A big data-based technical service consultation recommendation system and method

Abstract

The application discloses a technical service consultation recommendation system and method based on big data, relates to the field of information technology, and solves the technical problem that the experience of a customer is reduced because the technical product and the technical service selected by the customer do not reach the expectation of the customer due to the insufficient understanding of the technical product by the customer; the basic information and the consultation information of the customer are acquired, the basic information of the customer is processed to obtain customer information; the corresponding technical problem is determined according to the consultation information of the customer; the solution and the corresponding technical personnel are determined based on the technical problem; the customer is classified to obtain the type of the customer; the corresponding technical field of the technical service is determined according to the type of the customer; the demand information of the customer is acquired, and the corresponding technical product and the technical service are recommended for the customer according to the demand information; the scoring information of the customer on the technical personnel is acquired, and the star level of the technical personnel is adjusted according to the scoring information; and the experience of the customer is improved.

Description

Technical Field

[0001] This invention belongs to the field of information technology and relates to technical service consultation and recommendation technology, specifically a technical service consultation and recommendation system and method based on big data. Background Technology

[0002] Technical services constitute the main business model and scope of the technology market. They refer to various services provided by a technology-owning party to another party to solve a specific problem. These services include calculation, design, measurement, analysis, installation, commissioning, providing technical information, improving processes, and conducting technical diagnostics. Consulting and recommendations can introduce professional technical advisors or experts, enabling higher-quality technical services to clients. They can also help clients identify and resolve potential technical problems, ensuring the stability and reliability of services. Professional technical consulting can provide customized solutions, avoiding unnecessary resource waste and time delays. It allows clients to find the most suitable technical services for their needs more quickly, thereby improving overall work efficiency. Consulting and recommended services are often more aligned with clients' actual needs, thus gaining greater client approval and satisfaction. Furthermore, it enhances clients' trust in the technical service provider, building a more stable cooperative relationship.

[0003] The prior art (patent application CN116756397A) discloses a big data-based technical service consultation system, including a consultation unit for assisting consultants in providing technical consultations, a data collection unit for collecting answers related to the technical questions raised by consultants from a knowledge base, a technician selection unit for consultants to select technicians, an auditing unit for auditing consultants' questions and technicians' answers, a knowledge base for storing questions and answers audited by the auditing unit, and an analysis unit for analyzing and organizing the distribution of consultants' technical questions and the different technicians' answers to the same technical questions. The prior art analyzes and organizes consultants' technical questions to improve the knowledge base, thus improving the efficiency of technical service consultations. However, when customers need new technical products and services, their insufficient understanding of the technical products leads to the selected technical products and services not meeting their expectations, resulting in a decreased customer experience.

[0004] This invention provides a big data-based technical service consultation recommendation system and method to solve the above-mentioned technical problems. Summary of the Invention

[0005] This invention aims to solve at least one of the technical problems existing in the prior art; to this end, this invention proposes a big data-based technical service consultation and recommendation system and method to solve the technical problem that when customers need new technical products and services, their insufficient understanding of the technical products leads to the purchase and selection of technical products and services that do not meet their expectations, resulting in a decline in customer experience.

[0006] To achieve the above objectives, the first aspect of the present invention provides a big data-based technical service consultation and recommendation system, comprising: a consultation and recommendation module, and an information collection module and a personnel evaluation module connected thereto;

[0007] Information collection module: used to acquire basic customer information and inquiry information, and process the basic customer information to obtain customer information;

[0008] Consultation and Recommendation Module: This module is used to identify the corresponding technical problems based on customer consultation information; determine solutions and corresponding technical personnel based on the technical problems; classify customers according to their information to obtain customer types; determine the technical fields corresponding to the technical services based on the customer types; obtain customer demand information; and recommend corresponding technical products and services to customers based on the demand information.

[0009] Personnel Evaluation Module: Used to obtain customer ratings of technical personnel and adjust the star ratings of technical personnel based on the ratings.

[0010] Preferably, the process of processing the customer's basic information to obtain customer information includes:

[0011] Retrieve basic customer information; this includes: customer name, business scope, and industry.

[0012] Determine whether the customer's basic information includes all sub-information; if yes, retain the customer's basic information; otherwise, complete the missing sub-information.

[0013] The retained basic information and the supplemented basic information are marked as customer information.

[0014] It should be noted that the data type of customer information is consistent with that of basic information, and the sub-information includes customer name, experience range or technical information.

[0015] This invention analyzes the completeness of collected customer basic information. When there is missing sub-information in the basic information, the missing sub-information is supplemented to ensure the completeness of the basic information, which helps to ensure the validity of the data and provides a foundation for subsequent analysis.

[0016] Preferably, determining the corresponding technical problem based on the customer's inquiry information includes:

[0017] Retrieve customer consultation information, including the consultation area and consultation questions; determine the technical field based on the consultation area in the consultation information; obtain the technical question database corresponding to the technical field;

[0018] Extract keywords from the consultation information and match them with the technical question database to obtain the corresponding technical questions.

[0019] It should be noted that the consultation information is extracted from the questions raised by the client, and the corresponding consultation areas and questions are extracted from the questions raised by the client. The keywords in the consultation questions refer to professional terms related to the corresponding technical fields. The keywords are set manually and are updated regularly.

[0020] This invention determines the technical field based on customer consultation information, extracts keywords from the consultation questions, and matches them with a corresponding technical question database to obtain the corresponding technical questions. It can accurately locate the technical problems that customers need to solve, laying the foundation for providing solutions in the future and improving the customer experience.

[0021] Preferably, the step of determining the solution and corresponding technical personnel based on the technical problem includes:

[0022] Retrieve the technical problem and its corresponding technical field, and obtain the problem-solving database for the corresponding technical field; match the technical problem with the corresponding problem-solving database to obtain the corresponding solution;

[0023] Obtain the dispatch information of technical personnel, and dispatch technical personnel in the corresponding fields to the customer's site to solve technical problems based on the dispatch information;

[0024] When a technical problem fails to match the corresponding problem-solving database, the technical personnel in the corresponding technical field are notified to propose a solution to the technical problem and technical personnel are dispatched to solve the technical problem; and the technical problem and the corresponding solution are added to the problem-solving database.

[0025] It should be noted that the dispatch information refers to the basic information of the dispatched technical personnel. If a technical personnel has already been dispatched, a technical personnel who has not been dispatched will be selected to solve the customer's technical problems. If all technical personnel have been dispatched, the technical personnel closest to the customer will be selected based on the dispatched location to solve the customer's technical problems.

[0026] This invention matches corresponding solutions to customers' technical problems and dispatches technical personnel based on their scheduling information. If the corresponding solution is not found in the problem-solving database, the invention notifies the relevant technical personnel to provide a solution and dispatches them to handle the problem. This approach can resolve the technical problems raised by customers and improve their experience.

[0027] Preferably, the step of classifying customers based on customer information to obtain customer types includes:

[0028] Retrieve the business scope from customer information and mark the business scope as a type analysis sequence;

[0029] The type analysis model is invoked, the type analysis sequence is input into the type analysis model to obtain the corresponding type label, and the type label is used to match the type of the corresponding customer; the type analysis model is built based on an artificial intelligence model; the type label is set to a positive integer.

[0030] This invention classifies customers based on their business scope using a type analysis model, thus determining their customer types. This lays the foundation for subsequent recommendations of technical products and services and helps in understanding the customer's needs.

[0031] Preferably, the type analysis model is built based on an artificial intelligence model, including:

[0032] Obtain the standard dataset; the standard dataset includes standard input data consistent with the content attributes of the type analysis sequence, and standard output data consistent with the content attributes of the type label.

[0033] The standard dataset is divided into a training set, a validation set, and a test set; the training set is used to train the artificial intelligence model; the validation set is used to adjust the internal parameters of the artificial intelligence model; and the test set is used to test the artificial intelligence model to obtain performance parameters.

[0034] Determine if the performance parameters of the artificial intelligence exceed the parameter threshold; if yes, mark the artificial intelligence model as a type analysis model; otherwise, retrain the artificial intelligence model.

[0035] Preferably, determining the technical field corresponding to the technical service based on the customer type includes:

[0036] Retrieve the customer type and obtain the type expertise database; compare the customer type with the type expertise database to obtain the customer's corresponding technical expertise.

[0037] By acquiring general technical fields related to enterprise management and integrating professional and general technical fields, we can obtain the technical fields of our clients.

[0038] Preferably, recommending corresponding technical products and services to customers based on demand information includes:

[0039] Retrieve the customer's technical field and determine the customer's technical product library based on the technical field.

[0040] Retrieve customer needs information and match it with the corresponding product library to obtain several products; match the products with corresponding technical services; construct a service recommendation table based on the products and corresponding technical services, and display the service recommendation table to the customer for selection.

[0041] This invention determines a corresponding product library based on the customer's technical field, matches several products and corresponding technical services according to the customer's needs, constructs a service recommendation table based on the several products and corresponding technical services, and recommends products and technical services. It can recommend products and technical services according to the customer's needs, which helps to ensure that the technical services corresponding to the products meet the customer's needs and improves customer satisfaction.

[0042] Preferably, adjusting the star rating of the technicians based on the rating information includes:

[0043] Retrieve customer rating information; obtain raw ratings from several technical personnel; the rating information includes technical ratings, service ratings, and on-site attendance ratings;

[0044] The average of the sub-information in the rating information and the original rating is calculated to obtain the final rating of the corresponding technician; the final rating is compared with the star rating threshold to obtain the star rating of the technician.

[0045] It should be noted that the maximum score for the technical score, service score, and on-duty score in the rating information is 5.0.

[0046] This invention calculates the final score of the corresponding technician based on the customer's rating information, and determines the star rating of the technician based on the final score; it can evaluate the work performance of technicians and help improve their work enthusiasm.

[0047] A second aspect of this invention provides a method for recommending technical services based on big data, comprising:

[0048] Step S1: Obtain basic customer information and consultation information, and process the basic customer information to obtain customer information;

[0049] Step S2: Determine the corresponding technical problem based on the customer's consultation information; determine the solution and corresponding technical personnel based on the technical problem;

[0050] Step S3: Classify customers according to their information to obtain customer types; determine the technical field corresponding to the technical service based on the customer type;

[0051] Step S4: Obtain customer needs information and recommend corresponding technical products and services to customers based on the needs information;

[0052] Step S5: Obtain customer ratings for technical personnel and adjust the star ratings of technical personnel based on the ratings.

[0053] Compared with the prior art, the beneficial effects of the present invention are:

[0054] 1. This invention acquires basic customer information and consultation information, analyzes the completeness of the collected basic customer information, and supplements missing sub-information when it exists, ensuring the completeness of the basic information and facilitating data validity, thus providing a foundation for subsequent analysis. It determines the technical field based on customer consultation information, extracts keywords from the consultation questions, and matches them with a corresponding technical problem database to obtain the relevant technical problem. This allows for precise identification of the technical problem the customer needs to solve, laying the foundation for providing solutions and improving customer experience. It matches corresponding solutions to customer technical problems and dispatches technical personnel based on their scheduling information. If the corresponding solution database does not contain a solution, it notifies the relevant technical personnel to provide a solution and dispatches them to handle the issue, thus resolving the customer's technical problem and improving customer experience.

[0055] 2. This invention categorizes customers based on their business scope using a type analysis model, thus identifying customer types. This lays the foundation for subsequent recommendations of technical products and services, facilitating an understanding of customer needs. It determines a corresponding product library based on the customer's technical field and matches several products and corresponding technical services according to the customer's requirements. A service recommendation table is constructed based on these products and corresponding technical services to recommend products and services. This ability to recommend products and services based on customer needs helps ensure that the corresponding technical services meet customer requirements, improving customer satisfaction. Furthermore, it calculates the final score for the corresponding technical personnel based on the customer's rating information, determining the technical personnel's star rating. This allows for the evaluation of technical personnel's work performance, which helps improve their work enthusiasm. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a schematic diagram illustrating the specific steps of the system of the present invention;

[0058] Figure 2 This is a schematic diagram illustrating the service consultation steps of this invention;

[0059] Figure 3 A schematic diagram illustrating the recommended service steps of this invention;

[0060] Figure 4 This is a schematic diagram illustrating the specific steps of the method of the present invention. Detailed Implementation

[0061] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Please see Figure 1 The first aspect of the present invention provides a big data-based technical service consultation and recommendation system, including: a consultation and recommendation module, and an information collection module and a personnel evaluation module connected thereto;

[0063] Information collection module: used to acquire basic customer information and inquiry information, and process the basic customer information to obtain customer information;

[0064] Consultation and Recommendation Module: This module is used to identify the corresponding technical problems based on customer consultation information; determine solutions and corresponding technical personnel based on the technical problems; classify customers according to their information to obtain customer types; determine the technical fields corresponding to the technical services based on the customer types; obtain customer demand information; and recommend corresponding technical products and services to customers based on the demand information.

[0065] Personnel Evaluation Module: Used to obtain customer ratings of technical personnel and adjust the star ratings of technical personnel based on the ratings.

[0066] Please see Figure 2The system obtains the customer's basic information, which includes the customer's name, business scope, and industry. It then determines whether the customer's basic information contains all sub-information. If yes, the basic information is retained; otherwise, the missing sub-information is supplemented. The retained basic information and the supplemented basic information are then marked as customer information.

[0067] For example: Suppose we have basic information on three customers, namely customer A, customer B, and customer C. We analyze the basic information of the three customers. The basic information of customer A and customer C includes all sub-information. The basic information of customer B is missing the industry. So we supplement the industry of customer B. The retained basic information of customer A and customer C, as well as the supplemented basic information of customer B, are marked as customer information.

[0068] Obtain customer consultation information, including consultation areas and consultation questions; determine the technical field based on the consultation areas in the consultation information; obtain a technical question database corresponding to the technical field; extract keywords from the consultation questions in the consultation information, and match the keywords with the technical question database to obtain the corresponding technical questions.

[0069] For example: Suppose that customers A and B are consulting with each other. Obtain the consultation information of customers A and B, and determine that customer A's technical field is F and customer B's technical field is G based on the consultation information. Obtain the technical question databases for technical fields F and G respectively.

[0070] Keywords for the consultation questions from clients A and B are extracted respectively. Client A's keywords are matched with the technical question database of technology field F; client B's keywords are matched with the technical question database of technology field G, thus obtaining the technical questions of clients A and B respectively.

[0071] The system retrieves the technical problem and its corresponding technical field, and obtains a problem-solving database for that technical field. It then matches the technical problem with the corresponding problem-solving database to obtain a suitable solution. The system also retrieves the scheduling information of technical personnel and dispatches personnel from the relevant field to the customer's location to resolve the technical problem. If a technical problem fails to match the corresponding problem-solving database, the system notifies the technical personnel in the relevant technical field to propose a solution and dispatches personnel to resolve the problem. Finally, the system adds the technical problem and its corresponding solution to the problem-solving database.

[0072] For example: Suppose we need to solve the technical problems of customers A and B. We obtain problem-solving libraries F and G for the corresponding technical fields. We match customer A's technical problem with problem-solving library F to obtain the corresponding solution. We match customer B's technical problem with problem-solving library G, but the match fails. We notify the technical personnel in the corresponding technical field to propose a solution to the technical problem, and dispatch the technical personnel to solve customer B's technical problem. The solution is then added to problem-solving library G.

[0073] Please see Figure 3 The system retrieves the business scope from the customer information and marks it as a type analysis sequence. It then calls the type analysis model, inputs the type analysis sequence into the model, obtains the corresponding type label, and matches the type of the corresponding customer based on the type label. The type analysis model is built based on an artificial intelligence model, and the type label is set to a positive integer.

[0074] Retrieve customer types and obtain a type expertise database; compare customer types with the type expertise database to obtain the customer's corresponding technical expertise; obtain the general technical expertise of enterprise management; integrate the technical expertise and general technical expertise to obtain the customer's technical expertise.

[0075] For example: Suppose we are now analyzing the type of customer C, and we mark the business scope in customer C's customer information as a type analysis sequence; we call the type analysis model, input the type analysis sequence into the type analysis model, and obtain the corresponding type label; based on the type label, we match the type of customer C to class H.

[0076] By comparing the type of customer C with the type professional database, we find that the customer's corresponding professional technical field is mechanical manufacturing; by integrating the general technical field of enterprise management with the professional technical field, we find that the customer's technical fields are mechanical manufacturing and enterprise management.

[0077] The system retrieves the client's technical field information and determines a product library based on that field. It then retrieves the client's needs information and matches it with the corresponding product library to obtain several relevant products. Finally, it matches these products with corresponding technical services and constructs a service recommendation table based on the products and their corresponding technical services, presenting the table to the client for selection.

[0078] For example: Suppose we want to make recommendations to customer C. We obtain customer C's needs, which are production equipment in mechanical manufacturing. We match the corresponding product library to obtain product 12 and product 15, as well as the corresponding technical services. We build a service recommendation table and display the service recommendation table to the customer for selection.

[0079] Obtain customer rating information; obtain raw ratings from several technical personnel; the rating information includes technical rating, service rating, and on-duty rating; calculate the average of the sub-information in the rating information and the raw rating to obtain the final rating of the corresponding technical personnel; compare the final rating with the star rating threshold to obtain the star rating of the technical personnel.

[0080] For example: Suppose we are adjusting the star ratings of Technician 07 and Technician 09; obtain the original scores and score information of Technician 07 and Technician 09;

[0081] Technician 07: Original rating 4.8, technical rating 4.7, service rating 4.9, on-duty rating 4.4; therefore, Technician 07's final rating is 4.7; Technician 07's star rating is adjusted to 4 stars;

[0082] Technician 09: The original rating is 3.9, the technical rating is 4.8, the service rating is 5.0, and the on-duty rating is 4.6; therefore, the final rating of Technician 09 is 4.575; so the star rating of Technician 09 will be adjusted from 3 stars to 4 stars.

[0083] It is worth noting that the type analysis model is built on an artificial intelligence model, including:

[0084] Obtain the standard dataset; the standard dataset includes standard input data consistent with the content attributes of the type analysis sequence, and standard output data consistent with the content attributes of the type label.

[0085] The standard dataset is divided into a training set, a validation set, and a test set; the training set is used to train the artificial intelligence model; the validation set is used to adjust the internal parameters of the artificial intelligence model; and the test set is used to test the artificial intelligence model to obtain performance parameters.

[0086] Determine if the performance parameters of the artificial intelligence exceed the parameter threshold; if yes, mark the artificial intelligence model as a type analysis model; otherwise, retrain the artificial intelligence model.

[0087] It should be noted that the performance parameters include accuracy, F1 score, recall, and stability; the parameter thresholds are set manually based on practical experience.

[0088] Please see Figure 4 The second aspect of this invention provides a method for recommending technical services based on big data, comprising:

[0089] Step S1: Obtain basic customer information and consultation information, and process the basic customer information to obtain customer information;

[0090] Step S2: Determine the corresponding technical problem based on the customer's consultation information; determine the solution and corresponding technical personnel based on the technical problem;

[0091] Step S3: Classify customers according to their information to obtain customer types; determine the technical field corresponding to the technical service based on the customer type;

[0092] Step S4: Obtain customer needs information and recommend corresponding technical products and services to customers based on the needs information;

[0093] Step S5: Obtain customer ratings for technical personnel and adjust the star ratings of technical personnel based on the ratings.

[0094] Some of the data in the above formula are calculated by removing dimensions and taking their numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0095] The working principle of this invention is as follows: This invention acquires basic customer information and consultation information, processes the basic customer information to obtain customer information; determines the corresponding technical problem based on the customer's consultation information; determines the solution and corresponding technical personnel based on the technical problem; classifies customers according to customer information to obtain customer types; determines the technical field corresponding to the technical service based on the customer type; acquires customer demand information, recommends corresponding technical products and technical services to the customer based on the demand information; acquires customer rating information for technical personnel, and adjusts the star rating of technical personnel based on the rating information.

[0096] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A big data-based technology service consultation and recommendation system, comprising: The system includes a consultation and recommendation module, as well as connected information collection and personnel evaluation modules; its features are as follows: Information collection module: used to acquire basic customer information and inquiry information, and process the basic customer information to obtain customer information; Consultation and Recommendation Module: This module is used to identify the corresponding technical problems based on customer consultation information; determine solutions and corresponding technical personnel based on the technical problems; classify customers according to their information to obtain customer types; determine the technical fields corresponding to the technical services based on the customer types; obtain customer demand information; and recommend corresponding technical products and services to customers based on the demand information. Personnel Evaluation Module: Used to obtain customer ratings of technical personnel and adjust the star ratings of technical personnel based on the ratings.

2. The big data-based technical service consultation and recommendation system according to claim 1, characterized in that, The process of processing basic customer information to obtain customer information includes: Retrieve basic customer information; this includes: customer name, business scope, and industry. Determine whether the customer's basic information includes all sub-information; if yes, retain the customer's basic information; otherwise, complete the missing sub-information. The retained basic information and the supplemented basic information are marked as customer information.

3. The big data-based technical service consultation and recommendation system according to claim 1, characterized in that, The process of determining the corresponding technical issues based on customer inquiries includes: Retrieve customer consultation information, including the consultation area and consultation questions; determine the technical field based on the consultation area in the consultation information; obtain the technical question database corresponding to the technical field; Extract keywords from the consultation information and match them with the technical question database to obtain the corresponding technical questions.

4. The big data-based technical service consultation and recommendation system according to claim 3, characterized in that, The process of determining solutions and corresponding technical personnel based on technical problems includes: Retrieve the technical problem and its corresponding technical field, and obtain the problem-solving database for the corresponding technical field; match the technical problem with the corresponding problem-solving database to obtain the corresponding solution; Obtain the dispatch information of technical personnel, and dispatch technical personnel in the corresponding fields to the customer's site to solve technical problems based on the dispatch information; When a technical problem fails to match the corresponding problem-solving database, the technical personnel in the corresponding technical field are notified to propose a solution to the technical problem and technical personnel are dispatched to solve the technical problem; and the technical problem and the corresponding solution are added to the problem-solving database.

5. The big data-based technical service consultation and recommendation system according to claim 1, characterized in that, The process of classifying customers based on customer information to obtain customer types includes: Retrieve the business scope from customer information and mark the business scope as a type analysis sequence; The type analysis model is invoked, the type analysis sequence is input into the type analysis model to obtain the corresponding type label, and the type label is matched with the corresponding customer type; the type analysis model is built based on an artificial intelligence model; the type label is set to a positive integer.

6. The big data-based technical service consultation and recommendation system according to claim 5, characterized in that, The type analysis model is built based on an artificial intelligence model and includes: Obtain the standard dataset; the standard dataset includes standard input data consistent with the content attributes of the type analysis sequence, and standard output data consistent with the content attributes of the type label. The standard dataset is divided into a training set, a validation set, and a test set; the training set is used to train the artificial intelligence model; the validation set is used to adjust the internal parameters of the artificial intelligence model; and the test set is used to test the artificial intelligence model to obtain performance parameters. Determine if the performance parameters of the artificial intelligence exceed the parameter threshold; if yes, mark the artificial intelligence model as a type analysis model; otherwise, retrain the artificial intelligence model.

7. The big data-based technical service consultation and recommendation system according to claim 1, characterized in that, The process of determining the technical field corresponding to the technical service based on the customer type includes: Retrieve the customer type and obtain the type expertise database; compare the customer type with the type expertise database to obtain the customer's corresponding technical expertise. By acquiring general technical fields related to enterprise management and integrating professional and general technical fields, we can obtain the technical fields of our clients.

8. The big data-based technical service consultation and recommendation system according to claim 1, characterized in that, The process of recommending corresponding technical products and services to customers based on their needs includes: Retrieve the customer's technical field and determine the customer's technical product library based on the technical field. Retrieve customer needs information and match it with the corresponding product library to obtain several products; match the products with corresponding technical services; construct a service recommendation table based on the products and corresponding technical services, and display the service recommendation table to the customer for selection.

9. A big data-based technical service consultation and recommendation system according to claim 1, characterized in that, The adjustment of the star rating of technicians based on the scoring information includes: Retrieve customer rating information; obtain raw ratings from several technical personnel; the rating information includes technical ratings, service ratings, and on-site attendance ratings; The average of the sub-information in the rating information and the original rating is calculated to obtain the final rating of the corresponding technician; the final rating is compared with the star rating threshold to obtain the star rating of the technician.

10. A method for recommending technical services based on big data, applied to the big data-based technical service recommendation system as described in any one of claims 1-9, characterized in that, include: Step S1: Obtain basic customer information and consultation information, and process the basic customer information to obtain customer information; Step S2: Determine the corresponding technical problem based on the customer's consultation information; determine the solution and corresponding technical personnel based on the technical problem; Step S3: Classify customers according to their information to obtain customer types; determine the technical field corresponding to the technical service based on the customer type; Step S4: Obtain customer needs information and recommend corresponding technical products and services to customers based on the needs information; Step S5: Obtain customer ratings for technical personnel and adjust the star ratings of technical personnel based on the ratings.

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