User digitization level-based digitization transformation service recommendation method and system

Abstract

The invention provides a user digitization level-based digitization transformation service recommendation method and a user digitization level-based digitization transformation service recommendation system. The method comprises the following steps: constructing a five-dimensional evaluation model; acquiring multi-source data in real time; inputting the multi-source data into a five-dimensional evaluation model, and evaluating the digital level of the enterprise based on a preset evaluation algorithm and weight; constructing an industry knowledge graph comprising enterprises, service providers, solutions and technical standard nodes; the digital service is matched based on a mixed recommendation algorithm, the mixed recommendation algorithm comprises a collaborative filtering algorithm, a content matching algorithm and a knowledge graph reasoning algorithm, and a recommendation result is generated through a vectorization retrieval and optimization algorithm; and constructing a capability matrix including technical capability, implementation experience and service capability dimensions, and recording historical performance data of the service provider through a block chain technology to dynamically evaluate the capability of the service provider. The actual digital level of an enterprise can be accurately evaluated, and the accuracy of service provider matching is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise digital transformation services, and in particular, to a method, system, device and medium for recommending digital transformation services based on the digital level of users. Background Art

[0002] With the rapid development of the digital economy, the digital transformation of enterprises has become an important starting point for promoting the innovative development of enterprises. Accurately evaluating the digital level of enterprises and accordingly matching suitable digital service solutions and service providers for enterprises has become an urgent problem to be solved.

[0003] However, the existing technologies known to the inventors mainly rely on manual experience and are difficult to accurately evaluate the digital level of enterprises. Manual screening or simple rule matching based on keywords is inefficient and relies on subjective experience, and cannot match suitable digital service solutions and service providers for enterprises, seriously restricting the efficiency and effect of digital transformation. Summary of the Invention

[0004] The present invention provides a method, system, device and medium for recommending digital transformation services based on the digital level of users, and solves the problem of how to accurately evaluate the digital level of enterprises and match suitable service providers for enterprises.

[0005] To achieve the above object, the present application adopts the following technical solutions: In the first aspect, a method for recommending digital transformation services based on the digital level of users is provided, including: Construct a five-dimensional evaluation model; the model includes a dynamic questionnaire engine for dynamically evaluating problem generation, and the model dimensions include production manufacturing, supply chain, marketing, internal management and data governance; Obtain multi-source data in real time, and the multi-source data includes enterprise internal data, production equipment operation data and external public opinion data; Input the multi-source data into the five-dimensional evaluation model, evaluate the digital level of the enterprise based on a preset evaluation algorithm and weight, and generate an enterprise digital level evaluation report; Construct an industry knowledge graph including nodes of enterprises, service providers, solutions and technical standards, and establish associations between the nodes through supply-demand relationships, technical association relationships and successful case relationships; Match digital services based on a hybrid recommendation algorithm, and the hybrid recommendation algorithm includes a collaborative filtering algorithm, a content matching algorithm and a knowledge graph reasoning algorithm, and generate a recommendation result through vectorized retrieval and an optimization algorithm; Construct a capability matrix including dimensions of technical capabilities, implementation experience and service capabilities, and record the historical performance data of service providers through blockchain technology to dynamically evaluate the capabilities of service providers.

[0006] In the first possible implementation of the first aspect, the recommendation method further includes: At regular intervals, determine whether it is necessary to adjust the weights of the evaluation system according to industry trends and technological development dynamics; if so, adjust the weights of each dimension in the five-dimensional evaluation model according to industry trends and technological development dynamics.

[0007] In the second possible implementation of the first aspect, the construction of an industry knowledge graph including enterprise, service provider, solution, and technical standard nodes, where the nodes are associated through supply-demand relationships, technical association relationships, and successful case relationships, specifically includes: Use NLP technology to process unstructured data and extract key information; Build an industry knowledge graph based on the extracted key information; Establish associations between nodes according to supply-demand, technical association, and successful cases as relationships; Among them, establish a supply-demand relationship according to the functional matching between the digital needs of the enterprise and the solution; establish a technical association relationship according to the corresponding relationship between the technology adopted by the solution and the technical standard; establish a successful case relationship according to the cases where the service provider implements the solution for the enterprise.

[0008] In the third possible implementation of the first aspect, the matching of digital services based on a hybrid recommendation algorithm, the hybrid recommendation algorithm includes a collaborative filtering algorithm, a content matching algorithm, and a knowledge graph reasoning algorithm, and generates a recommendation result through vectorized retrieval and an optimization algorithm, specifically including: Construct a hybrid recommendation algorithm: use the collaborative filtering algorithm to recommend based on the historical cases of service providers; use the content matching algorithm to recommend based on the similarity between the enterprise's needs and the solution technology stack; use the knowledge graph reasoning algorithm to reason and recommend based on the relationships in the knowledge graph; Convert the enterprise's needs into vectors, store them in a vector database, and retrieve similar vectors in the vector database, and return the top recommendation results according to the similarity; Obtain an optimized matching result for the preliminary matching result using an optimization algorithm.

[0009] In the second aspect, a recommendation system for digital transformation services based on the user's digital level is provided, including: An enterprise digital level evaluation module for: constructing a five-dimensional evaluation model; the model includes a dynamic questionnaire engine for dynamically evaluating problem generation, and the model dimensions include production manufacturing, supply chain, marketing, internal management, and data governance; Real-time acquisition of multi-source data, where the multi-source data includes enterprise internal data, production equipment operation data, and external public opinion data; Input multi-source data into a five-dimensional evaluation model, evaluate the digital level of enterprises based on preset evaluation algorithms and weights, and generate an evaluation report on the digital level of enterprises; An industry knowledge graph construction module for constructing an industry knowledge graph containing nodes of enterprises, service providers, solutions, and technical standards, and establishing associations between nodes through supply-demand relationships, technical association relationships, and successful case relationships; A digital service matching module for matching digital services based on a hybrid recommendation algorithm, where the hybrid recommendation algorithm includes a collaborative filtering algorithm, a content matching algorithm, and a knowledge graph reasoning algorithm, and generating recommendation results through vectorized retrieval and optimization algorithms; A service provider capability dynamic evaluation module for constructing a capability matrix including dimensions of technical capabilities, implementation experience, and service capabilities, and recording the historical performance data of service providers through blockchain technology to dynamically evaluate the capabilities of service providers.

[0010] In the first possible implementation manner of the second aspect, the recommendation system further includes: An evaluation system dynamic adjustment module for judging every set time whether it is necessary to adjust the weights of the evaluation system according to industry trends and technological developments; if so, adjusting the weights of each dimension in the five-dimensional evaluation model according to industry trends and technological developments.

[0011] In the second possible implementation manner of the second aspect, the industry knowledge graph construction module is specifically used for: Using NLP technology to process unstructured data and extract key information; Constructing an industry knowledge graph based on the extracted key information; Establishing associations between nodes according to supply-demand, technical associations, and successful cases as relationships; Among them, a supply-demand relationship is established based on the functional matching between the digital needs of enterprises and the solutions; a technical association relationship is established based on the corresponding relationship between the technologies adopted by the solutions and the technical standards; a successful case relationship is established based on the cases where service providers implement solutions for enterprises.

[0012] In the third possible implementation manner of the second aspect, the digital service matching module is specifically used for: Constructing a hybrid recommendation algorithm: using a collaborative filtering algorithm to make recommendations based on the historical cases of service providers; using a content matching algorithm to make recommendations based on the similarity between enterprise needs and solution technology stacks; using a knowledge graph reasoning algorithm to make reasoning recommendations based on the relationships in the knowledge graph; Converting enterprise needs into vectors, storing them in a vector database and retrieving similar vectors in the vector database, and returning top recommendation results according to the similarity; Obtaining an optimized matching result for the preliminary matching result using an optimization algorithm.

[0013] In a third aspect, an electronic device is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the method for recommending digital transformation services based on the user's digital level as described in the first aspect.

[0014] In a fourth aspect, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, it implements the steps of the method for recommending digital transformation services based on the user's digital level as described in the first aspect.

[0015] The method for recommending digital transformation services based on the user's digital level of the present invention has the following advantages: This application constructs an objective and comprehensive enterprise digital level evaluation system, including a five-dimensional evaluation model and multi-source data collection, which can accurately evaluate the actual digital level of an enterprise; adopts an efficient intelligent matching engine, including a hybrid recommendation algorithm and a real-time matching process, which can achieve fast and accurate digital service matching; introduces a dynamic evaluation mechanism for service providers, including constructing a service provider capability matrix and blockchain evidence storage, which can timely understand the latest technical capabilities, implementation experience, and service capabilities of service providers, and improve the accuracy of matching.

[0016] The system, electronic device, and readable storage medium corresponding to the method for recommending digital transformation services based on the user's digital level of the present invention can achieve the same technical effects. To avoid repetition, they will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic flowchart of a method for recommending digital transformation services based on the user's digital level provided by an embodiment of this application; Figure 2 It is a schematic structural diagram of a system for recommending digital transformation services based on the user's digital level provided by an embodiment of this application; Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined purpose, the technical solutions in the embodiments of this application are clearly described. Obviously, the described embodiments are part of the embodiments of this application, rather than all of them. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of this application.

[0019] The terms "first", "second", etc. in the description and claims of this application are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that such terms can be interchanged under appropriate circumstances, so that the embodiments of this application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of the same category, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the description and claims means at least one of the connected objects, and the character " / ", generally indicates that the associated objects before and after are in an "or" relationship.

[0020] In the description of the method flow in the specification of this application and the steps in the flowchart in the accompanying drawings of the present invention, it is not necessary to strictly execute according to the step numbers. The execution order of the method steps can be changed. Moreover, certain steps can be omitted, multiple steps can be combined into one step for execution, and / or one step can be decomposed into multiple steps for execution.

[0021] The following will be described in detail the recommendation method, device, equipment and medium of the digital transformation service based on the user's digital level provided by the embodiments of this application in combination with the accompanying drawings and preferred embodiments.

[0022] Enterprise digital transformation services need to comprehensively evaluate the digital level of enterprises and match suitable digital service solutions and service providers for enterprises according to the evaluation results. The traditional evaluation methods known to the inventors mainly rely on manual experience, lack objectivity, and are difficult to comprehensively consider the actual situation of enterprises. At the same time, the existing service matching mechanism is inefficient, unable to achieve real-time matching, and lacks a dynamic evaluation mechanism for the capabilities of service providers, resulting in inaccurate matching results. Specifically: 1. There is a lack of an objective and comprehensive enterprise digital level evaluation system. The traditional evaluation methods mainly rely on manual experience and are difficult to accurately evaluate the actual digital level of enterprises.

[0023] 2. There is a lack of an efficient digital service matching mechanism. The existing matching methods are inefficient and unable to achieve real-time matching, resulting in inaccurate matching results.

[0024] 3. There is a lack of a dynamic evaluation mechanism for the capabilities of service providers, and it is impossible to timely understand the latest technical capabilities, implementation experience and service capabilities of service providers, affecting the accuracy of matching.

[0025] 4. There is a lack of a mechanism to adjust the evaluation system in combination with the industry development trend and the development dynamics of digital technologies, and the evaluation system lacks foresight and adaptability.

[0026] 5. There is a lack of in-depth optimization of data collection, integration, analysis and visualization, affecting the accuracy of evaluation and matching.

[0027] Based on this, the embodiments of the present application provide a digital service matching method, system, device and medium based on the digital level of enterprises, which can comprehensively evaluate the digital level of enterprises through intelligent means, construct an industry knowledge graph, adopt an efficient matching algorithm to match suitable digital service solutions and service providers for enterprises, and dynamically evaluate the capabilities of service providers, thereby promoting the process of enterprise digital transformation.

[0028] Please refer to Figure 1 , the embodiments of the present application provide a recommendation method for digital transformation services based on the digital level of users. As Figure 1 shown, the recommendation method of the embodiments of the present application includes: Step S1, construct a five-dimensional evaluation model; the model includes a dynamic questionnaire engine for dynamically evaluating problem generation, and the model dimensions include production manufacturing, supply chain, marketing, internal management, and data governance.

[0029] Adopt a dynamic questionnaire engine to construct a five-dimensional evaluation model. Specifically, the dynamic questionnaire engine contains 1500 - 3000 evaluation questions (preferably 2200), covering all aspects of the above five dimensions. For example: The production manufacturing dimension includes the digital level of production equipment, the digital level of production processes, etc.; the supply chain dimension includes the digital level of supplier management, the digital level of logistics management, etc.; the marketing dimension includes the digital level of customer relationship management, the digital level of marketing channels, etc.; the internal management dimension includes the digital level of human resource management, the digital level of financial management, etc.; the data governance dimension includes data collection capabilities, data analysis capabilities, etc.

[0030] Step S2, obtain multi-source data in real time. The multi-source data includes enterprise internal data, production equipment operation data, and external public opinion data.

[0031] In specific implementation, through the docking of enterprise ERP / MES system APIs, including RESTful API, SOAP API, etc., extract enterprise internal data in real time; the data collection methods for Internet of Things device operation data include MQTT protocol, OPC UA protocol, etc., to obtain production equipment operation data; through public opinion big data analysis, such as web crawlers, natural language processing, etc., obtain enterprise external public opinion data.

[0032] Step S3, input the multi-source data into the five-dimensional evaluation model, evaluate the digital level of the enterprise based on a preset evaluation algorithm and weights, and generate an enterprise digital level evaluation report.

[0033] Preferably, in this step, the evaluation algorithm adopts a comprehensive evaluation algorithm based on principal component analysis and BP neural network, and the weights are determined by a combination of subjective weighting and objective weighting based on the entropy method.

[0034] Through the above steps S1 - S3, an enterprise digital level evaluation system is constructed. Based on the five - dimensional evaluation model and multi - source data, a comprehensive evaluation of the enterprise digital level is carried out.

[0035] Step S4: Construct an industry knowledge graph including nodes of enterprises, service providers, solutions, and technical standards. The nodes are associated through supply - demand relationships, technical association relationships, and successful case relationships.

[0036] Specifically, step S4 includes: Step S401: Use NLP technology to process unstructured data and extract key information. Specifically, NLP technology includes named entity recognition, relation extraction, etc. Perform natural language processing on unstructured text data in the industry such as news reports and technical white papers to extract key information such as enterprise names, service provider names, solution names, and technical standard names.

[0037] Step S402: Construct an industry knowledge graph based on the extracted key information. It includes nodes such as enterprises, service providers, solutions, and technical standards. The knowledge graph construction tool can use the Neo4j graph database.

[0038] Step S403: Establish associations between nodes according to supply - demand, technical association, and successful cases as relationships. The relationship extraction methods include rule - based methods, machine - learning - based methods, etc. Specifically, establish supply - demand relationships according to the functional matching between the digital needs of enterprises and the functions of solutions; establish technical association relationships according to the corresponding relationships between the technologies adopted by solutions and technical standards; establish successful case relationships according to the cases where service providers implement solutions for enterprises.

[0039] Step S5: Match digital services based on a hybrid recommendation algorithm. The hybrid recommendation algorithm includes collaborative filtering algorithm, content matching algorithm, and knowledge graph reasoning algorithm, and generates recommendation results through vectorized retrieval and optimization algorithms.

[0040] Specifically, step S5 includes: Step S501: Construct a hybrid recommendation algorithm: Use the collaborative filtering algorithm to make recommendations based on the historical cases of service providers; use the content matching algorithm to make recommendations based on the similarity between enterprise needs and solution technology stacks; use the knowledge graph reasoning algorithm to make reasoning recommendations based on the relationships in the knowledge graph. Specifically, the collaborative filtering algorithm can use the user - based collaborative filtering algorithm; the content matching algorithm can use the TF - IDF - based text similarity calculation method; the knowledge graph reasoning algorithm can use the link prediction algorithm based on the TransE model.

[0041] Step S502: Convert the enterprise requirements into vectors, store them in the vector database, retrieve similar vectors in the vector database, and return the top recommended results according to the similarity. Specifically, the vectorization method uses the BERT model to encode the requirement text to obtain a vector representation, and the vector database uses the Milvus vector search engine. Sort the matching results according to the similarity. The similarity calculation method uses cosine similarity and sorts them according to the similarity with the enterprise requirement vector.

[0042] Step S503: Use an optimization algorithm for the preliminary matching results to obtain optimized matching results. Considering the synergy effect among service providers and avoiding conflicts between the recommended solutions, optimize and combine the preliminary matching results. Preferably, the optimization algorithm uses a combinatorial optimization algorithm based on the genetic algorithm.

[0043] Based on Step S5, the recommendation method of the embodiment of the present application introduces a real-time matching process to improve the matching accuracy.

[0044] Step S6: Construct a capability matrix including dimensions of technical capabilities, implementation experience, and service capabilities, and record the historical performance data of service providers through blockchain technology to dynamically evaluate the capabilities of service providers.

[0045] Furthermore, the evaluation indicators for the technical capabilities dimension include the number of patents, technical certification qualifications, etc.; the evaluation indicators for the implementation experience dimension include the number of industry cases, project scale, etc.; the evaluation indicators for the service capabilities dimension include response speed, customer satisfaction, etc.

[0046] Exemplarily: Among the three dimensions of the service provider capability matrix, the weight range of the evaluation indicators for technical capabilities is 0.2 - 0.4, the weight range of the evaluation indicators for implementation experience is 0.3 - 0.5, and the weight range of the evaluation indicators for service capabilities is 0.2 - 0.4. Use the Hyperledger Fabric blockchain platform to record the performance records of service providers providing services to enterprises on the blockchain for evidence preservation. The blockchain type can adopt a consortium chain, and the consensus algorithm can adopt the PBFT algorithm.

[0047] Dynamically evaluate the capabilities of service providers, that is, dynamically evaluate the capabilities of service providers according to the capability matrix and performance data. Specifically, regularly update the capability matrix according to the latest patent number, case number, customer evaluation, etc. of service providers, and dynamically evaluate the comprehensive capabilities of service providers in combination with the performance data. Furthermore, this evaluation method can adopt a comprehensive scoring model based on the entropy weight method.

[0048] In some possible implementation manners, the above recommendation method further includes: Step S7: Every set time, determine whether it is necessary to adjust the weights of the evaluation system according to the industry trends and technological development dynamics; if so, adjust the weights of each dimension in the five-dimensional evaluation model according to the industry trends and technological development dynamics.

[0049] Exemplarily, the judgment period can be set to be judged once every six months. For each dimension in the five-dimensional evaluation model, based on the obtained external public opinion data to analyze information such as industry policies and technological development trends, the weights determined by the analytic hierarchy process for each dimension are dynamically adjusted, so that the evaluation system can timely adapt to the industry development needs. For example: increasing the weight of data governance to meet privacy compliance requirements.

[0050] Based on the above recommendation method, the embodiments of the present application achieve the following beneficial effects: 1. Constructed an objective and comprehensive enterprise digitalization level evaluation system, including a five-dimensional evaluation model and multi-source data collection, which can accurately evaluate the actual digitalization level of enterprises; 2. Adopted an efficient intelligent matching engine, including a hybrid recommendation algorithm and a real-time matching process, which can achieve fast and accurate digital service matching; 3. Introduced a dynamic evaluation mechanism for service providers, including constructing a service provider capability matrix and blockchain evidence storage, which can timely understand the latest technical capabilities, implementation experience and service capabilities of service providers, and improve the accuracy of matching; 4. Has a dynamic weight adjustment mechanism, which can dynamically adjust the evaluation system according to the industry development trend and the development of digital technologies, and improve the forward-looking and adaptability of the evaluation system; 5. Adopted advanced technologies such as distributed architecture, microservices architecture and knowledge graph, and deeply optimized data collection, integration, analysis and visualization, improving the accuracy of evaluation and matching; 6. In the specific implementation process, in terms of visualization, the embodiments of the present application can introduce a digital twin sandbox and a three-dimensional visualization decision-making cabin, so as to be able to simulate the transformation effect and provide intuitive decision-making support for enterprises.

[0051] See Figure 2 , corresponding to the above embodiments of the recommendation method for digital transformation services based on the user's digitalization level, the embodiments of the present application provide a recommendation system for digital transformation services based on the user's digitalization level, and the recommendation system includes: An enterprise digitalization level evaluation module 1001, configured to: construct a five-dimensional evaluation model; the model includes a dynamic questionnaire engine for dynamically evaluating problem generation, and the model dimensions include production manufacturing, supply chain, marketing, internal management and data governance; Obtain multi-source data in real time, and the multi-source data includes enterprise internal data, production equipment operation data and external public opinion data; Input the multi-source data into the five-dimensional evaluation model, and evaluate the enterprise digitalization level based on a preset evaluation algorithm and weights, and generate an enterprise digitalization level evaluation report; The industry knowledge graph construction module 1002 is used to construct an industry knowledge graph including nodes of enterprises, service providers, solutions, and technical standards, and the nodes are associated through supply-demand relationships, technical association relationships, and successful case relationships; The digital service matching module 1003 is used to match digital services based on a hybrid recommendation algorithm, and the hybrid recommendation algorithm includes a collaborative filtering algorithm, a content matching algorithm, and a knowledge graph reasoning algorithm, and generates recommendation results through vectorized retrieval and optimization algorithms; The service provider ability dynamic evaluation module 1004 is used to construct an ability matrix including dimensions of technical ability, implementation experience, and service ability, and records the historical performance data of service providers through blockchain technology to dynamically evaluate the abilities of service providers.

[0052] Furthermore, the recommendation system further includes: The evaluation system dynamic adjustment module 1005 is used to determine whether it is necessary to adjust the weights of the evaluation system according to industry trends and technological developments every set time; if so, adjust the weights of each dimension in the five-dimensional evaluation model according to industry trends and technological developments.

[0053] Furthermore, the industry knowledge graph construction module is specifically used for: Adopt NLP technology to process unstructured data and extract key information; Construct an industry knowledge graph based on the extracted key information; Establish associations between nodes according to supply-demand, technical associations, and successful cases as relationships; Among them, establish a supply-demand relationship according to the functional matching between the digital needs of enterprises and solutions; establish a technical association relationship according to the corresponding relationship between the technologies adopted by solutions and technical standards; establish a successful case relationship according to the cases of service providers implementing solutions for enterprises.

[0054] Furthermore, the digital service matching module is specifically used for: Construct a hybrid recommendation algorithm: use a collaborative filtering algorithm to recommend based on the historical cases of service providers; use a content matching algorithm to recommend based on the similarity between enterprise needs and solution technology stacks; use a knowledge graph reasoning algorithm to perform reasoning and recommendation based on the relationships in the knowledge graph; Convert enterprise needs into vectors, store them in a vector database and retrieve similar vectors in the vector database, and return top recommendation results according to the similarity; Use an optimization algorithm for the preliminary matching results to obtain optimized matching results.

[0055] The recommendation system for digital transformation services based on the user's digital level implements the steps and various processes of the above-mentioned embodiment of the recommendation method for digital transformation services based on the user's digital level, and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0056] See Figure 3 , corresponding to the above-mentioned embodiment of the recommendation method for digital transformation services based on the user's digital level, an embodiment of the present application provides an electronic device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps and various processes of the above-mentioned embodiment of the recommendation method for digital transformation services based on the user's digital level, and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0057] The memory 1009 can be used to store software programs and various data. The memory 1009 mainly includes a first storage area for storing programs or instructions and a second storage area for storing data. Among them, the first storage area can store an operating system, applications or instructions required for at least one function (such as a sound playback function, an image playback function, etc.). In addition, the memory 1009 can include volatile memory or non-volatile memory, or the memory 1009 can include both volatile and non-volatile memory. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDR SDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM), and a direct rambus random access memory (DRRAM). The memory 1009 in the embodiments of the present application includes, but is not limited to, these and any other suitable types of memory.

[0058] The processor 1010 may include one or more processing units; optionally, the processor 1010 integrates an application processor and a modem processor. Among them, the application processor mainly processes operations related to the operating system, user interface, application programs, etc., and the modem processor mainly processes wireless communication signals, such as a baseband processor. It can be understood that the above modem processor may not be integrated into the processor 1010 either.

[0059] Corresponding to the above-described embodiment of the method for recommending digital transformation services based on the user's digital level, an embodiment of the present application further provides a readable storage medium. A program or instruction is stored on the readable storage medium. When the program or instruction is executed by a processor, the steps and processes of the above-described embodiment of the method for recommending digital transformation services based on the user's digital level are implemented, and the same technical effects can be achieved. To avoid repetition, they will not be elaborated here.

[0060] Among them, the processor is the processor in the electronic device described in the above embodiment of the present application. The readable storage medium includes computer-readable storage media, such as computer read-only memory ROM, random access memory RAM, magnetic disks, or optical discs, etc.

[0061] It should be noted that in this article, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including that element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in a reverse order according to the functions involved. For example, the described method may be executed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, the features described with reference to certain examples may be combined in other examples.

[0062] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, in essence or the part that contributes to the prior art, can be embodied in the form of a computer software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present application.

[0063] It can be understood that the embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific implementation manners. The above specific implementation manners are merely illustrative and not restrictive. As those skilled in the art know, without departing from the spirit and scope of the present invention, various changes or equivalent replacements can be made to these features and embodiments. In addition, those of ordinary skill in the art can modify these features and embodiments under the inspiration or teaching of the present application to adapt to specific situations and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of the present application belong to the scope protected by the present invention.

Claims

1. A method for recommending digital transformation services based on user digitalization level, characterized in that: include: Construct a five-dimensional evaluation model; the model includes a dynamic questionnaire engine for dynamic evaluation question generation, and the model dimensions include manufacturing, supply chain, marketing, internal management, and data governance; Real-time acquisition of multi-source data, including internal enterprise data, production equipment operation data, and external public opinion data; Input multi-source data into the five-dimensional evaluation model, evaluate the enterprise's digitalization level based on the preset evaluation algorithm and weights, and generate an enterprise digitalization level evaluation report; Build an industry knowledge graph that includes enterprise, service provider, solution and technical standard nodes, and establish connections between nodes through supply and demand relationships, technical association relationships and successful case relationships; Matching digital services based on a hybrid recommendation algorithm, which includes a collaborative filtering algorithm, a content matching algorithm, and a knowledge graph reasoning algorithm, and generating recommendation results through vectorized retrieval and optimization algorithms; Build a capability matrix that includes technical capabilities, implementation experience, and service capabilities, and use blockchain technology to record the service provider's historical performance data to dynamically evaluate the service provider's capabilities.

2. The method for recommending digital transformation services based on user digitalization level according to claim 1, characterized in that: The recommended method also includes: At set intervals, determine whether the evaluation system weights need to be adjusted based on industry trends and technological developments; if so, adjust the weights of each dimension in the five-dimensional evaluation model based on industry trends and technological developments.

3. The method for recommending digital transformation services based on user digitalization level according to claim 1, characterized in that: The construction includes an industry knowledge graph of enterprises, service providers, solutions and technical standard nodes. The nodes are associated with each other through supply and demand relationships, technical association relationships and successful case relationships, including: Use NLP technology to process unstructured data and extract key information; Build an industry knowledge graph based on the extracted key information; Establish connections between nodes based on supply and demand, technical connections, and success stories as relationships; Among them, the supply and demand relationship is established based on the matching of the enterprise's digital needs with the functions of the solution; the technical association relationship is established based on the correspondence between the technology adopted by the solution and the technical standards; and the successful case relationship is established based on the cases in which the service provider implements the solution for the enterprise.

4. The method for recommending digital transformation services based on user digitalization level according to claim 1, characterized in that: The digital services are matched based on the hybrid recommendation algorithm, which includes a collaborative filtering algorithm, a content matching algorithm, and a knowledge graph reasoning algorithm, and generates recommendation results through vectorized retrieval and optimization algorithms, specifically including: Build a hybrid recommendation algorithm: use collaborative filtering algorithm to make recommendations based on historical cases of service providers; use content matching algorithm to make recommendations based on the similarity between enterprise needs and solution technology stacks; use knowledge graph reasoning algorithm to make inference recommendations based on relationships in knowledge graphs; Convert enterprise requirements into vectors, store them in the vector database, search for similar vectors in the vector database, and return top recommendation results based on similarity; An optimization algorithm is used on the preliminary matching results to obtain optimized matching results.

5. A recommendation system for digital transformation services based on the user's digital level, characterized by: include: The enterprise digital level assessment module is used to: build a five-dimensional assessment model; the model includes a dynamic questionnaire engine for dynamic assessment question generation, and the model dimensions include manufacturing, supply chain, marketing, internal management, and data governance; Real-time acquisition of multi-source data, including internal enterprise data, production equipment operation data, and external public opinion data; Input multi-source data into the five-dimensional evaluation model, evaluate the enterprise's digitalization level based on the preset evaluation algorithm and weights, and generate an enterprise digitalization level evaluation report; The industry knowledge graph construction module is used to build an industry knowledge graph containing enterprise, service provider, solution and technical standard nodes. The nodes are associated with each other through supply and demand relationships, technical association relationships and successful case relationships. A digital service matching module is used to match digital services based on a hybrid recommendation algorithm, which includes a collaborative filtering algorithm, a content matching algorithm, and a knowledge graph reasoning algorithm, and generates recommendation results through a vectorized retrieval and optimization algorithm; The service provider capability dynamic assessment module is used to build a capability matrix that includes technical capabilities, implementation experience and service capability dimensions, and records the service provider's historical performance data through blockchain technology to dynamically assess the service provider's capabilities.

6. The recommendation system for digital transformation services based on user digitalization level according to claim 5 is characterized in that: The recommendation system further includes: The dynamic adjustment module of the evaluation system is used to determine whether the evaluation system weight needs to be adjusted at set intervals based on industry trends and technological development dynamics; if so, the weights of each dimension in the five-dimensional evaluation model are adjusted based on industry trends and technological development dynamics.

7. The recommendation system for digital transformation services based on user digitalization level according to claim 5 is characterized in that: The industry knowledge graph construction module is specifically used for: Use NLP technology to process unstructured data and extract key information; Build an industry knowledge graph based on the extracted key information; Establish connections between nodes based on supply and demand, technical connections, and success stories as relationships; Among them, the supply and demand relationship is established based on the matching of the enterprise's digital needs with the functions of the solution; the technical association relationship is established based on the correspondence between the technology adopted by the solution and the technical standards; and the successful case relationship is established based on the cases in which the service provider implements the solution for the enterprise.

8. The recommendation system for digital transformation services based on user digitalization level according to claim 5 is characterized in that: The digital service matching module is specifically used for: Build a hybrid recommendation algorithm: use collaborative filtering algorithm to make recommendations based on historical cases of service providers; use content matching algorithm to make recommendations based on the similarity between enterprise needs and solution technology stacks; use knowledge graph reasoning algorithm to make inference recommendations based on relationships in knowledge graphs; Convert enterprise requirements into vectors, store them in the vector database, search for similar vectors in the vector database, and return top recommendation results based on similarity; An optimization algorithm is used on the preliminary matching results to obtain optimized matching results.

9. An electronic device, characterized in that: The electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of the method for recommending digital transformation services based on the user's digitalization level as described in any one of claims 1 to 4 are implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, which, when executed by a processor, implements the steps of the method for recommending digital transformation services based on the user's digitalization level as described in any one of claims 1 to 4.

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