EPx of Y i ):

[0108] On the one hand, the contribution of the relative index changes of the sub-items (i.e., the line body) can be calculated.

[0109] On the other hand, the contribution of the changes in the proportion of each component (i.e., the line body) to the whole can be calculated.

[0110] Then, the contribution of indicator fluctuations and the contribution of structural changes The sum of these values ​​represents the contribution of the changes in the time utilization rate of each production line to the changes in the project's time utilization rate. (i.e., sub-contribution):

[0111] Among them, the contribution of indicator fluctuations This can be viewed as: Change in line utilization rate (difference between target and actual) * Percentage of line load time in the project's total load time in the previous period. Structural change contribution. It can be viewed as: Change in the proportion of line load time * (current period line time utilization rate - previous period project time utilization rate).

[0112] The following describes the accuracy of the sub-contribution determined by the two-factor analysis algorithm under the aggregation relationship of relative numerical indicators in the embodiments of this disclosure.

[0113] In the exemplary embodiment, "Beijing" and "Shanghai" are two lower-level relative indicators, and "total value" is their adjacent higher-level indicator. By obtaining the values ​​(including numerator and denominator) of "Beijing" and "Shanghai" in the previous and current periods, respectively, the indicator results for "Beijing" and "Shanghai" in the previous and current periods, respectively, as well as the numerator, denominator, and indicator results for "total value" in the previous and current periods, respectively, can be calculated.

[0114] After taking the values ​​in the first case, the results of calculating the sub-contributions of "Beijing" and "Shanghai" to the "total value" using the sensitivity analysis algorithm and the two-factor analysis algorithm are shown in Table 3.

[0115] Table 3

[0116]

[0117] As shown in Table 3, in this embodiment, the total denominator of the lower-level indicators remains unchanged, while the actual change is numerator +3 for Beijing and numerator +8 for Shanghai. The sub-contribution obtained by using the two-factor analysis algorithm can better reflect this information.

[0118] The results of calculating the sub-contributions of "Beijing" and "Shanghai" to the "total value" using the second two-factor analysis algorithm are shown in Table 4.

[0119] Table 4

[0120]

[0121] As shown in Table 4, the overall index in this embodiment increased (0.2->0.25). Although the sub-index Beijing also increased (0.1->0.15), the increase in its numerator was too small and the increase in its denominator was too large, according to the detailed factors. Therefore, for the overall change, the sub-index Beijing actually made a negative contribution. The sub-contribution value obtained using the two-factor analysis algorithm better reflects this information.

[0122] Furthermore, as shown in Tables 3 and 4 above, after calculating the impact of changes in each factor using the two-factor analysis method, the sum of the contributions of each factor can equal the rate of change of the overall index, and there will be no unclear parts.

[0123] The above comparison verifies that the contribution calculated by the two-factor analysis algorithm is more reasonable, and the calculated sub-contribution can more reasonably and accurately reflect the impact of changes in the indicators themselves and changes in the denominator on changes in adjacent higher-level indicators in the relative number lower-level indicators.

[0124] In step S350, in response to the relationship type being an additive relationship, a sensitivity analysis algorithm is used to determine the sub-contribution of the lower-level indicator to the adjacent upper-level indicator.

[0125] In this embodiment of the disclosure, when there is an additive relationship between a lower-level indicator and its adjacent higher-level indicator, a sensitivity analysis algorithm is used to determine the sub-contribution. Sensitivity analysis can quantify the contribution of each lower-level indicator to the overall change by calculating the sensitivity (i.e., rate of change or elasticity) of each lower-level indicator to its adjacent higher-level indicator.

[0126] In an exemplary embodiment, additive index aggregation methods include types such as sum and count, characterized in that the total index equals the sum of the index values ​​of each dimension item, and its EP (i.e., sub-contribution) calculation method can be:

[0127]

[0128] Among them EP ij This represents the contribution of the i-th and j-th dimension terms. This represents the current value of the i-th and j-th dimension items. Y represents the base period value of the i-th and j-th dimension terms. 1 Y represents the current value of the overall KPI indicator. 0 This represents the base period value of the overall KPI. The EP value under the summation relationship can be intuitively understood as the percentage change of each sub-indicator relative to the total change of the KPI.

[0129] These contribution algorithms, corresponding to different relationship types, ensure that for different types of operational relationships, the sub-contribution of lower-level indicators to adjacent higher-level indicators can be accurately and reasonably quantified. By applying appropriate contribution algorithms, the accuracy and reliability of root cause analysis can be improved, thereby more effectively identifying the fundamental factors affecting the indicators under analysis.

[0130] Figure 4 This is a flowchart illustrating the construction of an indicator tree in an indicator root cause analysis method according to some embodiments of this disclosure. In some embodiments of this disclosure, constructing the indicator tree based on the computational logic of the indicator to be analyzed may include the following steps.

[0131] Step S410: Create a root node corresponding to the indicator to be analyzed.

[0132] Step S420: Determine each lower-level indicator and its hierarchical relationship according to the calculation logic.

[0133] Step S430: Create non-root nodes corresponding to each lower-level indicator, and determine the node connection relationship in the indicator tree according to the operation hierarchy relationship.

[0134] In this embodiment, nodes can be connected to form a complete index tree based on the hierarchical relationship determined by the computational logic. Specifically, each non-root node is connected to its parent node. When connecting nodes, the previously determined hierarchical order and computational logic must be followed to ensure the correctness and integrity of the tree structure.

[0135] This embodiment demonstrates how a complete indicator tree can be constructed based on the computational logic of the indicators to be analyzed. This indicator tree can not only be used for data visualization and display but also serve as the foundation for subsequent data analysis, calculation, and optimization. In practical applications, the indicator tree may need further customization and optimization based on specific needs and business scenarios.

[0136] Figure 5 This is a flowchart illustrating the determination of cumulative contribution in a root cause analysis method for an indicator, according to some embodiments of this disclosure. In some embodiments of this disclosure, determining the cumulative contribution of a lower-level indicator to the indicator to be analyzed, based on the hierarchical sub-contribution corresponding to the structural relationship between the lower-level indicator and the indicator to be analyzed in the indicator tree, may include the following steps.

[0137] Step S510: Determine the contribution path from the non-root node to the root node corresponding to the lower-level indicator based on the indicator tree.

[0138] In this embodiment, the contribution path can be obtained by starting from the root node of the indicator tree and traversing downwards along the tree structure until the non-root node corresponding to the lower-level indicator of interest is reached. During the traversal, the nodes visited and their corresponding edges are recorded, forming a path from the root node to the non-root node.

[0139] Step S520: The sub-contribution degree generated on the contribution path is determined as the step-by-step sub-contribution degree.

[0140] In this embodiment of the disclosure, for each node on the contribution path, its sub-contribution to the parent node is calculated based on its position in the index tree and the computational logic. These sub-contributions are arranged in the order of the path, forming a hierarchical sub-contribution system.

[0141] Step S530: Determine the cumulative contribution of the lower-level indicator to the indicator to be analyzed based on the progressive sub-contribution.

[0142] In this embodiment of the disclosure, this can be achieved by performing a predetermined calculation on all sub-contributions along the contribution path. The specific calculation method depends on the calculation logic of the indicator tree and the definition of the sub-contributions.

[0143] In some embodiments of this disclosure, step S530 may include: multiplying the successive sub-contribution values ​​to obtain the cumulative contribution of the lower-level indicator to the indicator to be analyzed.

[0144] Through the embodiments disclosed herein, a tree structure can be used as an auxiliary tool to calculate the degree of influence of each lower-level indicator on the indicator to be analyzed, thereby identifying key influencing factors and providing strong support for subsequent decision-making and optimization.

[0145] Figure 6 This is a schematic diagram of a tree structure in an index root cause analysis method according to some embodiments of this disclosure. For example... Figure 6 As shown, this is an indicator tree, where the indicator X (root node) to be analyzed can be deconstructed as X = A + B; the lower-level indicator A can be deconstructed as A = C × D; the lower-level indicator B is a relative number indicator, the value of which is calculated based on the numerator and denominator of its three lower-level relative number indicators b1, b2, and b3; the lower-level indicator C can be written in the form C = c1 + c2.

[0146] refer to Figure 6 Sensitivity analysis models can be used to calculate the contributions of changes in A and B to changes in X, thus yielding NA and NB.

[0147] The LMDI (Logarithmic Mean Dichotomy) model was used to calculate the contributions of changes in C and D to changes in A, and N was derived. C and N D ;

[0148] The contribution of changes in b1, b2, and b3 to the change in B was calculated using a two-factor analysis model (two-factor analysis algorithm), and N was obtained. b1 N b2 and N b3 ;

[0149] The sensitivity analysis model (sensitivity analysis algorithm) is used to calculate the contribution of changes in c1 and c2 to the change in C, and N is obtained. c1 and N c2 .

[0150] Finally, for each leaf indicator, its sub-contributions are multiplied sequentially to obtain the contribution of each leaf indicator's change to the change in X. For example:

[0151] To calculate the contribution of the change in c1 to the change in X, one can calculate the value of Nc1 × NC × NA. To calculate the contribution of the change in b1 to the change in X, one can calculate the value of Nb1 × NB.

[0152] In some embodiments of this disclosure, determining the root cause index affecting the index to be analyzed based on the cumulative contribution of the lower-level index to the index to be analyzed includes: determining the lower-level index corresponding to the leaf node in the index tree; selecting the target cumulative contribution of the lower-level index to the index to be analyzed in descending order, and calculating the sum of the contribution of the selected target cumulative contributions; in response to the sum of the contribution being greater than or equal to a contribution threshold, determining the lower-level index corresponding to the selected target cumulative contribution as the root cause index.

[0153] In this embodiment, leaf nodes represent the lowest-level, indivisible indicators, i.e., lower-level indicators. These indicators are the starting point for data collection and analysis, and also potential factors that may affect higher-level indicators (including the indicators to be analyzed). The contribution threshold is a preset threshold used to determine whether the cumulative contribution of the selected target is large enough to identify the corresponding lower-level indicator as the root cause indicator.

[0154] Through the embodiments disclosed herein, the root cause indicators that have the greatest impact on the indicators to be analyzed can be screened from the lower-level indicators, providing strong support for subsequent decision-making and optimization.

[0155] It should be noted that the above figures are merely illustrative representations of the processes included in methods according to some embodiments of this disclosure, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may, for example, be executed synchronously or asynchronously in multiple modules.

[0156] The following are embodiments of the apparatus disclosed herein, which can be used to execute embodiments of the method disclosed herein. For details not disclosed in the apparatus embodiments of this disclosure, please refer to the embodiments of the method disclosed herein.

[0157] Figure 7 This is a block diagram illustrating an indicator root cause analysis device according to some embodiments of the present disclosure. (Refer to...) Figure 7 The device includes: a construction unit 701, a sub-contribution determination unit 702, a cumulative contribution determination unit 703, and a root cause determination unit 704.

[0158] A construction unit 701 is used to construct an indicator tree based on the operational logic of the indicator to be analyzed; wherein the root node in the indicator tree corresponds to the indicator to be analyzed, and the non-root nodes correspond to the lower-level indicators included in the operational logic; a sub-contribution determination unit 702 is used to determine the adjacent upper-level indicators of the lower-level indicators in the indicator tree, and determine the sub-contribution of the lower-level indicators to the adjacent upper-level indicators based on the operational relationship between the lower-level indicators and the adjacent upper-level indicators; a cumulative contribution determination unit 703 is used to determine the cumulative contribution of the lower-level indicators to the indicator to be analyzed based on the hierarchical sub-contribution corresponding to the structural relationship between the lower-level indicators and the indicator to be analyzed in the indicator tree; and a root cause determination unit 704 is used to determine the root cause indicators affecting the indicator to be analyzed based on the cumulative contribution of the lower-level indicators to the indicator to be analyzed.

[0159] In some embodiments of this disclosure, the sub-contribution determination unit 702 determines the sub-contribution of the lower-level indicator to the adjacent upper-level indicator based on the operational relationship between the lower-level indicator and the adjacent upper-level indicator, including: determining the operational relationship between the lower-level indicator and the adjacent upper-level indicator according to the operational logic; determining the relationship type to which the operational relationship belongs; and using a contribution algorithm corresponding to the relationship type to determine the sub-contribution of the lower-level indicator to the adjacent upper-level indicator.

[0160] In some embodiments of this disclosure, the sub-contribution determination unit 702 is further configured to: determine the sub-contribution of the lower-level indicator to the adjacent upper-level indicator using the log-mean Dijkstra index method in response to the relationship type being a product relationship.

[0161] In some embodiments of this disclosure, the sub-contribution determination unit 702 is further configured to: in response to the relationship type being a summary relationship of relative numerical indicators, use a two-factor analysis algorithm to determine the sub-contribution of the lower-level indicator to the adjacent upper-level indicator.

[0162] In some embodiments of this disclosure, the sub-contribution determination unit 702 is further configured to: in response to the relationship type being an additive relationship, use a sensitivity analysis algorithm to determine the sub-contribution of the lower-level indicator to the adjacent upper-level indicator.

[0163] In some embodiments of this disclosure, the construction unit 701 constructs an indicator tree according to the operation logic of the indicator to be analyzed, including: creating a root node corresponding to the indicator to be analyzed; determining each lower-level indicator and its operation hierarchy relationship according to the operation logic; creating non-root nodes corresponding to each lower-level indicator, and determining the node connection relationship in the indicator tree according to the operation hierarchy relationship.

[0164] In some embodiments of this disclosure, the sub-contribution determination unit 702 determines the sub-contribution of the lower-level indicator to the adjacent upper-level indicator based on the operational relationship between the lower-level indicator and the adjacent upper-level indicator. This includes: traversing the indicator tree and determining the sub-contribution of each child node to its corresponding parent node in sequence based on the operational relationship between each child node and its corresponding parent node, so as to serve as the sub-contribution of the corresponding lower-level indicator to the adjacent upper-level indicator.

[0165] In some embodiments of this disclosure, the cumulative contribution determination unit 703 determines the cumulative contribution of the lower-level indicator to the indicator to be analyzed based on the hierarchical sub-contribution corresponding to the structural relationship between the lower-level indicator and the indicator to be analyzed in the indicator tree. This includes: determining the contribution path from the non-root node corresponding to the lower-level indicator to the root node according to the indicator tree; determining the sub-contribution generated on the contribution path as the hierarchical sub-contribution; and determining the cumulative contribution of the lower-level indicator to the indicator to be analyzed based on the hierarchical sub-contribution.

[0166] In some embodiments of this disclosure, the cumulative contribution determination unit 703 determines the cumulative contribution of the lower-level indicator to the indicator to be analyzed based on the progressive sub-contribution, including: multiplying the progressive sub-contribution to obtain the cumulative contribution of the lower-level indicator to the indicator to be analyzed.

[0167] In some embodiments of this disclosure, the root cause determination unit 704 determines the root cause index affecting the index to be analyzed based on the cumulative contribution of the lower-level index to the index to be analyzed, including: determining the lower-level index corresponding to the leaf node in the index tree; selecting the target cumulative contribution of the lower-level index to the index to be analyzed in descending order, and calculating the sum of the contribution of the selected target cumulative contributions; in response to the sum of the contribution being greater than or equal to the contribution threshold, determining the lower-level index corresponding to the selected target cumulative contribution as the root cause index.

[0168] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0169] Figure 8 This is a block diagram illustrating an apparatus 800 for root cause analysis of indicators, according to some embodiments of the present disclosure. For example, apparatus 800 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0170] Reference Figure 8The device 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0171] Processing component 802 typically controls the overall operation of device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0172] Memory 804 is configured to store various types of data to support the operation of device 800. Examples of this data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0173] The power supply component 806 provides power to the various components of the device 800. The power supply component 806 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power to the device 800.

[0174] Multimedia component 808 includes a screen that provides an output interface between the device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0175] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0176] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0177] Sensor assembly 814 includes one or more sensors for providing status assessments of various aspects of device 800. For example, sensor assembly 814 may detect the on / off state of device 800, the relative positioning of components such as the display and keypad of device 800, changes in the position of device 800 or a component of device 800, the presence or absence of user contact with device 800, the orientation or acceleration / deceleration of device 800, and temperature changes of device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0178] Communication component 816 is configured to facilitate wired or wireless communication between device 800 and other devices. Device 800 can access wireless networks based on communication standards, such as WiFi, 3G, 4G, 5G, other communication standards, or combinations thereof. In some embodiments of this disclosure, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In some embodiments of this disclosure, communication component 816 further includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0179] In some embodiments of this disclosure, the apparatus 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0180] In some embodiments of this disclosure, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions that can be executed by a processor 820 of device 800 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0181] A non-transitory computer-readable storage medium, when instructions in the storage medium are executed by a processor of a mobile terminal, enables the mobile terminal to execute a root cause analysis method for an indicator. The method includes: constructing an indicator tree based on the computational logic of an indicator to be analyzed; wherein the root node in the indicator tree corresponds to the indicator to be analyzed, and non-root nodes correspond to lower-level indicators included in the computational logic; determining the adjacent higher-level indicators of the lower-level indicators in the indicator tree, and determining the sub-contribution degree of the lower-level indicators to the adjacent higher-level indicators based on the computational relationship between the lower-level indicators and the adjacent higher-level indicators; determining the cumulative contribution degree of the lower-level indicators to the indicator to be analyzed based on the hierarchical sub-contribution degree corresponding to the structural relationship between the lower-level indicators and the indicator to be analyzed in the indicator tree; and determining the root cause indicators affecting the indicator to be analyzed based on the cumulative contribution degree of the lower-level indicators to the indicator to be analyzed.

[0182] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0183] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for root cause analysis of indicators, characterized in that, include: An indicator tree is constructed based on the calculation logic of the indicator to be analyzed; wherein, the root node in the indicator tree corresponds to the indicator to be analyzed, and the non-root nodes correspond to the lower-level indicators included in the calculation logic. Determine the adjacent parent indicators of the lower-level indicator in the indicator tree, and determine the sub-contribution degree of the lower-level indicator to the adjacent parent indicators based on the operational relationship between the lower-level indicator and the adjacent parent indicators. Based on the hierarchical sub-contribution degree corresponding to the structural relationship between the lower-level indicator and the indicator to be analyzed in the indicator tree, the cumulative contribution degree of the lower-level indicator to the indicator to be analyzed is determined. Based on the cumulative contribution of the lower-level indicators to the indicator to be analyzed, the root cause indicators affecting the indicator to be analyzed are determined.

2. The method according to claim 1, characterized in that, Based on the computational relationship between the lower-level indicators and adjacent higher-level indicators, the sub-contribution degree of the lower-level indicators to adjacent higher-level indicators is determined, including: The operational relationship between the lower-level indicators and their adjacent higher-level indicators is determined based on the operational logic. Determine the relation type to which the operation constitutes the relation; The contribution degree of the lower-level indicator to the adjacent upper-level indicator is determined using the contribution algorithm corresponding to the relationship type.

3. The method according to claim 2, characterized in that, The method further includes: In response to the relationship type being a product relationship, the log-mean Dijkstra index method is used to determine the sub-contribution of the lower-level indicator to the adjacent higher-level indicator.

4. The method according to claim 2, characterized in that, The method further includes: In response to the relationship type being a summary relationship of relative indicators, a two-factor analysis algorithm is used to determine the sub-contribution of the lower-level indicator to the adjacent higher-level indicator.

5. The method according to claim 2, characterized in that, The method further includes: In response to the relationship type being additive, a sensitivity analysis algorithm is used to determine the sub-contribution of the lower-level indicator to the adjacent higher-level indicator.

6. The method according to claim 1, characterized in that, Construct an indicator tree based on the calculation logic of the indicator to be analyzed, including: Create a root node corresponding to the indicator to be analyzed; Determine each lower-level indicator and its hierarchical relationship based on the aforementioned operational logic; Create non-root nodes corresponding to each lower-level indicator, and determine the node connection relationship in the indicator tree according to the operation hierarchy relationship.

7. The method according to claim 1, characterized in that, Based on the computational relationship between the lower-level indicators and adjacent higher-level indicators, the sub-contribution degree of the lower-level indicators to adjacent higher-level indicators is determined, including: Traverse the index tree and determine the sub-contribution of each sub-node to its corresponding parent node based on the operational relationship between each sub-node and its corresponding parent node. This sub-contribution is then used as the sub-contribution of the corresponding lower-level index to its adjacent higher-level index.

8. The method according to claim 1, characterized in that, Based on the hierarchical sub-contribution degree corresponding to the structural relationship between the lower-level indicator and the indicator to be analyzed in the indicator tree, the cumulative contribution degree of the lower-level indicator to the indicator to be analyzed is determined, including: The contribution path from the non-root node to the root node corresponding to the lower-level indicator is determined based on the indicator tree. The sub-contribution degree generated on the contribution path is determined as the step-by-step sub-contribution degree; The cumulative contribution of the lower-level indicator to the indicator to be analyzed is determined based on the progressive sub-contribution.

9. The method according to claim 8, characterized in that, Determining the cumulative contribution of the lower-level indicator to the indicator to be analyzed based on the hierarchical sub-contribution degree includes: The cumulative contribution of the lower-level indicators to the indicator to be analyzed is obtained by multiplying the successive sub-contribution values.

10. The method according to claim 1, characterized in that, Based on the cumulative contribution of the lower-level indicators to the indicator to be analyzed, the root cause indicators affecting the indicator to be analyzed are determined, including: Determine the underlying sub-indicators corresponding to the leaf nodes in the indicator tree; Select the cumulative contribution of the lower-level indicators to the indicator to be analyzed in descending order of their contribution, and calculate the sum of the cumulative contributions of the selected indicators. In response to the sum of the contributions being greater than or equal to the contribution threshold, the underlying lower-level indicator corresponding to the selected target cumulative contribution is determined as the root cause indicator.

11. A root cause analysis device for indicators, characterized in that, include: A construction unit is used to construct an indicator tree based on the calculation logic of the indicator to be analyzed; wherein, the root node in the indicator tree corresponds to the indicator to be analyzed, and the non-root nodes correspond to the lower-level indicators included in the calculation logic. The sub-contribution determination unit is used to determine the adjacent upper-level indicators of the lower-level indicators in the indicator tree, and to determine the sub-contribution of the lower-level indicators to the adjacent upper-level indicators based on the operational relationship between the lower-level indicators and the adjacent upper-level indicators. The cumulative contribution determination unit is used to determine the cumulative contribution of the lower-level indicator to the indicator to be analyzed based on the step-by-step sub-contribution corresponding to the structural relationship between the lower-level indicator and the indicator to be analyzed in the indicator tree. The root cause determination unit is used to determine the root cause indicators affecting the indicator to be analyzed based on the cumulative contribution of the lower-level indicators to the indicator to be analyzed.

12. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to implement the steps of the method according to any one of claims 1-10.

13. A non-transitory computer-readable storage medium, wherein instructions in the storage medium, when executed by a processor of a mobile terminal, enable the mobile terminal to perform an indicator root cause analysis method, the method comprising: An indicator tree is constructed based on the calculation logic of the indicator to be analyzed; wherein, the root node in the indicator tree corresponds to the indicator to be analyzed, and the non-root nodes correspond to the lower-level indicators included in the calculation logic. Determine the adjacent parent indicators of the lower-level indicator in the indicator tree, and determine the sub-contribution degree of the lower-level indicator to the adjacent parent indicators based on the operational relationship between the lower-level indicator and the adjacent parent indicators. Based on the hierarchical sub-contribution degree corresponding to the structural relationship between the lower-level indicator and the indicator to be analyzed in the indicator tree, the cumulative contribution degree of the lower-level indicator to the indicator to be analyzed is determined. Based on the cumulative contribution of the lower-level indicators to the indicator to be analyzed, the root cause indicators affecting the indicator to be analyzed are determined.

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