CN116050887A - Supplier assessment method based on big data and related device - Google Patents
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Abstract
The application discloses a provider assessment method and a related device based on big data, wherein the method comprises the following steps: generating a supplier portrait according to the supply quality, the delivery information, the maintenance information and the qualification information in the target information by acquiring the target information of the supplier to be evaluated; updating the tag weight in the vendor representation in combination with the evaluation purpose; generating a vendor capability map from the updated vendor representation; generating a quality evaluation report of the supplier according to the supplier capability map when the supplier capability map meets preset evaluation conditions; updating the vendor representation according to different evaluation purposes on the basis of generating the vendor representation by using vendor target information; thereby generating vendor representation information more reasonable in terms of evaluation purpose; further enabling more accurate vendor quality assessment reporting.
Description
Technical Field
The present disclosure relates to the field of image data evaluation, and in particular, to a provider evaluation method based on big data and a related device.
Background
In the context of the current high economic development, enterprises are required to examine and evaluate the whole supply chain with a systematic and comprehensive idea so as to realize the integrated management of logistics, information flow and fund flow among the enterprises from suppliers, enterprises and users. Purchase provider management in the whole supply chain management process is the object of the important research of enterprises. The fine management can improve the product quality of enterprises, ensure the product delivery period, reduce the management operation cost and strengthen the competitiveness of the enterprises. The conventional vendor management has problems of unsmooth information communication, low cooperation level, no sharing of service data and the like, so that a layout mode of an intelligent vendor management system capable of overcoming the problems is urgently needed. In addition, in vendor management, vendor evaluation plays an important role in admission to qualification, supervisory inspection, and performance assessment. However, for the evaluation of suppliers, the evaluation indexes of most enterprises are more general, classification formulation or differential screening is not available, and the subjective factor influence of the using method is large, and the method is not objective and reasonable enough.
Therefore, how to accurately evaluate suppliers becomes a technical problem to be solved.
Disclosure of Invention
In order to accurately evaluate suppliers, the application provides a supplier evaluation method based on big data and a related device.
In a first aspect, the present application provides a vendor evaluation method based on big data, which adopts the following technical scheme:
a big data based vendor assessment method comprising:
acquiring target information of a supplier to be evaluated, wherein the target information comprises supply quality, delivery information, maintenance information and qualification information;
generating a vendor representation from the supply quality, the delivery information, the maintenance information, and the qualification information;
acquiring a current evaluation purpose, and updating weight information corresponding to a feature tag in the provider image according to the evaluation purpose;
generating a vendor capability map from the current vendor representation;
judging whether a preset evaluation condition is met in the supplier capability map;
if yes, generating a provider quality evaluation report according to the provider energy map.
Optionally, the step of generating a vendor representation by the supply quality, the delivery information, the maintenance information, and the qualification information includes:
generating a quality tag from the supply quality;
generating a delivery label through the delivery information;
generating an after-market label through the maintenance information;
generating a basic label through the qualification information;
and generating a provider portrait according to the quality label, the delivery label, the after-sales label and the basic label.
Optionally, the step of generating a vendor representation according to the quality label, the delivery label, the after-market label, and the base label includes:
determining quality secondary label information according to the quality label;
determining delivery secondary label information according to the delivery label;
determining after-sales secondary label information according to the after-sales label;
and generating a provider portrait according to the quality secondary label information, the delivery secondary label information and the after-sales secondary label information and combining the basic label.
Optionally, the step of obtaining the current evaluation objective and updating the weight information corresponding to the feature tag in the provider image according to the evaluation objective includes:
acquiring current evaluation information, and determining evaluation tendency according to the evaluation information;
matching a target weight strategy in a preset weight adjustment rule table according to the evaluation tendency;
and updating weight information corresponding to the feature labels in the provider image according to the target weight strategy.
Optionally, the step of updating weight information corresponding to the feature tag in the provider image according to the target weight policy includes:
acquiring strategy adjustment information of the target weight strategy;
matching a target feature tag in the provider image according to the policy adjustment information;
and updating the weight information corresponding to the target feature tag.
Optionally, the step of generating the vendor capability map according to the current vendor representation includes:
acquiring all label information in the current provider image;
acquiring label categories corresponding to preset capability map conditions;
screening the tag information according to the tag category to obtain a screening result;
and generating a provider capability map according to the label information in the screening result.
Optionally, the step of determining whether a preset evaluation condition is met in the vendor capability map includes:
acquiring a specific value of each capability class in the vendor capability map;
acquiring the lowest threshold value of each capability class in preset evaluation conditions;
judging whether the current specific value is greater than the lowest threshold value in each capability class;
if not, judging that the provider can not meet the preset evaluation condition.
In a second aspect, the present application provides a big data based vendor estimation device, the big data based vendor estimation device comprising:
the information acquisition module is used for acquiring target information of the supplier to be evaluated, wherein the target information comprises supply quality, delivery information, maintenance information and qualification information;
a representation generation module for generating a vendor representation from the supply quality, the delivery information, the maintenance information, and the qualification information;
the label updating module is used for acquiring the current evaluation purpose and updating the weight information corresponding to the feature label in the provider image according to the evaluation purpose;
the energy diagram module is used for generating a provider energy diagram according to the current provider portrait;
the condition evaluation module is used for judging whether a preset evaluation condition is met in the supplier capability map;
and the report generation module is used for generating a provider quality evaluation report according to the provider energy map if yes.
In a third aspect, the present application provides a computer device, the device comprising: a memory, a processor which, when executing the computer instructions stored by the memory, performs the method as claimed in any one of the preceding claims.
In a fourth aspect, the present application provides a computer readable storage medium comprising instructions which, when run on a computer, cause the computer to perform a method as described above.
In summary, the present application includes the following beneficial technical effects:
the method comprises the steps of obtaining target information of a supplier to be evaluated, and generating a supplier portrait in the target information according to supply quality, delivery information, maintenance information and qualification information; updating the tag weight in the vendor representation in combination with the evaluation purpose; generating a vendor capability map from the updated vendor representation; generating a quality evaluation report of the supplier according to the supplier capability map when the supplier capability map meets preset evaluation conditions; updating the vendor representation according to different evaluation purposes on the basis of generating the vendor representation by using vendor target information; thereby generating vendor representation information more reasonable in terms of evaluation purpose; further enabling more accurate vendor quality assessment reporting.
Drawings
FIG. 1 is a schematic diagram of a computer device in a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flow chart of a first embodiment of a big data based vendor evaluation method of the present invention;
FIG. 3 is a flow chart of a second embodiment of the big data based vendor evaluation method of the present invention;
fig. 4 is a block diagram showing the construction of a first embodiment of the big data based supplier evaluating apparatus of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail by means of the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
Referring to fig. 1, fig. 1 is a schematic diagram of a computer device structure of a hardware running environment according to an embodiment of the present invention.
As shown in fig. 1, the computer device may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
Those skilled in the art will appreciate that the architecture shown in fig. 1 is not limiting of a computer device and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a vendor evaluation program based on big data may be included in the memory 1005 as one storage medium.
In the computer device shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the computer device of the present invention may be provided in a computer device, where the computer device invokes the big data based vendor evaluation program stored in the memory 1005 through the processor 1001, and executes the big data based vendor evaluation method provided in the embodiment of the present invention.
The embodiment of the invention provides a provider assessment method based on big data, and referring to fig. 2, fig. 2 is a flow chart of a first embodiment of the provider assessment method based on big data.
In this embodiment, the provider assessment method based on big data includes the following steps:
step S10: target information of a supplier to be evaluated is obtained, wherein the target information comprises supply quality, delivery information, maintenance information and qualification information.
It should be noted that, suppliers are enterprises and individuals who supply various required resources to enterprises and competitors thereof, including providing raw materials, equipment, energy sources, labor, and the like. How they have a great impact on the marketing campaign of the enterprise, such as raw material price changes, shortages, etc., can affect the price and delivery period of the enterprise's products and can thus impair the long-term collaboration and benefits of the enterprise with the customer, and thus marketers must have a more comprehensive understanding and thorough analysis of the supplier's situation. Vendor classification is an important part of vendor system management. It decides which suppliers you want to develop strategic partnerships, which you want to grow business, which remain current, which are actively eliminated, and which are identity-indeterminate. Accordingly, suppliers may be classified into strategic suppliers (Strategic Suppliers), priority suppliers (Preferred Suppliers), survey suppliers (Provisional Suppliers), passive elimination (Exit Passive), active elimination (Exit Active), and identity-indeterminate suppliers (indeterminate). Of course, the division and definition of different companies may be slightly different. Generally, a transaction type refers to a provider with a plurality of suppliers but a small transaction amount; the strategic suppliers refer to a few suppliers necessary for the strategic development of the company; large suppliers refer to suppliers with large transaction amounts and general strategic meanings.
It should be noted that, in this embodiment, the manner of acquiring the target information of the provider may be by manually inputting information, or may be by acquiring the related information of the target provider through the internet.
It can be understood that the supply quality refers to the supply quality counted according to the supply quality in the historical supply record of the supplier as the reference data. Through a preset quality calculation rule, corresponding deduction items in different scenes are different. The quality of the supplier is thus information obtained in connection with the quality scores corresponding to the supply records known to the supplier.
The delivery information is information recorded during the delivery with the execution body of the present embodiment, and includes credit information of the delivery, timely information of the delivery, and security information of the delivery.
It will be appreciated that the maintenance information, i.e., the information that the supplier presents during the after-sales process, includes the timeliness of the maintenance, the completion of the maintenance, and the assessment information of the maintenance.
In this embodiment, the qualification information refers to basic information of the enterprise, including operation information of the enterprise, business information, and other basic information. The operation condition can be known through the qualification information of the enterprise, and the enterprise risk is estimated.
Step S20: a vendor representation is generated from the supply quality, delivery information, maintenance information, and qualification information.
The provider portrait generated by the quality of goods, delivery information, maintenance information and qualification information is one type of enterprise portrait, and the enterprise portrait (Enterprise Profile, EP) is used as an enterprise big data comprehensive service platform for scenes such as smart cities, financial supervision, enterprise information, enterprise assessment and the like, so that a billion-level enterprise knowledge map can be constructed, and complex network relations among enterprises, high-level management, legal persons, products and industry chains can be deeply mined. Providing services such as urban industry analysis, regional macro economic analysis, quotation and recommendation for government, and guiding local industry development; monitoring the development situation of a target enterprise for a finance or supervision institution, and early warning risks in the first time; and providing a plurality of comprehensive services such as enterprise public opinion, accurate marketing and the like for enterprises. Enterprise portraits are oriented to scenes such as smart cities, campus tenderers, financial supervision, enterprise evaluation and the like. Providing regional macro economic analysis and guiding local industry development. Evaluating and monitoring are carried out on important enterprises and supporting enterprises in places, and the development situation of the enterprises is monitored. The method and the system can accurately recommend the recruiter to target enterprises for government parks and provide accurate, professional and real-time recruiter recommendation services for local governments or parks. Enterprise portraits are based on Tencentrated cloud big data ecology, basic components such as elastic MapReduce, elasticsearch Service and a graph database are comprehensively utilized, and data values are deeply mined according to user scenes by means of deep learning, natural language processing, knowledge maps and other technologies. National enterprise information including enterprise business, high management, stakeholders, actual controllers, tax ratings, products, recruitments, written patents, administrative, judicial penalties, and the like. National industry data, including industry head enterprises, industry regional distribution, industry policies, industry upstream and downstream, etc., is a matter of years public opinion data accumulation in the financial/government/enterprise domain.
Further, in order to enhance the accuracy of the provider representation generation, the step of generating the provider representation by the supply quality, the delivery information, the maintenance information, and the qualification information includes: generating a quality tag from the supply quality; generating a delivery label through the delivery information; generating an after-market label through the maintenance information; generating a basic label through the qualification information; and generating a provider portrait according to the quality label, the delivery label, the after-sales label and the basic label.
The delivery label includes a label generated by the evaluation information corresponding to the delivery end.
It is understood that quality label means label information generated from the evaluation result after the quality evaluation of the supplier, including the evaluation of the quality of each part of the product of the supplier.
The after-market label refers to an after-market label generated after acquiring and evaluating after-market records of a provider, and the after-market label includes an after-market evaluation grade, an after-market delivery grade and an after-market completion grade, and the grades are all determined according to actual conditions by preset after-market grade calculation conditions.
It can be understood that the basic label of the provider performs qualification grade evaluation according to the basic information of the provider, and the content of the basic label is determined according to the evaluation result.
In a specific implementation, the step of generating a vendor representation according to the quality label, the delivery label, the after-market label, and the base label includes: determining quality secondary label information according to the quality label; determining delivery secondary label information according to the delivery label; determining after-sales secondary label information according to the after-sales label; and generating a provider portrait according to the quality secondary label information, the delivery secondary label information and the after-sales secondary label information and combining the basic label.
The secondary label information is secondary label information such as a delivery time interval, a delivery speed, a delivery result and the like, wherein the secondary label information is a secondary label set generated by other factors in the delivery process.
It can be understood that the quality secondary label information refers to secondary label information determined in the quality evaluation process of the provider, and includes; a commodity type tag; cost performance labels, and the like.
The after-market secondary label information, that is, the secondary label generated according to the after-market label, includes: after-market docking information, after-market time limit distribution information, and the like.
Step S30: and acquiring the current evaluation purpose, and updating the weight information corresponding to the feature tag in the provider image according to the evaluation purpose.
Note that, in the case where the evaluation purposes are different, the emphasis division should be performed according to the specific evaluation contents, and the evaluation tendency includes: evaluating the qualification of the suppliers, namely evaluating mainly qualification information; evaluating delivery of the supplier, namely evaluating the delivery information and the quality of the goods as the main materials; after-market assessment of the supplier, i.e. assessment based on maintenance information. Proper adjustment of weights in the vendor representation for each different assessment purpose may generate a more rational assessment report.
Step S40: a vendor capability map is generated from the current vendor representation.
Further, to improve the accuracy of generating the vendor capability map, the step of generating the vendor capability map according to the current vendor representation includes: acquiring all label information in the current provider image; acquiring label categories corresponding to preset capability map conditions; screening the tag information according to the tag category to obtain a screening result; and generating a provider capability map according to the label information in the screening result.
The embodiment of the performance form of the vendor capability map is not limited herein, and may be performed in the form of a bar graph, a fan graph, or a five-dimensional graph, and specific use needs to be set by a system administrator according to actual situations.
Step S50: it is determined in the vendor profile whether a preset evaluation condition is satisfied.
Further, in order to increase the requirement for the evaluation of the supplier, the step of determining whether the preset evaluation condition is satisfied in the supplier capability map includes: acquiring a specific value of each capability class in the vendor capability map; acquiring the lowest threshold value of each capability class in preset evaluation conditions; judging whether the current specific value is greater than the lowest threshold value in each capability class; if not, judging that the provider can not meet the preset evaluation condition.
In particular implementations, a failed classification is considered to occur if there is a condition in the corresponding capability classification that does not meet the minimum threshold. A provider is considered to be under-rated when a provider is under-rated. The minimum threshold may be raised appropriately to lower the requirement criteria depending on the particular situation of use.
Step S60: if yes, generating a quality evaluation report of the supplier according to the capability map of the supplier.
The method comprises the steps of obtaining target information of a supplier to be evaluated, and generating a supplier portrait in the target information according to supply quality, delivery information, maintenance information and qualification information; updating the tag weight in the vendor representation in combination with the evaluation purpose; generating a vendor capability map from the updated vendor representation; generating a quality evaluation report of the supplier according to the supplier capability map when the supplier capability map meets preset evaluation conditions; updating the vendor representation according to different evaluation purposes on the basis of generating the vendor representation by using vendor target information; thereby generating vendor representation information more reasonable in terms of evaluation purpose; further enabling more accurate vendor quality assessment reporting.
Referring to fig. 3, a flowchart of a second embodiment of the big data based vendor evaluation method of the present invention is shown.
Based on the above-described first embodiment, the step S30 of the vendor estimation method based on big data of the present embodiment further includes:
step S301: and acquiring current evaluation information, and determining evaluation tendency according to the evaluation information.
It should be noted that, the evaluation tendency in this embodiment refers to a specific requirement for the evaluation of the provider, and includes: delivery assessment, after-market assessment, and base assessment.
Step S302: and matching a target weight strategy in a preset weight adjustment rule table according to the evaluation tendency.
It should be noted that, the preset weight adjustment rule, that is, adjusting the weight of the label in the provider portrait for different evaluation trends, ultimately affects the specific performance value.
Step S303: and updating weight information corresponding to the feature labels in the provider image according to the target weight strategy.
After determining the target weight policy, determining the label to be adjusted, and matching the corresponding label in the provider image to perform adjustment.
According to the embodiment, the current evaluation information is acquired, and the evaluation tendency is determined according to the evaluation information; matching a target weight strategy in a preset weight adjustment rule table according to the evaluation tendency; updating weight information corresponding to the feature labels in the provider image according to the target weight strategy; the method realizes the updating of the weight of the label in the provider portrait accurately according to the evaluation tendency.
Furthermore, an embodiment of the present invention also proposes a computer-readable storage medium, on which a program of big data based vendor evaluation is stored, which when executed by a processor implements the steps of the method of big data based vendor evaluation as described above.
Referring to fig. 4, fig. 4 is a block diagram showing the construction of a first embodiment of the big data based supplier evaluating apparatus of the present invention.
As shown in fig. 4, the provider assessment device based on big data according to the embodiment of the present invention includes:
an information acquisition module 10 for acquiring target information of a supplier to be evaluated, the target information including supply quality, delivery information, maintenance information, and qualification information;
a portrait creation module 20 for creating a vendor portrait from the supply quality, the delivery information, the maintenance information, and the qualification information;
the label updating module 30 is configured to obtain a current evaluation objective, and update weight information corresponding to a feature label in the provider image according to the evaluation objective;
a capability map module 40 for generating a vendor capability map from the current vendor representation;
a condition evaluation module 50 for determining whether a preset evaluation condition is satisfied in the vendor capability map;
and the report generating module 60 is configured to generate a quality evaluation report of the provider according to the provider capability map if yes.
It should be understood that the foregoing is illustrative only and is not limiting, and that in specific applications, those skilled in the art may set the invention as desired, and the invention is not limited thereto.
The method comprises the steps of obtaining target information of a supplier to be evaluated, and generating a supplier portrait in the target information according to supply quality, delivery information, maintenance information and qualification information; updating the tag weight in the vendor representation in combination with the evaluation purpose; generating a vendor capability map from the updated vendor representation; generating a quality evaluation report of the supplier according to the supplier capability map when the supplier capability map meets preset evaluation conditions; updating the vendor representation according to different evaluation purposes on the basis of generating the vendor representation by using vendor target information; thereby generating vendor representation information more reasonable in terms of evaluation purpose; further enabling more accurate vendor quality assessment reporting.
In one embodiment, the image generation module 20 is further configured to generate a quality tag based on the supply quality; generating a delivery label through the delivery information; generating an after-market label through the maintenance information; generating a basic label through the qualification information; and generating a provider portrait according to the quality label, the delivery label, the after-sales label and the basic label.
In one embodiment, the portrait creation module 20 is further configured to determine quality secondary label information according to the quality label; determining delivery secondary label information according to the delivery label; determining after-sales secondary label information according to the after-sales label; and generating a provider portrait according to the quality secondary label information, the delivery secondary label information and the after-sales secondary label information and combining the basic label.
In an embodiment, the tag updating module 30 is further configured to obtain current evaluation information, and determine an evaluation tendency according to the evaluation information; matching a target weight strategy in a preset weight adjustment rule table according to the evaluation tendency; and updating weight information corresponding to the feature labels in the provider image according to the target weight strategy.
In an embodiment, the tag updating module 30 is further configured to obtain policy adjustment information of the target weight policy; matching a target feature tag in the provider image according to the policy adjustment information; and updating the weight information corresponding to the target feature tag.
In one embodiment, the capability map module 40 is further configured to obtain all tag information in the current vendor image; acquiring label categories corresponding to preset capability map conditions; screening the tag information according to the tag category to obtain a screening result; and generating a provider capability map according to the label information in the screening result.
In one embodiment, the condition evaluation module 50 is further configured to obtain a specific value for each capability class in the vendor capability map; acquiring the lowest threshold value of each capability class in preset evaluation conditions; judging whether the current specific value is greater than the lowest threshold value in each capability class; if not, judging that the provider can not meet the preset evaluation condition.
It should be noted that the above-described working procedure is merely illustrative, and does not limit the scope of the present invention, and in practical application, a person skilled in the art may select part or all of them according to actual needs to achieve the purpose of the embodiment, which is not limited herein.
In addition, technical details not described in detail in this embodiment may refer to the method for evaluating suppliers based on big data provided in any embodiment of the present invention, which is not described herein.
Furthermore, it should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. Read Only Memory)/RAM, magnetic disk, optical disk) and including several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. A big data based vendor assessment method, comprising:
acquiring target information of a supplier to be evaluated, wherein the target information comprises supply quality, delivery information, maintenance information and qualification information;
generating a vendor representation from the supply quality, the delivery information, the maintenance information, and the qualification information;
acquiring a current evaluation purpose, and updating weight information corresponding to a feature tag in the provider image according to the evaluation purpose;
generating a vendor capability map from the current vendor representation;
judging whether a preset evaluation condition is met in the supplier capability map;
if yes, generating a provider quality evaluation report according to the provider energy map.
2. The big data based supplier assessment method of claim 1, wherein the step of generating a supplier representation from the supply quality, the delivery information, the maintenance information, and the qualification information comprises:
generating a quality tag from the supply quality;
generating a delivery label through the delivery information;
generating an after-market label through the maintenance information;
generating a basic label through the qualification information;
and generating a provider portrait according to the quality label, the delivery label, the after-sales label and the basic label.
3. The big data based vendor assessment method of claim 2, wherein the step of generating a vendor representation from the quality label, the delivery label, the after-market label, and the base label comprises:
determining quality secondary label information according to the quality label;
determining delivery secondary label information according to the delivery label;
determining after-sales secondary label information according to the after-sales label;
and generating a provider portrait according to the quality secondary label information, the delivery secondary label information and the after-sales secondary label information and combining the basic label.
4. The big data based supplier evaluation method according to claim 1, wherein the step of obtaining a current evaluation purpose, and updating weight information corresponding to a feature tag in the supplier image according to the evaluation purpose, comprises:
acquiring current evaluation information, and determining evaluation tendency according to the evaluation information;
matching a target weight strategy in a preset weight adjustment rule table according to the evaluation tendency;
and updating weight information corresponding to the feature labels in the provider image according to the target weight strategy.
5. The big data based provider assessment method of claim 4, wherein the step of updating weight information corresponding to feature labels in the provider image according to the target weight policy comprises:
acquiring strategy adjustment information of the target weight strategy;
matching a target feature tag in the provider image according to the policy adjustment information;
and updating the weight information corresponding to the target feature tag.
6. The big data based vendor assessment method of claim 1, wherein the step of generating a vendor capability map from the current vendor representation comprises:
acquiring all label information in the current provider image;
acquiring label categories corresponding to preset capability map conditions;
screening the tag information according to the tag category to obtain a screening result;
and generating a provider capability map according to the label information in the screening result.
7. The big data based supplier evaluation method according to claim 1, wherein the step of judging whether a preset evaluation condition is satisfied in the supplier capability map comprises:
acquiring a specific value of each capability class in the vendor capability map;
acquiring the lowest threshold value of each capability class in preset evaluation conditions;
judging whether the current specific value is greater than the lowest threshold value in each capability class;
if not, judging that the provider can not meet the preset evaluation condition.
8. A big data based vendor estimation device, the big data based vendor estimation device comprising:
the information acquisition module is used for acquiring target information of the supplier to be evaluated, wherein the target information comprises supply quality, delivery information, maintenance information and qualification information;
a representation generation module for generating a vendor representation from the supply quality, the delivery information, the maintenance information, and the qualification information;
the label updating module is used for acquiring the current evaluation purpose and updating the weight information corresponding to the feature label in the provider image according to the evaluation purpose;
the energy diagram module is used for generating a provider energy diagram according to the current provider portrait;
the condition evaluation module is used for judging whether a preset evaluation condition is met in the supplier capability map;
and the report generation module is used for generating a provider quality evaluation report according to the provider energy map if yes.
9. A computer device, the device comprising: a memory, a processor which, when executing the computer instructions stored by the memory, performs the method of any one of claims 1 to 7.
10. A computer readable storage medium comprising instructions which, when run on a computer, cause the computer to perform the method of any one of claims 1 to 7.
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| JP2022035965A (en) * | 2020-08-20 | 2022-03-04 | 株式会社日立製作所 | Intelligent supplier managing system and intelligent supplier managing method |
| CN113641828A (en) * | 2021-07-01 | 2021-11-12 | 福建亿榕信息技术有限公司 | Power grid provider portrait imaging method based on knowledge graph and storage device |
| CN114386840A (en) * | 2022-01-12 | 2022-04-22 | 中国工商银行股份有限公司 | Marketing evaluation method, apparatus, device, medium, and program product |
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