Enterprise service-oriented component adaptive processing method, device and equipment

Abstract

The application provides a component adaptive processing method, device and equipment for enterprise service. The method comprises the following steps: acquiring a plurality of heterogeneous data sources of an enterprise; processing the plurality of heterogeneous data sources to obtain multi-dimensional enterprise portrait multi-dimensional data; performing multi-dimensional attribute correlation processing on the multi-dimensional enterprise data portrait to obtain a correlation matching result between multi-dimensional attributes of a service subject of the enterprise; performing co-occurrence or co-cited analysis on the multi-dimensional enterprise data portrait according to the correlation matching result to obtain a coupling degree of the enterprise and the service subject; constructing a qualitative index analysis model in an enterprise value chain according to the coupling degree; performing collaborative enterprise evaluation according to the qualitative index analysis model to obtain an evaluation result and outputting the evaluation result. The scheme of the application can help an enterprise user to select an optimal collaborative enterprise, improve the collaborative design, manufacturing and supply capacity between enterprises, and enhance the market competitiveness of the enterprise.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer information processing, and further relates to a component adaptive processing method, device and equipment for enterprise services. BACKGROUND

[0002] In the development process of service-oriented components, the existing component assembly does not separate the computing model of the component and the collaborative system for discussion, and the assembly mode is static and not flexible enough. In order to ensure that the service components in the network collaborative integration platform can run smoothly and effectively achieve the established functional or non-functional targets, the service components need to implement an adaptive mechanism to adapt to the frequent changes of user demand, the dynamic changes of the running environment, the reconstruction changes of computing resources, etc. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a component adaptive processing method, device and equipment for enterprise services. The optimal collaborative enterprise can be selected for enterprise users, the collaborative design, manufacturing and supply capabilities between enterprises are improved, and the market competitiveness of enterprises is enhanced.

[0004] To solve the above technical problems, the technical solutions of the present application are as follows:

[0005] A component adaptive processing method for enterprise services, comprising:

[0006] Obtaining a plurality of heterogeneous data sources of an enterprise;

[0007] Processing the plurality of heterogeneous data sources to obtain a multi-dimensional enterprise data portrait;

[0008] Performing multi-dimensional attribute association processing on the multi-dimensional enterprise data portrait to obtain an association matching result between the multi-dimensional attributes of the service subject of the enterprise;

[0009] According to the association matching result, performing co-occurrence or co-cited analysis on the multi-dimensional enterprise data portrait to obtain a coupling degree of the enterprise and the service subject;

[0010] According to the coupling degree, constructing a qualitative index analysis model in the enterprise value chain;

[0011] According to the qualitative index analysis model, performing collaborative enterprise evaluation to obtain an evaluation result and outputting.

[0012] Optionally, the processing of the plurality of heterogeneous data sources to obtain a multi-dimensional enterprise data portrait comprises:

[0013] Performing multi-dimensional data feature classification processing on at least one of the structured data, semi-structured data and unstructured data in the plurality of heterogeneous data sources to obtain a classification result;

[0014] storing the classification result in a basic database;

[0015] performing classification screening on data stored in the basic database to obtain at least one special database;

[0016] adding a category label to metadata in the at least one special database to obtain a multi-dimensional enterprise data portrait.

[0017] Optionally, the multi-dimensional enterprise data portrait is subjected to multi-dimensional attribute correlation processing to obtain a correlation matching result between multi-dimensional attributes of the service subject of the enterprise, including:

[0018] obtaining at least one business requirement of the enterprise according to the multi-dimensional enterprise data portrait;

[0019] analyzing a correlation relationship between multi-dimensional attributes of the service subject according to the at least one business requirement of the enterprise through multi-modal feature decomposition multi-dimensional attribute correlation processing to obtain the correlation matching result between multi-dimensional attributes of the service subject.

[0020] Optionally, the correlation relationship between multi-dimensional attributes of the service subject is analyzed, including:

[0021] through a formula: and analyzing the correlation relationship between multi-dimensional attributes of the service subject to obtain the correlation matching result between multi-dimensional attributes of the service subject;

[0022] wherein, V, Z, W≥0 X is a multi-dimensional enterprise data portrait, represented as a space stereogram;

[0023] wherein, v g is a rank-1 tensor of a gth node of a multi-dimensional attribute F of the multi-dimensional enterprise portrait data X, z gk is a rank-1 tensor of a node set L between multi-dimensional attributes F of the multi-dimensional enterprise portrait data X, w k is a rank-1 tensor of a life cycle T of the multi-dimensional enterprise portrait data X;

[0024] V is a vector of the multi-dimensional attribute F, Z is a vector of the node set L, and W is a vector of the life cycle T;

[0025] K is a total number of dimensions of the multi-dimensional attribute, and G is a total number of nodes in a node set corresponding to an attribute of each dimension;

[0026] k is a variable of the number of dimensions, g is a variable of the number of nodes in the node set corresponding to the attribute of the dimension, and η(·) is a cost function; the correlation matching result is a minimum value of the cost function.

[0027] f is an algebraic representation form of a multi-dimensional attribute F, l is an algebraic representation form of a node set L, v fg is a rank-1 tensor of the gth node in the node set L of the attribute f, z gkl is a rank-1 tensor of the gth node in the node set L of the kth dimensional attribute.

[0028] Optionally, according to the association matching result, co-occurrence or co-cited analysis is performed on the multi-dimensional enterprise data portrait to obtain a coupling degree of the enterprise and other service subjects, including:

[0029] According to the association matching result, co-occurrence frequency statistics is performed on the multi-dimensional enterprise data portrait to obtain a statistical result.

[0030] According to the statistical result, an association degree between feature items co-occurring or co-cited in the multi-dimensional enterprise data portrait is predicted to obtain the coupling degree of the enterprise and the service subject.

[0031] Optionally, according to the coupling degree, a qualitative index analysis model in an enterprise value chain is constructed, including:

[0032] According to the coupling degree and at least one index calculation function, a qualitative index analysis model in an enterprise value chain is constructed.

[0033] Optionally, according to the qualitative index analysis model, a collaborative enterprise is evaluated to obtain an evaluation result, including:

[0034] From the qualitative index analysis model, n target service subjects are selected, each target service subject has m indexes, a decision matrix is established, and n and m are positive integers.

[0035] The weight values of the indexes are obtained.

[0036] According to the weight values of the indexes and the decision matrix, a weighted decision matrix is obtained.

[0037] According to the distance between each index in the weighted decision matrix and a preset target value, a score of each service subject is obtained.

[0038] According to the score of each service subject, an optimal collaborative enterprise is obtained.

[0039] The application also provides a component adaptive processing device for enterprise service, including:

[0040] An acquisition module is configured to acquire a plurality of heterogeneous data sources of an enterprise.

[0041] The processing module is used for processing the multiple heterogeneous data sources to obtain a multi-dimensional enterprise data portrait, performing multi-dimensional attribute correlation processing on the multi-dimensional enterprise data portrait to obtain a correlation matching result between multi-dimensional attributes of the service subject of the enterprise, and performing co-occurrence or co-cited analysis on the multi-dimensional enterprise data portrait according to the correlation matching result to obtain a coupling degree of the enterprise and the service subject.

[0042] According to the correlation matching result, co-occurrence or co-cited analysis is performed on the multi-dimensional enterprise data portrait to obtain a coupling degree of the enterprise and the service subject, a qualitative index analysis model in the enterprise value chain is constructed according to the coupling degree, collaborative enterprise evaluation is performed according to the qualitative index analysis model to obtain an evaluation result, and the evaluation result is output.

[0043] The application further provides a computing device, including a processor and a memory storing a computer program, wherein the computer program is executed by the processor to perform the method described above.

[0044] The application further provides a computer readable storage medium, including a storage instruction, wherein the storage instruction is executed on a computer to make the computer perform the method described above.

[0045] The above scheme of the application has at least the following beneficial effects:

[0046] The above scheme of the application can select the optimal collaborative enterprise for the enterprise user, improve the collaborative design, manufacturing and supply capacity between enterprises, and enhance the market competitiveness of the enterprise. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 is a flowchart of the component adaptive processing method for enterprise service of the application;

[0048] Figure 2 is a module schematic diagram of the component adaptive processing device for enterprise service of the application. DETAILED DESCRIPTION

[0049] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the present disclosure to those skilled in the art.

[0050] As shown in Figure 1 An embodiment of the present disclosure proposes a component adaptive processing method for enterprise service, comprising:

[0051] Step 11, obtaining a plurality of heterogeneous data sources of an enterprise;

[0052] Step 12, processing the plurality of heterogeneous data sources to obtain a multi-dimensional enterprise data portrait;

[0053] Step 13, performing multi-dimensional attribute correlation processing on the multi-dimensional enterprise data portrait to obtain a correlation matching result between multi-dimensional attributes of a service subject of the enterprise;

[0054] Step 14, according to the correlation matching result, performing co-occurrence or co-cited analysis on the multi-dimensional enterprise data portrait to obtain a coupling degree of the enterprise and the service subject;

[0055] Step 15, constructing a qualitative index analysis model in an enterprise value chain according to the coupling degree;

[0056] Step 16, performing collaborative enterprise evaluation according to the qualitative index analysis model to obtain an evaluation result and output.

[0057] In this embodiment, the plurality of heterogeneous data sources can include data collected by different data collection systems, such as various data collected by sensors of Internet of Things devices, cache, and processing; whole-process process data collected by an enterprise process management system, production data collected by an enterprise production management system, operation data collected by an enterprise operation management system, quality data collected by an enterprise quality management system, model data collected by an enterprise model management system, enterprise management data, and graph documents.

[0058] In this embodiment of the present disclosure, according to the plurality of heterogeneous data sources of the enterprise, a multi-dimensional enterprise data portrait is obtained, and after multi-dimensional attribute correlation processing on the multi-dimensional enterprise data portrait, co-occurrence or co-cited analysis is performed to obtain a coupling degree of the enterprise and the service subject. According to the qualitative index analysis model constructed based on the coupling degree, the collaborative enterprise is evaluated to obtain an evaluation result. In this way, the optimal collaborative enterprise can be selected for the user according to the evaluation result, the collaborative design, manufacturing, and supply capabilities between enterprises are improved, and the market competitiveness of the enterprise is enhanced.

[0059] In an optional embodiment of the present application, step 12 can include:

[0060] Step 121, performing multi-dimensional data feature classification processing on at least one of structured data, semi-structured data and unstructured data in the plurality of heterogeneous data sources, to obtain a classification result;

[0061] Step 122, storing the classification result in a basic database;

[0062] Step 123, performing classification filtering on the data stored in the basic database, to obtain at least one special database;

[0063] Step 124, adding a category label to the metadata in the at least one special database, to obtain a multi-dimensional enterprise data portrait.

[0064] The structured data includes data obtained by processing an original data source using a preset processing tool, where the preset processing tool can be ETL, and ETL is a data warehouse that extracts data from a source end, converts the data, and loads the data to a destination end;

[0065] The semi-structured data includes data obtained by performing feature extraction processing on a text data source; for example, data obtained by performing translation, summarization, classification processing on text data, and performing metadata and feature information extraction operations;

[0066] The unstructured data includes data obtained by performing feature extraction on a file, picture, audio, or video data source.

[0067] In this embodiment, at least one of the structured data, semi-structured data and unstructured data is subjected to multi-dimensional data feature classification processing, and the classified data is subjected to warehouse building processing to form a basic database of an enterprise. The multi-dimensional data feature classification can be, for example, data feature classification according to a feature dimension of a manufacturing enterprise, which can include dimensions such as asset and liability situation, R&D department, procurement department, production department, product price, delivery flexibility, compatibility degree, bonus item, etc.

[0068] Further, the data stored in the basic database is classified and screened to obtain at least one special database; specifically, the data stored in the basic database can be classified and screened in combination with the industry characteristics of the enterprise to form various special databases, and the special databases can be special databases corresponding to each part of the manufacturing enterprise, for example, a special database corresponding to the R&D department, a special database corresponding to the procurement department, a special database corresponding to the production department, and the like, and can further include special databases of potential customers and intended customers of the enterprise, and the special databases of potential customers and intended customers of the enterprise can be generated according to at least one of the dimensions of product price, delivery flexibility, cooperation compatibility, and bonus items of the enterprise. In this way, the multiple special databases collectively constitute data resources supporting network collaborative manufacturing applications, and centralized storage management of enterprise resource data is achieved.

[0069] In each of the special databases, metadata, data resources, data directories, and the like are included, and further, the method further includes management of each of the special databases, for example, creation, deletion, permission setting of shared libraries, standard databases, and the like in each of the special databases, and life cycle management of these databases, and the like, to provide complete, comprehensive, and full life cycle device information and enterprise information archives for various application scenarios.

[0070] The following takes a manufacturing enterprise as an example to illustrate a multi-dimensional enterprise data portrait obtained by adding category labels to the metadata in the at least one special database; specifically, in combination with the identification of the characteristics of the manufacturing enterprise by each department, analysis requirements, and based on the analysis of the full amount of enterprise information, a tag system is constructed to realize the formation of a multi-dimensional enterprise data portrait; wherein the asset and liability situation, the R&D department, the procurement department, the production department, the product price, the delivery flexibility, the cooperation compatibility, and the bonus items are attribute dimensions of the manufacturing enterprise, each attribute dimension corresponds to corresponding metadata, and each metadata corresponds to a category label, which can be the asset and liability situation included in Table 1 below: asset-liability ratio, total asset turnover rate, quick ratio, total assets, enterprise credit, and the like. Similarly, the category label can also be the R&D department included in the following table: new product development cycle, new product development cycle, R&D team quality, R&D input return rate, R&D expense ratio, and R&D input cost, and the like.

[0071] The multi-dimensional enterprise data portrait is shown in Table 1 below:

[0072]

[0073]

[0074] Table 1

[0075] In an optional embodiment of the present application, step 13 can comprise:

[0076] Step 131, obtaining at least one business requirement of the enterprise according to the multi-dimensional enterprise data portrait;

[0077] Step 132, according to the at least one business requirement of the enterprise, analyzing the correlation between the multi-dimensional attributes of the service subject through multi-modal feature decomposition and multi-dimensional attribute correlation processing, and obtaining the correlation matching result between the multi-dimensional attributes of the service subject.

[0078] In this embodiment, based on the multi-dimensional enterprise data portrait, the market department, service department and risk control department of the enterprise can analyze the correlation between the multi-dimensional attributes of the service subject through multi-modal feature decomposition and multi-dimensional attribute correlation processing, i.e. through the evaluation of asset and liability situation, R&D department personnel composition, procurement department procurement completion, order production and equipment aging, product price, etc. to control the inherent asset consumption in production, enterprise running fund situation, production material procurement completion and new product R&D, etc.

[0079] Then analyze the complex correlation interaction behavior between the multi-dimensional attributes of the service subject, and mine the node set between the network collaborative manufacturing service subject and the multi-dimensional attributes. The node set here can be the labeled metadata corresponding to the attributes of one dimension, for example, the nodes in the node set corresponding to the attributes of the cooperation compatibility dimension include: strategic concept compatibility, management system compatibility, enterprise culture compatibility, information level, etc.

[0080] Taking the automobile manufacturing process as an example, the complex correlation interaction behavior between the multi-dimensional attributes includes:

[0081] First, the investigation and research stage, mainly for some product design and planning requirements, such as defined performance specifications and appearance, etc.

[0082] Second, the manufacturing process, which is the core part, contains many elements. First, process the various raw materials purchased, such as punching, pressing, coating, washing, etc. of the purchased iron sheet, and assemble various qualified parts and components purchased.

[0083] Third, the marketing process, including the use of marketing methods and how to build the brand. The product orders received by the marketing department are responsible for distribution and delivery by the distribution department.

[0084] Fourth, the operation and maintenance process, mainly for transportation problems or customer dissatisfaction with the product for secondary inspection or recycling.

[0085] According to the interaction of the above four steps, the complex correlation interaction behavior between the multi-dimensional attributes of the service subject can be obtained; it should be noted that the service subject is at least one target cooperative enterprise in the upstream and downstream enterprises of the enterprise that meets the business needs of the enterprise.

[0086] Specifically, in step 132, analyzing the correlation relationship between the multi-dimensional attributes of the service subject can include:

[0087] In step 1321, the correlation relationship between the multi-dimensional attributes of the service subject is analyzed by the formula: And The correlation matching result between the multi-dimensional attributes of the service subject is obtained.

[0088] Wherein, V, Z, W≥0, X is a multi-dimensional enterprise data portrait, represented as a space stereogram.

[0089] Wherein, v g is a rank-1 tensor of the gth node of the multi-dimensional attribute F of the multi-dimensional enterprise portrait data X, z gk is a rank-1 tensor of the node set L between the multi-dimensional attributes F of the multi-dimensional enterprise portrait data X, w k is a rank-1 tensor of the life cycle T of the multi-dimensional enterprise portrait data X.

[0090] V is the vector of the multi-dimensional attribute F, Z is the vector of the node set L, and W is the vector of the life cycle T.

[0091] K is the total number of dimensions of the multi-dimensional attribute, and G is the total number of nodes in the node set corresponding to the attribute of each dimension.

[0092] K is the total number of dimensions of the multi-dimensional attribute, and G is the total number of nodes in the node set corresponding to the attribute of each dimension.

[0093] f is the algebraic representation of the multi-dimensional attribute F, l is the algebraic representation of the node set L, v fg is a rank-1 tensor of the gth node of the attribute f, z gkl is a rank-1 tensor of the gth node of the kth dimension of the node set L.

[0094] is the Kronecker product, which represents the operation between two matrices of any size, and the Kronecker product is a special form of tensor product.

[0095] The implementation process of this embodiment is as follows:

[0096] A multi-dimensional enterprise data portrait of network collaborative manufacturing service subjects in multiple business fields of multiple types of enterprises is established, where the multi-dimensional enterprise data portrait of the service subject is formed in the same way as the multi-dimensional enterprise portrait data of the enterprise, and the network distribution structure and the correlation matching relationship between the service objects are obtained by constructing a correlation tensor graph structure and decomposing a multi-modal feature tensor model;

[0097] The model tensor decomposition form of the multi-dimensional enterprise data portrait is as follows:

[0098]

[0099] The above tensor decomposition problem is converted into a minimization problem for solving, as follows:

[0100]

[0101] Wherein, V, Z, W≥0,

[0102] The divergence function η(·) is taken as a cost function, and normalized to obtain the divergence function expression, as follows:

[0103]

[0104] Wherein, k is a variable of the number of attribute dimensions, g is a variable of the number of nodes in the node set corresponding to the attribute, l is the algebraic expression of the node set L, f is the algebraic expression of the multi-dimensional attribute F, and t is the algebraic expression of the life cycle T;

[0105] x flt is the algebraic expression of X, and v fg is the rank-1 tensor of the gth node in the node set L of attribute f, gkl is the rank-1 tensor of the gth node in the node set L of the kth dimensional attribute w kt is the rank-1 tensor of the kth dimensional attribute in the life cycle T of the multi-dimensional enterprise portrait data X;

[0106] Using the multiplicative update rule, the above v fg , z gkl , w kt are updated respectively by the following formula:

[0107]

[0108]

[0109]

[0110] Wherein, for v fg the value before update, for z gkl the value before update, for w kt the value before update.

[0111] In another optional embodiment of the present application, step 14 can include:

[0112] Step 141, according to the association matching result, co-occurrence frequency statistics of the multi-dimensional enterprise data portrait is performed to obtain a statistical result.

[0113] Step 142, according to the statistical result, the association degree between the co-occurring or co-cited feature items of the multi-dimensional enterprise data portrait is predicted to obtain the coupling degree of the enterprise and the service subject.

[0114] In the embodiment, the association degree between the co-occurring / co-cited feature items can be measured by the co-occurrence frequency, where the co-occurrence frequency refers to the number of times that the multi-dimensional enterprise data portrait and the associated feature items of the service subject are the same in the life cycle of data processing. The use of co-occurrence / co-citation feature analysis for the analysis of the multi-dimensional enterprise data portrait can present the coupling degree between different industries and different enterprises.

[0115] In another optional embodiment of the present application, step 15 can include:

[0116] Step 151, according to the coupling degree and at least one index calculation function, a qualitative index analysis model in the enterprise value chain is constructed, where the index calculation function can be any index calculation function corresponding to the indexes in Table 2.

[0117] In the embodiment, each index data of each department of each service subject has a corresponding preset qualitative index analysis model. Through the preset qualitative index analysis model, the actual data of each index of the service subject can be obtained, and then the optimal cooperative enterprise can be selected according to the business requirements of the enterprise user.

[0118] Specifically, according to the coupling degree, a qualitative index analysis model in the enterprise value chain is constructed, and time series analysis of the hotspots / main fields of the structure is performed to form a trend development model of the corresponding index, reflecting the future development of the enterprise and the industry. The qualitative index analysis model is shown in Table 2.

[0119]

[0120]

[0121] Table 2

[0122] Different links have different emphases. For the supply link, more emphasis is placed on the quality and price of the provided raw materials. For the production link, more emphasis is placed on procurement, research and development, and production. For the marketing link, more emphasis is placed on the attitude of customers and the handling of order problems.

[0123] In an optional embodiment of the present application, step 16 can include:

[0124] Step 161, from the qualitative index analysis model, select n target service subjects, each of which has m indexes, establish a decision matrix, and n and m are positive integers.

[0125] Step 162, obtain the weight values of the indexes.

[0126] Step 163, according to the weight values of the indexes and the decision matrix, obtain a weighted decision matrix.

[0127] Step 164, according to the distance between each index in the weighted decision matrix and a preset target value, obtain the score corresponding to each element of each service subject.

[0128] Step 165, according to the score corresponding to each element of each service subject, obtain the optimal cooperation enterprise.

[0129] In this embodiment, each index of the service subject can be evaluated, and the optimal cooperation enterprise can be selected for the enterprise user.

[0130] In specific implementation, the selection process of the optimal cooperation enterprise can include the following parts:

[0131] Selecting indexes, establishing a decision matrix, calculating weights, constructing a weighted decision matrix, evaluating, and obtaining an evaluation result. The specific evaluation process can include:

[0132] 1) Select the indexes of the service subject from the above-mentioned qualitative index analysis model.

[0133] 2) Establish a decision matrix: there are n service subjects, denoted as A1, A2, … An, and each service subject has m indexes. The data of the m indexes are processed by dimensionless, and then a matrix A can be established:

[0134]

[0135] 3), the weight is calculated, and weight values W=(W1, W2, W3,..., Wn) of each index can be obtained. The weight here can be the importance proportion of each index to all indexes of the enterprise. For example, the indexes of a service subject include asset-liability ratio, R&D expense ratio, product qualification rate, and sales profit rate. The importance of the R&D expense ratio accounts for 30%, the importance of the product qualification rate accounts for 20%, the importance of the sales profit rate accounts for 40%, and the importance of the R&D expense ratio accounts for 10%. Therefore, the weight of the R&D expense ratio is 0.3, the weight of the product qualification rate is 0.2, the weight of the sales profit rate is 0.4, and the weight of the R&D expense ratio is 0.1. Of course, this is only an example and is not limited thereto.

[0136] 4), the weighted decision matrix is calculated by using the weight and the decision matrix, and the service subject is evaluated by using the approximation ideal value method; the weighted decision matrix R=W*A;

[0137] 5), then the distance between each element in the weighted decision matrix R and the preset target value is calculated, and the scores S1, S2, S3,..., Sn corresponding to each element of the service subject are solved. S1 is the distance between the first element in R and the preset target value, S2 is the distance between the second element in R and the preset target value, and Sn is the distance between the n-th element in R and the preset target value.

[0138] According to the scores S1, S2, S3,..., Sn corresponding to each element of the service subject, the optimal cooperative enterprise is selected.

[0139] In the implementation of this embodiment, according to the specific business needs of the enterprise, a corresponding target cooperative enterprise is selected as the service subject of the enterprise from different dimensions. For example, if the service subject with the highest order completion rate needs to be selected as the target cooperative enterprise, the maximum value of the score of the element corresponding to the order completion rate in S1, S2, S3,..., Sn needs to be calculated, and the enterprise corresponding to the maximum value is selected as the optimal target cooperative enterprise of the enterprise.

[0140] If the enterprise user needs to select the service subject with the lowest customer complaint rate as the cooperative enterprise, the minimum value of the score of the element corresponding to the customer complaint rate in S1, S2, S3,..., Sn needs to be calculated, and the enterprise corresponding to the minimum value is selected as the optimal target cooperative enterprise of the enterprise.

[0141] If the enterprise user needs to select the service subject with the highest R&D expense ratio as the cooperative enterprise, the maximum value of the score of the element corresponding to the R&D expense ratio in S1, S2, S3,..., Sn needs to be calculated, and the enterprise corresponding to the maximum value is selected as the optimal target cooperative enterprise of the enterprise.

[0142] The optimal target cooperative enterprise of the enterprise can be finally obtained, including: a union of the optimal target cooperative enterprise selected according to the scores of the elements or the optimal target cooperative enterprise determined according to the weights of the dimension attributes corresponding to the actual business requirements.

[0143] In the above embodiment of the application, the component adaptive processing method for enterprise service can select the optimal target cooperative enterprise for the enterprise user, improve the collaborative design, manufacturing and supply capacity between enterprises, and enhance the market competitiveness of the enterprise.

[0144] As shown in Figure 2 The embodiment of the application further provides a component adaptive processing device 20 for enterprise service, and the device 20 includes:

[0145] The acquisition module 21 is configured to acquire a plurality of heterogeneous data sources of an enterprise.

[0146] The processing module 22 is configured to process the plurality of heterogeneous data sources to obtain a multi-dimensional enterprise data portrait, perform multi-dimensional attribute association processing on the multi-dimensional enterprise data portrait to obtain an association matching result between multi-dimensional attributes of a service subject of the enterprise, perform co-occurrence or co-cited analysis on the multi-dimensional enterprise data portrait according to the association matching result to obtain a coupling degree between the enterprise and the service subject, construct a qualitative index analysis model in an enterprise value chain according to the coupling degree, perform cooperative enterprise evaluation according to the qualitative index analysis model to obtain an evaluation result, and output the evaluation result.

[0147] Optionally, the processing of the plurality of heterogeneous data sources to obtain the multi-dimensional enterprise data portrait includes:

[0148] Performing multi-dimensional data feature classification processing on at least one of structured data, semi-structured data and unstructured data in the plurality of heterogeneous data sources to obtain a classification result.

[0149] Storing the classification result in a basic database.

[0150] Performing classification and screening on the data stored in the basic database to obtain at least one special database.

[0151] Adding a category label to metadata in the at least one special database to obtain the multi-dimensional enterprise data portrait.

[0152] Optionally, the multi-dimensional attribute association processing of the multi-dimensional enterprise data portrait to obtain the association matching result between the multi-dimensional attributes of the service subject of the enterprise includes:

[0153] Obtaining at least one business requirement of the enterprise according to the multi-dimensional enterprise data portrait.

[0154] According to at least one business requirement of the enterprise, by decomposing the multi-dimensional attribute correlation processing of the multi-modal feature, the correlation between the multi-dimensional attributes of the service subject is analyzed, and the correlation matching result between the multi-dimensional attributes of the service subject is obtained.

[0155] Optionally, the correlation between the multi-dimensional attributes of the service subject is analyzed, including:

[0156] According to the formula: And The correlation between the multi-dimensional attributes of the service subject is analyzed, and the correlation matching result between the multi-dimensional attributes of the service subject is obtained.

[0157] Wherein, V, Z, W≥0, X is a multi-dimensional enterprise data portrait, represented as a space stereogram;

[0158] Wherein, v g is a rank-1 tensor of the gth node of the multi-dimensional attribute F of the multi-dimensional enterprise portrait data X, z gk is a rank-1 tensor of the node set L between the multi-dimensional attributes F of the multi-dimensional enterprise portrait data X, w k is a rank-1 tensor of the life cycle T of the multi-dimensional enterprise portrait data X;

[0159] V is the vector of the multi-dimensional attribute F, Z is the vector of the node set L, and W is the vector of the life cycle;

[0160] K is the total number of dimensions of the multi-dimensional attribute, and G is the total number of nodes in the node set corresponding to the attribute of each dimension;

[0161] K is the total number of dimensions of the multi-dimensional attribute, and G is the total number of nodes in the node set corresponding to the attribute of each dimension;

[0162] f is the algebraic representation of the multi-dimensional attribute F, l is the algebraic representation of the node set L, v fg is a rank-1 tensor of the gth node of the attribute f, z gkl is a rank-1 tensor of the gth node of the kth dimension of the node set L.

[0163] Optionally, according to the correlation matching result, the multi-dimensional enterprise data portrait is analyzed for co-occurrence or co-cited analysis, and the coupling degree of the enterprise and other service subjects is obtained, including:

[0164] According to the correlation matching result, the co-occurrence frequency of the multi-dimensional enterprise data portrait is counted, and a statistical result is obtained.

[0165] According to the statistical result, a coupling degree between the enterprise and the service subject is predicted according to the correlation degree between the common or co-cited feature items of the multi-dimensional enterprise data portrait, and the coupling degree is obtained.

[0166] Optionally, according to the coupling degree, a qualitative index analysis model in the enterprise value chain is constructed, and the qualitative index analysis model comprises the following steps.

[0167] According to the coupling degree and at least one index calculation function, a qualitative index analysis model in the enterprise value chain is constructed.

[0168] Optionally, according to the qualitative index analysis model, a cooperative enterprise is evaluated, and an evaluation result is obtained, and the evaluation result comprises the following steps.

[0169] From the qualitative index analysis model, n target service subjects are selected, each target service subject has m indexes, a decision matrix is established, n and m are positive integers;

[0170] The weight values of the indexes are obtained.

[0171] According to the weight values of the indexes and the decision matrix, a weighted decision matrix is obtained.

[0172] According to the distance between each index in the weighted decision matrix and a preset target value, a score of each service subject is obtained.

[0173] According to the score of each service subject, an optimal cooperative enterprise is obtained.

[0174] It should be noted that the device corresponds to the above method, and all implementation manners of the above method embodiments are applicable to the embodiments of the device and can achieve the same technical effects.

[0175] Embodiments of the present application also provide a computing device, comprising a processor and a memory storing a computer program, when the computer program is run by the processor, the method described above is executed. All implementation manners of the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0176] Embodiments of the present application also provide a computer readable storage medium, comprising: storing instructions, when the instructions are run on a computer, the computer executes the method described above. All implementation manners of the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0177] Those skilled in the art can clearly understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0178] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0179] In the embodiments provided by the present application, it should be understood that the disclosed device and method can be implemented in other ways. For example, the device embodiments described above are only schematic. The division of the units is only a logical function division. There can be another division during actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between the units can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0180] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e. can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0181] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically, or two or more units can be integrated into one unit.

[0182] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that make contributions to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a ROM, a RAM, a magnetic disk or an optical disk, and various program code storage media.

[0183] Furthermore, it is pointed out that in the device and method of the present application, obviously, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations are to be considered as equivalent solutions of the present application. Moreover, the steps of performing the above series of processes can naturally be executed in time sequence according to the order of description, but do not necessarily have to be executed in time sequence. Some steps can be executed in parallel or independently of each other. It is understood by those skilled in the art that all or any of the steps or components of the method and device of the present application can be implemented in hardware, firmware, software, or a combination thereof, in any computing device (including a processor, a storage medium, etc.) or network of computing devices, using the basic programming skills of those skilled in the art upon reading the description of the present application.

[0184] Therefore, the object of the present application can also be achieved by running a program or a set of programs on any computing device. The computing device can be a commonly known general-purpose device. Therefore, the object of the present application can also be achieved only by providing a program product containing program code for implementing the method or device. That is, such a program product also constitutes the present application, and a storage medium storing such a program product also constitutes the present application. Obviously, the storage medium can be any commonly known storage medium or any storage medium developed in the future. It is also pointed out that in the device and method of the present application, obviously, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations are to be considered as equivalent solutions of the present application. Moreover, the steps of performing the above series of processes can naturally be executed in time sequence according to the order of description, but do not necessarily have to be executed in time sequence. Some steps can be executed in parallel or independently of each other.

[0185] The above is the preferred embodiment of the present application. It should be pointed out that for those skilled in the art, several improvements and refinements can be made without departing from the principles of the present application, and these improvements and refinements are also to be considered as the protection scope of the present application.

Claims

1. A component adaptation processing method for enterprise service, characterized by, The method comprises the following steps: acquiring a plurality of heterogeneous data sources of an enterprise; processing the plurality of heterogeneous data sources to obtain a multi-dimensional enterprise data portrait; performing multi-dimensional attribute correlation processing on the multi-dimensional enterprise data portrait to obtain a correlation matching result between multi-dimensional attributes of a service subject of the enterprise; performing co-occurrence or co-citation analysis on the multi-dimensional enterprise data portrait according to the correlation matching result to obtain a coupling degree between the enterprise and the service subject; constructing a qualitative index analysis model in an enterprise value chain according to the coupling degree; performing collaborative enterprise evaluation according to the qualitative index analysis model to obtain an evaluation result and outputting the evaluation result; wherein the multi-dimensional attribute correlation processing on the multi-dimensional enterprise data portrait to obtain the correlation matching result between the multi-dimensional attributes of the service subject of the enterprise comprises: obtaining at least one business requirement of the enterprise according to the multi-dimensional enterprise data portrait; analyzing the correlation between the multi-dimensional attributes of the service subject through multi-modal feature decomposition and multi-dimensional attribute correlation processing according to the at least one business requirement of the enterprise to obtain the correlation matching result between the multi-dimensional attributes of the service subject; wherein the analysis of the correlation between the multi-dimensional attributes of the service subject comprises: By formula: And The correlation between the multi-dimensional attributes of the service subject is analyzed to obtain the correlation matching result between the multi-dimensional attributes of the service subject. wherein, ; X is a multi-dimensional enterprise data portrait, represented as a spatial three-dimensional figure; wherein, is a rank-1 tensor of the gth node of the multi-dimensional attribute F of the multi-dimensional enterprise portrait data X, is a rank-1 tensor of the node set L between the multi-dimensional attributes F of the multi-dimensional enterprise portrait data X, is a rank-1 tensor of the life cycle T of the multi-dimensional enterprise portrait data X. is a vector of multi-dimensional attributes F, is a vector of node sets L, is a vector of life cycles; K is the total number of dimensions of multi-dimensional attributes, and G is the total number of nodes in a node set corresponding to each dimension attribute; is a variable for the number of dimensions, is a variable for the number of nodes in the node set corresponding to the attribute of the dimension. is a cost function; the associated matching result is a minimum value of the cost function; Let F be the algebraic representation of the multidimensional attribute. Let L be the algebraic representation of the node set L. For attributes The rank-1 tensor of the g-th node, Let g be the rank-1 tensor of the g-th node in the k-th dimension of the node set L.

2. The enterprise service oriented component adaptation processing method according to claim 1, wherein, processing the plurality of heterogeneous data sources to obtain a multi-dimensional enterprise data portrait comprises: performing multi-dimensional data feature classification processing on at least one of structured data, semi-structured data and unstructured data in the plurality of heterogeneous data sources to obtain a classification result; storing the classification result in a basic database; performing classification filtering on the data stored in the basic database to obtain at least one special database; adding a category label to the metadata in the at least one special database to obtain a multi-dimensional enterprise data portrait.

3. The enterprise service oriented component adaptation processing method of claim 1, wherein, performing co-occurrence or co-citation analysis on the multi-dimensional enterprise data portrait according to the correlation matching result to obtain a coupling degree between the enterprise and other service subjects comprises: performing co-occurrence frequency statistics on the multi-dimensional enterprise data portrait according to the correlation matching result to obtain a statistical result; predicting the correlation degree between the common or co-cited feature items of the multi-dimensional enterprise data portrait according to the statistical result to obtain the coupling degree between the enterprise and the service subject.

4. The enterprise service oriented component adaptation processing method according to claim 3, wherein, constructing a qualitative index analysis model in an enterprise value chain according to the coupling degree comprises: constructing a qualitative index analysis model in an enterprise value chain according to the coupling degree and at least one index calculation function.

5. The enterprise service oriented component adaptation processing method of claim 4, wherein, performing collaborative enterprise evaluation according to the qualitative index analysis model to obtain an evaluation result comprises: selecting n target service subjects from the qualitative index analysis model, each target service subject having m indexes, establishing a decision matrix, n and m being positive integers; obtaining a weight value of each index; obtaining a weighted decision matrix according to the weight value of each index and the decision matrix; obtaining a score of each service subject according to the distance between each index in the weighted decision matrix and a preset target value. According to the score of each service subject, an optimal cooperative enterprise is obtained.

6. A component adaptation processing apparatus for enterprise service, characterized by, The method comprises the steps of: an acquisition module, configured to acquire a plurality of heterogeneous data sources of an enterprise; a processing module, configured to process the plurality of heterogeneous data sources to obtain a multi-dimensional enterprise data portrait; performing multi-dimensional attribute correlation processing on the multi-dimensional enterprise data portrait to obtain a correlation matching result between multi-dimensional attributes of the service subject of the enterprise; performing co-occurrence or co-citation analysis on the multi-dimensional enterprise data portrait according to the correlation matching result to obtain a coupling degree between the enterprise and the service subject; constructing a qualitative index analysis model in the enterprise value chain according to the coupling degree; performing cooperative enterprise evaluation according to the qualitative index analysis model to obtain an evaluation result and outputting the evaluation result; wherein the multi-dimensional attribute correlation processing on the multi-dimensional enterprise data portrait to obtain the correlation matching result between the multi-dimensional attributes of the service subject of the enterprise comprises: obtaining at least one business requirement of the enterprise according to the multi-dimensional enterprise data portrait; analyzing the correlation between the multi-dimensional attributes of the service subject through multi-modal feature decomposition and multi-dimensional attribute correlation processing to obtain the correlation matching result between the multi-dimensional attributes of the service subject according to the at least one business requirement of the enterprise; wherein the analysis of the correlation between the multi-dimensional attributes of the service subject comprises: By formula: And The correlation between the multi-dimensional attributes of the service subject is analyzed to obtain the correlation matching result between the multi-dimensional attributes of the service subject. wherein, ; X is a multi-dimensional enterprise data portrait, represented as a spatial three-dimensional figure; wherein, is a rank-1 tensor of the gth node of the multi-dimensional attribute F of the multi-dimensional enterprise portrait data X, is a rank-1 tensor of the node set L between the multi-dimensional attributes F of the multi-dimensional enterprise portrait data X, is a rank-1 tensor of the life cycle T of the multi-dimensional enterprise portrait data X. is a vector of multi-dimensional attributes F, is a vector of node sets L, is a vector of life cycles; K is the total number of dimensions of the multi-dimensional attributes, and G is the total number of nodes in the node set corresponding to each dimension attribute; is a variable for the number of dimensions, is a variable for the number of nodes in the node set corresponding to the attribute of the dimension. is a cost function; the associated matching result is a minimum value of the cost function; is an algebraic representation of the multi-dimensional attribute F, is an algebraic representation of the node set L, is the attribute of the g-th node of rank-1 tensor, is the g-th node of rank-1 tensor of the k-th dimension of the node set L.

7. A computing device, comprising: The method comprises the steps of: a processor and a memory storing a computer program, wherein the computer program is executed by the processor to perform the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The method comprises the steps of: a storage instruction, when the instruction is executed on a computer, the computer executes the method according to any one of claims 1 to 5.

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