Management method and management system for post-market service items of numerical control machine tool

Abstract

The invention relates to the field of intelligent management, and provides a numerical control machine tool post-market service item management method and management system, which mainly divides service item management into pre-sales management, in-sales management and after-sales management, in customer feedback management of the after-sales management, a data set fed back by a customer is extracted, and the customer feedback management of the after-sales management is completed. Semantic understanding of the customer feedback is carried out by adopting a data processing algorithm based on deep learning, so that whether the user satisfaction is positive or negative is automatically assessed according to significant dynamic aggregation representation of a distribution field domain between customer feedback semantic features; and it can be ensured that interpretation and classification of client feedback have higher objectivity and consistency. And moreover, nuisance in customer feedback can be captured, and customer emotion and potential problems behind can be perceived, so that more comprehensive feedback analysis can be provided.

Description

Technical Field

[0001] The present application relates to the field of intelligent management, and more specifically, to a CNC machine tool after-market service project management method and management system. Background Art

[0002] Modern CNC machine tools usually integrate complex mechanical structures, sophisticated electronic control systems, and advanced software programs, which makes equipment maintenance and technical support increasingly important. Customers' demand for CNC machine tools is no longer just to purchase the equipment itself, but also includes a series of service support, such as pre-sales technical consultation, project management during sales, and after-sales technical support. These service needs require manufacturers to provide not only high-quality products, but also comprehensive service guarantees. Therefore, high-quality service has become a key factor in enhancing customer satisfaction and loyalty.

[0003] However, the decentralized data management in the traditional CNC machine tool aftermarket service project management has led to serious information islands, making it difficult for departments to share and synchronize information, which has affected data integration and analysis. For example, if key data such as customer service records, maintenance history, and spare parts usage cannot be centrally managed, a complete service history record cannot be formed, thus affecting the effectiveness of decision-making and service efficiency. In addition, the customer feedback mechanism is not sound, so that customer opinions and suggestions cannot be collected and processed in a timely manner, affecting the continuous improvement of service quality.

[0004] Therefore, an optimized CNC machine tool aftermarket service project management solution is needed. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present application provides a CNC machine tool after-market service project management method and management system.

[0006] According to one aspect of the present application, a CNC machine tool aftermarket service project management system is provided, which includes: a pre-sales management module, a sales management module and an after-sales management module; The after-sales management module includes a service request management unit, a maintenance management unit, a customer feedback management unit and a maintenance plan unit. The service request management unit is used to record and classify customer after-sales service requests and track the processing status; the maintenance management unit is used to manage the maintenance process; the customer feedback management unit is used to collect customer feedback and analyze customer satisfaction; the maintenance plan unit is used to formulate and implement regular maintenance plans; Wherein, the customer feedback management unit includes: a customer feedback data collection subunit, which is used to extract a data set of customer feedback; a customer feedback data semantic encoding subunit, which is used to semantically encode each customer feedback in the customer feedback data set to obtain a set of customer feedback semantic encoding feature vectors; a customer feedback significant aggregation subunit, which is used to dynamically aggregate the set of customer feedback semantic encoding feature vectors in a feature distribution field to obtain a customer feedback significant aggregation representation, wherein the customer feedback significant aggregation subunit includes: a customer feedback semantic static energy factor calculation secondary subunit, which is used to calculate the static energy factor of each customer feedback semantic encoding feature vector in the set of customer feedback semantic encoding feature vectors to obtain a set of customer feedback semantic static energy factors; a customer feedback semantic feature aggregation secondary subunit, which is used to perform feature significant aggregation based on an imitation force field on the set of customer feedback semantic encoding feature vectors based on the set of customer feedback semantic static energy factors to obtain the customer feedback significant aggregation representation; a satisfaction evaluation result generation subunit, which is used to obtain a satisfaction evaluation result based on the customer feedback significant aggregation representation.

[0007] In the above-mentioned CNC machine tool after-market service project management system, the pre-sales management module includes a customer demand collection unit, a technical consultation and support unit, a solution design unit and an estimated quotation unit. The customer demand collection unit is used to record the customer's basic information and demand description; the technical consultation and support unit is used to provide technical support and consulting services to the customer; the solution design unit is used to provide customized solutions based on the customer's demand description; the estimated quotation unit is used to determine the estimated cost based on the customized solution and generate a quotation; the in-sales management module includes a contract signing unit, a project allocation unit and a progress tracking unit. The contract signing unit is used to manage the creation and approval process of the sales contract; the project allocation unit is used to allocate appropriate technicians and resources according to project requirements; the progress tracking unit is used to monitor the progress of the project in real time.

[0008] In the above-mentioned CNC machine tool after-market service project management system, the customer feedback semantic static energy factor calculation secondary subunit is used to: calculate the mean and variance of the customer feedback semantic encoding feature vector to obtain the customer feedback semantic mean and customer feedback semantic variance; subtract the customer feedback semantic encoding feature vector from the customer feedback semantic mean by position, and calculate the fourth power of each position of the feature vector after subtraction to obtain the customer feedback semantic difference modulation vector; calculate the expected value of the customer feedback semantic difference modulation vector to obtain the customer feedback semantic expected value; divide the customer feedback semantic expected value by the square of the customer feedback semantic variance, and input the divided value into the sigmoid function to obtain the customer feedback semantic static energy factor.

[0009] In the above-mentioned CNC machine tool after-market service project management system, the customer feedback semantic feature aggregation secondary subunit includes: a customer feedback semantic clustering initial center vector determination third subunit, which is used to select the customer feedback semantic encoding feature vector corresponding to the largest customer feedback semantic static energy factor in the set of customer feedback semantic static energy factors as the customer feedback semantic clustering initial center vector; a customer feedback semantic dynamic aggregation energy factor calculation third subunit, which is used to calculate the customer feedback semantic dynamic aggregation energy factor based on the spatial span between each customer feedback semantic encoding feature vector in the set of customer feedback semantic encoding feature vectors and the customer feedback semantic clustering initial center vector, as well as the static energy factor of each customer feedback semantic encoding feature vector and the customer feedback semantic The static energy factor of the clustering initial center vector is used to calculate the dynamic aggregation energy factor of each customer feedback semantic encoding feature vector in the set of customer feedback semantic encoding feature vectors to obtain a set of customer feedback semantic dynamic aggregation energy factors; the customer feedback semantic dynamic aggregation energy factor gated mask three-level sub-unit is used to input the set of customer feedback semantic dynamic aggregation energy factors into the gated mask unit to obtain a set of customer feedback semantic dynamic aggregation weight factors; the customer feedback significant aggregation representation generation three-level sub-unit is used to calculate the weighted sum of the set of customer feedback semantic encoding feature vectors with the set of customer feedback semantic dynamic aggregation weight factors to obtain a customer feedback significant aggregation representation vector as the customer feedback significant aggregation representation.

[0010] In the above-mentioned CNC machine tool after-market service project management system, the customer feedback semantic dynamic aggregation energy factor calculation three-level sub-unit is used to: multiply the static energy factor of the customer feedback semantic encoding feature vector and the static energy factor of the customer feedback semantic clustering initial center vector with a first weighting parameter to obtain a first customer feedback semantic dynamic aggregation energy factor; multiply the square of the spatial span between the customer feedback semantic encoding feature vector and the customer feedback semantic clustering initial center vector with a second weighting parameter to obtain a second customer feedback semantic dynamic aggregation energy factor; divide the first customer feedback semantic dynamic aggregation energy factor by the second customer feedback semantic dynamic aggregation energy factor to obtain the customer feedback semantic dynamic aggregation energy factor.

[0011] In the above-mentioned CNC machine tool after-market service project management system, the customer feedback semantic dynamic aggregation energy factor gated mask three-level sub-unit includes: a dynamic aggregation energy factor normalization four-level sub-unit, which is used to normalize the set of customer feedback semantic dynamic aggregation energy factors to obtain a set of normalized customer feedback semantic dynamic aggregation energy factors; a normalized dynamic aggregation energy factor mask four-level sub-unit, which is used to mask the set of normalized customer feedback semantic dynamic aggregation energy factors to obtain a set of customer feedback semantic dynamic aggregation weight factors.

[0012] In the above-mentioned CNC machine tool after-market service project management system, the dynamic aggregation energy factor normalization four-level sub-unit is used to: calculate the exponential function value with the natural constant e as the base and the negative number of each customer feedback semantic dynamic aggregation energy factor in the set of customer feedback semantic dynamic aggregation energy factors as the exponent to obtain a set of customer feedback semantic dynamic aggregation energy index factors; after adding the set of customer feedback semantic dynamic aggregation energy index factors and the constant one by position, calculate the reciprocal of each customer feedback semantic dynamic aggregation energy index modulation factor in the obtained set of customer feedback semantic dynamic aggregation energy index modulation factors to obtain the set of normalized customer feedback semantic dynamic aggregation energy factors.

[0013] In the above-mentioned CNC machine tool after-market service project management system, the normalized dynamic aggregation energy factor mask four-level sub-unit is used to: in response to each normalized customer feedback semantic dynamic aggregation energy factor in the set of normalized customer feedback semantic dynamic aggregation energy factors being greater than a predetermined threshold, set the normalized customer feedback semantic dynamic aggregation energy factor to the original value, and set the rest to zero, so as to obtain a set of customer feedback semantic dynamic aggregation weight factors.

[0014] In the above-mentioned CNC machine tool after-market service project management system, the satisfaction evaluation result generating subunit is used to: input the customer feedback significant aggregation representation vector into the classifier-based customer satisfaction evaluation module to obtain the satisfaction evaluation result, and the satisfaction evaluation result is used to indicate that the user satisfaction is positive or the user satisfaction is negative.

[0015] According to another aspect of the present application, a method for managing a CNC machine tool aftermarket service project is provided, which includes: Pre-sales management steps: record the customer's basic information and demand description; provide technical support and consulting services to the customer; provide customized solutions based on the customer's demand description; determine the estimated cost based on the customized solution and generate a quotation; Sales management steps: manage the creation and approval process of sales contracts; allocate appropriate technical personnel and resources according to project requirements; monitor project progress in real time; After-sales management steps: record and classify customers' after-sales service requests and track processing status; manage maintenance processes; collect customer feedback and analyze customer satisfaction; formulate and implement regular maintenance plans.

[0016] This application has significant technical effects due to the adoption of the above technical solutions: The present application has at least the following technical effects: Compared with the prior art, the CNC machine tool aftermarket service project management method and management system provided by the present application mainly divides service project management into pre-sales management, in-sales management and after-sales management. In the customer feedback management of after-sales management, by extracting the data set of customer feedback and using a data processing algorithm based on deep learning to perform semantic understanding of the customer feedback, the user satisfaction is automatically evaluated as positive or negative based on the significant dynamic aggregation representation of the distribution field between the semantic features of each customer feedback, which can ensure that the interpretation and classification of customer feedback have higher objectivity and consistency. It can also capture the subtle differences in customer feedback, perceive the customer emotions and potential problems behind it, and thus provide a more comprehensive feedback analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0018] Figure 1 It is a block diagram of a customer feedback management unit in a CNC machine tool after-market service project management system according to an embodiment of the present application.

[0019] Figure 2 This is a data flow diagram of a customer feedback management unit in a CNC machine tool after-market service project management system according to an embodiment of the present application.

[0020] Figure 3 It is a block diagram of a customer feedback significant aggregation subunit in a CNC machine tool after-market service project management system according to an embodiment of the present application.

[0021] Figure 4 It is a block diagram of a secondary subunit for customer feedback semantic feature aggregation in a CNC machine tool after-market service project management system according to an embodiment of the present application.

[0022] Figure 5 It is a block diagram of the three-level sub-unit of the customer feedback semantic dynamic aggregation energy factor gated mask in the CNC machine tool after-market service project management system according to the embodiment of the present application.

[0023] Figure 6 The present invention is a flowchart of a method for managing a CNC machine tool aftermarket service project according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0025] As CNC machine tools continue to increase in complexity, they not only contain sophisticated mechanical structures, but also integrate advanced electronic control and software technologies. This technological advancement makes machine tool maintenance and customer service critical. Customers' expectations for CNC machine tools have gone beyond simply purchasing products, and they also seek a range of services including technical consulting, project management, and technical support.

[0026] In order to meet these needs, manufacturers must provide not only high-quality products, but also comprehensive service guarantees. Excellent service is the key to improving customer satisfaction and loyalty. However, traditional CNC machine tool service project management faces the problems of data dispersion and information islands, which hinder information sharing and synchronization between different departments and affect the efficiency of data integration and analysis. For example, if key data such as customer service records, maintenance history, and spare parts usage cannot be centrally managed, it is impossible to build a complete service history archive, which will affect the accuracy of decision-making and the response speed of service. In addition, if the customer feedback mechanism is not perfect, customer opinions and suggestions cannot be collected and processed in a timely manner, which will hinder the continuous improvement of service quality.

[0027] Based on this, the present application proposes an optimized CNC machine tool after-market service project management system, which includes: a pre-sales management module, the pre-sales management module includes a customer demand collection unit, a technical consulting and support unit, a solution design unit and an estimated quotation unit, the customer demand collection unit is used to record the customer's basic information and demand description; the technical consulting and support unit is used to provide technical support and consulting services to the customer; the solution design unit is used to provide customized solutions based on the customer's demand description; the estimated quotation unit is used to determine the estimated cost based on the customized solution and generate a quotation; the in-sales management module, the in-sales management module includes a contract signing unit, a project allocation unit, and a project allocation unit. Yuan and progress tracking unit, the contract signing unit is used to manage the creation and approval process of sales contracts; the project allocation unit is used to allocate appropriate technical personnel and resources according to project requirements; the progress tracking unit is used to monitor project progress in real time; after-sales management module, the after-sales management module includes a service request management unit, a maintenance management unit, a customer feedback management unit and a maintenance plan unit, the service request management unit is used to record and classify customers' after-sales service requests and track the processing status; the maintenance management unit is used to manage the maintenance process; the customer feedback management unit is used to collect customer feedback and analyze customer satisfaction; the maintenance plan unit is used to formulate and implement regular maintenance plans.

[0028] The optimized CNC machine tool aftermarket service project management system provided by this application realizes comprehensive management from pre-sales to after-sales, including customer demand collection, technical consultation and support, solution design, estimated quotation, contract signing, project allocation, progress tracking, service request management, maintenance management, customer feedback management and maintenance plan, etc. It breaks the information island by centrally managing all data before, during and after sales, and realizes information sharing and synchronization between departments.

[0029] Among them, customer feedback management is a crucial step in the CNC machine tool aftermarket service project management system. Specifically, customer feedback directly reflects the problems in the service process and the real feelings of customers. By collecting and analyzing these feedbacks, service providers can promptly identify problems and take measures to improve them, thereby continuously improving service quality. However, the interpretation and classification of customer feedback in traditional methods often rely on manual judgment, which makes the results vulnerable to personal experience and subjective bias and lacks unified objective standards. In addition, traditional analysis methods usually only stay at the surface text level, making it difficult to deeply understand customers' emotional tendencies, implicit needs or the root causes of potential problems, resulting in deviations in analysis results.

[0030] Accordingly, in the customer feedback management unit, the technical concept of the present application is to extract the customer feedback data set, and use the data processing and parsing algorithm based on deep learning to perform semantic understanding of the customer feedback, so as to automatically evaluate the user satisfaction as positive or negative according to the significant dynamic aggregation representation of the distribution field between the semantic features of each customer feedback, which can reduce the subjectivity and bias caused by manual judgment, ensure that the interpretation and classification of customer feedback has higher objectivity and consistency. It can also capture the subtle differences in customer feedback, perceive the customer emotions and potential problems behind it, and thus provide a more comprehensive feedback analysis, realizing the automation of customer feedback management.

[0031] Figure 1 It is a block diagram of a customer feedback management unit in a CNC machine tool after-market service project management system according to an embodiment of the present application. Figure 2 Schematic diagram of data flow of the customer feedback management unit in the CNC machine tool aftermarket service project management system according to the embodiment of the present application. Figure 1 and Figure 2 As shown, in the customer feedback management unit 100, it includes: a customer feedback data collection subunit 110, which is used to extract a data set of customer feedback; a customer feedback data semantic encoding subunit 120, which is used to semantically encode each customer feedback in the customer feedback data set to obtain a set of customer feedback semantic encoding feature vectors; a customer feedback significant aggregation subunit 130, which is used to dynamically aggregate the set of customer feedback semantic encoding feature vectors in the feature distribution field to obtain a customer feedback significant aggregation representation; a satisfaction evaluation result generation subunit 140, which is used to obtain a satisfaction evaluation result based on the customer feedback significant aggregation representation.

[0032] In the embodiment of the present application, the customer feedback data collection subunit 110 is used to extract a customer feedback data set. The customer feedback data set generally contains feedback content information of different customers' specific evaluations of the service, which are comments, ratings, suggestions, etc. in text form. By extracting and analyzing the customer feedback data set, it can help service providers to deeply understand the emotional tendencies and needs of customers, thereby achieving more effective satisfaction evaluation and promoting continuous improvement of service quality.

[0033] In an embodiment of the present application, the customer feedback data semantic encoding subunit 120 is used to semantically encode each customer feedback in the customer feedback data set to obtain a set of customer feedback semantic encoding feature vectors. Accordingly, considering that each customer feedback in the customer feedback data set covers the semantic information of the customer's various views and feelings about the product or service, and in order to help the model better understand the emotional color behind the text, so as to automatically evaluate user satisfaction, in the technical solution of the present application, each customer feedback in the customer feedback data set is semantically encoded to obtain a set of customer feedback semantic encoding feature vectors. In particular, in a specific implementable embodiment of the present application, a semantic encoder including an embedding layer can be used to semantically encode each customer feedback in the customer feedback data set to obtain a set of customer feedback semantic encoding feature vectors.

[0034] In an embodiment of the present application, the customer feedback significant aggregation subunit 130 is used to perform dynamic aggregation of the set of customer feedback semantic encoding feature vectors in the feature distribution field to obtain a customer feedback significant aggregation representation. Among them, there is a mutual semantic association relationship between the customer feedback semantic encoding feature vectors, and different customer feedback semantic information has different effects and influences on the overall satisfaction evaluation. Based on this, in order to better highlight those features that appear more frequently or have a greater impact in customer feedback, so as to more accurately reflect the main concerns and opinions of the customer group, thereby improving the accuracy of the satisfaction evaluation, in the technical solution of the present application, the set of customer feedback semantic encoding feature vectors is dynamically aggregated in the feature distribution field to obtain a customer feedback significant aggregation representation vector as a customer feedback significant aggregation representation.

[0035] Specifically, first, the static energy factor of each customer feedback semantic encoding feature vector in the set of customer feedback semantic encoding feature vectors is calculated to obtain a set of customer feedback semantic static energy factors. Specifically, the static energy factor can be used to quantify the importance of each customer feedback semantic encoding feature vector and evaluate its stability in the feature space, indicating the status and influence of the feature vector in the data set, and providing data support for the subsequent selection of aggregation centers. Then, based on the set of customer feedback semantic static energy factors, the set of customer feedback semantic encoding feature vectors is subjected to feature significant aggregation simulating a force field to obtain a customer feedback significant aggregation representation vector. Among them, each feature vector is like a "particle" in a force field, and the mutual influence between them includes both their positional relationship in the multidimensional space and their inherent properties (such as static energy factors), while also taking into account the characteristics of the overall data distribution. This method simulates the concept of force field in physics, and optimizes the layout of feature vectors in the multidimensional space by calculating the attraction and repulsion between feature vectors, thereby achieving the effect of significant aggregation. Specifically, the customer feedback semantic encoding feature vector corresponding to the maximum energy factor in the set of customer feedback semantic static energy factors is selected as the initial center vector of customer feedback semantic clustering, so as to be the representative and influential part in the integrated set. Then, the dynamic aggregation energy factor of each customer feedback semantic encoding feature vector is calculated based on the spatial span and static energy factor of each customer feedback semantic encoding feature vector and the center vector. Specifically, the dynamic energy factor not only takes into account the spatial span, but also takes into account the importance of the intrinsic characteristics of each data point to more accurately simulate the interaction between samples, while providing a flexible and detailed method to evaluate the role and importance of a single sample in the overall data set. Furthermore, considering that the dynamic aggregation energy factor reflects the relative importance and significant features of each feature vector in the dynamic aggregation process, in order to further dynamically adjust these energy factors and retain those with high influence and representativeness, the set of each dynamic aggregation energy factor is input into the gated mask unit to convert it into a set of dynamic aggregation weight factors. Finally, the set of weight factors is used to weight the set of customer feedback semantic encoding feature vectors to obtain the final customer feedback significant aggregation representation vector.

[0036] Specifically, Figure 3 FIG. 1 is a block diagram of a customer feedback aggregation subunit in a CNC machine tool aftermarket service project management system according to an embodiment of the present application. Figure 3As shown, the customer feedback significant aggregation subunit 130 includes: a customer feedback semantic static energy factor calculation secondary subunit 131, which is used to calculate the static energy factor of each customer feedback semantic encoding feature vector in the set of customer feedback semantic encoding feature vectors to obtain a set of customer feedback semantic static energy factors; a customer feedback semantic feature aggregation secondary subunit 132, which is used to perform feature significant aggregation based on the imitation field on the set of customer feedback semantic encoding feature vectors based on the set of customer feedback semantic static energy factors to obtain the customer feedback significant aggregation representation.

[0037] More specifically, in an embodiment of the present application, the customer feedback semantic static energy factor calculation secondary subunit is used to: calculate the mean and variance of the customer feedback semantic encoding feature vector to obtain the customer feedback semantic mean and the customer feedback semantic variance; subtract the customer feedback semantic encoding feature vector from the customer feedback semantic mean by position, and calculate the fourth power of each position of the feature vector after subtraction to obtain the customer feedback semantic difference modulation vector; calculate the expected value of the customer feedback semantic difference modulation vector to obtain the customer feedback semantic expected value; divide the customer feedback semantic expected value by the square of the customer feedback semantic variance, and input the divided value into the sigmoid function to obtain the customer feedback semantic static energy factor.

[0038] More specifically, Figure 4 FIG. 1 is a block diagram of a secondary subunit for customer feedback semantic feature aggregation in a CNC machine tool aftermarket service project management system according to an embodiment of the present application. Figure 4As shown, the customer feedback semantic feature aggregation secondary subunit 132 includes: a customer feedback semantic clustering initial center vector determination tertiary subunit 1321, which is used to select the customer feedback semantic encoding feature vector corresponding to the largest customer feedback semantic static energy factor in the set of customer feedback semantic static energy factors as the customer feedback semantic clustering initial center vector; a customer feedback semantic dynamic aggregation energy factor calculation tertiary subunit 1322, which is used to calculate the customer feedback semantic dynamic aggregation energy factor based on the spatial span between each customer feedback semantic encoding feature vector in the set of customer feedback semantic encoding feature vectors and the customer feedback semantic clustering initial center vector and the static energy factor of each customer feedback semantic encoding feature vector and the customer feedback semantic clustering initial center vector. The static energy factor of the quantity is calculated, and the dynamic aggregation energy factor of each customer feedback semantic encoding feature vector in the set of customer feedback semantic encoding feature vectors is calculated to obtain a set of customer feedback semantic dynamic aggregation energy factors; the customer feedback semantic dynamic aggregation energy factor gated mask three-level sub-unit 1323 is used to input the set of customer feedback semantic dynamic aggregation energy factors into the gated mask unit to obtain a set of customer feedback semantic dynamic aggregation weight factors; the customer feedback significant aggregation representation generation three-level sub-unit 1324 is used to calculate the weighted sum of the set of customer feedback semantic encoding feature vectors with the set of customer feedback semantic dynamic aggregation weight factors to obtain a customer feedback significant aggregation representation vector as the customer feedback significant aggregation representation.

[0039] More specifically, in an embodiment of the present application, the customer feedback semantic dynamic aggregation energy factor calculation three-level sub-unit is used to: multiply the static energy factor of the customer feedback semantic encoding feature vector and the static energy factor of the customer feedback semantic clustering initial center vector by a first weighting parameter to obtain a first customer feedback semantic dynamic aggregation energy factor; multiply the square of the spatial span between the customer feedback semantic encoding feature vector and the customer feedback semantic clustering initial center vector by a second weighting parameter to obtain a second customer feedback semantic dynamic aggregation energy factor; divide the first customer feedback semantic dynamic aggregation energy factor by the second customer feedback semantic dynamic aggregation energy factor to obtain the customer feedback semantic dynamic aggregation energy factor.

[0040] More specifically, Figure 5 The block diagram of the three-level sub-unit of the customer feedback semantic dynamic aggregation energy factor gated mask in the CNC machine tool after-market service project management system according to the embodiment of the present application. Figure 5As shown, the customer feedback semantic dynamic aggregation energy factor gated mask third-level subunit 1323 includes: a dynamic aggregation energy factor normalization fourth-level subunit 13231, which is used to normalize the set of customer feedback semantic dynamic aggregation energy factors to obtain a set of normalized customer feedback semantic dynamic aggregation energy factors; a normalized dynamic aggregation energy factor mask fourth-level subunit 13232, which is used to mask the set of normalized customer feedback semantic dynamic aggregation energy factors to obtain a set of customer feedback semantic dynamic aggregation weight factors.

[0041] More specifically, in an embodiment of the present application, the dynamic aggregation energy factor normalization four-level subunit is used to: calculate the exponential function value with the natural constant e as the base and the negative number of each customer feedback semantic dynamic aggregation energy factor in the set of customer feedback semantic dynamic aggregation energy factors as the exponent to obtain a set of customer feedback semantic dynamic aggregation energy index factors; after adding the set of customer feedback semantic dynamic aggregation energy index factors and the constant one by position, calculate the reciprocal of each customer feedback semantic dynamic aggregation energy index modulation factor in the obtained set of customer feedback semantic dynamic aggregation energy index modulation factors to obtain the set of normalized customer feedback semantic dynamic aggregation energy factors.

[0042] More specifically, in an embodiment of the present application, the normalized dynamic aggregation energy factor mask four-level sub-unit is used to: in response to each normalized customer feedback semantic dynamic aggregation energy factor in the set of normalized customer feedback semantic dynamic aggregation energy factors being greater than a predetermined threshold, set the normalized customer feedback semantic dynamic aggregation energy factor to the original value, and set the rest to zero, so as to obtain a set of customer feedback semantic dynamic aggregation weight factors.

[0043] In the embodiment of the present application, specifically, the customer feedback significant aggregation subunit is used to: dynamically aggregate the set of customer feedback semantic encoding feature vectors in the feature distribution field according to the following formula; wherein the formula is: in, is a set of semantically encoded feature vectors of the customer feedback, are the first, second, ..., and third in the set of customer feedback semantic encoding feature vectors. ,..., Customer feedback semantic encoding feature vector, It is The feature value of each position in the semantic encoding feature vector of customer feedback, To calculate the expected value, and They are The mean and variance of yes function, yes The corresponding customer feedback semantic static energy factor, To select the maximum value corresponding to value, is the maximum matching value, is the initial center vector of customer feedback semantic clustering, yes The corresponding customer feedback semantic static energy factor, express and The space span between and is the weighting parameter, for The corresponding customer feedback semantic dynamic aggregation energy factor, yes The corresponding normalized customer feedback semantic dynamic aggregation energy factor, For masking, is a predetermined threshold, yes The corresponding customer feedback semantic dynamic aggregation weight factor, is the number of vectors in the set of semantic encoding feature vectors of customer feedback, is the significant aggregate representation vector of the customer feedback.

[0044] In an embodiment of the present application, the satisfaction evaluation result generation subunit 140 is used to obtain a satisfaction evaluation result based on the significant aggregation representation of the customer feedback. Specifically, in an embodiment of the present application, the satisfaction evaluation result generation subunit is used to: input the significant aggregation representation vector of the customer feedback into a customer satisfaction evaluation module based on a classifier to obtain the satisfaction evaluation result, and the satisfaction evaluation result is used to indicate that the user satisfaction is positive or the user satisfaction is negative. Specifically, the significant aggregation representation of the customer feedback obtained by dynamically aggregating the set of semantically encoded feature vectors of the customer feedback is classified to automatically evaluate whether the user satisfaction is positive or negative, which can reduce the subjectivity and bias caused by manual judgment and ensure that the interpretation and classification of customer feedback have higher objectivity and consistency. It can also capture the subtle differences in customer feedback, perceive the customer emotions and potential problems behind it, thereby providing a more comprehensive feedback analysis and realizing the automation of customer feedback management.

[0045] In particular, the present application takes into account that each customer feedback semantic encoding feature vector in the set of customer feedback semantic encoding feature vectors respectively represents the text semantic encoding features of each customer feedback in the customer feedback data set. Taking into account that each customer feedback in the customer feedback data set has relatively significant content and expression differences in the text source domain, this makes each customer feedback semantic encoding feature vector in the set of customer feedback semantic encoding feature vectors have relatively significant semantic feature expression heterogeneity, which makes that when performing dynamic aggregation of feature distribution field based on the imitation force field, the uneven distribution of the semantic space will also make the semantic dynamic aggregation representation of the customer feedback significant aggregation representation vector have semantic representation diversity, thereby affecting the iterative consistency of classification mapping and reducing the accuracy of classification results.

[0046] Preferably, in an example of the present application, inputting the customer feedback significant aggregation representation vector into a classifier-based customer satisfaction evaluation module to obtain a satisfaction evaluation result includes: Determine a customer feedback significant aggregation probability value corresponding to the customer feedback significant aggregation representation vector, wherein the customer feedback significant aggregation probability value represents a probability that customer satisfaction is positive; Calculate the product of the first customer feedback significant aggregation feature value of the customer feedback significant aggregation representation vector and the customer feedback significant aggregation probability value to obtain a first customer feedback significant aggregation feature probability product; Calculate the product of the second customer feedback significant aggregation feature value of the customer feedback significant aggregation representation vector and the difference between one and the customer feedback significant aggregation probability value to obtain a second customer feedback significant aggregation feature inverse probability product; Calculate the quotient of the first customer feedback significant aggregate feature value divided by the difference between one and the customer feedback significant aggregate probability value to obtain a first customer feedback significant aggregate feature inverse probability quotient; Calculating the quotient of the second customer feedback significant aggregate feature value and the customer feedback significant aggregate probability value to obtain a second customer feedback significant aggregate feature probability quotient; After adding the probability product of the first customer feedback significant aggregation feature and the inverse probability product of the second customer feedback significant aggregation feature, subtract the difference between the inverse probability quotient of the first customer feedback significant aggregation feature and the probability quotient of the second customer feedback significant aggregation feature to obtain a customer feedback significant aggregation corrected feature value; For each first customer feedback significant aggregate feature value and second customer feedback significant aggregate feature value of the customer feedback significant aggregate representation vector, matrix multiplying a customer feedback significant aggregate correction feature matrix composed of the customer feedback significant aggregate correction feature values ​​with the customer feedback significant aggregate representation vector to obtain a corrected customer feedback significant aggregate representation vector; and The corrected customer feedback significant aggregation representation vector is input into a classifier-based customer satisfaction evaluation module to obtain a satisfaction evaluation result.

[0047] Among them, the customer feedback significant aggregation feature vector The correction process is expressed as: in, represents the significant aggregation representation vector of the customer feedback, a first customer feedback significant aggregation feature value representing the customer feedback significant aggregation representation vector, a second customer feedback significant aggregation feature value representing the customer feedback significant aggregation representation vector, represents the probability that customer satisfaction is positive, represents the significant aggregate corrected eigenvalue of the customer feedback, A customer feedback significant aggregate corrected feature matrix representing the customer feedback significant aggregate corrected feature values, represents matrix multiplication, A significant aggregate representation vector of customer feedback representing the correction.

[0048] Therefore, the present application aims at the region-boundary integral relationship of the high-dimensional feature manifold of the customer feedback significant aggregation representation vector, and defines the probability distribution of the feature set of the customer feedback significant aggregation representation vector as a manifold constraint boundary to approximate the high-dimensional manifold of the feature set of the customer feedback significant aggregation representation vector, which is composed of a single connected region representation of eigenvalue pairs, so as to avoid the ambiguity of mapping the diversified feature representation of the customer feedback significant aggregation representation vector to the local manifold representation in the high-dimensional feature class convergence space, improve the execution iteration consistency of each local feature distribution in the mapping task, improve the feature classification convergence effect, and improve the accuracy of the satisfaction evaluation result obtained by the customer feedback significant aggregation representation vector through the customer satisfaction evaluation module based on the classifier. In this way, the subjectivity and bias caused by manual judgment can be reduced, and the interpretation and classification of customer feedback can be ensured to have higher objectivity and consistency. It can also capture the subtle differences in customer feedback, perceive the customer emotions and potential problems behind it, thereby providing a more comprehensive feedback analysis and realizing the automation of customer feedback management.

[0049] In summary, the CNC machine tool aftermarket service project management system based on the embodiment of the present application is explained, which mainly divides service project management into pre-sales management, in-sales management and after-sales management. In the customer feedback management of after-sales management, by extracting the data set of customer feedback and using a data processing algorithm based on deep learning to perform semantic understanding of the customer feedback, the user satisfaction is automatically evaluated as positive or negative based on the significant dynamic aggregation representation of the distribution field between the semantic features of each customer feedback, which can ensure that the interpretation and classification of customer feedback has higher objectivity and consistency. And it can capture the subtle differences in customer feedback, perceive the customer emotions and potential problems behind it, and thus provide a more comprehensive feedback analysis.

[0050] As described above, the CNC machine tool aftermarket service project management system according to the embodiment of the present application can be implemented in various terminal devices, such as a server for CNC machine tool aftermarket service project management. In one example, the CNC machine tool aftermarket service project management system according to the embodiment of the present application can be integrated into the terminal device as a software module and / or hardware module. For example, the CNC machine tool aftermarket service project management system can be a software module in the operating system of the terminal device, or it can be an application developed for the terminal device; of course, the CNC machine tool aftermarket service project management system can also be one of the many hardware modules of the terminal device.

[0051] Alternatively, in another example, the CNC machine tool after-market service project management system and the terminal device may also be separate devices, and the CNC machine tool after-market service project management system may be connected to the terminal device via a wired and / or wireless network and transmit interactive information in accordance with an agreed data format.

[0052] Figure 6 Flow chart of the CNC machine tool aftermarket service project management method according to the embodiment of the present application. Figure 6 As shown, the CNC machine tool aftermarket service project management method includes: pre-sales management steps: recording the customer's basic information and demand description; providing technical support and consulting services to the customer; providing customized solutions based on the customer's demand description; determining the estimated cost based on the customized solution and generating a quotation; in-sales management steps: managing the creation and approval process of sales contracts; allocating appropriate technicians and resources based on project requirements; monitoring project progress in real time; after-sales management steps: recording and classifying customers' after-sales service requests and tracking processing status; managing maintenance processes; collecting customer feedback and analyzing customer satisfaction; formulating and implementing regular maintenance plans.

[0053] Among them, the specific operations of each step in the above CNC machine tool after-market service project management method have been referenced above. Figures 1 to 5 The description of the CNC machine tool after-market service project management system has been introduced in detail, and therefore, its repeated description will be omitted.

[0054] In summary, the CNC machine tool aftermarket service project management method based on the embodiment of the present application is explained, which mainly divides service project management into pre-sales management, in-sales management and after-sales management. In the customer feedback management of after-sales management, by extracting the data set of customer feedback and using a data processing algorithm based on deep learning to perform semantic understanding of the customer feedback, the user satisfaction is automatically evaluated as positive or negative based on the significant dynamic aggregation representation of the distribution field between the semantic features of each customer feedback, which can ensure that the interpretation and classification of customer feedback has higher objectivity and consistency. And it can capture the subtle differences in customer feedback, perceive the customer emotions and potential problems behind it, and thus provide a more comprehensive feedback analysis.

[0055] The above is only a preferred embodiment of the present application and does not constitute any form of limitation to the present application. Although the present application has been disclosed as a preferred embodiment as above, it is not intended to limit the present application. Any technician familiar with the profession can make some changes or modifications to equivalent embodiments of the technical contents disclosed above without departing from the scope of the technical solution of the present application. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present application without departing from the content of the technical solution of the present application still falls within the scope of the technical solution of the present application.

Claims

1. The CNC machine tool after-market service project management system is characterized by: include: Pre-sales management module, sales management module and after-sales management module; wherein the after-sales management module includes a service request management unit, a maintenance management unit, a customer feedback management unit and a maintenance plan unit, wherein the service request management unit is used to record and classify customer after-sales service requests and track processing status; the maintenance management unit is used to manage the maintenance process; the customer feedback management unit is used to collect customer feedback and analyze customer satisfaction; the maintenance plan unit is used to formulate and execute regular maintenance plans; Wherein, the customer feedback management unit includes: A customer feedback data collection subunit, used to extract a data set of customer feedback; A customer feedback data semantic encoding subunit, used for semantically encoding each customer feedback in the customer feedback data set to obtain a set of customer feedback semantic encoding feature vectors; A customer feedback significant aggregation subunit is used to perform feature distribution field dynamic aggregation on the set of customer feedback semantic encoding feature vectors to obtain a customer feedback significant aggregation representation; wherein, the customer feedback significant aggregation subunit includes: a customer feedback semantic static energy factor calculation secondary subunit, used to calculate the static energy factor of each customer feedback semantic encoding feature vector in the set of customer feedback semantic encoding feature vectors to obtain a set of customer feedback semantic static energy factors; a customer feedback semantic feature aggregation secondary subunit, used to perform feature significant aggregation based on an imitation force field on the set of customer feedback semantic encoding feature vectors based on the set of customer feedback semantic static energy factors to obtain the customer feedback significant aggregation representation; The satisfaction evaluation result generating subunit is used to obtain the satisfaction evaluation result based on the significant aggregation representation of the customer feedback.

2. The CNC machine tool aftermarket service project management system according to claim 1 is characterized in that: The pre-sales management module includes a customer demand collection unit, a technical consultation and support unit, a solution design unit and an estimated quotation unit. The customer demand collection unit is used to record the customer's basic information and demand description; the technical consultation and support unit is used to provide technical support and consulting services to the customer; The solution design unit is used to provide a customized solution based on the customer's demand description; the estimated quotation unit is used to determine the estimated cost based on the customized solution and generate a quotation; The sales management module includes a contract signing unit, a project allocation unit and a progress tracking unit. The contract signing unit is used to manage the creation and approval process of the sales contract; the project allocation unit is used to allocate appropriate technical personnel and resources according to project requirements; and the progress tracking unit is used to monitor project progress in real time.

3. The CNC machine tool aftermarket service project management system according to claim 2 is characterized in that: The customer feedback semantic static energy factor calculation secondary subunit is used to: Calculating the mean and variance of the customer feedback semantic encoding feature vector to obtain a customer feedback semantic mean and a customer feedback semantic variance; Subtracting the customer feedback semantic encoding feature vector from the customer feedback semantic mean by position, and calculating the fourth power of each position of the feature vector after the subtraction to obtain a customer feedback semantic difference modulation vector; Calculating the expected value of the customer feedback semantic difference modulation vector to obtain the customer feedback semantic expected value; The customer feedback semantic expected value is divided by the square of the customer feedback semantic variance, and the value obtained by the division is input into a sigmoid function to obtain the customer feedback semantic static energy factor.

4. The CNC machine tool aftermarket service project management system according to claim 3 is characterized in that: The customer feedback semantic feature aggregation secondary sub-unit includes: The initial center vector of the customer feedback semantic clustering determines a three-level subunit, which is used to select the customer feedback semantic encoding feature vector corresponding to the largest customer feedback semantic static energy factor in the set of customer feedback semantic static energy factors as the initial center vector of the customer feedback semantic clustering; The customer feedback semantic dynamic aggregation energy factor calculation tertiary subunit is used to calculate the dynamic aggregation energy factor of each customer feedback semantic encoding feature vector in the set of customer feedback semantic encoding feature vectors based on the spatial span between each customer feedback semantic encoding feature vector in the set of customer feedback semantic encoding feature vectors and the initial center vector of the customer feedback semantic clustering and the static energy factor of each customer feedback semantic encoding feature vector and the static energy factor of the initial center vector of the customer feedback semantic clustering to obtain a set of customer feedback semantic dynamic aggregation energy factors; A customer feedback semantic dynamic aggregation energy factor gated mask tertiary subunit, used for inputting the set of customer feedback semantic dynamic aggregation energy factors into a gated mask unit to obtain a set of customer feedback semantic dynamic aggregation weight factors; The customer feedback significant aggregation representation generation three-level subunit is used to calculate the weighted sum of the set of customer feedback semantic encoding feature vectors with the set of customer feedback semantic dynamic aggregation weight factors to obtain a customer feedback significant aggregation representation vector as the customer feedback significant aggregation representation.

5. The CNC machine tool aftermarket service project management system according to claim 4 is characterized in that: The customer feedback semantic dynamic aggregation energy factor calculation three-level sub-unit is used to: Multiplying the static energy factor of the customer feedback semantic encoding feature vector, the static energy factor of the customer feedback semantic clustering initial center vector and a first weighting parameter to obtain a first customer feedback semantic dynamic aggregation energy factor; Multiplying the square of the spatial span between the customer feedback semantic encoding feature vector and the customer feedback semantic clustering initial center vector by a second weighting parameter to obtain a second customer feedback semantic dynamic aggregation energy factor; The first customer feedback semantic dynamic aggregation energy factor is divided by the second customer feedback semantic dynamic aggregation energy factor to obtain the customer feedback semantic dynamic aggregation energy factor.

6. The CNC machine tool aftermarket service project management system according to claim 5 is characterized in that: The customer feedback semantic dynamic aggregation energy factor gated mask three-level sub-unit includes: The dynamic aggregation energy factor normalization four-level subunit is used to normalize the set of customer feedback semantic dynamic aggregation energy factors to obtain a set of normalized customer feedback semantic dynamic aggregation energy factors; The normalized dynamic aggregation energy factor masking fourth-level sub-unit is used to perform masking processing on the set of normalized customer feedback semantic dynamic aggregation energy factors to obtain the set of customer feedback semantic dynamic aggregation weight factors.

7. The CNC machine tool aftermarket service project management system according to claim 6 is characterized in that: The dynamic aggregation energy factor normalized four-level subunit is used for: Calculate an exponential function value with the natural constant e as the base and the negative number of each customer feedback semantic dynamic aggregation energy factor in the set of customer feedback semantic dynamic aggregation energy factors as the exponent to obtain a set of customer feedback semantic dynamic aggregation energy exponential factors; After adding the set of customer feedback semantic dynamic aggregation energy index factors and the constant one by position, the inverse of each customer feedback semantic dynamic aggregation energy index modulation factor in the set of customer feedback semantic dynamic aggregation energy index modulation factors is calculated to obtain the set of normalized customer feedback semantic dynamic aggregation energy factors.

8. The CNC machine tool aftermarket service project management system according to claim 7 is characterized in that: The normalized dynamic aggregation energy factor masking four-level sub-unit is used to: in response to each normalized customer feedback semantic dynamic aggregation energy factor in the set of normalized customer feedback semantic dynamic aggregation energy factors being greater than a predetermined threshold, set the normalized customer feedback semantic dynamic aggregation energy factor to the original value, and set the rest to zero, so as to obtain the set of customer feedback semantic dynamic aggregation weight factors.

9. The CNC machine tool aftermarket service project management system according to claim 8, characterized in that: The satisfaction evaluation result generating subunit is used to: input the customer feedback significant aggregation representation vector into a classifier-based customer satisfaction evaluation module to obtain the satisfaction evaluation result, and the satisfaction evaluation result is used to indicate that the user satisfaction is positive or the user satisfaction is negative.

10. A method for managing a CNC machine tool aftermarket service project, the method being applied to the management system according to claim 9, characterized in that: include: Pre-sales management steps: record the customer's basic information and demand description; provide technical support and consulting services to the customer; provide customized solutions based on the customer's demand description; Determine estimated costs and generate a quotation based on the customized solution; Sales management steps: manage the creation and approval process of sales contracts; Allocate appropriate technical staff and resources based on project needs; Monitor project progress in real time; After-sales management steps: record and classify customers' after-sales service requests and track the processing status; Manage the maintenance process; Collect customer feedback and analyze customer satisfaction; Develop and implement a regular maintenance schedule.

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