CN112785104B - Information processing method, device, equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses an information processing method, an information processing device, information processing equipment and a storage medium. The method comprises the following steps: acquiring preset evaluation elements of an object to be evaluated, wherein the preset evaluation elements comprise the automatic degree of system docking and/or the complexity of a reported work order; and inputting the preset evaluation factors into the trained capacity evaluation model, and determining the information technology capacity of the object to be evaluated according to the output result of the capacity evaluation model. The technical scheme of the embodiment of the invention can determine the IT capability of the object to be evaluated, especially the IT capability of the object supplied by the cooperated goods, in a universal and accurate manner.
Description
Technical Field
The embodiment of the invention relates to the technical field of information processing, in particular to an information processing method, an information processing device, information processing equipment and a storage medium.
Background
At present, an automatic and systematic data interaction mode between enterprises is more and more efficient, and cooperation between an electronic commerce and a supplier is more and more intimate. The information technology (Information Technology, IT) capability of the suppliers as important partners of the electronic suppliers is an important guarantee of supply chain process systemization of purchasing, supplying and the like, and is also an important factor influencing the cost and benefit of the electronic suppliers.
But IT capability of different suppliers is greatly different, which makes cooperative communication between suppliers and electronic suppliers more and more costly. Therefore, with the scale expansion and business complexity of the electronic commerce, real-time accurate portrait information of the vendor is important, particularly the evaluation information of IT capability, which can provide decision support for accurate service and resource matching of the electronic commerce.
In the process of implementing the present invention, the inventor finds that at least the following problems exist in the prior art: on one hand, the prior art has few studies on how to determine the IT capability of a provider, and has more studies on how to determine the business capability of the provider or enterprises in other fields, and the determination scheme of the business capability has stronger field performance and is more an empirical index, so that the reference significance for researching the IT capability is limited; on the other hand, in the prior art, the calculation is finished through a plurality of simple index values, and the accuracy is required to be improved.
Disclosure of Invention
The embodiment of the invention provides an information processing method, an information processing device, information processing equipment and a storage medium, so as to realize the effect of determining universality and accuracy of IT (information technology) capability.
In a first aspect, an embodiment of the present invention provides an information processing method, which may include:
Acquiring preset evaluation elements of an object to be evaluated, wherein the preset evaluation elements comprise the automatic degree of system docking and/or the complexity of a reported work order;
and inputting the preset evaluation factors into the trained capacity evaluation model, and determining the information technology capacity of the object to be evaluated according to the output result of the capacity evaluation model.
Optionally, obtaining the automation degree of the system docking of the object to be evaluated may include:
Determining whether an object to be evaluated can be accessed into a first preset system, wherein the first preset system comprises an electronic data exchange system;
If yes, determining the access quantity of the target access modes and/or the access types of all the target access modes according to the target access modes when the object to be evaluated is accessed to the first preset system;
and acquiring the automatic degree of system docking of the object to be evaluated according to whether the object to be evaluated can be accessed to the first preset system, the access quantity and/or the access type.
Optionally, obtaining the complexity of the submitted worksheet of the object to be evaluated may include:
Determining the first reporting quantity and the difficulty level of each type of reported worksheets of the objects to be evaluated in a second preset system, and acquiring the complexity of the reported worksheets of the objects to be evaluated according to the first reporting quantity and the difficulty level, wherein the second preset system comprises an information technology infrastructure library system.
Optionally, according to the first reporting number and the difficulty level, obtaining the complexity of the reported worksheet of the object to be evaluated may include:
And determining the second reporting quantity of all the reported worksheets of each type in the second preset system, and acquiring the complexity of the reported worksheets of the object to be evaluated according to the numerical relation between the second reporting quantity and the first reporting quantity and the difficulty level.
Optionally, on the basis of the method, the method may further include: and allocating the resources matched with the object to be evaluated to the object to be evaluated according to the information technology capability of the object to be evaluated.
Optionally, inputting the preset evaluation element into the trained capacity evaluation model may include:
Connecting the first characteristic vector corresponding to the automatic degree of the system butt joint and the second characteristic vector corresponding to the complexity of the reported work order end to generate a joint vector;
The joint vector is input into a trained capacity assessment model.
Optionally, the object to be evaluated includes a goods supply object of the e-commerce platform, and/or the capability evaluation model includes a support vector machine.
In a second aspect, an embodiment of the present invention further provides an information processing apparatus, which may include:
The system comprises a preset evaluation element acquisition module, a control module and a control module, wherein the preset evaluation element acquisition module is used for acquiring a preset evaluation element of an object to be evaluated, and the preset evaluation element comprises the automatic degree of system butt joint and/or the complexity of a submitted work order;
The information technology capability determining module is used for inputting preset evaluation factors into the trained capability evaluation model, and determining the information technology capability of the object to be evaluated according to the output result of the capability evaluation model.
In a third aspect, an embodiment of the present invention further provides an apparatus, which may include:
One or more processors;
a memory for storing one or more programs;
When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the information processing method provided by any embodiment of the present invention.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the information processing method provided by any embodiment of the present invention.
According to the technical scheme provided by the embodiment of the invention, the information technology capability of the object to be evaluated can be determined according to the output result of the capability evaluation model by inputting the preset evaluation elements into the trained capability evaluation model, wherein the degree of automation of system docking and/or the complexity of the reported work order are reference factors related to various industries, and the universality is strong. The technical scheme can determine the IT capability of the object to be evaluated, especially the IT capability of the matched goods supply object, universally and accurately.
Drawings
Fig. 1 is a flowchart of an information processing method in a first embodiment of the present invention;
FIG. 2 is a flow chart of an information processing method in a second embodiment of the present invention;
fig. 3 is a flowchart of an information processing method in the third embodiment of the present invention;
fig. 4 is a block diagram showing the structure of an information processing apparatus according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of an apparatus in a fifth embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Example 1
Fig. 1 is a flowchart of an information processing method provided in a first embodiment of the present invention. The embodiment can be applied to the situation of determining the IT capability of an object to be evaluated, and is particularly applicable to the situation of determining the IT capability of a goods supply object of an electronic commerce platform. The method may be performed by an information processing apparatus provided by an embodiment of the present invention, which may be implemented by software and/or hardware, and which may be integrated on various devices.
Referring to fig. 1, the method of the embodiment of the present invention specifically includes the following steps:
S110, acquiring preset evaluation elements of the object to be evaluated, wherein the preset evaluation elements comprise the automatic degree of system docking and/or the complexity of the submitted work order.
On one hand, each industry is related to operation and maintenance work, and in a broad sense, operation and maintenance generally refers to maintenance of network software and hardware which are already established by a large organization; in a narrow sense, an operation is typically an information technology operation (i.e., an IT operation). IT follows that the reference factors often involved in operation and maintenance work are critical in determining the IT capabilities of the object under evaluation. Accordingly, the complexity of the proposed work order which is universally involved in operation and maintenance work can be used as an element to be evaluated. In general, the higher the complexity of a reported worksheet of an object under evaluation, the more IT can be, because only objects under evaluation with a higher IT can report more complex worksheets.
On the other hand, the automation and systemization data interaction between the objects based on the preset data system is more and more frequent, and the interaction cost between the objects is directly influenced by the degree of informatization, so that the automation degree of the system docking can be used as an element to be evaluated. The automation degree of the system docking may be the automation degree when the object to be evaluated accesses the preset data system, for example, whether the object to be evaluated can access the preset data system, can access the preset data system based on several modes, and so on. For the degree of automation of the system docking, various industries may be involved, such as a goods-offering object, in particular a goods-offering object that cooperates with an e-commerce platform, which may be a vendor. Therefore, the degree of automation of the system docking is particularly important in determining the IT capabilities of the goods-offering objects of the e-commerce platform.
Of course, besides the two preset evaluation elements, namely the automation degree of the system docking and the complexity of the reported worksheet, other reference factors which are applicable to various industries, have universality and can be used for determining the IT capability of the object to be evaluated can be used as the preset evaluation elements, and the method is not particularly limited.
S120, inputting preset evaluation factors into the trained capacity evaluation model, and determining the information technology capacity of the object to be evaluated according to the output result of the capacity evaluation model.
Wherein, the capability assessment model for the trained completion can be obtained by the following steps: and acquiring a history evaluation element and a history evaluation result of the history object, taking the history evaluation element and the history evaluation result as a group of training samples, and training an original machine learning model based on a plurality of training samples to obtain the capability evaluation model. Since the untrained raw machine learning model is a machine learning model, the resulting capability assessment model is also a machine learning model, such as a neural network model, a classification model, etc., which may be a support vector machine, K-nearest neighbor classification, etc.
According to the technical scheme provided by the embodiment of the invention, the information technology capability of the object to be evaluated can be determined according to the output result of the capability evaluation model by inputting the preset evaluation elements into the trained capability evaluation model, wherein the degree of automation of system docking and/or the complexity of the reported work order are reference factors related to various industries, and the universality is strong. The technical scheme can determine the IT capability of the object to be evaluated, especially the IT capability of the matched goods supply object, universally and accurately.
An optional technical solution, the preset evaluation element may be presented in the form of a feature vector, for example, a first feature vector corresponding to the automation degree of system docking, and a second feature vector corresponding to the complexity of the reported work order may be generated based on a serial combination mode, that is, the two feature vectors are connected end to end, and feature extraction is performed in a vector space with a higher dimension to generate a joint vector; the joint vector may also be generated based on parallel combining, i.e. combining the two feature vectors together with a complex vector, performing feature extraction in the complex vector space to generate the joint vector. On this basis, the joint vector may be input into a trained capacity assessment model.
Further, optionally, after the IT capability of the object to be evaluated is obtained, a resource matched with the object to be evaluated may be allocated to the object to be evaluated according to the IT capability, where the resource may be a customer service team resource, which has the advantage that if the object to be evaluated and the customer service team resource with matched capabilities work together, the working efficiency of each other may be improved, and the communication cost may be reduced. Of course, the resource may also be a substance resource, an informationized resource, a human resource, etc., which is not specifically limited herein.
Illustratively, taking a customer service team resource as an example, an appropriate customer service team resource may be matched for the object to be evaluated according to ITs IT capability. Specifically, if the IT capability of the object to be evaluated is strong, client team resources with strong professionals can be allocated to the object to be evaluated, and the members in the client team resources can be senior technicians, because the problems posed by the object to be evaluated are more professional problems; conversely, a customer team resource with general professionals may be allocated to the object to be evaluated, where the member may be a general customer service person, because the problem posed by the object to be evaluated is often a pervasive problem.
Further exemplary, as shown in the following table, IT capabilities of the objects to be evaluated are divided into a plurality of levels (high, medium and low), and the objects to be evaluated of different levels correspond to matched customer service team resources. For example, the members in the A customer service team resource are the senior technicians, and the members in the C customer service team resource are the common customer service personnel. Taking the example that the object to be evaluated is a supplier which cooperates with the electronic supplier, the electronic supplier can be matched with proper customer service team resources according to the IT capacity level of the supplier, namely, accurate customer service is provided for the supplier, so that the working efficiency of the electronic supplier is improved, the time and cost consumed in personnel communication between the electronic supplier and the supplier are reduced, and the pressure of operation and maintenance work is further reduced.
| IT capability | Customer service team resource |
| High height | A |
| In (a) | B |
| Low and low | C |
Example two
Fig. 2 is a flowchart of an information processing method provided in the second embodiment of the present invention. The present embodiment is optimized based on the above technical solutions. In this embodiment, optionally, obtaining the automation degree of the system docking of the object to be evaluated may specifically include: determining whether an object to be evaluated can be accessed into a first preset system, wherein the first preset system comprises an electronic data exchange system; if yes, determining the access quantity of the target access modes and/or the access types of all the target access modes according to the target access modes when the object to be evaluated is accessed to the first preset system; and acquiring the automatic degree of system docking of the object to be evaluated according to whether the object to be evaluated can be accessed to the first preset system, the access quantity and/or the access type. Wherein, the explanation of the same or corresponding terms as the above embodiments is not repeated herein.
Referring to fig. 2, the method of this embodiment may specifically include the following steps:
S210, acquiring the automatic degree of system docking of the object to be evaluated, and determining whether the object to be evaluated can be accessed into a first preset system, wherein the first preset system comprises an electronic data exchange system.
The first preset system may be an Electronic data exchange (Electronic DATA INTERCHANGE, EDI) system, which is widely used in various industries, or in various production links, such as manufacturers, suppliers, electronic suppliers, etc. Of course, any system related to electronic data exchange may be attached to the first preset system, which is not specifically limited herein.
Taking an EDI system as an example, the access manner of the current EDI system may include at least one of the following: file transfer Protocol (FILE TRANSFER Protocol, FTP), secure file transfer Protocol (SSH FILE TRANSFER Protocol, SFTP), hypertext transfer Protocol (HyperText Transfer Protocol, HTTP), webService, applicability Statement (AS 2), and so forth. The difficulty of various access modes is gradually increased, wherein FTP is a set of standard protocols for file transmission on a network, SFTP can provide a secure encryption mode for transmitting files, webService is a remote call technology of cross programming language and cross operating system platform, AS2 is a point-to-point electronic data exchange specification of trade parties, and aims to ensure that data can be transmitted safely and reliably on the internet. Of course, in addition to the above several access modes, with the development of electronic data technology, more access modes may be extended.
S220, if so, determining the access quantity of the target access modes and/or the access types of the target access modes according to the target access modes when the object to be evaluated is accessed to the first preset system.
If the object to be evaluated can be accessed to the first preset system, determining a target access mode of the object to be evaluated, and the access number of the target access modes and/or the access types of the target access modes. For example, if the target access manner of the object a to be evaluated is FTP, SFTP, and AS2, that is, the object a to be evaluated may be accessed to the first preset system based on FTP, SFTP, and AS2, the number of accesses of the object a to be evaluated is 3, and the access types are FTP, SFTP, and AS2.
S230, acquiring the automatic degree of system butt joint of the object to be evaluated according to whether the object to be evaluated can be accessed to a first preset system, the access quantity and/or the access type.
In general, IT is considered that the IT capability of the object to be evaluated, which cannot access the first preset system, is low, because of ITs poor automatization and systemization capability in terms of data exchange; the IT capability of the object to be evaluated, which can be accessed into the first preset system based on various modes, is stronger, because different service scenes may relate to different access modes, and the higher the diversity of the access modes, the more service scenes related to the object to be evaluated, the higher the IT capability of the object to be evaluated naturally; or the object to be evaluated can be considered to be accessed into the first preset system in a more difficult access mode, and the IT capacity of the object to be evaluated is higher. Of course, the diversity of access modes is a major reference factor.
S240, inputting preset evaluation elements into the trained capacity evaluation model, and determining the information technology capacity of the object to be evaluated according to the output result of the capacity evaluation model, wherein the preset evaluation elements comprise the degree of automation of system butt joint and/or the complexity of the submitted work order.
According to the technical scheme of the embodiment of the invention, the automatic degree of the system butt joint of the object to be evaluated can be accurately determined according to whether the object to be evaluated can be accessed into the first preset system, the access quantity of the target access mode and/or the access type of each target access mode when the object to be evaluated is accessed into the first preset system, and the first preset system can be an EDI system with universality in various industries, so that the universality of the information processing method disclosed by the embodiment of the invention is further enhanced.
In order to better understand the specific implementation procedure of the above steps, an information processing method of the present embodiment will be described below by taking an example in which the object to be evaluated is the provider X.
Illustratively, according to the stored enterprise information of the provider X (denoted by X), the provider profile code (denoted by vendorCode) corresponding to X is retrieved from the EDI system, where the provider profile code may be a unique identifier of X in the EDI system; inquiring whether X is accessed to the EDI system or not according to vendorCode (symbol is istrue), if X is formally accessed to the EDI system, istrue =1, otherwise istrue =0; inquiring which access modes are supported by X, and sequentially judging whether X supports FTP, SFTP, HTTP, webService and AS2 docking modes, wherein specific judging logic can be that if X supports FTP, accesstype 1 =1, otherwise accesstype 1 =0; if X supports SFTP, accesstype 2 =1, whereas accesstype 2 =0; if X supports HTTP then accesstype 3 =1, whereas accesstype 3 =0; accesstype 4 =1 if X supports WebService, and accesstype 4 =0 otherwise; accesstype 5 =1 if X supports AS2, accesstype 5 0 vice versa; the information thus obtained for the X access EDI system is as follows:
Gi＝{istruei,accesstype1i,accesstype2i,accesstype3i,accesstype4i,accesstype5i} Wherein G i represents the automation degree of the system docking, i represents the provider X, accesstype 1～accesstype5 represents the automatic access mode of the provider X.
Example III
Fig. 3 is a flowchart of an information processing method provided in the third embodiment of the present invention. The present embodiment is optimized based on the above technical solutions. In this embodiment, optionally, acquiring the complexity of the submitted worksheet of the object to be evaluated may specifically include: determining the first reporting quantity and the difficulty level of each type of reported worksheets of the objects to be evaluated in a second preset system, and acquiring the complexity of the reported worksheets of the objects to be evaluated according to the first reporting quantity and the difficulty level, wherein the second preset system comprises an information technology infrastructure library system. Wherein, the explanation of the same or corresponding terms as the above embodiments is not repeated herein.
Referring to fig. 3, the method of this embodiment may specifically include the following steps:
s310, acquiring the complexity of the submitted worksheet of the object to be evaluated.
S320, determining the first reporting quantity and the difficulty level of each type of reported worksheets of the objects to be evaluated in a second preset system, and acquiring the complexity of the reported worksheets of the objects to be evaluated according to the first reporting quantity and the difficulty level, wherein the second preset system comprises an information technology infrastructure library system.
The second preset system may be an information technology infrastructure library (Information Technology Infrastructure Library, ITIL) system, which may be used as an intermediary for inter-system connection communication between any two objects. For example, if the object a uses the system a daily and the object B uses the system B daily, the ITIL system can be used as an intermediate medium for connecting and communicating between the system a and the system B, and can realize electronic information interaction, such as daily communication and communication, between the object a and the object B. Of course, any system related to electronic information interaction may be attached to the second preset system, which is not specifically limited herein.
Taking the ITIL system as an example, the processed reported worksheets can be divided into a simple consultation class, a system Bug class and an optimization suggestion class, and the difficulty level of each type of reported worksheets is gradually increased. Wherein, the simple consultation class can be a simple consultation aiming at the related problems between the cooperation of the two parties; the system Bug class may be a proposal for a Bug existing in a system that is being used by both parties respectively, for example, a Bug existing in system a and/or system B in the above example; the optimization suggestion class can be optimization suggestions for systems respectively used by both parties, so that the difficulty level of each type of reported worksheet is gradually increased.
Therefore, the complexity of the submitted worksheets of the object to be evaluated can be determined according to the first submitted number of the worksheets of each type submitted by the object to be evaluated in the second preset system and the difficulty degree of each type. For example, for all the worksheets of the object to be evaluated, if the proportion of the worksheets in the high-difficulty type in all the worksheets is larger, that is, the first reporting number in the high-difficulty type is larger, IT can be stated that the IT capability of the object to be evaluated is higher, because the concerns of the object to be evaluated with higher IT capability are more complex problems; otherwise, IT may be indicated that the IT capability of the object under evaluation is low. For example, the work order submitted by the object to be evaluated A relates to three types of RST, the difficulty level of each type is gradually increased, and if the number of the first submitted of the three types is 20, 50 and 100, IT can be indicated that the IT capability of the object to be evaluated is higher.
For another example, to reflect the relative height of the IT capability between the objects, a second reporting number of all the reported worksheets of each type in the second preset system may be determined, where the second reporting number is the reporting number of all the objects that interact with the second preset system; further, according to the numerical relation between the second reporting number and the first reporting number and the difficulty level, the complexity of the reported worksheet of the object to be evaluated can be determined, and the numerical relation can be a numerical ratio between the first reporting number and the second reporting number of each type of reported worksheets. This is because, for the first reporting number and the second reporting number that are at a higher difficulty, if the numerical ratio corresponding to the object to be evaluated is larger, IT may be indicated that IT capability of the object to be evaluated is higher in all the objects related to the second preset system. Of course, the numerical relationships of the various types may be considered in combination to accurately derive the complexity of the proposed work order of the object under evaluation. Continuing with the above example, if the second reporting amounts of the three types of all the objects related to the second preset system are 10000, 8000 and 5000 respectively, since the value ratio of the object to be evaluated corresponding to the type T with higher difficulty is larger and the value ratio of the object to be evaluated corresponding to the type R with lower difficulty is smaller, IT can be said that the IT capability of the object to be evaluated in all the objects related to the second preset system is higher.
In order to better understand the specific implementation procedure of the above steps, the information processing method of the present embodiment will be described by taking the example in the second embodiment as an example based on the ITIL system.
Illustratively, the full-volume worksheet data of the ITIL system is obtained and denoted as y= (Σy 1,∑Y2,∑Y3), wherein Y 1 represents the second reporting number of simple consultation class worksheets, Y 2 represents the second reporting number of system Bug class worksheets, and Y 3 represents the second reporting number of optimization advice class worksheets; inquiring the first report number of the ITIL work orders reported by X through vendorCode, and recording as Y i＝(Y1i,Y2i,Y3i), wherein i represents X, calculating the percentage value of each type of work orders contained by X through Y i and Y, thereby obtaining the complexity P i of the report work orders of X in the ITIL system, and the specific formula is as follows: At this time, if/> Smaller and/>And/>If IT is larger, IT is stated that the IT capability of the provider X is higher, because IT is only possible for the provider X with higher IT capability to discover the system BUG and make optimization suggestions.
S330, inputting preset evaluation elements into the trained capacity evaluation model, and determining the information technology capacity of the object to be evaluated according to the output result of the capacity evaluation model, wherein the preset evaluation elements comprise the degree of automation of system docking and/or the complexity of the submitted work order.
According to the technical scheme of the embodiment of the invention, the complexity of the reported worksheets of the objects to be evaluated can be accurately determined according to the first reporting quantity of the reported worksheets of the types of the objects to be evaluated in the second preset system and the difficulty of the types of the reported worksheets, and the complexity of the reported worksheets can relate to operation and maintenance work of various industries and has stronger universality.
On this basis, in order to better understand the generation manner of the joint vector and the training process of the capability assessment model described in the first embodiment, the foregoing will be exemplarily described by taking examples in the second embodiment and the third embodiment as examples.
Exemplary, a first feature vector Gi＝{istruei,accesstype1i,accesstype2i,accesstype3i,accesstype4i,accesstype5i}, corresponding to the degree of automation for system docking and a second feature vector corresponding to the complexity of the proposed work orderConstructing a joint vector H i＝{Gi,Pi of the corresponding provider level of X based on a serial combination, the joint vector H i consisting of two parts: EDI system access information G i, information P i of the report worksheet in ITIL system.
Further, training the training sample including the joint vector H i by using one or more classification methods of the K-nearest neighbor method or the support vector machine to obtain a capacity assessment model, wherein the capacity assessment model is a classification model. Specifically, if an SVM classifier model (i.e., a capability assessment model) is constructed based on a learning classification algorithm of a support vector machine (Support Vector Machine, SVM), the generation of the classifier model specifically includes the following steps: 1) Dividing the supplier data into a training sample set Q and a test sample set T, wherein Q= { Q 1,q2,…,qe }, and e represents the number of suppliers in the training sample set; t= { T 1,t2,…,tn }, n represents the number of suppliers in the test sample set; 2) Extracting EDI system access information G j and information P j of a report work order in an ITIL system from a training sample set Q respectively, constructing H j in a serial combination mode, and marking the H= { H 1,H2,…,Hj…,He }, wherein the range of j is 0-e; 3) The method comprises the steps of taking a feature vector H extracted from a training sample set Q, a class label H lable corresponding to the training sample set Q and related parameters as inputs of a training classifier model, and outputting the classifier model as a result, wherein the value range of H lable＝{lable1,lable2,…,lablej,…,lablee},lablej is 1-n, n is=3, and the high, medium and low are respectively represented; 4) And identifying the provider data to be identified in the test sample set T by using the classifier model, wherein the identification result is measured by the identification accuracy D, D=d/n, D represents the number of identification pairs in the test sample, and n represents the number of providers in the test sample set.
Thus, the SVM classifier model (i.e., the capability assessment model) is trained. Subsequently, the class range corresponding to the IT capability class lable i,lablei corresponding to X is identified as high, medium, and low by training the model of the SVM classifier, wherein i represents the provider X.
Example IV
Fig. 4 is a block diagram of an information processing apparatus according to a fourth embodiment of the present invention, which is configured to execute the information processing method according to any of the above-described embodiments. The apparatus belongs to the same inventive concept as the information processing method of the above embodiments, and reference may be made to the above embodiments of the information processing method for details which are not described in detail in the embodiments of the information processing apparatus. Referring to fig. 4, the apparatus may specifically include: a preset evaluation element acquisition module 410 and an information technology capability determination module 420.
The preset evaluation element obtaining module 410 is configured to obtain a preset evaluation element of an object to be evaluated, where the preset evaluation element includes an automation degree of system docking and/or a complexity of a reported work order;
The information technology capability determining module 420 is configured to input a preset evaluation element into the trained capability evaluation model, and determine the information technology capability of the object to be evaluated according to the output result of the capability evaluation model.
Optionally, the preset evaluation element acquisition module 410 may specifically include:
the first preset system access unit is used for determining whether an object to be evaluated can be accessed into the first preset system, wherein the first preset system comprises an electronic data exchange system;
The access type determining unit is used for determining the access quantity of the target access modes and/or the access types of all the target access modes aiming at the target access modes when the object to be evaluated is accessed to the first preset system if yes;
And the system docking automation degree acquisition unit is used for acquiring the system docking automation degree of the object to be evaluated according to whether the object to be evaluated can be accessed into the first preset system, the access quantity and/or the access type.
Optionally, the preset evaluation element acquisition module 410 may specifically include:
the difficulty level determining unit is used for determining the first reporting quantity of each type of reported worksheets of the object to be evaluated in a second preset system and the difficulty level of each type, wherein the second preset system comprises an information technology infrastructure library system;
The complexity acquisition unit is used for acquiring the complexity of the submitted worksheets of the objects to be evaluated according to the first submitted quantity and the difficulty level.
Optionally, the complexity obtaining unit of the reported worksheet may be specifically configured to:
And determining the second reporting quantity of all the reported worksheets of each type in the second preset system, and acquiring the complexity of the reported worksheets of the object to be evaluated according to the numerical relation between the second reporting quantity and the first reporting quantity and the difficulty level.
Optionally, on the basis of the above device, the device may further include:
and the resource allocation module is used for allocating the resources matched with the object to be evaluated to the object to be evaluated according to the information technology capability of the object to be evaluated.
Optionally, the information technology capability determining module 420 may specifically include:
the joint vector generation unit is used for connecting the first characteristic vector corresponding to the automatic degree of system butt joint and the second characteristic vector corresponding to the complexity of the reported work order end to generate a joint vector;
And the joint vector input unit is used for inputting the joint vector into the trained capacity assessment model.
Optionally, the object to be evaluated includes a goods supply object of the e-commerce platform, and/or the capability evaluation model includes a support vector machine.
According to the information processing device provided by the fourth embodiment of the invention, the preset evaluation element acquisition module and the information technology capability determination module are matched with each other, the preset evaluation element is input into the trained capability evaluation model, and the information technology capability of the object to be evaluated can be determined according to the output result of the capability evaluation model, wherein the degree of automation of system butt joint and/or the complexity of the submitted work order are reference factors related to various industries, and the universality is high. The device can determine the IT capability of the object to be evaluated, especially the IT capability of the matched goods supply objects, in a universal and accurate manner.
The information processing device provided by the embodiment of the invention can execute the information processing method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
It should be noted that, in the above-described embodiment of the information processing apparatus, each unit and module included is divided according to the functional logic only, but is not limited to the above-described division, as long as the corresponding function can be realized; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
Example five
Fig. 5 is a schematic structural diagram of an apparatus according to a fifth embodiment of the present invention, and as shown in fig. 5, the apparatus includes a memory 510, a processor 520, an input device 530, and an output device 540. The number of processors 520 in the device may be one or more, one processor 520 being taken as an example in fig. 5; the memory 510, processor 520, input means 530 and output means 540 in the device may be connected by a bus or other means, in fig. 5 by way of example by a bus 550.
The memory 510 is a computer-readable storage medium that can be used to store a software program, a computer-executable program, and modules, such as program instructions/modules corresponding to the information processing method in the embodiment of the present invention (for example, the preset evaluation element acquisition module 410 and the information technology capability determination module 420 in the information processing apparatus). The processor 520 executes various functional applications of the device and data processing, i.e., implements the information processing method described above, by running software programs, instructions, and modules stored in the memory 510.
The memory 510 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for functions; the storage data area may store data created according to the use of the device, etc. In addition, memory 510 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some examples, memory 510 may further include memory located remotely from processor 520, which may be connected to the device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 530 may be used to receive input numeric or character information and to generate key signal inputs related to user settings and function control of the device. The output 540 may include a display device such as a display screen.
Example six
A sixth embodiment of the present invention provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are for performing an information processing method, the method comprising:
Acquiring preset evaluation elements of an object to be evaluated, wherein the preset evaluation elements comprise the automatic degree of system docking and/or the complexity of a reported work order;
and inputting the preset evaluation factors into the trained capacity evaluation model, and determining the information technology capacity of the object to be evaluated according to the output result of the capacity evaluation model.
Of course, the storage medium containing the computer executable instructions provided in the embodiments of the present invention is not limited to the method operations described above, and may also perform the related operations in the information processing method provided in any embodiment of the present invention.
From the above description of embodiments, it will be clear to a person skilled in the art that the present invention may be implemented by means of software and necessary general purpose hardware, but of course also by means of hardware, although in many cases the former is a preferred embodiment. In light of such understanding, the technical solution of the present invention may be embodied essentially or in part in the form of a software product, which may be stored in a computer-readable storage medium, such as a floppy disk, read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), FLASH Memory (FLASH), hard disk, optical disk, or the like, of a computer, which may be a personal computer, a server, a network device, or the like, including instructions for causing a computer device (which may be a personal computer, a server, or the like) to perform the methods described in the various embodiments of the present invention.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (8)
1. An information processing method, characterized by comprising:
acquiring preset evaluation elements of an object to be evaluated, wherein the preset evaluation elements comprise the automatic degree of system docking and/or the complexity of a submitted work order;
Inputting the preset evaluation elements into a trained capacity evaluation model, and determining the information technology capacity of the object to be evaluated according to the output result of the capacity evaluation model;
The obtaining the automation degree of the system docking of the object to be evaluated comprises the following steps:
Determining whether an object to be evaluated can be accessed into a first preset system, wherein the first preset system comprises an electronic data exchange system;
If yes, determining the access quantity of the target access modes and/or the access types of the target access modes according to the target access modes when the object to be evaluated is accessed to the first preset system;
Acquiring the automatic degree of system docking of the object to be evaluated according to whether the object to be evaluated can be accessed into a first preset system, the access quantity and/or the access type;
And/or the number of the groups of groups,
The obtaining the complexity of the submitted worksheet of the object to be evaluated comprises the following steps:
Determining the first reporting quantity of each type of reported worksheets of the object to be evaluated in a second preset system and the difficulty level of each type;
And acquiring the complexity of the submitted worksheets of the objects to be evaluated according to the first submitted number and the difficulty level, wherein the second preset system comprises an information technology infrastructure library system.
2. The method according to claim 1, wherein the obtaining the complexity of the submitted worksheet of the object to be evaluated according to the first number of submitted worksheets and the difficulty level comprises:
And determining the second reporting quantity of all the reported worksheets of each type in the second preset system, and acquiring the complexity of the reported worksheets of the object to be evaluated according to the numerical relation between the second reporting quantity and the first reporting quantity and the difficulty degree.
3. The method as recited in claim 1, further comprising:
and distributing the resources matched with the object to be evaluated to the object to be evaluated according to the information technology capability of the object to be evaluated.
4. The method of claim 1, wherein the inputting the preset evaluation element into a trained capacity evaluation model comprises:
Connecting the first characteristic vector corresponding to the automatic degree of the system butt joint and the second characteristic vector corresponding to the complexity of the submitted work order end to generate a joint vector;
The joint vector is input into a trained capacity assessment model.
5. The method of claim 1, wherein the object under evaluation comprises a goods provision object of an e-commerce platform and/or the capability assessment model comprises a support vector machine.
6. An information processing apparatus, characterized by comprising:
the system comprises a preset evaluation element acquisition module, a control module and a control module, wherein the preset evaluation element acquisition module is used for acquiring a preset evaluation element of an object to be evaluated, and the preset evaluation element comprises the automatic degree of system butt joint and/or the complexity of a submitted work order;
The information technology capability determining module is used for inputting the preset evaluation factors into a trained capability evaluation model, and determining the information technology capability of the object to be evaluated according to the output result of the capability evaluation model;
the preset evaluation element acquisition module includes:
the system comprises a first preset system access unit, a second preset system access unit and a first data processing unit, wherein the first preset system access unit is used for determining whether an object to be evaluated can be accessed into a first preset system, and the first preset system comprises an electronic data exchange system;
The access type determining unit is used for determining the access number of the target access modes and/or the access types of the target access modes aiming at the target access modes when the object to be evaluated accesses the first preset system if the object to be evaluated is in the first preset system;
the system docking automation degree acquisition unit is used for acquiring the system docking automation degree of the object to be evaluated according to whether the object to be evaluated can be accessed into a first preset system, the access quantity and/or the access type;
And/or the number of the groups of groups,
The preset evaluation element acquisition module includes:
The difficulty level determining unit is used for determining the first reporting quantity of each type of reported worksheets of the object to be evaluated in the second preset system and the difficulty level of each type;
the complexity acquisition unit is used for acquiring the complexity of the submitted worksheets of the objects to be evaluated according to the first submitted quantity and the difficulty level, wherein the second preset system comprises an information technology infrastructure library system.
7. An apparatus, the apparatus comprising:
One or more processors;
a memory for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the information processing method of any of claims 1-5.
8. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the information processing method according to any one of claims 1-5.
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