Process resource analysis method, device and machine-readable storage medium

Abstract

The present application discloses a process resource analysis method, device and machine-readable storage medium, which relate to the field of intelligent production technology. The method includes: based on the type of preset metadata model, identifying and collecting target metadata in the process resources to be analyzed, obtaining different types of meta-models to be analyzed, and determining the first data quality score of each meta-model to be analyzed based on the data attributes in each meta-model to be analyzed; determining the importance score of the process resources to be analyzed based on the preset importance index, and determining the resource similarity data of the process resources to be analyzed based on the preset similarity index; determining the data utilization rate of the process resources to be analyzed based on the preset isolation index. Then, the analysis results of the process resources to be analyzed are determined based on the importance score, resource similarity data, data utilization rate and all first data quality scores. It realizes the quantitative analysis of process resources, provides objective and effective analysis results, and provides data reference for the effective utilization of process resources.

Description

Technical Field

[0001] The present application relates to the field of intelligent production technology, and specifically to a process resource analysis method, device and machine-readable storage medium. Background Art

[0002] Process resources refer to all tangible and intangible resources used in the production process. These resources jointly support the execution of the process flow and ensure that the product can be produced according to the established design and quality standards. The effective management and optimization of process resources are of great significance to improving production efficiency, reducing costs and ensuring product quality. In-depth analysis and intelligent management of process resources are the core driving force for promoting intelligent production.

[0003] Existing process resource analysis and management are usually based on basic data and the experience of relevant designers, without specific quantitative data as a measurement standard. It is difficult for users to intuitively understand the value of relevant data. Although this analysis and management method that relies too much on personal experience and intuition can make effective analysis based on the solid professional knowledge of relevant personnel in some cases, it also has obvious limitations. Different designers and process personnel may have different evaluation criteria and preferences, which may lead to inconsistent standards when analyzing process resources, and personal experience may be limited by knowledge scope and subjective bias, and it is impossible to fully consider all influencing factors. Therefore, how to improve the intelligence of process resource analysis and management is an urgent problem that needs to be solved. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the purpose of the embodiments of the present application is to provide a process resource analysis method, device and machine-readable storage medium.

[0005] In order to achieve the above objectives, the present application provides a process resource analysis method in a first aspect, comprising:

[0006] Based on the type of preset metadata model, target metadata in the process resources to be analyzed is identified and collected to obtain different types of meta-models to be analyzed;

[0007] Determine a first data quality score of each meta-model to be analyzed based on the data attributes in each meta-model to be analyzed;

[0008] Determine the importance score of the process resource to be analyzed based on the preset importance index, wherein the preset importance index includes in-degree centrality, out-degree centrality and traceability depth;

[0009] Determining resource similarity data of the process resources to be analyzed based on a preset similarity index;

[0010] Determine data utilization of process resources to be analyzed based on preset isolation index;

[0011] The analysis result of the process resource to be analyzed is determined based on the importance score, the resource similarity data, the data utilization rate and all the first data quality scores.

[0012] In the embodiment of the present application, determining the first data quality score of each meta-model to be analyzed based on the data attributes in each meta-model to be analyzed includes:

[0013] For each type of metamodel to be analyzed, determining the sub-data quality score of each target metadata based on the data attributes of each target metadata in the metamodel to be analyzed;

[0014] determining a second data quality score based on an average of all sub-data quality scores;

[0015] Determine the data link integrity score based on the lineage attribute of the metamodel to be analyzed, wherein the lineage attribute belongs to the data attribute;

[0016] A first data quality score of the meta-model to be analyzed is determined based on the second data quality score and the data link integrity score.

[0017] In the embodiment of the present application, for each type of metamodel to be analyzed, the sub-data quality score of each target metadata is determined based on the data attributes of each target metadata in the metamodel to be analyzed, including:

[0018] For each target metadata in each type of metamodel to be analyzed, based on the verification attributes of the target metadata, all data attributes of the target metadata are verified respectively;

[0019] Determine the attribute score of each data attribute based on the verification result and attribute weight of each data attribute;

[0020] The ratio of the sum of the attribute scores of all data attributes based on the target metadata to the sum of the weights of all data attributes is the sub-data quality score of the target metadata.

[0021] In the embodiment of the present application, determining the data link integrity score based on the lineage attribute of the meta-model to be analyzed includes:

[0022] Determine all the lineage nodes of the metamodel to be analyzed based on the lineage attributes of the metamodel to be analyzed;

[0023] Determine the target lineage node that has collected the target metadata among all lineage nodes;

[0024] The data link integrity score is determined based on the proportion of the target lineage node among all lineage nodes.

[0025] In the embodiment of the present application, the importance score of the process resource to be analyzed is determined based on the preset importance index, including:

[0026] Determine the correlation between all data points in the process resources to be analyzed;

[0027] For each data point, the in-degree centrality score, out-degree centrality score and traceability depth score of the data point are determined based on the association relationship, and the sum of the in-degree centrality score, out-degree centrality score and traceability depth score is used as the importance score of the process resource to be analyzed, where the in-degree centrality score is the number of references of the data point by other data points, the out-degree centrality score is the number of other data points that the data point depends on, the traceability depth score is the number of other data points corresponding to the maximum traceability depth of the data point, and other data points are data points other than the data point among all data points.

[0028] In the embodiment of the present application, the resource similarity data of the process resources to be analyzed is determined based on the preset similarity index, including:

[0029] Determine the correlation between all data points in the process resources to be analyzed;

[0030] For each data point in the process resource to be analyzed, all adjacent data points of the data point are determined based on the association relationship;

[0031] For each adjacent data point, determining a union of a first data value of the adjacent data point and a second data value of the data point;

[0032] Determine the number of identicalities and differences between the first data value and the second data value;

[0033] Determine resource similarity sub-data of adjacent data points and data points, wherein the resource similarity sub-data includes a first proportion and a second proportion, the first proportion is the proportion of the same number in the union, and the second proportion is the proportion of the different numbers in the union;

[0034] The resource similarity data of the process resources to be analyzed are generated based on the resource similarity sub-data corresponding to all data points.

[0035] In the embodiment of the present application, the determination of data utilization of the process resources to be analyzed based on the preset isolation index includes:

[0036] Determine the correlation between all data points in the process resources to be analyzed;

[0037] For each data point, the in-degree centrality score and out-degree centrality score of the data point are determined based on the association relationship, where the in-degree centrality score is the number of references of the data point by other data points, and the out-degree centrality score is the number of other data points that the data point depends on, and other data points are all data points except the data point;

[0038] Determine whether the data point meets the preset isolation index based on the in-degree centrality score and the out-degree centrality score, where the preset isolation index is that both the in-degree centrality score and the out-degree centrality score are 0;

[0039] The proportion of data points that meet the preset isolation index among all data points is determined as data utilization.

[0040] In the embodiment of the present application, the steps of constructing the preset metadata model include:

[0041] Determine the type of initial metadata model based on input resource analysis requirements;

[0042] Based on the resource analysis requirements and types, determine the business attributes, data types, validation attributes, weights, scoring attributes, and traceability attributes of the metadata in the initial metadata model. Business attributes are used to define the data type of metadata, data types are used to define the representation of metadata, validation attributes are used to determine validation rules, scoring attributes are used to define whether metadata participates in data quality scoring, and traceability attributes are used to define whether the attribute value of metadata is derived from other attributes.

[0043] Determine the lineage attributes of each metadata based on resource analysis requirements and traceability attributes;

[0044] The business attributes, data types, verification attributes, weights, scoring attributes, and lineage attributes of the metadata are determined as data attributes of the metadata.

[0045] A second aspect of the present application provides a process resource analysis device, comprising:

[0046] a memory configured to store instructions;

[0047] The processor is configured to call instructions from a memory and implement the process resource analysis method as described in the above embodiment when executing the instructions.

[0048] A third aspect of the present application provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to enable a machine to execute the process resource analysis method as described in the above embodiment.

[0049] Through the above technical solution, based on the type of preset metadata model, the target metadata in the process resources to be analyzed is identified and collected to obtain different types of meta models to be analyzed, and the first data quality score of each meta model to be analyzed is determined based on the data attributes in each meta model to be analyzed; the first data quality score can determine the quality of the process resources to be analyzed, and provide effective reference data for managers to control the quality of process resources. The importance score of the process resources to be analyzed is determined based on the preset importance index, wherein the preset importance index includes in-degree centrality, out-degree centrality and traceability depth; the resource similarity data of the process resources to be analyzed is determined based on the preset similarity index; the data utilization rate of the process resources to be analyzed is determined based on the preset isolation index. The importance score, resource similarity data and data utilization rate of the process resources to be analyzed are determined respectively, so as to realize the multi-faceted analysis of the process resources to be analyzed, provide comprehensive analysis data, and improve the comprehensiveness and diversity of data analysis. Then, the analysis results of the process resources to be analyzed are determined based on the importance score, resource similarity data, data utilization rate and all first data quality scores. It realizes the quantitative analysis of process resources, reduces the deviation of subjective judgment, provides more objective and effective analysis results, and provides effective data reference for subsequent resource allocation, product cost reduction and efficiency improvement, and resource performance control.

[0050] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings:

[0052] Figure 1 A schematic diagram of a process resource analysis method according to an embodiment of the present application is schematically shown;

[0053] Figure 2 The schematic diagram shows the structure of a process resource analysis device according to an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0055] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back...), such directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0056] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0057] Figure 1 The following schematically shows a flow chart of a process resource analysis method according to an embodiment of the present application. Figure 1 As shown, the embodiment of the present application provides a process resource analysis method, which may include the following steps:

[0058] Step 100, identifying and collecting target metadata in the process resources to be analyzed based on the type of preset metadata model, and obtaining different types of metadata models to be analyzed;

[0059] In this embodiment, it should be noted that the metadata model is a model used to describe the structure and relationship of metadata, which helps people understand and manage metadata. Metadata is "data about data" and contains descriptive information about data items, such as entities, attributes, relationships, etc. The metadata model generally involves the definition of metadata items, the relationship between metadata, and how metadata is organized and stored. In this embodiment, process resource data is analyzed and managed based on the metadata model. The preset metadata model is a metadata model constructed based on the analysis needs of management personnel for process resources.

[0060] Specifically, in one embodiment, the steps of constructing the preset metadata model include:

[0061] Determine the type of initial metadata model based on input resource analysis requirements;

[0062] Based on the resource analysis requirements and types, determine the business attributes, data types, validation attributes, weights, scoring attributes, and traceability attributes of the metadata in the initial metadata model. Business attributes are used to define the data type of metadata, data types are used to define the representation of metadata, validation attributes are used to determine validation rules, scoring attributes are used to define whether metadata participates in data quality scoring, and traceability attributes are used to define whether the attribute value of metadata is derived from other attributes.

[0063] Determine the lineage attributes of each metadata based on resource analysis requirements and traceability attributes;

[0064] The business attributes, data types, verification attributes, weights, scoring attributes, and lineage attributes of the metadata are determined as data attributes of the metadata.

[0065] In this embodiment, it should be noted that the resource analysis requirements include the expectations of managers to analyze the quality of process resources, the impact between resources, and resource costs. The resource analysis requirements may include reference standards for classifying process resources. The types of process resources may include total process resources, professional process resources, process resources, raw material resources, workstation resources, and material resources. The initial metadata model refers to a model architecture for data conversion of a certain type of process resources. Based on the initial metadata model, different types of process resources can be converted into different types of metadata, for example, total process metadata, professional process metadata, process metadata, raw material metadata, workstation metadata, and material metadata. The initial metadata model includes the types of process resources that need to be collected, that is, different types of initial metadata models have different data sources for process resources, and different data sources may have different data collection methods. The data collection method may include accessing a database, EXCEL import, page editing import, and other methods to collect and maintain metadata.

[0066] It should be noted that for each type of initial metadata model, based on the expected analysis standard for the process resource of this type in the resource analysis requirement, the business attributes, data types, verification attributes, weights and scoring attributes of the metadata in the initial metadata model are defined, wherein the business attributes are used to define the data types of the metadata, such as basic metadata, management metadata and business metadata, etc. In this embodiment, the cost indicator formula of the metadata is also defined based on the business attributes to calculate the estimated cost of the metadata. The data type is used to define the representation form of the metadata, such as text, number, enumeration, reference and the like. The verification attribute is used to determine the verification rule, which may include regular expression verification, range verification, enumeration verification and the like. The scoring attribute is used to define whether the metadata participates in the data quality scoring. The weight is used to indicate the relative importance of the metadata, and the specific value is determined based on the resource analysis requirement. The traceability attribute is used to define whether the attribute value of the metadata is derived from other attributes. Based on the traceability attribute, the blood relationship between the attributes can be determined, and the blood relationship of each metadata can be determined based on the blood relationship. The business attributes, data types, verification attributes, weights, scoring attributes and blood relationship attributes of the metadata are the data attributes of the metadata.

[0067] For each different type of preset metadata model, resource data corresponding to the preset metadata model in the process resources to be analyzed is identified and collected from different data sources respectively, and the resource data is converted into target metadata based on the preset metadata model to generate the metamodel to be analyzed.

[0068] Step 200, determining a first data quality score of each meta-model to be analyzed based on the data attributes in each meta-model to be analyzed;

[0069] In this embodiment, for each different type of metamodel to be analyzed, the first data quality score of the metamodel to be analyzed of the type is calculated based on the data attributes in the metadata to be analyzed, that is, the first data quality score of the process resource to be analyzed corresponding to the type. The first data quality score includes the second data quality score and the data link integrity score, wherein the second data quality score refers to the data quality score of the metamodel to be analyzed.

[0070] Specifically, in one embodiment, determining the first data quality score of each meta-model to be analyzed based on the data attributes in each meta-model to be analyzed includes:

[0071] For each type of metamodel to be analyzed, determining the sub-data quality score of each target metadata based on the data attributes of each target metadata in the metamodel to be analyzed;

[0072] determining a second data quality score based on an average of all sub-data quality scores;

[0073] Determine the data link integrity score based on the lineage attribute of the metamodel to be analyzed, wherein the lineage attribute belongs to the data attribute;

[0074] A first data quality score of the meta-model to be analyzed is determined based on the second data quality score and the data link integrity score.

[0075] It should be noted that each type of metamodel to be analyzed includes multiple target metadata. Each target metadata corresponds to a defined data attribute. In this embodiment, a data quality score is calculated for the data attribute of each target metadata, that is, the sub-data quality score of the target metadata, and then the average of the sub-data quality scores of all target metadata is calculated to obtain the data quality score of the metamodel to be analyzed, that is, the second data quality score. Taking the process resource to be analyzed as BOM (Bill of Materials) as an example, all nodes of the BOM can correspond to a target metadata, determine the sub-data quality scores corresponding to all nodes of the BOM, and calculate the average based on the sub-data quality scores of all nodes in the BOM to obtain the data quality score corresponding to the BOM.

[0076] It should be noted that the lineage attribute can characterize the lineage relationship between target metadata. Based on the lineage attributes of each target metadata in the metamodel to be analyzed, the link association between each target metadata can be determined. Taking the production process resources as an example, the complete data link from design to manufacturing in the metamodel to be analyzed can be determined based on the lineage attributes of each target metadata. In this embodiment, whether the data link is complete is determined based on the lineage attributes of the metamodel to be analyzed, and a data link integrity score is obtained. The sum of the second data quality score and the data link integrity score is used as the first data quality score of the metamodel to be analyzed.

[0077] Specifically, in one embodiment, for each type of metamodel to be analyzed, the sub-data quality score of each target metadata is determined based on the data attributes of each target metadata in the metamodel to be analyzed, including:

[0078] For each target metadata in each type of metamodel to be analyzed, based on the verification attributes of the target metadata, all data attributes of the target metadata are verified respectively;

[0079] Determine the attribute score of each data attribute based on the verification result and attribute weight of each data attribute;

[0080] The ratio of the sum of the attribute scores of all data attributes based on the target metadata to the sum of the weights of all data attributes is the sub-data quality score of the target metadata.

[0081] In this embodiment, it should be noted that each data attribute of the target metadata includes a verification attribute. Based on the verification attribute, it can be determined whether all data attributes of the target metadata are compliant. Based on the verification rules in the verification attribute, all data attributes included in the target metadata are verified respectively. The data attributes that pass the verification will receive a preset basic score, and the basic score of the data attributes that fail the verification is 0. The basic score of the data attribute is multiplied by the weight of the data attribute, and the obtained product is used as the attribute score of the data attribute. In this embodiment, the basic score is set to 1. The sum of the attribute scores of all data attributes of the target metadata is calculated, and the sum of the weights of all data attributes of the target metadata is calculated; the percentage of the sum of the attribute scores and the sum of the weights is used as the sub-data quality score of the target metadata.

[0082] In one embodiment, determining a data link integrity score based on the lineage attribute of the metamodel to be analyzed includes:

[0083] Determine all the lineage nodes of the metamodel to be analyzed based on the lineage attributes of the metamodel to be analyzed;

[0084] Determine the target lineage node that has collected the target metadata among all lineage nodes;

[0085] The data link integrity score is determined based on the proportion of the target lineage node among all lineage nodes.

[0086] It should be noted that the metamodel to be analyzed includes multiple data nodes, and the data nodes are used to collect and store target metadata. Before collecting the target metadata, the data nodes have defined data attributes when they are in the preset metadata model, and the data attributes include bloodline attributes. That is, the data nodes in the metamodel to be analyzed can determine the bloodline relationship between the data nodes based on the bloodline attributes. In this embodiment, if all target metadata can constitute a complete data link, then in the metamodel to be analyzed that has completed the target metadata collection work, the data nodes with bloodline relationships should all collect and store the target metadata. If there are data nodes that have not collected the target metadata among the data nodes with bloodline relationships, it means that the data link is incomplete. In this embodiment, the integrity score of the data link is determined based on the bloodline attributes. Specifically, all data nodes with bloodline relationships in the metamodel to be analyzed are determined as bloodline nodes based on the bloodline attributes of the metamodel to be analyzed, and the target bloodline nodes that have collected the target metadata among all the bloodline nodes are determined. The percentage of the target bloodline node in all bloodline nodes is determined to obtain the data link integrity score of the metamodel to be analyzed.

[0087] In this embodiment, by determining the first data quality score of the meta-model to be analyzed, the quality of the process resources to be analyzed can be determined, thereby providing effective reference data for management personnel to control the quality of process resources.

[0088] Step 300, determining the importance score of the process resource to be analyzed based on preset importance indicators, wherein the preset importance indicators include in-degree centrality, out-degree centrality, and traceability depth;

[0089] Step 400, determining resource similarity data of the process resources to be analyzed based on a preset similarity index;

[0090] Step 500, determining data utilization of the process resources to be analyzed based on a preset isolation index;

[0091] Step 600 , determining analysis results of the process resources to be analyzed based on the importance score, resource similarity data, data utilization, and all first data quality scores.

[0092] It should be noted that in this embodiment, the importance, similarity and data utilization of the process resources to be analyzed will also be analyzed. Specifically, the importance of the process resources to be analyzed is analyzed based on preset importance indicators such as in-degree centrality, out-degree centrality and traceability depth; and the internal resource similarity of the process resources to be analyzed is analyzed based on preset similarity indicators; and further, isolated resources in the process resources to be analyzed are determined based on preset isolation indicators, so as to calculate the data utilization of the process resources to be analyzed based on the isolated resources. After determining the importance score, resource similarity data, and data utilization, the analysis results of the process resources to be analyzed are determined in combination with the first data quality scores of all types of meta-models to be analyzed.

[0093] In this embodiment, based on the type of preset metadata model, target metadata in the process resources to be analyzed is identified and collected to obtain different types of meta models to be analyzed, and the first data quality score of each meta model to be analyzed is determined based on the data attributes in each meta model to be analyzed; the first data quality score can determine the quality of the process resources to be analyzed, and provide effective reference data for managers to control the quality of process resources. The importance score of the process resources to be analyzed is determined based on the preset importance index, wherein the preset importance index includes in-degree centrality, out-degree centrality and traceability depth; the resource similarity data of the process resources to be analyzed is determined based on the preset similarity index; the data utilization rate of the process resources to be analyzed is determined based on the preset isolation index. The importance score, resource similarity data and data utilization rate of the process resources to be analyzed are determined respectively, so as to realize the multi-faceted analysis of the process resources to be analyzed, provide comprehensive analysis data, and improve the comprehensiveness and diversity of data analysis. Then, the analysis results of the process resources to be analyzed are determined based on the importance score, resource similarity data, data utilization rate and all first data quality scores. It realizes the quantitative analysis of process resources, reduces the deviation of subjective judgment, provides more objective and effective analysis results, and provides effective data reference for subsequent resource allocation, product cost reduction and efficiency improvement, and resource performance control.

[0094] In one embodiment, determining the importance score of the process resource to be analyzed based on a preset importance index includes:

[0095] Determine the correlation between all data points in the process resources to be analyzed;

[0096] For each data point, the in-degree centrality score, out-degree centrality score and traceability depth score of the data point are determined based on the association relationship, and the sum of the in-degree centrality score, out-degree centrality score and traceability depth score is used as the importance score of the process resource to be analyzed, where the in-degree centrality score is the number of times the data point is referenced by other data points, the out-degree centrality score is the number of other data points that the data point depends on, the traceability depth score is the number of other data points corresponding to the maximum traceability depth of the data point, and other data points are data points other than the data point among all data points.

[0097] In this embodiment, it should be noted that all data points in the process resources to be analyzed refer to various sub-resources. Taking the process resource to be analyzed as BOM as an example, all data points in the process resource to be analyzed are nodes in the BOM. In this embodiment, based on the association relationship of all data points in the process resource to be analyzed, the process resource to be analyzed is imported into the graph database, and the graph analysis tool is used to implement the analysis of the process resource to be analyzed. For example, the nodes in the BOM are regarded as nodes in the graph database, and the relationship between each node is regarded as an edge in the graph database.

[0098] It should be noted that the in-degree centrality score is the number of times a data point is referenced by other data points. The higher the in-degree centrality score, the more frequently the resource corresponding to the data point is used, and the wider the impact of resource changes. The out-degree centrality score is the number of other data points that the data point depends on. The higher the out-degree centrality score, the higher the resource processing complexity and supply chain risk corresponding to the data point. The traceability depth score is the number of other data points corresponding to the maximum traceability depth of the data point. Other data points are all data points except the data point. The lower the traceability depth score, the more important the resource corresponding to the current data point is, and the higher the traceability depth score, the less important the resource corresponding to the current data point is.

[0099] In this embodiment, for each data point, the in-degree centrality score, out-degree centrality score and traceability depth score of the data point are calculated based on the association relationship. The sum of the in-degree centrality score, out-degree centrality score and traceability depth score corresponding to the data point is calculated, and the sum of the scores is used as the importance score of the process resource to be analyzed.

[0100] In one embodiment, for the calculation of the in-degree centrality score, all data points can be further ranked based on the size of the in-degree centrality score, and each data point can be scored in the form of a graded scoring system based on the ranking result to determine the final in-degree centrality score of each data point. The calculation of the final out-degree centrality score of the data point and the final traceability depth score of the data point can be calculated with reference to the calculation method of the final in-degree centrality score of the data point. Importance score of the process resource to be analyzed. Weighting is performed based on the final in-degree centrality score, the final out-degree centrality score, and the final traceability depth score of the data point, and the final importance score of the process resource to be analyzed is obtained based on the weighted average.

[0101] In this embodiment, the importance scores of the process resources to be analyzed are calculated to determine the importance of the process resources to be analyzed, thereby providing an effective reference for subsequent resource allocation.

[0102] In one embodiment, determining resource similarity data of the process resources to be analyzed based on a preset similarity index includes:

[0103] Determine the correlation between all data points in the process resources to be analyzed;

[0104] For each data point in the process resource to be analyzed, all adjacent data points of the data point are determined based on the association relationship;

[0105] For each adjacent data point, determining a union of a first data value of the adjacent data point and a second data value of the data point;

[0106] Determine the number of identicalities and differences between the first data value and the second data value;

[0107] Determine resource similarity sub-data of adjacent data points and data points, wherein the resource similarity sub-data includes a first proportion and a second proportion, the first proportion is the proportion of the same number in the union, and the second proportion is the proportion of the different numbers in the union;

[0108] The resource similarity data of the process resources to be analyzed are generated based on the resource similarity sub-data corresponding to all data points.

[0109] In this embodiment, it should be noted that the process resources to be analyzed include multiple data points, each data point may have one or more adjacent data points, and all adjacent data points of each data point are determined based on the association relationship of all data points. The data value corresponding to the adjacent data point is the first data value, and the data value corresponding to the data point is the second data value. For each adjacent data point of the data point, the union of the first data value of the adjacent data point and the second data value of the data point is determined, and the same number and different number of the first data value and the second data value are determined, and then the first proportion and the second proportion are determined, wherein the first proportion is the proportion of the same number in the union, and the second proportion is the proportion of the different number in the union. For example, the first data value is 1, 2, 3; the second data value is 2, 3, 4; then the union is 1, 2, 3, 4, and the number of the union is 4. The data values ​​2 and 3 between the first data value and the second data value are the same, and the data values ​​1 and 4 are different, that is, the same number is 2, and the different number is 2. The first proportion is 1 / 2; the second proportion is 1 / 2. The resource similarity sub-data includes a first proportion and a second proportion, and resource similarity data of the process resources to be analyzed are generated based on the resource similarity sub-data corresponding to all data points.

[0110] In this embodiment, by analyzing the similarity of all data points in the process resources to be analyzed, an effective data basis is provided for subsequent decisions such as resource replacement and resource allocation.

[0111] In one embodiment, determining data utilization of the process resources to be analyzed based on a preset isolation index includes:

[0112] Determine the correlation between all data points in the process resources to be analyzed;

[0113] For each data point, the in-degree centrality score and out-degree centrality score of the data point are determined based on the association relationship, where the in-degree centrality score is the number of references of the data point by other data points, and the out-degree centrality score is the number of other data points that the data point depends on, and other data points are all data points except the data point;

[0114] Determine whether the data point meets the preset isolation index based on the in-degree centrality score and the out-degree centrality score, where the preset isolation index is that both the in-degree centrality score and the out-degree centrality score are 0;

[0115] The proportion of data points that meet the preset isolation index among all data points is determined as data utilization.

[0116] In this embodiment, it should be noted that for each data point in the process resource to be analyzed, the in-degree centrality score and out-degree centrality score of the data point can be determined based on the association relationship. The in-degree centrality score is the number of data points referenced by other data points. The higher the in-degree centrality score, the higher the frequency of use of the resource corresponding to the data point, and the wider the scope of the impact when the resource changes. The out-degree centrality score is the number of other data points that the data point depends on. The higher the out-degree centrality score, the higher the resource processing complexity and the higher the supply chain risk corresponding to the data point. Based on whether the in-degree centrality score and the out-degree centrality score are both 0, it is determined whether the data point meets the preset isolation index. When the preset isolation index is met, the in-degree centrality score and the out-degree centrality score are both 0. The preset isolation index can provide a reference for whether the resources are reasonably used or fully integrated into the production process. Determine the proportion of data points that meet the preset isolation index in all data points as data utilization.

[0117] In this embodiment, the resource utilization of the process resources to be analyzed is determined by calculating the data utilization rate, so as to provide effective data reference for subsequent decision-making activities such as resource replacement and resource scheduling.

[0118] Figure 2 The structure block diagram of a process resource analysis device 1000 according to an embodiment of the present application is schematically shown. Figure 2 As shown, the embodiment of the present application provides a process resource analysis device 1000, including:

[0119] Memory 1001, configured to store instructions;

[0120] The processor 1002 is configured to call instructions from the memory 1001 and implement the process resource analysis method described in the above embodiment when executing the instructions.

[0121] An embodiment of the present application also provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to enable a machine to execute the above-mentioned process resource analysis method.

[0122] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0123] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0124] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0125] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0126] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0127] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0128] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0129] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0130] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A process resource analysis method, characterized in that: include: Based on the type of preset metadata model, target metadata in the process resources to be analyzed is identified and collected to obtain different types of meta-models to be analyzed; Determine a first data quality score for each metamodel to be analyzed based on the data attributes in each metamodel to be analyzed, wherein the first data quality score includes a second data quality score and a data link integrity score, the second data quality score is determined based on the attribute score of the data attribute of each target metadata in the metamodel to be analyzed, the attribute score is determined based on the verification attribute in the data attribute of the target metadata, and the data link integrity score is determined based on the lineage attribute of the metamodel to be analyzed; Determine the importance score of the process resource to be analyzed based on preset importance indicators, wherein the preset importance indicators include an in-degree centrality score, an out-degree centrality score, and a traceability depth score, wherein, for each data point in the process resource to be analyzed, the in-degree centrality score is the number of references of the data point by other data points, the out-degree centrality score is the number of other data points that the data point depends on, and the traceability depth score is the number of other data points corresponding to the maximum traceability depth of the data point, and the other data points are data points other than the data point among all data points; Determining resource similarity data of the process resources to be analyzed based on a preset similarity index; Determining whether each of the data points meets a preset isolation index based on the in-degree centrality score and the out-degree centrality score, so as to determine the data utilization rate of the process resource to be analyzed; Determining an analysis result of the process resource to be analyzed based on the importance score, the resource similarity data, the data utilization rate, and all the first data quality scores; The determining of the resource similarity data of the process resources to be analyzed based on the preset similarity index includes: Determine the correlation between all data points in the process resources to be analyzed; For each of the data points in the process resources to be analyzed, determining all adjacent data points of the data point based on the association relationship; For each of the adjacent data points, determining a union of a first data value of the adjacent data point and a second data value of the data point; Determine the number of identicalities and the number of differentities between the first data value and the second data value; Determine resource similarity sub-data between the adjacent data point and the data point, wherein the resource similarity sub-data includes a first proportion and a second proportion, the first proportion is the proportion of the same number in the union, and the second proportion is the proportion of the different number in the union; The resource similarity data of the process resources to be analyzed are generated based on the resource similarity sub-data corresponding to all data points.

2. The process resource analysis method according to claim 1, characterized in that: The determining the first data quality score of each meta-model to be analyzed based on the data attributes in each meta-model to be analyzed respectively includes: For each type of metamodel to be analyzed, determining the sub-data quality score of each target metadata based on the data attributes of each target metadata in the metamodel to be analyzed; determining a second data quality score based on an average of all of the sub-data quality scores; Determine a data link integrity score based on the lineage attribute of the metamodel to be analyzed, wherein the lineage attribute belongs to a data attribute; A first data quality score of the meta-model to be analyzed is determined based on the second data quality score and the data link integrity score.

3. The process resource analysis method according to claim 2, characterized in that: The step of determining, for each type of metamodel to be analyzed, the sub-data quality scores of each target metadata based on the data attributes of each target metadata in the metamodel to be analyzed comprises: For each target metadata in each type of metamodel to be analyzed, based on the verification attribute of the target metadata, verify all data attributes of the target metadata respectively; Determine an attribute score for each data attribute based on the verification result and attribute weight of each data attribute; The proportion of the sum of the attribute scores of all data attributes of the target metadata to the sum of the weights of all data attributes is the sub-data quality score of the target metadata.

4. The process resource analysis method according to claim 2, characterized in that: The step of determining the data link integrity score based on the lineage attribute of the meta-model to be analyzed includes: Determine all the lineage nodes of the metamodel to be analyzed based on the lineage attributes of the metamodel to be analyzed; Determine a target lineage node that has collected target metadata among all the lineage nodes; A data link integrity score is determined based on the proportion of the target lineage node in all the lineage nodes.

5. The process resource analysis method according to claim 1, characterized in that: The determining the importance score of the process resource to be analyzed based on the preset importance index includes: Determine the correlation between all data points in the process resources to be analyzed; For each data point, the in-degree centrality score, out-degree centrality score and traceability depth score of the data point are determined based on the association relationship, and the sum of the in-degree centrality score, out-degree centrality score and traceability depth score is used as the importance score of the process resource to be analyzed.

6. The process resource analysis method according to claim 1, characterized in that: The determining whether each of the data points meets a preset isolation index based on the in-degree centrality score and the out-degree centrality score to determine the data utilization rate of the process resource to be analyzed includes: Determine the correlation relationship between all data points in the process resources to be analyzed; For each of the data points, determining an in-degree centrality score and an out-degree centrality score of the data point based on the association relationship; Determining whether the data point meets a preset isolation index based on the in-degree centrality score and the out-degree centrality score, wherein the preset isolation index is that both the in-degree centrality score and the out-degree centrality score are 0; The proportion of data points that meet the preset isolation index among all the data points is determined as data utilization.

7. The process resource analysis method according to claim 1, characterized in that: The steps of constructing the preset metadata model include: Determine the type of initial metadata model based on input resource analysis requirements; Determine the business attributes, data types, verification attributes, weights, scoring attributes and traceability attributes of the metadata in the initial metadata model based on the resource analysis requirements and the type, wherein the business attributes are used to define the data type of the metadata, the data types are used to define the representation of the metadata, the verification attributes are used to determine the verification rules, the scoring attributes are used to define whether the metadata participates in the data quality scoring, and the traceability attributes are used to define whether the attribute value of the metadata is derived from other attributes; Determine the lineage attribute of each of the metadata based on the resource analysis requirements and the traceability attributes; The business attribute of the metadata, the data type, the verification attribute, the weight, the scoring attribute, and the lineage attribute are determined as data attributes of the metadata.

8. A process resource analysis device, characterized in that: include: a memory configured to store instructions; A processor is configured to call the instructions from the memory and implement the process resource analysis method according to any one of claims 1 to 7 when executing the instructions.

9. A machine-readable storage medium, characterized in that: The machine-readable storage medium stores instructions, which are used to enable a machine to execute the process resource analysis method according to any one of claims 1 to 7.

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