Intelligent after-sales service method and system for instrument and apparatus products

Abstract

The invention relates to the technical field of instrument and meter maintenance and after-sales service, and discloses an intelligent after-sales service method for instrument and meter products, which comprises the following steps: completing normal production of the products and providing a production and manufacturing information data basis; establishing a data database provided by the intelligent after-sales service; determining a partner providing the intelligent after-sales service, and optimizing a task assignment process and a classification and distribution method of the networked collaborative technology service; the use preference characteristics of each instrument product user on the currently used product are obtained through the user behavior database, and in combination with the knowledge of the instrument expert and the service rules provided by the instrument product manufacturer, the influence of the differentiated behaviors of the user on the performance state of the instrument is analyzed; predicting the item and time for each user to receive the after-sales service next time by combining the prediction model, a standard service rule and a knowledge base; and establishing a temporary authority opening and withdrawing mechanism and a dynamic service node interference mechanism. A corresponding system is also disclosed.

Description

Technical Field

[0001] The present invention relates to the technical field of instrument maintenance and after-sales service, and in particular to an intelligent after-sales service method and system for instrument products. Background Art

[0002] With the increasingly fierce international market competition and the advancement of information technology, the competitive environment of economic globalization has gradually taken shape, market demand is changing rapidly, the personalized needs of instrumentation products are constantly diversified, the product manufacturing cycle is constantly shortened, and the technical content is constantly increasing. It is difficult for instrumentation manufacturers to effectively improve their overall agility by relying solely on their own internal information integration and equipment flexibility. There is an urgent need to improve their comprehensive competitiveness through strong cooperation between enterprises, thereby achieving a win-win situation.

[0003] In addition, at present, the after-sales department of instruments and meters mostly receives task calls and uses manual methods to assign tasks. After the after-sales personnel are dispatched, they cannot track the location of the personnel and can only passively wait for the reply of the after-sales personnel. After the after-sales personnel arrive at the site, if they do not bring all the information or are not familiar with the products sold, it will bring difficulties to the on-site work, waste time, and affect the image of the company in the minds of users. After the after-sales work is completed, the after-sales staff usually asks the user to sign some paper documents to obtain the user's confirmation and feedback on the service and product. After the after-sales personnel bring the paper documents signed by the user back to the company, the relevant personnel need to further summarize and process them, which is very inefficient. Therefore, although the domestic instrumentation product service has great potential for growth, the overall market started late, the after-sales service of instrumentation products is uneven, the channel network organization is disordered, the service measures need to be improved urgently, the quality of practitioners is low, professional talents are relatively scarce, user complaints are frequent, customer satisfaction rate is low, problems are more prominent, and there is a lack of advanced management ideas and technical means. After-sales service basically relies on the guidance and requirements of instrument manufacturers, lacks proactive market analysis, and is difficult to adapt to the complex market environment. Services are highly homogenized, with a single variety of services and a lack of targeted services. It is unable to automatically match user needs and the after-sales service staff's expertise in fault types in a reasonable and rapid manner. As a result, there are defects such as low efficiency in the after-sales service process and low professionalism in after-sales staff in handling faults, and most importantly, a failure to provide differentiated services. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent after-sales service method and system for instrumentation products. Based on the product data information obtained by the intelligent manufacturing of instrumentation products, a complete product information database is established to complete the basic information accumulation of the after-sales service system. On this basis, an after-sales service system is constructed to effectively connect manufacturers and product users through the existing scientific and technological network to form a shared, open, timely and effective communication, improve product quality, and improve user satisfaction. The system includes intelligent manufacturing products, databases, service systems and communication; at the after-sales service receiving end, a temporary permission opening and revocation mechanism and a dynamic service node interference mechanism are established to facilitate users to participate in the entire process of after-sales service.

[0005] A first aspect of the present invention is to provide an intelligent after-sales service method for instrumentation products, comprising: S1, at the production service information providing end, complete the normal production of the product through the intelligent manufacturing production standard and provide the production and manufacturing information data basis; establish a data database for intelligent after-sales service based on the production and manufacturing information data basis; wherein the data database is composed of multiple data sub-databases; S2, at the after-sales service provider, determine the partner who provides the intelligent after-sales service and optimize the networked collaborative technical service task assignment process, classification and allocation method; S3, at the after-sales service support end, by building a user behavior database and using tools for analysis and statistics, we can obtain the usage preference characteristics of each instrumentation product user for the current product. Combined with the knowledge of instrumentation experts and the service rules provided by instrumentation product manufacturers, we analyze the impact of user differentiated behavior on the performance status of instruments. Combined with the prediction model, standard service rules and knowledge base, we predict the next after-sales service item and time for each user, and finally provide support for service recommendations of instrumentation service providers. S4, at the receiving end of after-sales service, establish a temporary permission opening and revocation mechanism and a dynamic service node interference mechanism to facilitate users to participate in the entire process of after-sales service.

[0006] Preferably, the normal production of products is completed through intelligent manufacturing production standards and the production and manufacturing information data basis is provided, including: the instrument manufacturing enterprise completes the normal production of instrument products according to the intelligent manufacturing production standards of the smart factory, and uses the original material information and instrument product information of the normal production as the first basic data basis for providing the intelligent after-sales service; The data database provided by the intelligent after-sales service based on the production and manufacturing information data includes: (1) Establishing a product material identification information sub-database to ensure that the product materials are effectively and timely identified, the product identification information sub-database including identification codes, codes, packaging codes and graphic identification data; (2) Establishing a product information sub-database, wherein the product information sub-database includes production material collection information, assembly and debugging information, inspection information, sales information, delivery information, logistics information, maintenance information, service information, user information and warranty information; (3) Establishing a product information sub-database, which includes data on instructions, spare parts library, promotional materials and technical documents; (4) Establishing a service sub-database, which includes data on the entire on-site after-sales service process, maintenance and repair reporting process, and repair processing tracking; (5) Establishing a communication sub-database, which includes data on sales activities, service evaluation, user satisfaction tracking, user demand feedback, technical consultation, problem feedback and related queries.

[0007] Preferably, S2 includes: S21, based on the functional requirements of the networked collaborative technical service system, establish a functional model of the networked collaborative technical service system and a system architecture based on the multi-agent networked collaborative technical service system; the functional model of the networked collaborative technical service system includes: a technical service functional module for new product development decision-making, a technical service functional module for product design, a technical service functional module for process planning, a technical service functional module for production management, a technical service functional module for manufacturing, a technical service functional module for quality management, a technical service functional module for equipment management, a technical service functional module for suppliers, a technical service functional module for financial management, a technical service functional module for sales, a technical service functional module for after-sales, a technical service functional module for product recycling, a system performance evaluation functional module and a system management functional module; S22, established a comprehensive evaluation index system for virtual enterprise partner selection, adopted fuzzy comprehensive evaluation method to select partners, and optimized the combination of partners based on genetic algorithm; S23, combining RFID technology with barcode technology to collect manufacturing data of instrumentation products, and establishing an information collection model that combines distributed management and centralized storage, and using the information collection model to set up perception agents in the perception layer and data layer of the intelligent after-sales service system; S24, Ontology-based technical service knowledge fusion and reuse to build a networked collaborative technical service knowledge fusion architecture and a technical service domain knowledge ontology library; S25, optimizes the networked collaborative technical service task assignment process, classification and allocation method, and establishes a technical service queue sorting model and task assignment model based on the coefficient matrix element algorithm.

[0008] Preferably, the system architecture of the multi-agent networked collaborative technical service system in S21 includes: The top layer, the middle layer and the bottom layer; the top layer is the Internet, which is used to realize information resource sharing and complete the coordination of the design and manufacturing process based on the Internet; the middle layer is the Internet or a local area network, in which manufacturing enterprises complete planning, design, process planning, sales and inventory production activities, and can also realize information exchange within the enterprise through computer networks to achieve collaboration and sharing; the bottom layer is composed of the Internet and field bus.

[0009] Preferably, the S22 includes: (1) Establish a comprehensive evaluation index system for selecting virtual enterprise partners, including: quality, time, cost, innovation capability, advancement, corporate reputation, management and culture, environment, service, synergy and agility; (2) Use fuzzy comprehensive evaluation method to select partners, including: A. Establish factor set; B. Create alternative sets; C. Establish weight set; D. Perform fuzzy transformation; E. Conduct fuzzy comprehensive evaluation; (3) Use genetic algorithms to optimize the combination of partners, including: A. Set the minimum processing cost and the earliest completion time as the objective function; combine the two objective functions and assign weights to them respectively, transform the multi-objective problem into a single-objective problem, and obtain the final objective function; B. Determine the chromosome coding scheme Assume that each chromosome consists of m genes, corresponding to m sub-project partner selection classes, and the i-th sub-project has no partners from n i The position of the gene is used to represent the candidate enterprises of different sub-projects, and the value of the gene is used to represent the partners selected from the candidate enterprises; the length of each chromosome is the sum of multiple binary strings of different bits, each factor represents a gene of the chromosome, and each chromosome code string can represent a partner combination plan; C. Determine the fitness function: take the minimum value of the objective function as the fitness function; D. Determine the method of calculating the objective function based on selecting operators, checking and compiling operators, and determining termination conditions, so as to obtain an optimal combination of partners; wherein the termination condition is a fixed algebra, and the optimal combination of partners includes taking the better individuals in the group as the final result for decision makers to choose.

[0010] Preferably, the S23 includes: (1) RFID tag reading equipment is used in key manufacturing resource information areas and areas with particularly strong dynamic changes; barcode reading equipment is used in secondary manufacturing information areas and areas with slower status changes; (2) Establish a resource information acquisition grid for tracking and collecting manufacturing process information and collecting manufacturing resource information, so as to identify and track manufacturing resources with printed labels and provide users with information on manufacturing resources; (3) Establishing an information collection model that combines distributed management and centralized storage, the information collection model includes a perception layer, a data layer, a management layer, and an application layer; the perception layer is located at the lowest layer and is composed of perception nodes and perception agents; the perception nodes use a combination of RFID and barcodes to identify and perceive information, and the collection behavior is initiated automatically or by the perception agent. The information collection model is used to set up perception agents in the perception layer and data layer of the intelligent after-sales service system; (4) Use the object identification system to collect, process and store manufacturing resource information and establish a complete resource information management system.

[0011] Preferably, the S24 includes: (1) Establish a collaborative technical reasoning model based on the integration of CBR and RBR. Technical personnel or domain experts provide technical service support to producers or users based on user needs and technical service requirements through the collaborative technical service knowledge management interactive interface. RBR performs traversal reasoning on the rules and stores all possible technical problem descriptions and their solution strategies as cases, i.e., solution experience, in the case library. (2) Retrieval and solution of technical cases based on CBR and RBR, including: when providing technical services, first search the case library based on the technical service information input by the user to find out whether there are matching cases as reused cases. If there are no similar cases, search in the external database provided by the collaborating enterprise or expert through collaboration; if no matching cases can be found through searching the external case library, perform fusion solution based on similar cases, and first submit the solution results to the corresponding technical personnel for confirmation or modification, and then submit it to the required user. If the user successfully solves the technical problem by applying the solution, feedback information is obtained and the corresponding case library and rule library are updated.

[0012] Preferably, the S25 includes: (1) Build a task sorting model based on FAHP; (2) Classifying task assignments into four categories according to their nature: single-goal assignments, multi-goal assignments, parallel task assignments, and emergency task assignments; establishing a coefficient matrix based on the number of technical service tasks and the number of technical service personnel, wherein the coefficient matrix includes a plurality of coefficient matrix elements; When the number of technical service tasks is less than the number of technical service personnel, multiple virtual tasks are added and the time taken by each person to complete these virtual tasks is set to 0. At this time, the problem is transformed into an assignment problem in which the number of people is equal to the number of tasks. When the number of technical service tasks is equal to the number of technical service personnel, the time taken by each person to complete these virtual tasks is set to 0. At this time, the problem is an assignment problem in which the number of people is equal to the number of tasks. Then the coefficient matrix is ​​transformed to form independent 0 elements equal to the number of people. The number of tasks currently required to be completed in the solution matrix is ​​set to 1, and the rest are set to 0, thereby cross-setting the matching relationship between technical service personnel and technical services. When the number of technical service tasks is greater than the number of technical service personnel, if a new technical service task is submitted to the system while all technical service personnel are solving other tasks at this time, then the new task will enter the queue and wait for assignment, waiting for the new technical service personnel to solve it; when there are more than two tasks, the technical service personnel who completes the task first will be assigned the new service task, and when two or more technical service personnel complete the task at the same time, the technical service tasks will be assigned according to the Hungarian algorithm; when a new technical service personnel logs in, the technical service tasks existing in the system will be assigned to the new logged-in personnel in the order of the sorting queue; when the technical service personnel logs out, the system will no longer assign technical service tasks to the offline technical service personnel.

[0013] Preferably, S3 includes: S31, based on the analysis of historical user behavior patterns of instrument and meter products, determine the factor system that affects user behavior of instrument and meter products, and construct a user behavior index system based on the structural equation model, wherein the user behavior index system is used to analyze the influence mechanism between multiple factors and between behavioral factors and after-sales service; S32, constructing a user behavior database, and using tools to analyze and count the data in the user behavior database, so as to obtain the usage preference characteristics of each instrument product user for the product currently used; S33, selecting multiple types of data from the user behavior database, associating the multiple types of data with the user behavior indicator system to form user differentiated behavior data, integrating the user differentiated behavior data with a knowledge base formed by instrument expert knowledge and service rules provided by an instrument product manufacturer, and determining the impact of user differentiated behavior on instrument performance status; S34, establishing an after-sales prediction model based on the impact of the user's differentiated behavior on the performance status of the instrument; S35, based on the after-sales prediction model, the knowledge base formed by each user and the instrument expert knowledge and the service rules provided by the instrument product manufacturer are secondary integrated to predict the items and time of the next after-sales service each user will receive; the secondary integration is implemented based on the after-sales service dynamic game strategy.

[0014] A second aspect of the present invention is to provide an intelligent after-sales service system for instrumentation products, which is used to implement the method of the first aspect, including: A production service information providing module (101) is used to complete normal production of products through intelligent manufacturing production standards and provide a production and manufacturing information data basis; based on the production and manufacturing information data basis, a data database for intelligent after-sales service provision is established; wherein the data database is composed of a plurality of data sub-databases; An after-sales service providing module (102), used to determine a partner providing the intelligent after-sales service and optimize the networked collaborative technical service task assignment process, classification and allocation method; The after-sales service support module (103) is used to obtain the usage preference characteristics of each instrument product user for the current product by building a user behavior database and using tools for analysis and statistics, and analyze the impact of user differentiated behavior on the performance status of the instrument by combining the knowledge of instrument experts and the service rules provided by the instrument product manufacturer, and predict the items and time of the next after-sales service received by each user by combining the prediction model, standard service rules and knowledge base, and finally provide support for service recommendations of instrument service providers; The after-sales service receiving module (104) is used to establish a temporary permission opening and revocation mechanism and a dynamic service node interference mechanism to facilitate users to participate in the entire process of after-sales service.

[0015] A third aspect of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory stores a plurality of instructions, and the processor is configured to read the instructions and execute the method described in the first aspect.

[0016] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a plurality of instructions, and the plurality of instructions can be read by a processor and execute the method described in the first aspect.

[0017] Beneficial effects of the method, system, electronic device and computer-readable storage medium of the present invention: The intelligent after-sales service method and system for instrumentation products, based on the product data information obtained by the intelligent manufacturing of instrumentation products, establishes a complete product information database, completes the basic information accumulation of the after-sales service system, and builds an after-sales service system on this basis, effectively connecting manufacturers and product users through the existing scientific and technological network, forming a shared, open, timely and effective communication, improving product quality, and improving user satisfaction. The system includes intelligent manufacturing products, databases, service systems and communication; at the receiving end of after-sales service, a temporary permission opening and revocation mechanism and a dynamic service node interference mechanism are established to facilitate users to participate in the entire process of after-sales service. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the related technologies, the drawings required for use in the specific embodiments or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A flow chart of an intelligent after-sales service method for instrumentation products provided according to an embodiment of the present invention; Figure 2 This is a flow chart of step S2 of the intelligent after-sales service method for instrumentation products provided according to an embodiment of the present invention; Figure 3 This is a flow chart of step S3 of the intelligent after-sales service method for instrumentation products provided according to an embodiment of the present invention; Figure 4 A schematic diagram of the intelligent after-sales service system for instrumentation products provided according to an embodiment of the present invention; Figure 5 A structural diagram of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0022] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. Embodiment 1

[0023] See also Figure 1 This embodiment provides an intelligent after-sales service method for instrument products, including: S1, at the production service information providing end, complete the normal production of the product through the intelligent manufacturing production standard and provide the production and manufacturing information data basis; establish a data database for intelligent after-sales service based on the production and manufacturing information data basis; wherein the data database is composed of multiple data sub-databases; As a preferred implementation, the normal production of products is completed through intelligent manufacturing production standards and the production and manufacturing information data basis is provided, including: the instrument manufacturing enterprise completes the normal production of instrument products according to the intelligent manufacturing production standards of the smart factory, and uses the original material information and instrument product information of the normal production as the first basic data basis for providing the intelligent after-sales service.

[0024] As a preferred implementation, the data database provided by the intelligent after-sales service based on the production and manufacturing information data includes: (1) Establishing a product material identification information sub-database to ensure that the product materials are effectively and timely identified, the product identification information sub-database including identification codes, codes, packaging codes and graphic identification data; (2) Establishing a product information sub-database, wherein the product information sub-database includes production material collection information, assembly and debugging information, inspection information, sales information, delivery information, logistics information, maintenance information, service information, user information and warranty information; (3) Establishing a product information sub-database, which includes data on instructions, spare parts library, Sherwin-Williams information and technical documents; (4) Establishing a service sub-database, which includes data on the entire on-site after-sales service process, maintenance and repair reporting process, and repair processing tracking; (5) Establishing a communication sub-database, which includes data on sales activities, service evaluation, user satisfaction tracking, user demand feedback, technical consultation, problem feedback and related queries.

[0025] S2, at the after-sales service provider, determine the partner who provides the intelligent after-sales service and optimize the networked collaborative technical service task assignment process, classification and allocation method; like Figure 2 As shown, as a preferred embodiment, the S2 includes: S21, based on the functional requirements of the networked collaborative technical service system, establish the functional model of the networked collaborative technical service system and the system architecture based on the multi-agent networked collaborative technical service system; Technical services run through the entire life cycle of products and play an important role in all stages of the product. The diversification of technical service needs has brought about the complexity and variability of networked technical service transaction processing. The distributed networked collaborative technical service system based on service customization is an organic reorganization of all effective resources and efficient combination of service capabilities of all cooperating parties such as manufacturing enterprises, design partners, manufacturing alliances, sales groups, scientific research institutes, and academic institutions. Therefore, as a preferred implementation, the functional model of the networked collaborative technical service system includes: (1) Technical service function module for new product development decision-making With the increasingly fierce market competition, product innovation or service innovation has become the basis for the survival and development of enterprises. Product development is a large investment and long cycle activity, which can easily bring losses to enterprises. Therefore, it is necessary to be cautious in the development of new products. The technical service function module for new product development decision-making can greatly reduce risks, improve the success rate of product development, and provide decision makers with information services such as profit and loss balance analysis, development risk assessment and resource optimization scheduling.

[0026] (2) Technical service function module for product design Designers can obtain technical support from the system, other designers, process personnel, quality managers, workers, product users, collaborative enterprises and experts through the technical service function module for product design, thereby improving the advancement of product design.

[0027] (3) Technical service function module for process planning Process planners can obtain technical support from the system, other process personnel, designers, quality managers, workers, product users, cooperative enterprises and experts through the technical service function module for process planning to improve the advancement of product technology.

[0028] (4) Technical service function module for production management It is used to directly assign production tasks to the heads of corresponding design rooms, process rooms, workshops and other departments, and then the heads of these departments will assign tasks step by step, thereby realizing agile scheduling of tasks; in addition, it can also supervise and guide the completion of production tasks during the production process.

[0029] (5) Manufacturing-oriented technical service function module It is used to manage workshop production processes and production services, including technical services in parts processing and product assembly, as well as tool, gauge and fixture borrowing services. For workshop dispatchers, the system can be used to input the processing location of parts and product assembly status. Processing workers and assembly workers can query production tasks and material preparation status through the manufacturing-oriented technical service function module. Material management personnel can input material inbound and outbound information through the manufacturing-oriented technical service function module.

[0030] (6) Technical service function module for quality management Quality inspectors can input parts processing inspection records and product assembly inspection records through the quality management-oriented technical service function module; they can also evaluate the inspection quality through the quality management-oriented technical service function module and feed back the results to the processors and assemblers as prompts or warnings; they can also query relevant quality management documents and standards through the quality management-oriented technical service function module.

[0031] (7) Technical service function module for equipment management The technical service function module for equipment management can be used to query equipment status, issue equipment maintenance notices, handle equipment scrapping and purchase equipment, so that enterprises can obtain the best equipment resource allocation and maximize equipment utilization.

[0032] (8) Technical service function module for suppliers The technical service function module for suppliers consists of two parts: the external supply chain management submodule and the internal supply chain management submodule. The external supply chain management submodule is used to evaluate and manage suppliers and obtain comprehensive information, including supplier information, purchaser information, product information and purchase information; the internal supply chain management submodule is used to determine the product's delivery time, delivery quantity, recipient code, handler code, product location, product status and other related information.

[0033] (9) Technical service function module for financial management The technical service function module for financial management is used to provide various services including product cost accounting, labor time quota accounting and financial information query to the company's managers, relevant technical service personnel, workers, cooperative enterprises and product users.

[0034] (10) Sales-oriented technical service function module The sales-oriented technical service function module includes three parts: product sales management submodule, product online ordering submodule and virtual warehouse submodule. Among them: The product sales management submodule is used to manage sellers and agents. The management content is the basic information of sellers and agents, including name, education, sales experience and sales performance information; The product online ordering submodule is used to browse product related information online (including product introduction, etc.), conduct online consultation, order online, query order information, and other related services; The virtual warehouse sub-module is used to query and adjust information such as product entry and exit, and to grasp the product entry time, entry quantity, handler, corresponding product exit time, exit quantity and recipient information.

[0035] (11) Technical service function module for after-sales The after-sales technical service function module is used to provide remote technical training to the corresponding users and solve possible technical problems; conduct corresponding technical consultations through the Internet, and complete equipment operations or solve problems under the guidance of relevant technicians, thereby completing remote after-sales service; when the remote after-sales service is not sufficient to solve the problem, the user makes an on-site technical service agreement and agrees that the company will delegate relevant technicians to provide door-to-door service; purchase accessories and support querying the information of the accessories purchase at any time.

[0036] (12) Technical service function module for product recycling The technical service function module for product recycling is used to achieve product recall and reuse to a certain extent, thereby promoting the recycling of materials and parts and promoting the development of green manufacturing to a certain extent.

[0037] (13) System performance evaluation function module The system performance evaluation function module is used to comprehensively evaluate the implementation effect of the networked collaborative technical service system; the functions of the system performance evaluation function module include: determining the evaluation index system and weight distribution; managing expert information, conducting a comprehensive fuzzy evaluation of system performance, and then completing the comprehensive management of the evaluation results.

[0038] (14) System management function module The system management function module is used to set the relevant permissions of the log-in user (this is because different users have different permissions and each corresponds to its own system interface); the administrator can perform corresponding daily maintenance on the system management function module and clean up redundant information; the administrator uses the system management function module to publish information, announce meeting notices, adjust holiday schedules, announce business operations and personnel changes, and other relevant information involving the company or its employees.

[0039] As a preferred implementation, the system architecture based on the multi-agent networked collaborative technical service system includes: The top layer, the middle layer and the bottom layer; the top layer is the Internet, which uses the Internet as a platform to realize information resource sharing and complete the collaboration of the design and manufacturing process; the middle layer is the Internet or local area network, within which manufacturing companies can complete production activities such as planning, design, process planning, sales and inventory, and can also realize information exchange within the enterprise through computer networks to achieve collaboration and sharing; the bottom layer is composed of the Internet and field bus.

[0040] The network structure of the multi-agent networked collaborative technical service system is an open architecture. It has both multi-level and distributed characteristics. There is no dependency between the nodes (or subsystems) in the same level system. It is a completely loosely coupled relationship. This makes the system structure highly flexible and dynamic, ensuring the repeatability of the service, thereby ensuring that the technical service needs are not affected by time and region. Even if multiple users make the same technical service request at the same time, the multi-agent networked collaborative technical service system will respond quickly.

[0041] The multi-agent networked collaborative technical service system has the following characteristics: (1) The flatness of the network hierarchy.

[0042] (2) Distributed and open architecture. In this mode, a network system can be equipped with multiple managers. Each manager is only responsible for the management of a specific domain in the network system. Managers can communicate with each other and accept the coordination of senior managers. This management mode of distributed management can disperse management work throughout the entire manufacturing network. Due to the unified upper-level manager, the processing results can also be summarized. This method not only takes into account the autonomy of the terminal nodes, but also can effectively reduce network communication volume and ensure the efficiency of traffic.

[0043] (3) Have efficient analytical and coordination capabilities and be able to provide users with efficient and high-quality technical services.

[0044] (4) Parallelism of working methods.

[0045] (5) Scalability, reconfigurability, and good fault tolerance.

[0046] (6) Distribution of data, knowledge and information.

[0047] (7) It has good compatibility and can adapt to various hardware platforms and various operating systems.

[0048] S22, established a comprehensive evaluation index system for selecting virtual enterprise partners, adopted fuzzy comprehensive evaluation method to select partners, and used genetic algorithm to optimize the combination of partners; including: (1) The comprehensive evaluation index system for selecting virtual enterprise partners includes 11 first-level indicators: quality (Q), time (T), cost (C), innovation capability (I), advancement (AD), corporate reputation (CR), management and culture (MC), environment (E), service (S), synergy (SY), and agility (AG); among which: Quality indicators are divided into design quality, manufacturing quality and material quality indicators; Time indicators are divided into design time, processing time and transportation time indicators; Cost indicators are divided into design cost, processing cost and material cost indicators; Innovation capability indicators are divided into design capability, production capability and sales capability indicators; The advanced indicators are divided into standardization degree, automation degree, information degree and equipment advanced indicators; Corporate reputation indicators are divided into corporate reputation, corporate scale and corporate financial situation indicators; Management and culture indicators are divided into management system compatibility, corporate culture compatibility and corporate compatibility indicators; Environmental indicators are divided into manufacturing greenness, use greenness and recycling greenness indicators; Service indicators are divided into pre-sales service, in-sales service and after-sales service indicators.

[0049] Enterprises select several evaluation indicators for comprehensive evaluation based on actual conditions, or dynamically adjust evaluation indicators based on the characteristics of production tasks.

[0050] (2) Use fuzzy comprehensive evaluation method to select partners, including: A. Establishing Factor Sets (1); Indicates that the evaluation object is evaluated from the perspective of m evaluation indicators. is the mth factor in the first layer, which is composed of factors, namely: (2); B. Create alternative sets Also known as the evaluation set, it is a collection of various possible overall evaluation results that the evaluator may make on the evaluation object. The evaluation levels used in this invention are: (3); C. Establishing weight sets With the goal of achieving the task, taking into full consideration the different importance of each element to the task, a weight set is established to represent the weight set of each factor. This embodiment is represented in the following form: (4); All weights satisfy the normalization and non-negativity conditions.

[0051] D. Perform fuzzy transformation if is the fuzzy relationship matrix from U to V, then using R we can get a fuzzy transformation: (4); At this point, if you enter a weight distribution , a comprehensive decision can be obtained through fuzzy transformation:

[0052] (5); E. Conduct fuzzy comprehensive evaluation Assume that the lowest level single factor The membership of K elements in the alternative set is the membership of K elements

[0053] , then the second-level single factor evaluation matrix is: (6); The second layer of fuzzy comprehensive evaluation matrix is: (7).

[0054] (3) Use genetic algorithms to optimize the combination of partners, including: A. Set the minimum processing cost and the earliest completion time as the objective function; combine the two objective functions and assign weights to them respectively, transform the multi-objective problem into a single-objective problem, and obtain the final objective function; B. Determine the chromosome coding scheme Assume that each chromosome consists of m genes, corresponding to m sub-project partner selection classes, and the i-th sub-project partner is selected from n-i candidate partners. Therefore, the position of the gene is used to represent the candidate companies of different sub-projects, and the value of the gene is used to represent the partners selected from the candidate companies. The length of each chromosome is the sum of multiple binary strings with different bits, each factor represents a gene of the chromosome, and each chromosome code string can represent a partner combination scheme.

[0055] C. Determine the fitness function: Take the minimum value of the objective function as the fitness function.

[0056] D. Determine the method of calculating the objective function based on the selection operator, the check and compilation operator, and the determination of the termination condition, so as to obtain the optimal combination of partners. The termination condition is a fixed number of generations (50 in this embodiment), and the optimal combination of partners includes taking the better individuals in the group as the final result for the decision maker to choose.

[0057] S23, combining RFID technology with barcode technology to collect manufacturing data of instrumentation products, and establishing an information collection model that combines distributed management and centralized storage, and using the information collection model to set up perception agents in the perception layer and data layer of the intelligent after-sales service system; As a preferred implementation, the S23 includes: (1) RFID tag reading equipment is used in key manufacturing resource information areas and areas with particularly strong dynamic changes; barcode reading equipment is used in secondary manufacturing information areas and areas with slower status changes; (2) Establish a resource information acquisition grid for tracking and collecting manufacturing process information and collecting manufacturing resource information, so as to identify and track manufacturing resources with printed labels and provide users with information on manufacturing resources; (3) Establish an information collection model that combines distributed management and centralized storage. The information collection model includes a perception layer, a data layer, a management layer, and an application layer. The perception layer is located at the lowest layer and is composed of perception nodes and perception agents. The perception nodes use a combination of RFID and barcodes to identify and perceive information. The collection behavior is initiated automatically or by the perception agent. The information collection model is used to set up perception agents in the perception layer and data layer of the intelligent after-sales service system; (4) Use the object identification system to collect, process and store manufacturing resource information and establish a complete resource information management system.

[0058] S24, based on the fusion and reuse of technical service knowledge based on ontology, the architecture of networked collaborative technical service knowledge fusion and the technical service domain knowledge ontology library are constructed to improve the intelligence of the system, thereby improving the efficiency and quality of technical services; including: (1) Establish a collaborative technical reasoning model based on the integration of CBR and RBR. Technical personnel or domain experts provide technical service support to producers or users based on user needs and technical service requirements through the collaborative technical service knowledge management interactive interface. RBR performs traversal reasoning on the rules and stores all possible technical problem descriptions and their solution strategies as cases, i.e., solution experience, in the case library.

[0059] (2) Retrieval and solution of technical cases based on CBR and RBR, including: when providing technical services, first search in the case library according to the technical service information input by the user to find out whether there are matching cases as reused cases. If there are no similar cases, search in the external database provided by the collaborating enterprise or expert through collaboration. If no matching cases are found through searching the external case library, the system will perform fusion solution based on similar cases, and first submit the solution results to the corresponding technical personnel for confirmation or modification, and then submit it to the required users. If the user successfully solves the technical problem by applying the solution, the system will get feedback information and update the corresponding case library and rule library.

[0060] S25, optimize the networked collaborative technical service task assignment process, classification and allocation method, and establish a technical service queue sorting model and task assignment model based on coefficient matrix element algorithm; including: (1) Build a task sorting model based on FAHP; (2) Classifying task assignments into four categories according to their nature: single-goal assignments, multi-goal assignments, parallel task assignments, and emergency task assignments; establishing a coefficient matrix based on the number of technical service tasks and the number of technical service personnel, wherein the coefficient matrix includes a plurality of coefficient matrix elements; When the number of technical service tasks is less than the number of technical service personnel, multiple virtual tasks are added and the time taken by each person to complete these virtual tasks is set to 0. At this time, the problem is transformed into an assignment problem in which the number of people is equal to the number of tasks. When the number of technical service tasks is equal to the number of technical service personnel, the time taken by each person to complete these virtual tasks is set to 0. At this time, the problem is an assignment problem where the number of people is equal to the number of tasks. Then the coefficient matrix is ​​transformed to form independent 0 elements equal to the number of people. The number of tasks that need to be completed in the solution matrix is ​​set to 1, and the rest are set to 0, thereby cross-setting the matching relationship between technical service personnel and technical services; When the number of technical service tasks is greater than the number of technical service personnel, if a new technical service task is submitted to the system while all technical service personnel are solving other tasks at this time, then the new task will enter the queue and wait for assignment, waiting for the new technical service personnel to solve it; when there are more than two tasks, the technical service personnel who completes the task first will be assigned the new service task, and when two or more technical service personnel complete the task at the same time, the technical service tasks will be assigned according to the Hungarian algorithm; when a new technical service personnel logs in, the technical service tasks existing in the system will be assigned to the new logged-in personnel in the order of the sorting queue; when the technical service personnel logs out, the system will no longer assign technical service tasks to the offline technical service personnel.

[0061] S3, at the after-sales service support end, by building a user behavior database and using tools for analysis and statistics, we can obtain the usage preference characteristics of each instrumentation product user for the current product. Combined with the knowledge of instrumentation experts and the service rules provided by instrumentation product manufacturers, we analyze the impact of user differentiated behavior on the performance status of instruments. Combined with the prediction model, standard service rules and knowledge base, we predict the next after-sales service item and time for each user, and finally provide support for service recommendations of instrumentation service providers. like Figure 3 As shown, as a preferred implementation, S3 includes: S31, based on the analysis of historical user behavior patterns of instrument and meter products, determine the factor system that affects user behavior of instrument and meter products, and construct a user behavior index system based on the structural equation model, wherein the user behavior index system is used to analyze the influence mechanism between multiple factors and between behavioral factors and after-sales service; S32, constructing a user behavior database, and using tools to analyze and count the data in the user behavior database, so as to obtain the usage preference characteristics of each instrument product user for the product currently used; S33, selecting multiple types of data from the user behavior database, associating the multiple types of data with the user behavior indicator system to form user differentiated behavior data, integrating the user differentiated behavior data with a knowledge base formed by instrument expert knowledge and service rules provided by an instrument product manufacturer, and determining the impact of user differentiated behavior on instrument performance status; S34, establishing an after-sales prediction model based on the impact of the user's differentiated behavior on the performance status of the instrument; S35, based on the after-sales prediction model, a knowledge base formed by each user and the instrument expert knowledge and the service rules provided by the instrument product manufacturer are secondary integrated to predict the item and time of the next after-sales service to be received by each user; the secondary integration is implemented based on the after-sales service dynamic game strategy; For example, five equilibrium points are set in the game, two of which are unstable source starting points, one is a saddle point, and two are evolutionary stable states. This result shows that as long as the after-sales supplier of instrumentation products insists on carrying out automotive after-sales service recommendations, users will eventually gradually develop the habit of replacing instruments or related spare parts on time. Of course, if users have a strong demand for replacing instruments or related spare parts on time, after-sales suppliers will also be happy to carry out after-sales service recommendations to facilitate users to get timely replacement spare parts reminders, reduce the possibility of missing spare parts replacement time due to user errors or forgetfulness, and thus increase the spare parts replacement rate and obtain more benefits.

[0062] S4, at the receiving end of after-sales service, establish a temporary permission opening and revocation mechanism and a dynamic service node interference mechanism to facilitate users to participate in the entire process of after-sales service.

[0063] As a preferred implementation, the temporary permission opening and revocation mechanism and the dynamic service node interference mechanism will be activated multiple times in the following process, so that users can participate in the whole process of after-sales service.

[0064] (1) In the early stage of production, the identity of various products is identified through product orientation setting, and classification management is carried out at the same time. The relevant information is entered manually or through intelligent production equipment, and an initial information database is established and stored. If necessary, the user-prescribed products can be opened to users for production supervision and production stage inquiries; (2) Complete product inspection and shipment based on sales activities, register logistics information, and make it available to users so that they can understand the dynamics of cargo transportation; (3) After the product arrives, the user organizes the acceptance and authorizes the system function to save the arrival information record; (3) After the user initiates an appointment for door-to-door service, the system arranges personnel to provide service. The receiving personnel will contact the user to confirm, prepare on-site and schedule the time to log in to the system; (4) Service personnel provide door-to-door service, input service content, user acceptance and evaluation, understand service feedback, and collect information and opinions; (5) During the user's use, the system can be used to complete technical consultation, data replacement, product upgrades or promotions. If problems arise, an appointment can be made for product repair services; (6) After the product is returned for repair, it will be received, judged, inspected and shipped, and users can view the relevant process nodes; (7) Complete communication and interaction with users. Embodiment 2

[0065] like Figure 4 As shown, this embodiment provides an intelligent after-sales service system for instrument products, which is used to implement the method of embodiment 1, including: The production service information providing module 101 is used to complete the normal production of products through intelligent manufacturing production standards and provide a production and manufacturing information data basis; based on the production and manufacturing information data basis, a data database for intelligent after-sales service provision is established; wherein the data database is composed of multiple data sub-databases; After-sales service providing module 102, used to determine the partners who provide the intelligent after-sales service and optimize the networked collaborative technical service task assignment process, classification and allocation method; The after-sales service support module 103 is used to obtain the usage preference characteristics of each instrument product user for the current product by building a user behavior database and using tools for analysis and statistics. It combines the knowledge of instrument experts and the service rules provided by instrument product manufacturers to analyze the impact of user differentiated behavior on the performance status of instruments. It also combines the prediction model, standard service rules and knowledge base to predict the next after-sales service item and time for each user, and finally provides support for service recommendations of instrument service providers. The after-sales service receiving module 104 is used to establish a temporary permission opening and revocation mechanism and a dynamic service node interference mechanism to facilitate users to participate in the entire process of after-sales service.

[0066] The present invention also provides a memory storing a plurality of instructions, wherein the instructions are used to implement the method as in the first embodiment.

[0067] like Figure 5 As shown, the present invention also provides an electronic device, including a processor 301 and a memory 302 connected to the processor 301, the memory 302 stores a plurality of instructions, and the instructions can be loaded and executed by the processor so that the processor can execute the method as in the first embodiment.

[0068] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above embodiments can be implemented by software, or by means of software plus necessary general hardware platforms. Based on such understanding, the technical solutions of the above embodiments can be embodied in the form of software products, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0069] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent after-sales service method for instrumentation products, characterized in that: include: S1, at the production service information provider end, complete the normal production of products through intelligent manufacturing production standards and provide the production and manufacturing information data basis; A data database for intelligent after-sales service is established based on the manufacturing information data foundation; wherein the data database is composed of multiple data sub-databases; S2, at the after-sales service provider, determine the partner who provides the intelligent after-sales service and optimize the networked collaborative technical service task assignment process, classification and allocation method; S3, at the after-sales service support end, by building a user behavior database and using tools for analysis and statistics, we can obtain the usage preference characteristics of each instrumentation product user for the current product. Combined with the knowledge of instrumentation experts and the service rules provided by instrumentation product manufacturers, we can analyze the impact of user differentiated behavior on the performance status of instruments. Combined with the prediction model, standard service rules and knowledge base, we can predict the next after-sales service item and time for each user, and finally provide support for service recommendations of instrumentation service providers. S4, at the receiving end of after-sales service, establish a temporary permission opening and revocation mechanism and a dynamic service node interference mechanism to facilitate users to participate in the entire process of after-sales service.

2. The intelligent after-sales service method for instrument products according to claim 1, characterized in that: The normal production of products is completed through intelligent manufacturing production standards and the production and manufacturing information data basis is provided, including: the instrument manufacturing enterprise completes the normal production of instrument products according to the intelligent manufacturing production standards of the smart factory, and uses the original material information and instrument product information of the normal production as the first basic data basis for providing the intelligent after-sales service; The data database provided by the intelligent after-sales service based on the production and manufacturing information data includes: (1) Establishing a product material identification information sub-database to ensure that the product materials are effectively and timely identified, and the product material identification information sub-database includes identification codes, codes, packaging codes and graphic identification data; (2) Establishing a product information sub-database, wherein the product information sub-database includes production material collection information, assembly and debugging information, inspection information, sales information, delivery information, logistics information, maintenance information, service information, user information and warranty information; (3) Establishing a product information sub-database, which includes data on instructions, spare parts library, promotional materials and technical documents; (4) Establishing a service sub-database, which includes data on the entire on-site after-sales service process, maintenance and repair reporting process, and repair processing tracking; (5) Establishing a communication sub-database, which includes data on sales activities, service evaluation, user satisfaction tracking, user demand feedback, technical consultation, problem feedback and related queries.

3. The intelligent after-sales service method for instrument products according to claim 2, characterized in that: The S2 includes: S21, based on the functional requirements of the networked collaborative technical service system, establish a functional model of the networked collaborative technical service system and a system architecture based on the multi-agent networked collaborative technical service system; the functional model of the networked collaborative technical service system includes: a technical service functional module for new product development decision-making, a technical service functional module for product design, a technical service functional module for process planning, a technical service functional module for production management, a technical service functional module for manufacturing, a technical service functional module for quality management, a technical service functional module for equipment management, a technical service functional module for suppliers, a technical service functional module for financial management, a technical service functional module for sales, a technical service functional module for after-sales, a technical service functional module for product recycling, a system performance evaluation functional module and a system management functional module; S22, established a comprehensive evaluation index system for virtual enterprise partner selection, adopted fuzzy comprehensive evaluation method to select partners, and optimized the combination of partners based on genetic algorithm; S23, combining RFID technology with barcode technology to collect manufacturing data of instrumentation products, and establishing an information collection model that combines distributed management and centralized storage, and using the information collection model to set up perception agents in the perception layer and data layer of the intelligent after-sales service system; S24, Ontology-based technical service knowledge fusion and reuse to build a networked collaborative technical service knowledge fusion architecture and a technical service domain knowledge ontology library; S25, optimizes the networked collaborative technical service task assignment process, classification and allocation method, and establishes a technical service queue sorting model and task assignment model based on the coefficient matrix element algorithm.

4. The intelligent after-sales service method for instrument products according to claim 3, characterized in that: The system architecture of the multi-agent networked collaborative technical service system in S21 includes: The top layer, the middle layer and the bottom layer; the top layer is the Internet, which is used to realize information resource sharing and complete the coordination of the design and manufacturing process based on the Internet; the middle layer is the Internet or a local area network, in which manufacturing enterprises complete planning, design, process planning, sales and inventory production activities, and can also realize information exchange within the enterprise through computer networks to achieve collaboration and sharing; the bottom layer is composed of the Internet and field bus.

5. The intelligent after-sales service method for instrumentation products according to claim 4, characterized in that: The S22 includes: (1) Establish a comprehensive evaluation index system for selecting virtual enterprise partners, including: quality, time, cost, innovation capability, advancement, corporate reputation, management and culture, environment, service, synergy and agility; (2) Use fuzzy comprehensive evaluation method to select partners, including: A. Establish factor set; B. Create alternative sets; C. Establish weight set; D. Perform fuzzy transformation; E. Conduct fuzzy comprehensive evaluation; (3) Use genetic algorithms to optimize the combination of partners, including: A. Set the minimum processing cost and the earliest completion time as the objective function; combine the two objective functions and assign weights to them respectively, transform the multi-objective problem into a single-objective problem, and obtain the final objective function; B. Determine the chromosome coding scheme Assume that each chromosome consists of m genes, corresponding to m sub-project partner selection classes, and the i-th sub-project has no partners from n i The position of the gene is used to represent the candidate enterprises of different sub-projects, and the value of the gene is used to represent the partners selected from the candidate enterprises; the length of each chromosome is the sum of multiple binary strings of different bits, each factor represents a gene of the chromosome, and each chromosome code string can represent a partner combination plan; C. Determine the fitness function: take the minimum value of the objective function as the fitness function; D. Determine the method of calculating the objective function based on selecting operators, checking and compiling operators, and determining termination conditions, so as to obtain an optimal combination of partners; wherein the termination condition is a fixed algebra, and the optimal combination of partners includes taking the better individuals in the group as the final result for decision makers to choose.

6. The intelligent after-sales service method for instrumentation products according to claim 5, characterized in that: The S23 includes: (1) RFID tag reading equipment is used in key manufacturing resource information locations and locations where dynamic changes exceed a predetermined threshold; barcode reading equipment is used in secondary manufacturing information locations and locations where the status changes slowly; (2) Establish a resource information acquisition grid for tracking and collecting manufacturing process information and collecting manufacturing resource information, so as to identify and track manufacturing resources with printed labels and provide users with information on manufacturing resources; (3) Establishing an information collection model that combines distributed management and centralized storage, the information collection model includes a perception layer, a data layer, a management layer, and an application layer; the perception layer is located at the lowest layer and is composed of perception nodes and perception agents; the perception nodes use a combination of RFID and barcodes to identify and perceive information, and the collection behavior is initiated automatically or by the perception agent. The information collection model is used to set up perception agents in the perception layer and data layer of the intelligent after-sales service system; (4) Use the object identification system to collect, process and store manufacturing resource information and establish a complete resource information management system.

7. The intelligent after-sales service method for instrumentation products according to claim 6, characterized in that: The S24 includes: (1) Establish a collaborative technical reasoning model based on the integration of CBR and RBR. Technical personnel or domain experts provide technical service support to producers or users based on user needs and technical service requirements through the collaborative technical service knowledge management interactive interface. RBR performs traversal reasoning on the rules and stores all possible technical problem descriptions and their solution strategies as cases, i.e., solution experience, in the case library. (2) Retrieval and solution of technical cases based on CBR and RBR, including: when providing technical services, first search the case library based on the technical service information input by the user to find out whether there are matching cases as reused cases. If there are no similar cases, search in the external database provided by the collaborating enterprise or expert through collaboration; if no matching cases can be found through searching the external case library, perform fusion solution based on similar cases, and first submit the solution results to the corresponding technical personnel for confirmation or modification, and then submit it to the required user. If the user successfully solves the technical problem by applying the solution, feedback information is obtained and the corresponding case library and rule library are updated.

8. The intelligent after-sales service method for instrumentation products according to claim 7, characterized in that: The S25 includes: (1) Build a task sorting model based on FAHP; (2) Classifying task assignments into four categories according to their nature: single-goal assignments, multi-goal assignments, parallel task assignments, and emergency task assignments; establishing a coefficient matrix based on the number of technical service tasks and the number of technical service personnel, wherein the coefficient matrix includes a plurality of coefficient matrix elements; When the number of technical service tasks is less than the number of technical service personnel, multiple virtual tasks are added and the time taken by each person to complete these virtual tasks is set to 0. At this time, the problem is transformed into an assignment problem in which the number of people is equal to the number of tasks. When the number of technical service tasks is equal to the number of technical service personnel, the time taken by each person to complete these virtual tasks is set to 0. At this time, the problem is an assignment problem in which the number of people is equal to the number of tasks. Then the coefficient matrix is ​​transformed to form independent 0 elements equal to the number of people. The number of tasks currently required to be completed in the solution matrix is ​​set to 1, and the rest are set to 0, thereby cross-setting the matching relationship between technical service personnel and technical services. When the number of technical service tasks is greater than the number of technical service personnel, if a new technical service task is submitted to the system while all technical service personnel are solving other tasks at this time, then the new task will enter the queue and wait for assignment, waiting for the new technical service personnel to solve it; when there are more than two tasks, the technical service personnel who completes the task first will be assigned the new service task, and when two or more technical service personnel complete the task at the same time, the technical service tasks will be assigned according to the Hungarian algorithm; when a new technical service personnel logs in, the technical service tasks existing in the system will be assigned to the new logged-in personnel in the order of the sorting queue; when the technical service personnel logs out, the system will no longer assign technical service tasks to the offline technical service personnel.

9. The intelligent after-sales service method for instrumentation products according to claim 8, characterized in that: The S3 includes: S31, based on the analysis of historical user behavior patterns of instrument and meter products, determine the factor system that affects user behavior of instrument and meter products, and construct a user behavior index system based on the structural equation model, wherein the user behavior index system is used to analyze the influence mechanism between multiple factors and between behavioral factors and after-sales service; S32, constructing a user behavior database, and using tools to analyze and count the data in the user behavior database, so as to obtain the usage preference characteristics of each instrument product user for the product currently used; S33, selecting multiple types of data from the user behavior database, associating the multiple types of data with the user behavior indicator system to form user differentiated behavior data, integrating the user differentiated behavior data with a knowledge base formed by instrument expert knowledge and service rules provided by an instrument product manufacturer, and determining the impact of user differentiated behavior on instrument performance status; S34, establishing an after-sales prediction model based on the impact of the user's differentiated behavior on the performance status of the instrument; S35, based on the after-sales prediction model, the knowledge base formed by each user and the instrument expert knowledge and the service rules provided by the instrument product manufacturer are secondary integrated to predict the items and time of the next after-sales service each user will receive; the secondary integration is implemented based on the after-sales service dynamic game strategy.

10. An intelligent after-sales service system for instrumentation products, used to implement any of the methods described in claims 1-9, characterized in that: include: A production service information providing module (101), used to complete normal production of products through intelligent manufacturing production standards and provide a production and manufacturing information data basis; A data database for intelligent after-sales service is established based on the manufacturing information data foundation; wherein the data database is composed of multiple data sub-databases; An after-sales service providing module (102), used to determine a partner providing the intelligent after-sales service and optimize the networked collaborative technical service task assignment process, classification and allocation method; The after-sales service support module (103) is used to obtain the usage preference characteristics of each instrument product user for the current product by building a user behavior database and using tools for analysis and statistics, and analyze the impact of user differentiated behavior on the performance status of the instrument by combining the knowledge of instrument experts and the service rules provided by the instrument product manufacturer, and predict the items and time of the next after-sales service received by each user by combining the prediction model, standard service rules and knowledge base, and finally provide support for service recommendations of instrument service providers; The after-sales service receiving module (104) is used to establish a temporary permission opening and revocation mechanism and a dynamic service node interference mechanism to facilitate users to participate in the entire process of after-sales service.

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